esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 6 Challenges of Deep Learning
### Data Management Challenges
Keeping contextual metadata beside the sequencing data represents a challenge, particularly when there are huge amounts of data. In dataset preparation, the original dataset is split into three dissimilar sets called training, validation, and testing datasets [156]. The following represent the challenges of dataset preparation [156]: Data leakage can come from the training, validation, and test datasets not being portioned correctly. For example, identical instances of data in testing and training datasets represent data leakage. Data quality is another challenge. Data quality is considered important due to weak data quality leading to performance deterioration and overstated results. Consistency of data can be involved in determining data quality. Nevertheless, consistency of data is difficult to accomplish within many fields. Data testing is an important phase that tests the quality of the data and decreases the presence of flawed data that would impact the procedural efficiency [156]. When some test data are outdated or erroneous, this can prevent people from using the data at all [156]. The challenges of data testing are as follows: Performing testing of data is very expensive. It demands much time and effort. Tooling is a challenge that emerges in most stages of the data pipeline. If the data-utilizing systems have prepared to be deployed in production contexts, an unparalleled collection of challenges may be faced [156]: Training-serving skewing can occur if the data at the time of serving are dissimilar to the data employed in model training. Overfitting is another challenge. Overfitting appears whenever deep neural networks fit their training datasets very well and memorize extremely specific features. When this happens, they can fail to generalize and perform predictions with new data. Post deployment, although the deep learning method is already deployed, observation must remain continuous. The reasons for that are that distribution alterations can occur, sudden additions of data, and much else may take place in the real world; and the model keeps learning continuously [156]. The following challenges come up beyond deployment [156]: Again, changes in data sources and distribution are challenges. If the distribution of data is altered, the resulting distribution may not be processed well by the deep neural network. Results that are unforeseen can be obtained due to abrupt alterations in the source of data. Data drifting is another challenge. Data drifts are also called data shifts, and can happen unexpectedly over time. Deep learning models may provide faulty and strange outcomes because of data drifts. Another challenge is feedback loops. Feedback loops can be harmful sometimes and advantageous other times.
# A Review on Mixed Reality: Current Trends, Challenges and Prospects
## 3 Concept of Mixed Reality
### Algorithm
#### 3.2.3 Object Recognition  
Identifying a real object and tracking enables the embedding of a virtual object in real space in order to implement an MR scenario. This section discusses object recognition methods, and the next section describes tracking techniques. In object recognition, the object may be static or dynamic in nature. Dynamic object recognition is an interesting topic. A good example of dynamic object recognition is moving human recognition. Ref. [36] employed an image-processing-based approach using probability-based segmentation to detect a moving object (a human) from the foreground to insert the virtual object, such as a home, in a real scene. Ref. [37] proposed the use of image processing based on semantic segmentation using conditional random fields as recurrent neural networks (CRF-RNN) to provide an appropriate context for display in a material-aware platform. Ref. [38] proposed image processing using deep learning and labeling based on voice commands for object detection from images. This method was suitable for small-size classification. Ref. [39] employed spatial object recognition for displaying information (such as a building) based on the user field of view. This model used filtering based on the distance from the user to display information.  
Despite the great benefits of the proposed models, there are various challenges that still need to be addressed. In image-based approaches based on deep learning, choosing the right training data set to identify the general object of the MR system is an important point. This data provides a useful context in MR systems to improve the quality of interaction [37]. In the image-processing-based approach based on labeling with voice commands, the accuracy of the method depends on concepts such as good synchronization of voice commands with the gaze. Additionally, the scalability of this method for large classification is a challenge and needs future research [38]. Spatial object detection encounters unwanted errors caused by tracking tools such as the global positioning system (GPS). This problem needs to be combined with an image-processing technique to extract information such as contour data, followed by comparison of the extracted data with the map to correct errors [39].
# Comparison of Manual and Automated Capillary Morphometry Measurements in Oral Mucosa: A Pilot Study
## 4 Discussion
### Role of Videocapillaroscopy  
Videocapillaroscopy played a central role in this study, providing high-resolution images that enabled detailed visualization and quantification of capillary structures. The use of pre-existing data from healthy subjects ensured a robust baseline for comparison between the automated and manual methods. However, the variability in image quality and capillary morphology across different sites and subjects also highlighted the challenges associated with automating microvascular analysis. Future studies could explore the use of additional preprocessing steps or advanced image enhancement techniques to improve the consistency and reliability of automated measurements.
###### Abstract
To solve the resource allocation problem with opportunity constraints, they utilized hybrid ant colony optimization and a support vector machine (SVM). [PERSON] et al. suggested a collaborative energy-aware resource allocation and scheduling strategy based on the TOPSIS multi-criteria decision-making method to maximize resource sharing and utilization efficiency in offloading IoT tasks [8]. [PERSON] et al. used an improved ACA to address resource allocation for 5G C-RAN [9]. The challenge is to design logical joint mappings between user equipment (UE) and RRH, as well as between RRH and BBU. This was performed adaptively based on network load conditions to reduce overall network costs while maintaining user QoS and QoE. From the above discussion, we can see that there is no report on the ACA on resource allocation for CAEVs. the ACA is a swarm intelligence optimization algorithm that can solve discrete optimization problems, so it is suitable for the problem. This paper presents a road resource allocation method based on swarm intelligent computing to address the challenge of road resource sharing in CAEVs IoT. The method's fundamental principle and implementation process are thoroughly explained, followed by comprehensive experimental verification and performance testing. Furthermore, this paper discusses and analyses the potential applications and future research directions of this method, providing valuable insights into its prospects and future developments. The approach presented in the paper has two main contributions: First, we not only consider the time, cost and load factors under the resource allocation task, but also consider the security, reliability and latency issues specific to CAEVs IoT scenarios to effectively improve the performance of all aspects of resource scheduling. Secondly, we try to introduce other algorithms to integrate and complement each other with the ACA to optimize the pheromone of the probability transfer formula in the ACA, so as to find the optimized path solution. The proposed method provides some reference for the next step of resource allocation optimization.
## 2 Materials and Methods
### Datasets Used  
The trained dataset was collected from 2018 to 2021 at a commercial vineyard (Colinas do Douro) located in the Douro Superior wine-growing region, Portugal (40.49 N; 6.59 W), at an elevation of 463 to 477 m and a slope of 9%. The vines were spaced 1.0 m within and 2.4 m between east-west-oriented rows and trained on a vertical shoot positioning with a pair of movable wires. Shoots were trimmed at a height of 1.0 m. The climate is of the Mediterranean type with continental influence, presenting very hot and dry summers. The soil is of schist origin with a sandy loam texture, a pH (KCl) of 5.6, and 0.39% of organic matter. An in situ (Enviroscan) probe composed of four capacitance sensors located at 20, 40, 60, and 80 cm depths and a GPRS communication unit were used to assess soil moisture. An automatic weather station was established near the vineyard to measure the minimum, maximum, and average temperature, as well as the minimum, maximum, and average relative humidity, wind speed, rainfall, and solar radiation. The weather station platform also delivers the ETo (estimated by the Penman-Monteith equation [47]). During the experimental period (2018-2021), the mean air temperature ranged between \(-2.12\)\({}^{\circ}\)C and \(33.92\)\({}^{\circ}\)C and the mean total annual rainfall was 763.2, 417.2, 602.8, and 514.8 mm, in 2018, 2019, 2020, and 2021, respectively. The vineyards of Colinas do Douro are drip-irrigated. The irrigation strategy used is sustainable deficit irrigation consisting of a periodical application of 5.5 mm per irrigation event. Figure 3 shows the monthly rainfall, ETo, and temperature (average minimum, maximum, and average) at Colinas do Douro from 2018 to 2021. Further details of the climate data can be found in Table 1.  
Figure 2: Random Forest’s structure. Adapted from ref [46]. The ratio between the Available Soil Water at a given time (ASW) and the Total Transpirable Soil Water (TTSW) of a certain crop in a given soil is called the Fraction of Transpirable Soil Water (FTSW) [48]. The ability of the crop to absorb moisture from the soil is usually less than the theoretical volume of soil water calculated by the field capacity minus the permanent wilting point. Therefore, at a given date, the ASW was calculated as the difference between the soil water content on the day of the measurements and the lower limit of water uptake, reached at the end of the season of the driest year (achieved in 2020; Table 1), before the first rainfall. The FTSW was calculated as the ratio of the ASW to the TTSW [35]. The TTSW up to an 80 cm soil depth was estimated as the soil water reserve held between the field capacity and the lower limit mentioned above. Table 2 and Figure 4 show the details of the calculated FTSW from the soil moisture recorded by the sensor and the calculated FTSW at Colinas Do Douro, respectively.
# An Approach to Designing Critical Railway Voice Communication
## 1 Introduction
As the FRMCS user requirements and use cases only specify the need to provide secure and reliable communication applications, the technical details of FRMCS applications are not considered and are implementation-dependent. The main goal of the paper is to formulate an approach to the design of FRMCS critical voice applications, which is a more technologically acceptable option and is independent of a specific implementation or proprietary solution. In contrast with this goal, various platform-dependent proprietary solutions are not open sourced or available to the wider application developer community. To the best of our knowledge, there is no published research on railway-specific, and in particular, FRMCS-specific services, likely because this is a highly competitive market. The proposed approach facilitates the sharing and modification of Application Programming Interfaces (APIs), which allows them to improve over time and be tailored to specific needs. The rest of the paper is organized as follows. Section 2 discusses the related works. Section 3 presents the identified common functions that can be used as service bricks in application design. Section 4 describes the REST APIs for services that are used by FRMCS support-type applications. Use cases illustrating the logic of FRMCS critical voice communication applications that make use of FRMCS support applications, as well as the verification of the application logic, are discussed in Section 5. The verification of the approach is provided in Section 6. A discussion of some API performance metrics with a focus on the injected latency is provided in Section 7. Section 8 considers the pros and cons of the proposed approach. The conclusion summarizes the contribution.
## 5 Discussion  
The experiment shows that the optimizer has a significant influence on loss and accuracy in median-sized in-air signature classification tasks. After fine-tuning the learning rate and optimizer, we can obtain the best testing performance score. Table 6 shows the best configuration for each of the models when applied to the smartwatch in-air signature dataset. The ResNet model with a learning rate of \(1\times 10^{-4}\) and Adagrad as the optimizer gives the best performance of 99.8514% for the smartwatch in-air signature testing dataset.  
Figure 16: Transformer over-fitting phenomena on smartwatch dataset in 50 repetitions.  
Figure 17 and 18 show the learning and loss curves for four models in 1000 Epochs. The ResNet is the gray colored line, from which we could observe that ResNet shows a much better tendency compared with the rest of the models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{5}{c}{**Final Configuration for Each Model**} \\ \hline  & **LeNet** & **AlexNet** & **VGG** & **ResNet** & **Transformer** (Baseline) \\ \hline
**Learning Rate** & \(1\times 10^{-1}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) \\ \hline
**Optimizer** & SGD & Adamax & Adamax & Adagrad & Adamax \\ \hline
**Test Score (\%)** & 99.4086 & 99.4232 & 99.5205 & **99.8514** & 0.1268 \\ \hline \end{tabular}
\end{table}
Table 6: The table shows the best performance configuration that was tested in this experiment for each model and the corresponding testing score in percentage. Please note that the experiment does not enumerate every combination of parameters exhaustively. In theory, there is still a possible combination that gives a better performance for each model.  
Figure 17: Learning curve comparisons for the LeNet training process, LeNet testing process, AlexNet training process, AlexNet testing process, VGG training process, VGG testing process, ResNet training process, and ResNet testing process.
## 4 Results and Evaluation
### Recommendation System for Thermal Envelope
#### 4.1.1 Numerical Evaluation  
The evaluation metrics used for the numerical variables are Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).  
The calculation of MAE is a simple process, as described by Equation (1). It involves summing the absolute values of the errors, which represent the differences between the actual and predicted values, and then dividing this sum by the total number of instances. In contrast to alternative statistical methodologies, MAE assigns equal weight to all errors when evaluating performance.  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert y_{i}-f_{i}\rvert \tag{1}\]  
where \(y_{i}\) is the real, \(f_{i}\) is the predicted value, and \(N\) is the amount of observations.  
RMSE (Equation (3)) is defined as the square root of the mean squared distance between the actual and predicted values, which is equivalent to the square root of the Mean Squared Error (MSE) (Equation (2)). While the computations for these two metrics are quite similar (Equations (2) and (3)), the RMSE is commonly preferred due to its ability to be represented in the same units as the goal parameter in the given scenario. RMSE, as per its technical definition, assigns higher significance to larger errors, since the impact of each error on the overall sum is determined by its square rather than its magnitude.  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-f_{i})^{2} \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-f_{i})^{2}} \tag{3}\]  
where \(y_{i}\) is the real, \(f_{i}\) is the predicted value, and \(N\) is the number of observations.  
After executing the code, the following evaluation metric values were obtained (Table 5).  
The accuracy of the algorithm is acceptable in terms of MAE values; however, there are some values of RMSE that are outside the ranges stated in Table 4.  
To make a clear decision, other algorithms were also used to see how they perform in terms of these statistical measures. Table 6 presents these results.  
\begin{table}
\begin{tabular}{c c c} \hline
**Building Envelope Component** & **MAE** & **RMSE** \\ \hline Wall U value & 0.0326 & 0.0423 \\ Root U value & 0.0349 & 0.0430 \\ Wall insulation thickness & 0.0263 & 0.0329 \\ Root insulation thickness & 0.0238 & 0.0316 \\ Window U value & 0.0471 & 0.0647 \\ \hline \end{tabular}
\end{table}
Table 5: Random Forest model results.  
Based on Table 6, the RF algorithm generally performs better with lower MAE and RMSE values compared to the other algorithms for most of the target variables.
## 2 Materials and Methods
### MADDPG Solves UAV Path Planning and Task Offloading Problem
#### 2.3.2 Security Reinforcement Learning
In the algorithm proposed in this chapter, we choose \(\rho>||\lambda^{*}||_{\infty}\), where \(\lambda^{*}\) is the optimal Lagrange multiplier of the original problem in formula (34). This ensures that the soft constraint problem (48) produces equivalent solutions when the original problem of the formula is feasible. The Algorithm 1 is the SC-MADDPG algorithm process. ```
1. Random initialization the parameter \(\hat{\rho}_{i}\) of Online Cirtic network \(Q_{i}^{\pi}(x,a;\hat{\rho}_{i})\),The parameter \(\theta_{i}\) of the Online Actor network
2. Random initialization the parameter \(\hat{\beta}_{i}\) of Target Cirtic network. The parameter \(\hat{\theta}_{i}\) of Target Actor network \(\hat{\pi}_{i}\): \(\hat{\beta}_{i}\leftarrow\beta_{i}\), \(\hat{\theta}_{i}\leftarrow\theta_{i}\)
3. Initialize experience replay buffer pool \(R\)
4. for episode = 1, M do
5. Random initialization state \(x^{1}\)
6. for t = 1, T do
7. Input the state value \(x_{i}^{t}\) of each intelligent agent into the Encoder layer and output the state code value \(E_{i}\)
8. Obtain \(\eta_{i}\) by solving formula (36) and use it with the agent's state value as input to the policy network
9. Each agent's action is made through a policy network \(\pi_{i}^{L}(a|o_{i},g_{i})\) to make the final action \(a_{i}^{t}\)
10. Combine the actions of all intelligent agents: \(a^{t}=(a_{1}^{t},\ldots,a_{n}^{t})\)
11. Project \(a^{t}\) onto the safe action set by solving the formula
12. Injecting detection noise \(n\)
13. Execute action \(a^{t}\), Obtain reward \(R^{t}\) and state \(x^{t}\) for the next time slot
14. Put \((x^{t},a^{t},R^{t},x^{t})\) into the experience replay buffer pool \(R\)
15. Randomly select \(\lambda\) samples from the experience replay buffer pool \(R\), with an index of \(k\)
16. Obtained through Target Actor network \(\overset{\sim}{a}=(\hat{\pi}_{1}(x_{1}^{t};\hat{\theta}_{1}),\ldots,\hat{\pi }_{1}(x_{n}^{t};\hat{\theta}_{n}))\)
17. Set \(z_{i}^{t}=R_{i}^{k}+\gamma Q_{i}^{\pi}(x^{t},\overset{\sim}{a};\hat{\theta}_{i})\)
18. Update the Citric network of all agents by minimizing the loss function:
19. \(L_{i}=\frac{1}{\lambda}\sum_{k}\left(z_{i}^{t}-Q_{i}^{\pi}(x_{k},\overset{ \sim}{a};\hat{\theta}_{i})\right)^{2}\)
20. Sampling strategy gradient update Online Actor network:
21. \(\
abla_{\theta_{i}}\hat{\theta}_{i}\approx\frac{\sum_{k}\
abla_{x_{i}}Q_{i}^{ \pi}(x^{t},\hat{\theta}_{i};\hat{\theta}_{i})|_{\lambda}=\gamma(x_{i})}{\sum_ {k}\left(z_{i}^{t},\hat{\theta}_{i};\hat{\theta}_{i})\right)}{\sum_{k}\left(z_ {i}^{t},\hat{\theta}_{i};\hat{\theta}_{i}\right)\right)}\)
22. Each intelligent agent \(i\) updates the Target Citric network and Target Actor network parameters:
23. \(\hat{\theta}_{i}\leftarrow\tau\hat{\theta}_{i}+(1-\tau)\hat{\theta}_{i}\)
24. \(\hat{\theta}_{i}\leftarrow\tau\theta_{i}+(1-\tau)\hat{\theta}_{i}\)
25. endfor
26. endfor
```  
**Algorithm 1** SC-MADDPG
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 5 Conclusions  
According to the features of periodic roll mark defects of medium and heavy plates, a periodic defect generator was designed and an CNN + LSTM detection algorithm was proposed and tested. Subsequently, an improved detection method with an attention mechanism algorithm was proposed and tested.  
The experimental results showed that the periodic defect sample generator simulates real periodic defects well to some extent. The CNN + LSTM + Attention algorithm has a good ability to recognize periodic defects such as roll marks.  
Although the algorithm proposed in this study was able to recognize most of the defects, the defect recognition rate with strong scales was found to be insufficient. The attention mechanism also increases the complexity of the algorithm and improves the requirements for computing resources. Our future research will consider using a more powerful feature extraction network and different kinds of training samples.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L.; validation, Y.L.; investigation, Y.L.; resources, K.X., J.X.; data curation, K.X.; writing--original draft preparation, Y.L.; writing--review and editing, K.X.; visualization, Y.L.; supervision, K.X.; project administration, K.X.; funding acquisition, K.X., J.X.  
This research was funded by the National Natural Science Foundation of China, grant number 51674031 and 51874022.  
The authors declare no conflict of interest.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results
### Coupling Efficiency  
Figure 8 displays the coupling efficiency analysis for BDCOA with different population sizes i.e., 20, 30, 40, and 50. The evaluation of coupling efficiency with different optimization methods is represented in Figure 9. The evaluation demonstrates that BDCOA with population 30 demonstrates better performance than the other variations. Additionally, the coupling efficiency of BDCOA is higher than MOA, POA, and COA. The dynamic adjustment of control signals for DM from BDCOA helps enhance coupling efficiency. Accordingly, the WAs are compensated for and an optical signal concentration is enhanced in FSO.  
Figure 8: Coupling efficiency for different population sizes.  
Figure 7: RMS for different optimization approaches.