esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Modified Adaptive Selective Cuckoo Search Algorithm (MASCSA)
#### 3.2.2 Adaptive Mutation Mechanism (AMM)  
AMM in [24] is applied to use two different sizes of the updated step. In Equation (14), only the step with the deviation between two random solutions is applied. Consequently, the mechanism applies two different sizes for each considered solution, in which the small step size is established byusing two solutions, and the large step size is calculated by using four different solutions. The small size and the large size support the formation of new solutions, as shown in the following equations:  
\[S_{s}^{new}=S_{o_{s}}+\delta\times(S_{01}-S_{02}) \tag{15}\]  
\[S_{s}^{new}=S_{o_{s}}+\delta\times(S_{01}-S_{02})+\delta\times(S_{03}-S_{04}) \tag{16}\]  
However, ASCSA has still applied the condition of the comparison between \({\it{rd}}_{s}\) and \({\it{MF}}\), shown in Equation (14). Thus, either Equation (15) or Equation (16) is not used if \({\it{rd}}_{s}\) is higher than \({\it{MF}}\). Clearly, there is a high possibility that new solutions are not generated if \({\it{MF}}\) is set to close to zero. In order to avoid this shortcoming, \({\it{MF}}\) is set to one in the proposed MASCSA method.  
Furthermore, in order to determine the use of either Equation (15) or Equation (16), ASCSA has applied a condition much dependent on a high number of selections. A ratio of fitness function of each considered solution to the fitness function of the best solution is calculated and then the ratio is compared to a threshold, which is suggested to be \(10^{-5}\), \(10^{-4}\), \(10^{-3}\), \(10^{-2}\), and \(10^{-1}\). If the ratio is less than the threshold, Equation (15) is used. Otherwise, Equation (16) is selected. Clearly, the condition is time-consuming, due to the selection of five values for the threshold. Consequently, in order to tackle the main disadvantage of ASCSA, a modified adaptive mutation mechanism is proposed and shown in the next section.
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
###### Abstract  
This study investigates the performance of a mixed dual-hop free-space optical/underwater wireless optical communication (FSO-UWOC) system employing a decode-and-forward (DF) relay protocol, particularly under a comprehensive hybrid channel fading model. The FSO link is assumed to experience Gamma-Gamma atmospheric turbulence fading, combined with air path loss and pointing errors. Meanwhile, the UWOC link is modeled with generalized Gamma distribution (GGD) oceanic turbulence fading, along with underwater path loss and pointing errors. Based on the proposed hybrid channel fading model, closed-form expressions for the average outage probability (OP) and average bit error rate (BER) of the mixed dual-hop system are derived using the higher transcendental Meijer-G function. Similarly, the closed-form expression for the average ergodic capacity of the mixed relay system is obtained via the bivariate Fox-H function. Additionally, asymptotic performance analyses for the average outage probability and BER under high signal-to-noise ratio (SNR) conditions are provided. Finally, Monte Carlo simulations are conducted to validate the accuracy of the derived theoretical expressions and to illustrate the effects of key system parameters on the performance of the mixed relay FSO-UWOC system.  
decode and forward; free-space optical; underwater wireless optical communication; pointing error; turbulence fading; performance analysis +
Footnote †: journal: Electronics Letters  
1  
e lect
## 2 Code Length Estimation and Blind Synchronization
### Principles of the Proposed Recognition Algorithm in Soft Decision Situations
#### 2.2.3 Summary of the Recognition Steps  
According to Equation (20), Equation (21), Equation (22) and Equation (23), we propose the code length estimation and blind synchronization in the following steps for the convenience of computer program automatic processing:  
**Step 1**: According to some prior information, set the searching range of the degree \(m\), _i.e._, set the minimal and maximal degree \(m_{min}\) and \(m_{max}\).  
**Step 2**: Design a window \(W\) which has a length \(L\) at least \(5\times(2^{m_{max}}-1)\), _i.e._, \(M\geq 5\) in Equation (22).  
**Step 3**: Full fill the window \(W\) with the received soft decision bits.  
**Step 4**: Set the initial degree \(m=m_{min}\).  
**Step 5**: Set the code length \(l=2^{m-1}\).  
**Step 6**: Set the initial synchronization position \(t\) at \(0\), which is the starting position of \(W\).  
**Step 7**: Assume the code length is \(l\) and the synchronization position is \(t\) and calculate \(\Delta H\). Note that the window \(W\) has more than one assumed codewords, we calculate the \(\Delta H\) on all the codewords and compute the mean of them as \(\Delta H(l,t)\).  
**Step 8**: If \(t<l\), then let \(t=t+1\) and go back to **Step 7**; if \(t=l\), then jump to **Step 9**.  
**Step 9**: If \(l<2^{m}-1\), then let \(l=l+1\) and go back to **Step 6**; if \(l=2^{m}-1\), then jump to **Step 10**.  
**Step 10**: If \(m<m_{max}\), then let \(\operatorname{m}=\operatorname{m}+1\) and go back to **Step 5**; if \(m=m_{max}\), then jump to **Step 11**.  
**Step 11**: Compare all the calculated \(\Delta H(l,t)\), select the maximum one and get the corresponding values of \(l\), \(t\) and \(m\) as the estimated code length, synchronization position and the degree of the GF of the being recognized codes, respectively.  
By traversing all the possible \(l\) and \(t\), finally we can search out the parameters pair \((\hat{l},\hat{t})\) that maximizes the RIDEF \(\Delta H\). \(\hat{l}\) and \(\hat{t}+k\hat{l}(k\in\mathbb{Z})\) are the estimated code length and synchronization positions.
# Evaluation of PID-Based Algorithms for UGVs
## 2 Related Work  
### Advantages and Disadvantages of Different Control Methods  
The PID controller is one of the most widely used methods in UGV control systems due to its simplicity, robustness, and effectiveness in adjusting control variables such as speed and direction. In addition to this type of control, the literature also highlights three others: the ON-OFF controller, fuzzy controller, and Model Predictive Control (MPC).  
The ON-OFF controller is particularly simple and practical to implement, such that quick response capabilities can be realized in systems with on-off inputs, and thus the ON-OFF controller is widely employed for binary actuations. Despite this, it is power hungry and imprecise and therefore not suitable for applications that demand control with optimal or continuous control [13].  
The PID controller, with its three parameters, can control the steady-state and transient responses nicely and provides simple yet effective control solutions for a wide range of control problems. Nowadays, they are widely used in a variety of industries because of their simple structure and intuitive understanding of the controller principle, as compared to other types of controllers. As described, the behavior of PID controllers depends on these parameters. Yet, the potential vulnerability of their robustness and simplicity can make them challenging to parameterize [14].  
The fuzzy controller has significant benefits in energy consumption and convergence time. It also affords a more stable response across variable conditions in contrast to conventional approaches that may be responsive to initial torques and to inhomogeneity in friction. Despite these benefits, the fuzzy design has a number of problems, such as the accuracy being affected by the actuation deadband and the computational complexity, meaning that the design frequently needs to be simplified [15].  
Model Predictive Control (MPC) is a powerful control architecture that uses dynamic models to predict and optimize control actions within a time window. It can accommodate input and output constraints, making it ideal for complex and dynamic systems, such as industrial process level control. However, the validity of MPC depends upon the validity of the system models, and the absence of this validity may lead to suboptimal behavior. Moreover, real-time optimization procedures may be computationally heavy, resulting in implementation difficulty [16].  
PID controllers are widely preferred due to their simplicity, readability, and ability to handle both steady-state and transient responses. Although more sophisticated controllers such as fuzzy logic and Model Predictive Control (MPC) have specific advantages, e.g., a better energy efficiency or optimization for dynamic systems, they generally come with disadvantages, e.g., increased complexity or larger computational needs. Table 1 shows the advantages and disadvantages of each type of control.
## 3 Results and Discussion
### Teaching Satisfaction Evaluation
#### 3.4.1 Teaching Satisfaction Accuracy Assessment  
In the scores of the \"integrated teaching effect evaluation\" of the teaching methods of the two groups of students, where 95% is the percentage of confidence interval (CI = 95%), Sig = 0.698 (Sig \(\geq\) 0.05), and the variance is homogeneous, then t(9) = 3.666, \(p\) = 0.011 (\(p\)\(\leq\) 0.05), and thus the results showed significant differences. Therefore, the experimental group's satisfaction with the overall teaching effect of VR teaching was significantly better than that of the control group.  
Among the various indicators of satisfaction evaluation, the students in the experimental group were more satisfied with the teaching objectives of \"improving learning enthusiasm\" and \"cultivating hands-on practical ability\" in VR teaching, and the evaluation of the satisfaction of the experimental group students on these two items was statistically significant. This study used an independent \(t\)-test. In the scores of the two groups of students' satisfactions with the teaching method of \"enhancing learning enthusiasm\", where 95% is the percentage of confidence interval (CI = 95%), Sig = 0.364 (Sig \(\geq\) 0.05), and the variance is homogeneous, then t(9) = 8.610, \(p\) = 0.0000 (\(p\)\(\leq\) 0.05), and thus the results showed significant differences.  
In the scores of satisfactions between the two groups of students on the teaching method of \"cultivating hands-on practical ability\", where 95% is the percentage of confidence interval (CI = 95%), Sig = 0.488 (Sig \(\geq\) 0.05), and the variance is homogeneous, then t(9) =11.658, \(p\) = 0.0000 (\(p\)\(\leq\)0.05), and thus the results showed significant differences. Therefore, the satisfaction of the experimental group on VR teaching was significantly higher than that of the control group for the realization the goal of hands-on practical ability and the improvement of learning.  
In summary, in terms of teaching satisfaction, the use of VR technology can improve students' satisfaction with the overall teaching effect, especially in terms of \"enhancing learning enthusiasm\" and \"cultivating hands-on practical ability\".
## 5 Conclusions  
Time-averaged unsteady and unsteady numerical simulations have been conducted to analyze the internal flow and aerodynamic performance of a centrifugal compressor with different diffusers. Two vaned diffuser configurations, i.e., TVD and SVD, were mainly investigated at the stall operating point. The purpose of this paper was to clarify the flow field characteristics inside the compressor with different diffusers at the diffuser stall operating point and understand the mechanism of the performance improvement of SVD. Moreover, the unsteady coherent mechanism in TVD and SVD was studied. The main conclusions are summarized as follows:  
1. The centrifugal compressor with TVD has the highest total pressure ratio, the minimum \(C_{\text{pt}}\), and maximum \(C_{\text{ps}}\) at the design operating point. In contrast, SVD has better flow adaptability, and HVD has the same internal flow characteristics as VD, especially at a low mass flow rate.
2. The mechanism of the improvement in stability by the SVD is that the high-momentum fluid from the vane pressure surface to the suction surface flows to the shroud side through the vane root, which prevents the growth of the boundary layer and the generation of flow separation on the suction surface of the diffuser vane.
3. The rotating stall occurs inside the TVD and SVD diffuser passages at the 70% design mass flow rate. Results show that the stall cells propagate at 35.7% of impeller rotational speed in the semi-vaneless space and diffuser passages.
4. The POD and DMD analysis show that the amplitude of pressure fluctuations at the selected cross-section of TVD is higher than that of SVD under the same operating condition. The spatial distribution of the pressure field in the diffuser passages demonstrates that the large-scale flow structure almost blocks the part of diffuser passages, which is also a primary reason for the diffuser stall. Therefore, SVD can improve the stability and performance of the compressor stage when compared to TVD.  
The contribution of all the authors can be subdivided on an equal basis. Conceptualization, validation, writing original draft, visualization, S.L.; validation, resources, writing--review and editing, supervision, Y.L.; project administration, funding acquisition, H.L.; validation, writing--review and editing, M.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number U1808214.  
Not applicable.  
Figure 22: The pressure fields for DMD modes of two specific frequencies at 0.95 span: (**a**) 86.645 Hz; (**b**) BPF.  
Data Availability Statement: Not applicable.  
Acknowledgments: The authors thanks Shenyang Blower Works Group Corporation for providing the CAD geometry and experimental data for this centrifugal compressor stage.  
Conflicts of Interest: The authors declare no conflict of interest.
# Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection
## 1 Introduction  
The unique form of farming that has taken place inside or near the city rather than rural areas, or urban farming, is growing at a rapid rate. Its market share has also shown a steady increase. The lack of knowledge and inexperience, however, makes the novice farmers face challenges since the crops are vulnerable to climate changes that only the professional farmers can properly manage. There have been a number of attempts to detect crop diseases using cutting-edge techniques such as CNN [1]. However, those types of models show whether the plants were infected or not rather than giving the specific details such as the cause, the area, or the gravity of the diseases.  
Therefore, our research aims to develop a solution that can help the novice urban farmers who are struggling with maintaining their crops. Our proposed solution was designed to show the infected areas and diagnose the diseases with detailed explanations.  
Therefore, we employed the Image Captioning model that requires image and sentence inputs to generate the captions which play a key role in diagnosing diseases. Moreover, we also utilize the YOLOv5-based Object Detection model that shows bounding boxes to indicate the damaged area.  
Preventing the spread of diseases in crops is indispensable for farmers, and therefore, it is crucial to identify the characteristics of diseases and handle them in advance. However, most urban farmers are struggling with them since they are not familiar with farming. We expect our improved solution suggested in this paper can give helpful assistance to those who are not familiar with agriculture.  
We expected that our research model could contribute to boost the reliability and accuracy of detecting the crop diseases by presenting sentences of its diagnosis based on our Image Captioning model and boxing the damaged areas without the Object Detection model. We also anticipated that our model could help existing urban farmers with a lack of farming knowledge.  
Our research was conducted as follows. In Section 2, we presented background and the related work in Image Captioning and Object Detection. We also introduced data collection and preprocessing work along with the explanation of our two models in Section 3. We described the output of our model test by illustrating the tables and the plots of the qualitative and quantitative results in Section 4. We concluded our paper with an overall summary of our research in Section 5.
## 4 Simulation Results and Analysis
### Backlog vs. number of AI services  
We observe from Figure 5(b) that DQN reduces by an average of 60% backlog of distributed model training tasks than the benchmark in a large network. The average error between DQN and MILP is 47.68%.  
Figure 5: Performance comparisons in large network in terms of multi-objective optimization, (**a**) \(\alpha\):\(\beta\) = 100:1, (**b**) \(\alpha\):\(\beta\) = 0.8:0.2, (**c**) \(\alpha\):\(\beta\) = 0.9:0.1.  
Figure 6: Performance comparisons in large network in terms of (**a**) profit vs. No. of services, (**b**) backlog vs. No. of services, (**c**) running time vs. No. of services.  
Running time vs. number of AI services  
It can be observed in Figure 6c that MILP consumes the most running time among the three algorithms, especially as the number of AI services is more than 160. DQN reduces by an average of 98.27% running time than MILP. The running time between DQN and benchmark is not much different. This is reasonable because MILP shows high algorithm time complexity, especially in large networks.
## 2 System Model
### Cellular System  
A total of 200 MHz in the band n257 (28 GHz) is assumed to be available for use. This band is considered since it has industry support for the deployment of mmWave 5G networks. The channel bandwidth is chosen to be 50 MHz, with the shortest subcarrier spacings (SCS) applicable in the frequency range 2 (FR2), which is 60 KHz.  
SN cell association is based on maximum receive signal strength (RSS). The SN receiver considers the cell antenna from which the largest signal power is received as the signal source. The remaining cell antennas act as sources of interference.  
If round-robin scheduling is considered, users, or equivalently sensors, in the same cell are served in a sequence, and an equal bandwidth allocation policy among users in the same cell is assumed. Following the work in [45], the user spectral efficiency (USE) under full-load conditions can be obtained based on the Shannon capacity formula as follows:  
\[USE_{j}=\frac{1}{N_{i}}log_{2}\big{(}1+SINR_{j}\big{)} \tag{6}\]  
where \(N_{i}\) is the number of active users in cell \(i\), and \(SINR_{j}\) represents the signal-to-interference-plus-noise ratio for user \(j\). Cell spectral efficiency (CSE) for cell \(i\) can be obtained by calculating the sum of users' spectral efficiencies in that cell, as follows:  
\[CSE_{i}=\sum_{j\in i}USE_{j} \tag{7}\]The area spectral efficiency (\(ASE\)) can be calculated as the weighted arithmetic mean of the \(CSE\) for all cells as follows:  
\[ASE=\sum\frac{N_{i}}{N}CSE_{i} \tag{8}\]  
where \(N\) is the number of users in the area.
## 4 Case Study Application: Towards an Efficient Terminal Container in the Port of Augusta  
### Territorial Framework  
The situation of ports in Italy has evolved in recent years, particularly from an administrative point of view. In particular, since 2016, the government promoted a rationalization of port management with the introduction of 15 Port Systems Authorities, decision-making centres grouping together different ports considered part of the same territorial system from the point of view of sea traffic; if one single port authority can address the planning and operation of several ports, optimization and performance can be increased through an effective division of tasks. Such division allows the specialization of each port of the systems, such in the case of the Eastern Sicily Port System, which includes the two ports of Catania and Augusta: the first one is a commercial port located close to the city centre, with increasing freight traffic in the last few years but suffering a lack of space for further expansion; the second one is mainly an industrial port with large spaces whose commercial activity has been steady for years and which can be seen as a possible area for development of the maritime transport of goods of Eastern Sicily, starting indeed from container traffic. The Port of Augusta is currently more focused on its industrial activity (being the third port in Italy for liquid bulk), and it is the one with the highest city-port distance [24], which makes it a good candidate to become a major trading port, due to the lower impact that operation at terminal would have on the closest urban community. The upgrade of an effective terminal container in a port that for a long time has been in a deadlock situation should be planned to guarantee freight handling optimization, but must also pass through more sustainable scenarios in line with European Union policies [25; 26]. The current situation of the Easter Sicily Port System sees the port of Catania mainly focused on roll on-roll off (Ro-Ro) traffic, which is currently its main business. The future plans cover the shift of container traffic to Augusta where large areas are available and rapid development is possible. In this sense, the upgrade of an effective container terminal in a port that for a long time has been in a deadlock situation represents a great opportunity to relieve the port of Catania and increase container volume, making the terminal the most important one in Sicily. This could allow the port system to cover both segments within the Mediterranean Short Sea Shipping market [27] and to try to gain a role in international maritime traffic. In order to achieve that, an improvement of the current terminal capacities is needed, and attention should be paid to the consequent environmental impact.