esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 3 Related Work
### Physical Models  
Physical models were the predominant method for meteorological forecasting before the advent of Artificial Intelligence (AI) and generally produce more accurate results compared with statistical methods. Physical models are predicated upon a foundational set of physical principles, including but not limited to [PERSON]'s laws of motion, the laws of conservation of energy and mass, and the principles of thermodynamics. These governing equations are commonly expressed in mathematical form, with the Navier-Stokes equations serving as a quintessential example for describing fluid dynamics. At the core of these models lies the objective of simulating real-world phenomena in a computational setting with high fidelity. To solve these intricate equations, high-performance computing platforms are typically employed, complemented by specialized numerical methods and techniques such as Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA).  
In the context of atmospheric science, these physical models are especially pivotal for Numerical Weather Prediction (NWP) and climate modeling. NWP primarily focuses on short-to-medium-term weather forecasting, striving for highly accurate meteorological predictions within a span of days or weeks. In contrast, climate models concentrate on long-term changes and predictions, which can span months, years, or even longer time scales. Owing to their rigorous construction based on physical laws, physical models offer a high degree of accuracy and reliability, providing researchers with valuable insights into the underlying mechanisms of weather and climate variations.  
As mentioned before, statistical-based methods can analyze past weather data to make predictions, but they may often fail to accurately predict future weather trends [36], and physic-based models, despite being computationally intensive [37], help us understand atmospheric, oceanic, and terrestrial processes in detail. Recently, machine learning methods have begun to be applied to the field of meteorology [38], offering new ways to analyze and predict weather patterns and climate change [39]. Machine learning methods are increasingly being utilized in meteorology for forecasting. Compared to physical models, they offer faster predictions, and compared with statistical methods, they provide more accurate results [40]. Additionally, machine learning can be employed for error correction and Down-scaling, further enhancing its applicability in weather and climate predictions.  
In the critical fields of weather forecasting and climate prediction, achieving accuracy and efficiency is of paramount importance. Traditional methods, while foundational, inevitably present limitations, creating a compelling need for innovative approaches. Machine learning has emerged as a promising solution, demonstrating significant potential for enhancing prediction outcomes.
## 6 Conclusions  
We firmly believe that the tracker can overcome the four tracking problems, motion blur, deformation, illumination variation, and occlusion at the same time if the tracker has sufficient characteristics of the target object, more than one set of templates, dynamically updated templates, and less sensitivity to illumination variations. Our proposed ADMTCF has several features. Firstly, we convert the image of the pre-framed target object into the HSV color space, and then perform LBP conversion on the luminance to obtain the image features of the target object. So, our tracker will not degrade the stability and accuracy of tracking due to changes in ambient light. Secondly, our tracker has four sets of dynamically updatable templates. Our ADMTCF uses the template with the highest similarity to track objects while retaining the template with lower similarity to memorize the pose of the previous object to avoid the deformation of the target in the future. Therefore, the ADMTCF can maintain good tracking accuracy when the shape of the target changes or the movement is blurred during the tracking process. Thirdly, the template of our tracker can be adaptively resized as the surface features of the moving object change, maintaining a high similarity between the template and the target object. The ADMTCF can maintain good tracking accuracy even if the target is occluded during the tracking process. Experiments show that our proposed ADMTCF provides a good object tracker when object tracking encounters illumination variation, deformation, motion blur and occlusion.  
Conceptualization, K.-C.H.; methodology, K.-C.H.; validation, K.-C.H.; writing--original draft preparation, K.-C.H. and S.-F.L.; writing--review and editing, K.-C.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Informed Consent Statement: No consent required. OTB-100 dataset is publicly available and can be used for research purposes. Data Availability Statement: Not applicable.  
**Acknowledgments:** Thanks to friends who are keen on object tracking for their suggestions on my program.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Image Reconstruction Using Autofocus in Single-Lens System
## 2 Methodology
### Autofocus and Iterative Algorithm  
Based on the single-lens CDI system in Figure 1, the insertion and removal of the diffuser realize the switch of speckle illumination and coherent illumination. In this paper, speckle illumination is used to realize autofocus and coherent illumination is used for image construction. The flowchart is given in Figure 2, which includes three parts:  
1. Reconstruction of the scattered out-of-focus dataset by using the intensity patterns under speckle illumination.
2. Clarity evaluation of reconstructed speckle images, and curve drawing between estimated distances and clarity results.
3. Reconstruction of coherent patterns by using the quasi-focus distance in step 2.  
Figure 1: An optical setup for single-lens CDI with coherent and speckle illumination. AS: aperture stop; L\({}_{1}\), L\({}_{2}\): lens; D: diffuser; S: sample; SP: sensor plane.
## 4 Materials and Methods
### Theoretical Framework and Survey Design  
An extensive review of published academic and relevant literature was determined for a survey tool design [37; 38; 40; 75; 76; 77; 78; 79]. The survey tool was developed and validated. More details can be found elsewhere [46].  
Appreciative Inquiry (AI) theory was applied as a theoretical framework in creating the survey tool [46]. The closed- and open-ended questions were designed to collect information in line with the Discovery and Dream phases of the AI. Detailed information on the AI theory is published elsewhere [46]. The Discovery phase information gathering involved asking individuals to recall actual experiences of the best or the most effective practice, and also to focus on their aspirations to improve practice in their work settings [80]. The Dream phase information gathering occurred via open-ended questions, allowing expression of ideas about what the participant might have aspired to do in future practices (Figure 3).  
The survey tool consisted of four sections, i.e., 24 sociodemographic questions asking gender, age, education, workplace location and experience, and resources including antibiotic learning experience; 10 knowledge statements with three response options (yes, no, don't know) measuring knowledge of aspects of antibiotic use in Thailand including the legislation of antibiotics dispensing and awareness of antimicrobial resistance; 12 attitude questions with five-point Likert scale answer options (very good, good, unsure, poor, very poor) to assess participants' attitudes that reflected the Discovery phase of AI theory; three open-ended questions of the Dream phase of AI to allow the participants to reflect on the current practices and to suggest their proposals for ideal practices to promote smart antibiotic use in community pharmacies in Thailand.
## 2 Related Work
### Deep-Learning-Based Methods  
Compared with methods based on traditional machine learning, deep-learning-based methods have the advantages of the wide range of applications and high applicability. In the feature extraction step of detection processing, traditional machine-learning-based methods use manual feature engineering methods, which are labor-intensive and time-consuming. Deep-learning-based methods can achieve automatic feature learning through advanced network structures and complex features compared to simple ones. Therefore, with the development of deep learning technologies, increasing research about EM detection using deep learning methods is presented, such as in [81; 82; 83; 84; 85; 86; 87; 88; 89; 90]. In [81; 82; 83], CNN was employed for EM detection. In [81], a tubercle bacillus detector was designed based on CNN. In [83],a CNN-based method was proposed for actinobacterial species detection. In [88], a region convolutional neural network (R-CNN)-based detector was proposed for diatom detection. In addition, a you only look once (YOLO)-based detector is prepared for comparisons. The result indicates that YOLO performs better than R-CNN in diatom detection. In [84; 85; 86; 87], Faster R-CNN-based methods were employed for EM detection. In [85], a Faster R-CNN-based detector was proposed for parasite egg detection. In [87], Faster R-CNN was applied for algal detection. About 1859 samples were prepared for the test.  
After consulting all these related research studies, we found that classical image-processing-based methods were mainly used as preprocessing methods in current EM detection studies. The most widely used methods in EM detection are traditional machine-learning-based methods. Although there are a few studies about deep-learning-based methods, deep-learning-based methods show great potential in EM detection. Therefore, we designed a deep-learning-based detector for EM detection called SEM-RCNN.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
###### Abstract
Moreover, several illustrations of NC-NET, e.g., tactical targeting network technology (TTNT) [8], have adopted the general-purpose technologies of ad hoc network such as TDMA and CSMA/CA. From this point of view, NC-NET can also be investigated as a special form of ad hoc network. In previous literature, several effective technologies for MAC protocols of ad hoc network have been studied in depth, and can be used as a reference for our research, i.e., directional antennas [9; 10; 11], multi-channel diversity [12], differentiated-service mechanisms [13; 14; 15; 16; 17; 18], and cooperative communication [19; 20; 21; 22; 23; 24]. Following detailed analysis, it is clear that the results for challenge (i), challenge (ii), and their hybrid are extensive; however, in terms of challenge (iii), the integrated investigations on challenges (i) and (ii) are quite limited. From the above analysis, we identify the missing properties in the existing  
Figure 1: Typical illustration of networked UAVs based on NC-NET. work for QoS provisioning in NC-NET and introduce the design and implementation of a new MAC protocol for NC-NET. In this paper, we propose a hybrid approach, namely, a DiffServ-based dynamic cooperative MAC (DDC-MAC) protocol, in combination with the aforementioned techniques for NC-NET. The key contributions of this work and highlights of DDC-MAC are listed as follows: First, a system model is presented that exploits the merits of the aforementioned issues, including an adaptive array hybrid antenna system, multi-channel environment and cluster-based network topology. Second, the proposed DDC-MAC protocol is designed in detail with the following features: (i) a DiffServ mechanism is proposed to realize the parallel transmission of multiple services with QoS guarantee; (ii) a multi-priority policy is presented to reduce the probability of contention and collision, and to reassign the access sequence; (iii) an adaptive channel back-off sequence (ACBS) is proposed as a reference for lower-priority pairs to switch directly among data channels without additional negotiation packets; and (iv) an optimal-relay-based MAC procedure is deduced to handle link failures in long-range transmission. The performance of DDC-MAC is theoretically analyzed and evaluated. Finally, the results demonstrate the efficiency of DDC-MAC by comparing it with other contemporary protocols. The remainder of the paper is organized as follows. Section 3 presents the system description and traffic models. Section 4 provides a detailed description of the proposed DDC-MAC protocol. In Section 5, we analyze DDC-MAC in terms of the searching probability of next-hop nodes and saturated throughput. In Section 6, we evaluate DDC-MAC based on extensive simulations. Finally, conclusions are given in Section 7.
## 2 Related Work
This model, which incorporates embedded reinforcement learning, assumes that the defender's belief is centered around monitoring the progress of attackers through a network-based intrusion detection system (NIDS), prompting the defender to take actions aimed at luring attackers towards decoy nodes. [PERSON] et al. [23] determined the optimal deployment strategy for deception assets such as honeypots. They developeda Q-learning algorithm to intelligently deploy deception assets that can adapt to changes in the network security state. By analyzing the attacker's strategy under uncertainty and the defender's strategy under multiple deployment location strategies, they considered the attacker-defender game. Reinforcement learning and Q-learning training algorithms were employed to determine the most effective deployment strategy for deception assets. [PERSON] et al. [24] applied an infinite horizon semi-Markov decision process (SMDP) to describe the random transition and sojourn time of attackers in honeynets, quantified the reward-risk tradeoff, and designed adaptive long-term participation policies, showing risk aversion, cost effectiveness, and time efficiency. Numerical results show that the adaptive interaction strategy proposed in the literature can quickly attract attackers to the target honeypot and attract them for a long enough time to obtain valuable threat information while keeping the penetration probability at a low level. Finally, reinforcement learning is applied to SMDP to achieve fast and robust convergence of the optimal strategy and value. [PERSON] et al. [25] introduced a new game-theoretic framework of the interaction between a defender who used limited Security Resources (SRs) to harden the network and an attacker who adopted a multi-stage plan to attack the network. They constructed the model as a maze and represented the possible plans of the attacker using attack graphs. Simulation results exhibited that their proposed reinforcement learning-based SR allocation was feasible and efficient. However, all the above studies fail to consider the attacker model whose strategy changes in real time. In order to solve the problem of deception deployment without considering the real-time change of the attacker's strategy, researchers in recent years have begun to develop a multi-agent model, but there have been few studies. Therefore, under the framework of game theory and multi-agent reinforcement learning, this paper also studies the problem of deception asset deployment in the intranet. Different from previous works, this paper models the process of lateral movement of the attacker in the intranet and the defender's defense strategy as a maze pathfinding model according to [25], adopts a stochastic game [26] to describe the dynamic interaction between the attacker and defender, and uses the multi-agent reinforcement learning algorithm Nash Q-learning [27; 28] to find the Nash equilibrium in the game to solve for the deception asset deployment strategy.
## 3 RL-Inspired Controller
### Policy Update Algorithm  
The policy update algorithm calculates the estimate corresponding to the previous pair \((s,a)\in S\times A\) of the \(T^{\pi}_{(s,a)}\) each control cycle. At \(t_{i}\), the last state and action, that is, \((s_{t-1},a_{t-1})\), are updated by the policy function \(f_{\pi}\), using the previous estimation of the long-term expected reward \(T^{\pi}_{(s_{t-1},a_{t-1})}\) and the current reward \(r_{t}\), Equation (7).  
\[T^{\pi}_{(s_{t-1},a_{t-1})}(t_{i}):=f_{\pi}\bigg{(}T^{\pi}_{(s_{t-1},a_{t-1})} (t_{i-1}),r_{t},\ldots\bigg{)} \tag{7}\]  
Once the table \(T^{\pi}\) is updated, the table is searched for the action that maximizes the reward, Equation (8):  
\[f_{a}(s_{t}):=\underset{a}{\text{arg}}MAX\bigg{(}T^{\pi}_{(s,a)}(t_{i})\bigg{)} \tag{8}\]  
The different policy update algorithms \(f_{\pi}\) that have been implemented and compared in the experiments are the following  
Figure 1: Scheme of the reinforcement learning (RL) controller.  
One-reward (OR), the last one received. As it only takes into account the last reward (smallest memory), it may be very useful when the system to be controlled changes frequently, Equation (9)  
\[OR:T^{\pi}_{(s_{t-1},\mu_{t-1})}(t_{i}):=r_{t} \tag{9}\]  
Summation of all previous rewards (SAR). It may cause an overflow in the long term, that could be solved if the values are saturated to be maintained within some limits, Equation (10)  
\[SAR:T^{\pi}_{(s_{t-1},\mu_{t-1})}(t_{i}):=T^{\pi}_{(s_{t-1},\mu_{t-1})}(t_{i-1} )+r_{t} \tag{10}\]  
Mean of all previous rewards (MAR). This policy gives more opportunities to not yet selected actions than SAR, especially when there are many rewards with the same sign, Equation (11)  
\[MAR:T^{\pi}_{(s_{t-1},\mu_{t-1})}(t_{i}):=\frac{1}{i}\bigg{[}T^{\pi}_{(s_{t-1}, \mu_{t-1})}(t_{i-1})+r_{t}\bigg{]} \tag{11}\]  
Only learning with learning rate (OL-LR). It considers a percentage of all previous rewards, Equation (12), given by the learning rate parameter \(\alpha\in\mathbb{R}\ [0,1]\).  
\[OL-LR:T^{\pi}_{(s_{t-1},\mu_{t-1})}(t_{i}):=T^{\pi}_{(s_{t-1},\mu_{t-1})}(t_{ i-1})+\alpha\cdot r_{t} \tag{12}\]  
Learning and forgetting with learning rate (LF-LR). The previous methods do not forget any previous reward; this may be effective for steady systems but for changing models it might be advantageous to forget some previous rewards, Equation (13). The forgetting factor is modelled as the complementary leaning rate \((1-\alpha)\).  
\[LF-LR:T^{\pi}_{(s_{t-1},\mu_{t-1})}(t_{i}):=(1-\alpha)T^{\pi}_{(s_{t-1},\mu_{ t-1})}(t_{i-1})+\alpha\cdot r_{t} \tag{13}\]  
Q-learning (QL). The discount factor, \(\gamma\in\mathbb{R}\ [0,1]\) is included in the policy function, Equations (14) and (15).  
\[a_{max}=\underset{a}{\text{arg}MAX}\bigg{(}T^{\pi}_{(s_{t},a)}(t_{i-1})\bigg{)}, \tag{14}\]  
\[Q_{L}:T^{\pi}_{(s_{t-1},\mu_{t-1})}(t_{i})=(1-\alpha)\cdot T^{\pi}_{(s_{t-1}, \mu_{t-1})}(t_{i-1})+\alpha\bigg{[}r_{t}-\gamma\cdot T^{\pi}_{(s_{t-1},\mu_{ max})}(t_{i-1})\bigg{]} \tag{15}\]
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 4 Chitosan
### Parameters Influencing the Antibacterial Properties of Chitosan
#### 4.1.2 Concentration  
According to several studies, the antibacterial activity of chitosan increases with increasing concentration [75; 76; 77; 78]. However, according to the study of [PERSON] et al., when comparing the antimicrobial activity of different concentrations of chitosan coated on cotton fabrics, it has been found that smaller concentrations of 0.5-0.75% achieved the highest antimicrobial activity, whereas the concentration of 1% led to decreased antibacterial effects [79]. It has been found that at low concentrations, chitosan binds to the negatively charged cell surface and destroys the cell membrane, which ultimately kills the cell. At higher concentrations of chitosan, mutual repulsion of positively charged bacterial cells occurs and agglutination is avoided [80].  
Very low concentrations, on the other hand, do not destroy bacteria. According to the findings of another research, all chitosan samples exhibited excellent antibacterial activity against _Escherichia coli_ (_E. coli_) at concentrations above 0.02%, while all samples at lower concentrations of 0.002% may encourage _E. coli_ development. They proposed that chitosan might cause bacteria to flocculate and kill them, although at low concentrations of 0.002%, it could not flocculate and kill all of them, meaning their survival would continue via reproduction [81].
## 2 Materials and Methods
### Implementation of PID, MPC, and AMPC
#### 2.3.1 Implementation of PID  
PID controllers are commonly utilized for the control of closed-loop processes. In designing the PID controller, we considered a single input single output (SISO) system, where the primary regulated variable is the output temperature, while other process parameters, such as mass flow rate, are maintained at a constant level. The equation governing the PID controller is expressed as follows:  
\[R(t)=K_{\text{p}}\cdot x(t)+K_{i}\int_{0}^{t}x(t)dt+K_{d}\frac{dx(t)}{dt} \tag{1}\]  
The proportional term, \(\text{K}_{\text{p}}\), is determined by the difference between the desired reference temperature and the actual measured value of temperature at each time step, effectively representing the temperature error. The integral term, \(\text{K}_{\text{i}}\), \(K_{i}\int_{0}^{t}x(t)dt\), accounts for the cumulative sum of past errors, while the derivative term, \(\text{K}_{\text{d}}\), \(\text{dx(t)}/\text{dt}\), evaluates the rate at which the error changes, helping to assess whether the error decreases. In this context, \(\text{K}_{\text{p}}\) helps to reduce the rise time, \(\text{K}_{\text{i}}\) addresses the steady-state error, and \(\text{K}_{\text{d}}\) improves the settling time and minimizes overshoot. The PID controller was configured using the Zeigler-Nicholas tuning method to obtain the proportional (P) gain of 2.5, an integral (I) gain of 0.2, and a derivative (D) gain of 0 [27]. To reduce the influence of noise, voltage limits were imposed on the controller, constraining its operational range. These voltage limits were determined based on real-time data, taking into account variations in electrical conductivity and the system's power requirements.  
During the heating process, the product flow rate is maintained constant at 1 L/min. The PID controller is provided with a reference for the desired output temperature. The temperature at the outlet is monitored using two optical fiber temperature sensors positioned at the heater's outlet. Additionally, a thermocouple is placed outside the heating area to further validate the readings from the optical fiber sensors. The measured temperature is compared to the reference temperature, and the resulting error is fed into the PID controller. The PID controller then sends a dimensionless signal, ranging from 0 to 100, to the HV thyristor. This signal corresponds to a specific high voltage applied to the electrodes. The effective performance of the PID controller is achieved through careful tuning of the controller gains.