esa-sceva/satcom-chunk-collection huggingface dataset:
# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 4 Proposed Hybrid DL Model  
In this section, we present the proposed hybrid DL model of the MobiRes-Net model that can be used for olive disease recognition. Furthermore, the model's aim is a smart system that enables the processing of olive leaf images supported by machine learning algorithms as well as the detection of various symptoms of diseases affecting olive trees. In this section, we detail our approach for classifying olive leaf images into four statuses, namely: \"healthy\", \"Aculus Olearius disease\", \"olive scab\", and \"peacock spot disease\". In this proposed work, we perform several stages of image processing techniques for detection and classification of olive leaf diseases. Figure 3 portrays a schematic view of the proposed hybrid DL model.  
Firstly, after collecting data from olive groves using a drone, the input data are preprocessed by resizing the input image into the required pixels for training the dataset. Then, two methods, i.e., contrast enhancement and image normalization, are applied to change the value of the pixel intensity in order to acquire a better-enhanced image. The changed pixel intensity reveals the hidden information existing within the low range. Secondly, a data augmentation technique is used with the purpose of increasing the overall performance of this system by adding more diverse data to the existing limited dataset. The used data augmentation techniques are built on rotation and Gaussian blur.
## 3 Transmitter Hardware Impairments or 'RF Features' Overview
### BPF Models  
The band-pass filter (BPF) is equipped at transmitters to filter out undesired non-central frequencies signals. In this work, we model BPF using a finite impulse response (FIR) filter. A general form of an FIR filter output \(y[n]\) can be given by  
\[y[n]=\beta_{0}x[n]+\beta_{1}x[n-1]+\cdots+\beta_{k}x[n-k]+\cdots+\beta_{K}x[n-K] \tag{14}\]  
where \(\beta_{k}\) is the \(k_{th}\) impulse response, \(K\) is the order of the filter.  
We use the window design method for the genuine GNSS transmitter BPF and the least squares method for spoofer transmitter BPF. An example of BPFs used for a genuine GNSS transmitter versus a spoofer transmitter is shown in Figures 5(a,b), respectively. The exact parameters of the filters used in the genuine GNSS transmitters are not known, however, without loss of generality, the assumption here is that the passband and stop-band ripples of a BPF for a genuine transmitter are smaller than those for the BPF of a spoofer. This is expected to be more evident for spoofers based on SDR, which generally include configurable BPFs.
## 2 Apparatus
### Simulations
#### 2.3.1 High Resolution Ion Modes  
For the high resolution ion modes, ions are detected on the electron side of the spectrometer, and for the simulations, the choice was made to ground the front of the MCP and the flight tube (\(V_{\rm F}^{\rm e}=0\) kV). The repeller voltage was set to \(V_{\rm R}=3\) kV. Figure 2a shows the resulting mass resolution in Operation Mode 1(a) for extractor voltages from 1400-3000 V. For zero kinetic energy ions, the optimum extractor voltage was found to be \(V_{\rm E}^{\rm e}=2.354\) kV (\(\eta=0.78\), black curve). The resolution of \(m/\Delta m=35,\!000\) is clearly over-estimated, and for the more realistic case of ions with an initial drift velocity, the best resolution of \(m/\Delta m=2000\) was found for a voltage of \(V_{\rm E}^{\rm e}=2.275\) kV (\(\eta=0.76\), blue curve).  
Figure 2b shows the resulting energy resolution for ion VMI in Operation Mode 1(b) for three different settings for the extractor voltage, \(V_{\rm E}^{\rm e}\), calculated using the 27-particle model (blue, green and orange lines). The optimum extractor voltage, for which the best resolution was achieved for a photoion kinetic energy of 6 eV, was found to be \(V_{\rm E}^{\rm e}=2.275\) kV (\(\eta=0.76\)), and for this setting, the resulting energy resolution from the Monte Carlo simulation is shown (black line), indicating an energy resolution better than 1.5% for kinetic energies between 6 and 10 eV. In Figure 2c,d, the corresponding  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Operation Mode** & \(\eta\) **(Equation (1))** & \(V_{\rm F}^{\rm e}\) **(kV)** & \(V_{\rm E}^{\rm e}\) **(kV)** & \(V_{\rm R}\) **(kV)** & \(V_{\rm E}^{\rm i}\) **(kV)** & \(V_{\rm F}^{\rm i}\) **(kV)** \\ \hline
1(a) Ion TOF & 0.76–0.78 & 0 & 2.275–2.354 & 3.000 & not used & not used \\
1(b) Ion VMI & 0.76 & 0 & 2.275 & 3.000 & not used & not used \\
2(a) Electron VMI + ion TOF & 0.76 & 5.000 & \(-5.775\) & \(-9.200\) & \(-10.000\) & \(-10.000\) \\
2(b) Electron VMI + ion VMI & 0.76 & 5.000 & \(-5.775\) & \(-9.200\) & \(-8.802\) & \(-10.000\) \\ \hline \end{tabular}
\end{table}
Table 1: Simulated optimum voltages for the different operation modes. For Mode 1(a), the exact optimum voltage \(V_{\rm E}^{\rm e}\) depends on the initial velocity of the ions.  
simulated detector image and the 3D momentum distribution after inversion are shown, respectively. Figure 2e shows the kinetic energy spectrum calculated from the 3D momentum distribution.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Intrusion Detection
#### 2.1.5 Vgg16  
VGG16 (Visual Geometry Group 16) is a well-known deep convolutional neural network (CNN) that has had a significant influence on the areas of computer vision and picture categorization. This model marks an important step forward in the development of deep learning architectures, having demonstrated its adaptability and efficacy in a broad range of applications.  
The need for a deeper neural network capable of recognizing and categorizing sophisticated picture patterns and attributes led to the creation of VGG16. It was created by the University of Oxford's Visual Geometry Group and soon became a standard in the field of image recognition.  
This model is notable for its great depth, with 16 convolutional and fully linked layers. VGG16's core is made up of 13 convolutional layers followed by 3 fully linked layers. These layers, with varying filter widths and depths, are critical to collecting and processing picture data hierarchically. This deep architecture is the heart of VGG16, allowing it to learn complicated visual data representations.  
In image classification tasks, VGG16 has become associated with excellent accuracy. It is praised for its capacity to detect and discriminate between a wide range of object classes in large-scale datasets, most notably in the prestigious ImageNet Large Scale Visual Recognition Challenge. VGG16 is distinguished by its uniform and consistent architecture, which employs repeated 3\(\times\)3 convolutional filters. This standardization simplifies the model's construction and improves its generalization.  
VGG16's excellent picture categorization skills are put to use in the context of the SFSS. Because of its capacity to detect detailed patterns and objects inside photos, it is an excellent option for detecting intruders in agricultural fields. The adoption of VGG16 as one of the system's models guarantees that any unwanted presence, whether animal or human, is identified quickly. VGG16's excellent and constant performance record makes it a trustworthy instrument for agricultural land security, particularly when applied within cloud infrastructure.
## 3 System Model
### Network Model  
A wireless sensor network can be viewed as a directed graph \(G=\)\(<\)\(V_{node}\)U \(V_{sink}\), \(E_{node}\)U \(E_{sink}\)\(>\), where \(V_{node}\) and \(V_{sink}\) represents the set of sensor nodes and sink nodes respectively. \(E_{node}\) represents the set of all links _l(i,j)_, where \(i\) and \(j\) are neighboring sensor nodes, and \(E_{sink}\) represents the set of all links between sensor nodes and sink node. The randomly distributed wireless sensor network is illustrated in Figure 1.  
The sensor network here consists of a certain number of static sensor nodes as well as multiple mobile sink nodes which will move along the network boundary. The static sensor nodes are randomly dispersed in the monitored area and keep generating data packets. The mobile sink nodes are used to  
Figure 1: Network model.  
gather data packets in different location by moving along network boundary for two reasons. First, it is relatively easy to deploy mobile sink along network boundary rather than along certain optimal trajectory inside. More importantly, network performance is usually better with sink moving along the boundary than inside sensor network [19]. Here, the small squares \(P_{i}\) stands for parking positions of mobile sink nodes.  
Mobile sink nodes will periodically visit these parking positions and sojourn at each position for a certain time to collect data packets. We can divide the whole process of mobile sink node movement into a moving phase and a collecting phase. In the moving phase, mobile sink nodes only move with a certain speed from one parking position \(P_{i}\) to another position \(P_{j}\), and they will refuse to receive any monitored data. In the collecting phase, mobile sink nodes will broadcast a notification message to their nearby sensors. The message includes their arrival message and next location message, and then they will receive data packets in a single hop or multi-hop transmission manner.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 6 Open Research Issues and Challenges
### Mobility  
Control of the routing overhead is also important in UAWSNs [60]. The sensor nodes are assumed to be static. In the last few years, however, node mobility drew attention in many applications. For example, mobile sensor nodes are essential for medical applications and services. In such applications, sensor nodes need to transmit data continuously.
## 5 Results and Analyses
### Prediction Models
#### 5.3.2 First Scenario: Datasets without FS or Sampling  
Table 12 provides the results of AUC values for prediction models in seven datasets in the first scenario. These results indicate that RF outperformed all other prediction models, as RF provided higher AUC values in all datasets except antlr4, in which NB achieved a slightly better prediction accuracy (only 0.03 higher). KNN achieved the best performance among individual models and was the second-best prediction model.
## 4 Conclusions  
The influence of SS-PCM incorporations on properties and durability of mortars was examined. The rheological, structural, mechanical and thermal properties of SS-PCM mortars were measured and analyzed. Then, the durability study allowed assessing the volume (drying shrinkage), chemical (carbonation) and mechanical stabilities (over thermal storage cycles). The results showed that:  
* The increase of SS-PCM content in the fresh mortar induces a better workability and increases the total porosity, hence a drop in mechanical strength and thermal  
Figure 21: Compressive strength of mortars as a function of thermal aging cycles.  
conductivity. However, the mechanical performance of SS-PCM composite mortars remains sufficient (27-18 MPa) for most building applications.
* The drying shrinkage amplitude decreases with its SS-PCM content. This result should be related to the compressibility of the SS-PCM amortizing the local capillary pressure, leading to less measurable bulk deformation.
* An increase in mechanical strength with the carbonation rate was noted. The mechanical stability of SS-PCM composite mortars over the heat storage cycles is confirmed. This should be explained by the volume stability of SS-PCM during phase change unlike classic SL-PCM.  
The advantage of this innovative SS-PCM compared to the existing SL-PCM is its ability to store high thermal energy with a better durability thanks to its volume stability during solid-solid phase change. So, this storage material could be a solution for the durability problems inhibiting the use of phase change materials in building sector. The SS-PCM mortar was suitable for masonry coating, masonry bricks, roof coating, covering panels, etc. This allow to improve the thermal inertia of the building, thus avoiding sudden changes in the indoor temperature, all while maintaining the supporting structure capacity.  
Conceptualization: [PERSON], [PERSON], L.P. and [PERSON]; Methodology: [PERSON], K.N., L.P., Z.E.A.T., A.C. and Y.M.; Formal analysis: [PERSON], K.N., L.P., Z.E.A.T., A.C. and Y.M.; Investigation: [PERSON] and [PERSON]; Writing--original draft: [PERSON] and [PERSON]; Visualization: [PERSON] and K.N.; Editing: [PERSON] and [PERSON]; Writing--review: [PERSON], [PERSON], A.C. and Y.M.; Supervision: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The SS-PCM are supplied by the \"Laboratoire GEC\" of Cergy Paris University (France). The SEM analyses were carried out in the \"Microscopies & Analyses\" imaging facility, Federation I-Mat (FR4122) of the CY Cergy Paris University. The mercury intrusion porosimetry (MIP) was performed in the \"Laboratoire Materiaux et Durabilite des Constructions\" of the Toulouse University (France).  
The authors declare no conflict of interest.
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 3 Software Architecture
where \(\|\alpha\|_{1}=\sum_{i=0}^{N}|\alpha_{i}|\), \(\|\cdot\|_{1}\) and \(\|\cdot\|_{2}\) represents the \(l_{1}\)-norm and \(l_{2}\)-norm, respectively, and \(\varepsilon\) denotes the noise bound. Comparing to the traditional sampling-and-sending wireless communicating method, CS-based coding algorithm has the ability of transmitting far lesser encryption data than the original measurements and decrypting the information from received data with an optimization processing [22]. It is important to emphasize that the CS strategy is robust which can protect against lost data packages. For wireless communication, losing data packages is a great threat in transmission which causes unnecessary failures or disasters for monitoring and decision-making applications. Thanks to CS coding strategy, each value of the transmitting vector, \(\mathbf{y}\), contains the information of all the raw signal, which reduces the risk of \"losing all eggs from one basket\". The CS-based coding algorithm was embedded in the computational core of the Raspberry Pi's microcontroller by the Python language, and the decrypting algorithm was coded in MATLAB in the server with the corresponding random matrix. A simple example was tested by a programmable function generator (Tektronix Arbitrary Function Generator model AFG3022C) as a demo. The sampling frequency for generating was 1000 Hz, and the sample rate for sensing was 800 Hz. The time-history signal (Figure 3a) had 10 frequency components, which is sparse in the frequency domain (Figure 3b). It was coded by the aforementioned random linear measurement matrix to form encrypted data of length much smaller (40 points) than the previous one. After transmission to the server, the encrypted data was decoded by \(l_{1}\)-norm optimization algorithm. As we can see in Figure 3c, the recovered signal (red solid curve) had a good agreement with the initial measurements (color blue), which validates the functionality of the proposed data-compressive method. The compressive ratio was 1:16, which means 5440 bytes of acquired data regarding the ground motion could be transmitted in 340 bytes. Figure 3: Example of compressive sensing (CS)-based encrypted algorithm: (**a**) Sampling points and original time-history signal containing 10 frequency components; (**b**) reconstructed sparse vector in the frequency domain; and (**c**) recovered signal from the random sampled points.
## 2 Cairns-Tsallis \((q,\alpha)\)-Distribution Function  
The 3-D \((q,\alpha)\)-distribution function, which is particularly the combination of the Cairns-Tsallis distribution, is defined as [36]  
\[f_{\text{CT}}(\mathbf{V}_{j})=D_{q\alpha}\left(\frac{2\pi}{1-q}\right)^{-\frac {3}{2}}\left(\frac{T_{j}}{m_{j}}\right)^{3}\left(1+\alpha\frac{m_{j}^{2}V_{j} ^{4}}{T_{j}^{2}}\right)\left[1-(q-1)\frac{m_{j}V_{j}^{2}}{2T_{j}}\right]^{(q-1 )^{-1}}, \tag{1}\]  
where \(T_{j}\), \(m_{j}\), and \(V_{j}\) are the temperature, mass, and velocity of \(j\)th plasma particles (\(j=e\) is for the electrons; \(Xe^{+}\) and \(Ar^{+}\) are for the positive ions; \(SF_{6}^{-}\) and \(F^{-}\) are the negative ions (and the letter \(d\) stands for negative charge dust grains); \(q\) and \(\alpha\) are the spectral indexes, associated with non-extensivity and the quantity of non-thermal electrons, and positive/negative ions, respectively. Here, \(D_{q\alpha}\) is a constant of normalization expressed by [37].  
\[\begin{array}{l}C_{q\alpha}=\left[\frac{(1-q)^{2}\Gamma\left(\frac{1}{1-q} \right)}{\Gamma\left(\frac{1}{1-q}-\frac{3}{2}\right)\left[3\alpha+(1-q)^{2} \left(\frac{1}{1-q}-\frac{3}{2}\right)\left(\frac{1}{1-q}-\frac{3}{2}\right) \right]}\right]\quad\text{for}-1<q\leq 1,\\ C_{q\alpha}=\left[\frac{(1-q)^{2}\Gamma\left(\frac{2}{1+\frac{1}{q}}\right) \left(\frac{3}{1+\frac{2}{q}}\right)\left(\frac{3}{1+\frac{2}{q}}\right) \left(\frac{5}{1+\frac{1}{q}}\right)}{\Gamma\left(1+\frac{2}{q}\right)\left[3 \alpha+(q-1)^{2}\left(\frac{3}{1+\frac{2}{q}}+\frac{1}{1-q}\right)\right]} \right]\quad\text{for}\quad q\geq 1,\end{array} \tag{2}\]  
\(\Gamma\) is called the Gamma function. Both values of \(C_{q\alpha}\) correspond to the Cairns distribution in the limit \(q\to 1\)[15], and the \((q,\alpha)\)-distribution simplifies to a pure Tsallis distribution for \(\alpha=0\)[22]. The \((q,\alpha)\)-distribution reduces to the Maxwellian distribution exactly in the case of \(\alpha=0\) and \(q\to 1\). It is watched that the real Tsallis \((q)\)-distribution function carries on distinctively in the limits, \(q>1\) and \(-1<q<1\)[23]. As in the previous extent, the real T-sallis distribution function is non-zero for the complete velocities limit \(-\infty\) to \(+\infty\), and it shows the high energy tail because of an abundance of super-thermal species. **These** standards, on a basic level, are like a power-law or \(\kappa-\)distribution function.
## 3 The Proposed Method
### Algorithm Complexity Analysis  
The first step of iteration needs to calculate \(\mathcal{R}\times_{1}\boldsymbol{A}_{1}^{T}\left(:,i_{1}\right)\times_{2} \boldsymbol{A}_{2}^{T}\left(:,i_{2}\right)\times_{3}\cdots\times_{N} \boldsymbol{A}_{N}^{T}\left(:,i_{N}\right)\), and the corresponding computational cost is \(\frac{2 MP\left(M^{N}-PN\right)}{M-1}\). In addition, the absolute maximum optimization should be calculated, which needs \(2M^{N}\) operations. Comparing with this, the classic matching pursuit method, such as orthogonal matching pursuit (OMP) [30; 32], requires the computation cost \(2M^{N}\left(P^{N}+1\right)\) in this step.  
With the application of the Cholesky decomposition, the computational complexity of estimating \(\boldsymbol{s}_{nz}\) can be reduced efficiently in the iteration. Even if every mode need to be updated, the total computational cost can not exceed \(\left(2 Nk\left(P+1\right)+Nk^{2}+2 Nk^{N+1}\right)\). By comparison, the computational cost of OMP algorithm is \(k^{N}\left(P^{N}+3k^{N}\right)\) in this step.  
In the residual calculation step, it is needed to compute \(\mathcal{S}_{nz}\times_{1}\boldsymbol{B}_{1}\times_{2}\boldsymbol{B}_{2} \times_{3}\cdots\times_{N}\boldsymbol{B}_{N}\) and subtract it from \(\mathcal{Y}\). The corresponding calculations are \(P^{N}\left(2k+1\right)\). The ones of the OMP algorithm in this step, by contrast, are \(P^{N}\left(2k^{N}+1\right)\).  
With regard to iterations, for a \(\left(K_{0},K_{0},\ldots,K_{0}\right)\)-structured sparse problem, the maximum iterations is \(NK_{0}\). But OMP algorithm needs \(K_{0}^{N}\) times iteration for the same problem.
# Design Patterns for Mobile Games Based on Structural Similarity
## 4 Design Patterns Based on Structural Similarity
### Different Parts  
This pattern may be used to develop mobile games with different parts, where one part usually implements the main game environment.  
1. Intent: This pattern suggests that game logic should be divided into different parts and communication between parts may be through an interface.
2. Motivation: In some games, the game logic is divided into different parts. The player has to enter and exit these parts at certain points. These parts are different enough to handle them differently. In the first type, one part usually represents the main game environment where a player has to deal with enemies with the help of friends to achieve the goal. Other parts are supporting the main gameplay in one way or the other. In the second type, the main game environment is partitioned where each part represents a different location or scene. Though, each part has main game-related tasks, they are sufficiently different from others to be implemented in a separate class. Adventure games are most likely to have this pattern.
3. Implementation: As each part is different from all others, a separate class would be used for each part. All of these classes will directly extend AbstractUpdateable because there is nothing common among the parts. It is quite possible that parts have to communicate with each other especially while leaving one part to entering another. Certain factors might be shared in this communication. For example, in a game, coins may be earned in the main part of the game, which may be spent in the shop part. Similarly, the powers or weapons bought in the shop would be used in the main game part. To address this sharing, a class with shared features is required. An appropriate name for this class may be GameStatus, since the status of the game is shared. (4) Diagram: Figure 16 represents class diagram of the Similar Levels pattern.  
Figure 15: Similar Levels pattern.  
Participants:  
* MainPart class includes the implementation of the main game logic and environment.
* SuppPart_j, SuppPart_k (an arbitrary number of parts) are classes implementing each supporting part.
* GameStatus class stores the status of the game, which is shared by different parts.
* Sample Code: The Appendix A depicts sample code for the Different Parts pattern.
* Consequences: Each part of a game can have entirely different logics and design. The diagram and sample code shown here are minimal. In practice, there can be a group of classes for each part. This design pattern does not take details and complexities of a part into account. The pattern does not limit the complexity of a part as long as it can be identified as a part.
* Relationships: Although Different Parts pattern seems a simple variation of Unrelated Levels, yet there is a definite difference between them. In Unrelated Levels, we talk about levels, while in Different Parts we talk about parts.