esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Cereal Crops Disease Detection: A Review
## 1 Introduction  
Advancements in the area of machine learning and computer vision in the past decade had had a profound effect on the utilization of machine learning techniques in different sectors [1]. Machine learning approaches are being used from the medical [2, 3, 4, 5, 6, 7, 8, 9] to the security sector [10]. Recently, many works [11] have been undertaken on the application of machine learning in the agriculture sector for the detection of plant diseases, such as coffee [12] and Enset [13], Crop yield prediction [14], quality and growth monitoring [15, 16], supply chain performance [17], and water stress determination [18].  
Plants constitute 98% of the world's diet, two-thirds of which are Cereals [19]. The eight major kinds of cereal, wheat, maize, rice, barley, sorghum, oats, millets, and rye cover 56 percent of the world's arable land. Wheat, maize, and rice account for 80% of global cereal production [19]. Plant diseases are the major cause of global crop yield reduction, resulting in 10% loss of all the global food production [20]. The major plant disease-causing pathogens are viruses, bacteria, Oomycetes, fungi, nematodes, and other parasitic plants [20]. When infections occur to a large extent, losses to cereal crop production could reach as high as 50% [21]. Many laboratory techniques are available for the identification and detection of plant pathogens [20], but rapid and early detection is an important factor in the successful containment and control [22]. In this work, we present a survey of machine learning, especially deep learning techniques in the detection of cereal crop diseases.  
We placed a special emphasis on the tools and available datasets, which we think are the major hurdles researchers face when planning the undertaking of research work on this area of study.  
The rest of the paper is organized into nine sections: In Section 2, we begin by reviewing the latest survey papers performed on machine learning-based plant disease detection and identification. In Section 3, we conduct a detailed discussion on the major cereal crop species and summarise the common and damaging types of diseases affecting each cereal crop. In Section 4, we discuss the methodology and criteria for the selection of works. In Section 5, we provide a detailed discussion on works performed on each cereal crop species, various machine learning approaches, and available datasets.
# RoseCliff Algorithm: Making Passwords Dynamic
## 6 Limitations and Future Recommendations  
In the course of this study, it is imperative to acknowledge certain limitations and propose avenues for further exploration and enhancement of the RoseCliff Algorithm. The study assumed the relative security of the encryption techniques employed for both symmetric and asymmetric aspects of the algorithm. While this assumption provides a foundational framework, future research should delve deeper into the resilience of these encryption techniques. Robustness against potential breaches and the algorithm's ability to safeguard users even in the event of a breach warrant thorough investigation.  
Multifactor authentication (MFA) and two-factor authentication (2 FA) constitute security processes wherein users furnish two distinct authentication factors, generally categorized as knowledge factors (something the user knows), possession factors (something the user has), and inference factors (something the user is) [1; 41]. In the specific context discussed here, only the knowledge factor (what the user knows) was employed. While this may be considered a less secure technique, research indicates that users generally prefer to minimize the frequency of providing input multiple times.Consequently, security professionals often restrict such requests to instances where they are deemed necessary, thus balancing security measures with user preferences [3]. Our study predominantly concentrated on the aspect of randomizing user passwords, thereby incorporating evolving techniques such as model building to assist users in crafting more effective passwords for use with the algorithm. However, further research avenues could explore additional dimensions, including the integration of emerging technologies and methodologies to enhance the overall effectiveness and security of password randomization.  
In the realm of future research, there is a need to delve into the development and detection of potential adversarial attacks, particularly in scenarios where the password undergoes segmentation, infusion with common characters, and the tracking of previously used dynamic passwords. Understanding the algorithm's vulnerability to adversarial exploits and developing countermeasures will be instrumental in fortifying its robustness and ensuring a resilient defense against sophisticated cyber threats.
## 5 LoRaWAN Simulation Tools
### FLoRa  
A framework for a LoRa (FLoRa) simulator was developed to evaluate the LoRa network's performance using the ADR mechanism. It proved the effectiveness of the ADR at increasing the PDR with improving energy efficiency. FLoRa is an end-to-end simulation framework for LoRa networks that relies on the OMNeT++ network simulator and also utilizes INET system components [76]. An open-source OMNeT++ library was designed to support the experimentation process for various network protocols. FLoRa code is created by C++, and it enables the development of LoRa networks that support the integration of LoRa nodes, gateways, and network server modules. Application logic can be implemented as separate modules that are linked to a network server. The network server and nodes support dynamic configuration parameters controlled via the ADR and considering collisions and the capture effect. The module includes an accurate modeling of the backhaul network and can simulate multiple gateways. At the end of the simulation, energy consumption statistics can be collected in each node and over the entire network [77].  
Aside from that, the modules of the LoRaWAN MAC protocol strive to simulate the physical layer [78]. This offers a very strong graphical interface compared with the other simulation applications, since it is based on OMNeT++ and a graphical network description. The developed simulation module includes a sample scenario in the FLoRa simulations directory. The scenario has several features to simulate a network with 10 nodes that are placed randomly in a square network topology, with one gateway that is linked to a network server. Each node transmits a packet at a time based on an exponential distribution with a defined mean. For simulating a LoRa network, several parameters need to be selected, such as the simulation time, warm-up period, SF, the transmission power for each LoRa end node, backhaul network configuration, and links. The simulation statistics and tracing files are generated upon completion of the run. The simulation statistics can be viewed through the OMNeT++ GUI as in Figure 11.
## 4 Performance Analysis
### Average Latency  
The mean time it takes for TMS to receive a message from the time it is created. That means that the period between when the sender begins transmitting the message and when the recipient accepts the message is referred to as average delivery delay.
# Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel
## 3 Initial Error Estimation of an UAV Autonomous Guidance System
### UAV Motion Simulation Based on Optimal Approach Landing Trajectory
For our simulation we used the following parameters: \(T_{1}=T_{3}=0.3\) s, \(T_{2}=0.8\) s, \(T_{4}=0.5\) s, \(k_{1}=30\), \(k_{2}=50\), \(\xi_{1}=\xi_{2}=1\), \(k_{\gamma}=k_{\delta}=1\), \(k_{\gamma}=k_{\delta}=0.2\) s. UAV speed vector initial track azimuth \(\gamma_{A0}=120^{\circ}\), vessel speed vector initial track inclination \(\delta_{A0}=20^{\circ}\), UAV minimum turning radius R \(=720\) m, UAV flight speed \(40\) m/s, vessel sailing speed \(10\) m/s, landing position \(x_{A0}=0\) m, \(z_{A0}=0\) m, \(y_{A0}=1000\) m of UAV at the initial landing time, Landing position \(x_{B0}=2500\) m, \(z_{B0}=2500\) m, \(y_{B0}=0\) m of the vessel at the initial landing moment, the sampling period \(\Delta\mathrm{T}=0.01\) s.  
The spatial path of the UAV trajectory (from point \(A_{0}\) to point \(A_{3}\)) is shown in Figure 4, and the projection in the vertical plane \(XY\) is shown in Figure 5. The projection in the horizontal plane \(XZ\) is shown in Figure 6.  
Figure 4: Space motion trajectory of the UAV. In Figures 4-6, the blue solid line indicates the calculated optimal approach landing trajectory, and the red dashed line indicates the actual approach landing trajectory. At the point \(A_{3}\), the coordinate deviation between the actual trajectory of the UAV and the optimal calculated trajectory is \(\Delta X=33.7\) m, \(\Delta Z=34.45\) m, and the total error \(\Delta S=53.8\) m. Such deviations can seriously affect the autonomous guidance of the last segment of the landing trajectory since, in many cases, directing the UAV to the autonomous guidance area of the landing device is an intractable task. At the _ii_ calculation point of the approaching landing trajectory between the UAV and the vessel, the direction angle \(\gamma_{tr}\) and dip angle \(\vartheta_{tr}\) of the UAV flight speed vector are shown in Figure 7.  
As shown in the simulation results of Figure 7 during the complete approach landing trajectory calculation, the direction angle \(\gamma_{tr}\) of the UAV flight speed vector changes more smoothly, while the dip angle \(\vartheta_{tr}\) of the UAV flight speed vector changes more sharply with a step change occurring at the points \(A_{1}\) and \(A_{2}\) of the approach landing trajectory. This step change is not conducive to an optimal UAV landing trajectory during actual flight. Figure 5: Projection of the space motion trajectory of the UAV in the vertical plane \(XY\). Figure 6: Projection of the space motion trajectory of the UAV in the horizontal plane \(XZ\).
## 4 Numerical Experimentation
### Forecasting Model Parameter Setting  
Wind speed series from four datasets are chosen to test the forecasting accuracy of the proposed hybrid model. The results of the proposed model are also used to compare with other conventional methods; namely, BP neural networks, the ARIMA model, and the RBF model. This paper followed the standard of energy industry NB/T31046-2013 and the rules for measuring wind sources, which were published and made by the National Energy Administration in 2013.  
(1) For BPNN, the newff function of the neural network toolbox is employed to build the network. The dimensions of the input, hidden, and output layers are 4, 5, and 1, respectively. The learning  
Figure 3: The original wind speed data and de-noised data from four datasets.  
rate is set to 0.1, the maximum number of iterations is set to 100, and the training precision is set to 0.00004.
2. For ARIMA, the forecasting results are influenced by the moving average and the order of auto-regressive. The observed value's fitting effect is measured by the AIC criterion, and the AIC also calculates the most suitable number for the parameters. When the AIC reaches the lowest value, the ARIMA method can achieve the best order.
3. For RBFNN, similar with BPNN, the newrb function of the neural network toolbox is employed to build the forecasting network. The same parameters as the BPNN are used in the RBFNN.
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 3 AI-Based Optical Performance Monitoring
### Al-Based Failure Management  
Link failures can be classified into hard failures and soft failures. Hard failures in the link cause immediate disruptions but can be easily detected and restored. Soft failures just gradually deteriorate the performance of the link and they are hard to be detected. In addition, the causes behind them are challenging to be identified. Therefore, detecting and identifying soft failures are of great importance and highly desired. In this section, we review some recent works for failure management based on AI techniques and they are listed in Table 4.  
For the soft failure detection, current detection methods in a deployed network usually rely on a pre-defined threshold. However, because of the high complexity of modern optical networks, it is hard to set an accurate threshold. If it is set too loose, some soft failures may be ignored, and if too tight, false detection may occur. For soft failure identification, it is generally difficult to accomplish accurate identifications using analytic methods. To address the challenges faced by the traditional methods, many works are proposed to utilize the ML techniques to perform failure detection and identification. In [70], finite state machine (FSM) is used to detect and identify the soft failures caused by laser and wavelength selective switch (WSS). In [71], the trend of the BER is monitored and analyzed. The statistical characteristics of BER are input to the RF and SVM to detect the soft failure, and an ANN with a hidden layer is applied to identify the cause of the soft failure between EDFA and WSS. In [15], the optical spectrum is monitored using an optical spectrum analyzer (OSA). The features of it are extracted and analyzed to detect the soft failure caused by WSS. Then, controllers identify the anomaly between filter shift (FS) and filter tightening (FT). In [72], the tap value of the adaptive filter is analyzed using one-class SVM to detect the soft failure caused by laser, WSS and fiber nonlinearity. To summarize, ML techniques pave a promising way to address the problems of failure detection and identification. With the powerful learning capability of ML, the hidden patterns of the monitored data can be learned to enable various failure management functionalities. As optical networks becoming more dynamic and heterogeneous, traditional techniques for soft failure detection and identification may not be able to adapt to the complex scenarios well. Therefore, more applications of ML techniques are expected to be investigated in this field.
## 5 Simulation Results and Discussion
In the presence of weak turbulence, the time-varying received optical intensity may be described by a log-normal probability density function (PDF) [4] as  
\[P(I)=\frac{1}{I\sqrt{2\pi\sigma_{I}^{2}}}\exp\left(\frac{-\left[\ln\left(\frac{I} {I(0,I)}\right)\right]^{2}+0.5\sigma_{I}^{2}}{2\sigma_{I}^{2}}\right) \tag{27}\]  
Also, in the presence of moderate and strong turbulence, we used the extended equations presented in [4] for Gamma-Gamma distribution, specifically for the uplink. More details can be found in [4]. In the case of coherent receiver without turbulence, for M-ary QAM, the symbol error rate is given by [31]  
\[P_{e}=4\left(1-\frac{1}{\sqrt{M}}\right)\,\mathrm{Q}\left(\sqrt{\frac{3\log_{ 2}M\rho}{M-1}}\right)\left(1-\left(1-\frac{1}{\sqrt{M}}\right)\mathrm{Q}\left( \sqrt{\frac{3\log_{2}M\rho}{M-1}}\right)\right), \tag{28}\]  
where \(\rho\) is the signal-to-noise ratio (SNR) per bit and \(\mathrm{Q}(. )\) is the \(\mathrm{Q}\)-function. In the presence of turbulence, the mean BER is calculated by  
\[<\mathrm{BER}>=\int\limits_{-\infty}^{\infty}p(I)\mathrm{BER}(I)dI, \tag{29}\]  
where \(p(I)\) is the probability density function of turbulence and \(I\) is the optical intensity.
## 1 Introduction
Because trial-and-error or Ziegler-Nichols methods often suffer from huge time consumption and optimal parameter selection uncertainty, it is highly required to discover a systematic optimal tuning process which can guarantee satisfactory results within minimum time and computation effort. Also, as the outer voltage control loop and the inner current loop are cascaded in nature, separate tuning will take time to get optimal results. Hence, simultaneous tuning is required to tune optimally. Moreover, the system response can be suboptimal when applying a conventional PI controller due to the fixed-order integrator. Hence the system can be more sophisticated by applying a well-tuned fractional order PI (FOPI) controller due to its capability of dealing with fractional order integrators. Again, this needs a systematic approach to tune the respective parameters. Thus, in this work, a novel plant propagation algorithm (PPA)-based simultaneous and cascaded outer DC link voltage and inner current loop controller is proposed. The proposed method is applied with fractional order PI controller instead of the conventional PI controller due to its increase in degree of freedom to control more precisely. The considered objective function consists of two terms (integral of absolute time error and peak difference) which effectively reduces the overshoot, settling time and steady-state error if the controller is tuned properly. The proposed controller is also compared with two other metaheuristic algorithms, namely particle swarm optimization (PSO) and elephant herding optimization (EHO). Using PSO, conventional PI is tuned; using EHO, FOPI is tuned. The differences in results and reasonings are covered in detail to understand the science behind better results with PPA over PSO and EHO. As systems are becoming more and more digitized, the real-time simulation is also required to develop appropriate digital twins to emulate physical systems in the cyber domain. A successful development of an optimal controller in real-time simulators can lead to such achievements proficiently. Thus, the proposed controllers were tested in real-time simulator OPAL-RT. The performance of the proposed controller was also extended by analysis of the total harmonic distortion (THD) of the inverter current and performance during voltage sags and varied irradiance in grid-connected mode. The paper is arranged in the following order. Section 2 describes the system, system components, and mathematical modeling of the considered system along with perturbation-and observation-basedMPPT algorithm. In Section 3 the objective function, working flow chart, and algorithms are discussed in detail. Section 4 deals with the experimental setup, parameter selection, and simulation results. Analyses of results and comparative analysis of the experimented optimization methods are also covered in the same section. In Section 5, performance of the selected controller is also tested under varied conditions and in terms of THD criteria. Finally, Section 6 concludes and summarizes the paper with a direction to future prospective work.
## 4 Experiments and Results
### Corrosion Detection Network Experiment Comparison and Analysis
For the small corrosion patch in the middle of image (a), YOLOv9t detects the area multiple times, and other YOLO series models also detect the same area multiple times. RT-DETR-L makes a false detection in the lower-left corner of image (a), while SSD only detects the corrosion in the center of the image. In image (b), only YOLOv10-vit detects all the corrosion areas completely, while the other models suffer from varying degrees of missed and false detections. In image (c), again, only YOLOv10-vit detects all corrosion areas fully, while the corrosion at the bottom of the image is missed by all other models. In image (d), YOLOv10-vit, RT-DETR-L, and YOLOv3-tiny detect all corrosion parts, but YOLOv3-tiny exhibits overlapping detection boxes. Other models show varying degrees of missed detections, with SSD exhibiting the most significant missed detections. Through the comparison of evaluation metrics, the mAP curves during the training process and the detection result images, the proposed YOLOv10-vit network consistently outperforms the other models indicating that integrating the MobileViTv3 module into the YOLOv10n architecture enhances the accuracy of corrosion detection in hydraulic metal structures.
## 1 Introduction
[PERSON] [6] found that the AE signals of the rock bridge in direct shear testing were dispersed in high- and low-frequency bands and that the dispersion of the peak frequency band to the medium frequency could be used as crucial rock failure precursor information. Through the uniaxial compression test of fractured mortar rock specimens, [PERSON] [14] discovered that the AE signals of the specimen failure revealed three distinct peak frequency bands: high, medium, and low. [PERSON] [20] found that the AE signals were dispersed in three frequency bands and studied how the frequency bands evolved at different loading stages. According to the findings of this study, a combination of AE and other characteristics can be utilized to thoroughly assess the presence of macroscopic rupture, boosting the accuracy of early warning of rock failure. As previously stated, little research has been conducted on the evolution of the amplitude and peak frequency distributions of tensile and tensile-shear failure. It is unknown whether similar patterns exist in tensile and tensile-shear experiments. Furthermore, it is evident that low-amplitude AE signals correspond to small-scale cracks, and high-amplitude AE signals belong to large-scale rupture. However, the link between the magnitude of the signal at the peak AE frequency and the crack types is more complex and has yet to be determined [19; 21; 22], and more research is required. To explore the AE characteristics and crack evolution of the same shape and size of rock specimens under three stress states, direct tensile, compressive-shear, and tensile-shear tests were performed on intact and jointed cubic cement mortar specimens utilizing self-developed multifunctional test equipment. AE monitored the interior cracking in specimens. AE amplitude and peak frequency were analyzed to understand crack sizes and types under different failure modes. We classified the internal crack types of the specimens using the conventional RA (rising time/max. amplitude)-AF (average frequency, ring down count/duration)-based method and compared the results with the newly proposed amplitude-PF-based method to provide new ideas for analyzing microscopic cracks and macroscopic ruptures in the failure process.