esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Harvest Time of Apple Trees: An RNN-Based Approach
## 3 Materials and Methods
### Evaluation Methodology
#### 3.3.2 Dataset  
The datasets used in this work are composed of data collected in apple orchards of agricultural institutions located in southern Brazil. Weather data come from weather stations located near the orchards. Dataset1 (DS1) comes from the municipality of Cacador located in western Santa Catarina (26\({}^{\circ}\)49\({}^{\prime}\)2.884\({}^{\prime\prime}\) S, 50\({}^{\circ}\)59\({}^{\prime}\)45.044\({}^{\prime\prime}\) W) with an altitude of 960 m comprising the period from 2000 to 2018. Dataset2 (DS2) comes from the municipality of Vacaria located in the northeast region of Rio Grande do Sul (29\({}^{\circ}\)32\({}^{\circ}\)30\({}^{\prime\prime}\) S, 50\({}^{\circ}\)54\({}^{\prime}\)54\({}^{\prime\prime}\) W) with an altitude of 962 m covering the period from 2016 to 2020.  
The meteorological data were organized in a CSV file. A small excerpt is presented in Table 2. These data were used as input to the implementation. The phenological stages data consist of visual observations of the plant. In order to contribute to the reproducibility of the results of this article, the data set is available in a publicly available repository (available: [[https://github.com/tboechel/dataset/](https://github.com/tboechel/dataset/)]([https://github.com/tboechel/dataset/](https://github.com/tboechel/dataset/)) accessed on 17 February 2022).
## 4 Results and Discussions
### Effect of CO\({}_{2}\) Injection on Coalled Methane Production
#### 4.1.3 Variation of Adsorbed Gas Concentration  
The core of the CO\({}_{2}\)-ECBM is to achieve the purpose of stable CO\({}_{2}\) storage through the adsorption of CO\({}_{2}\) by coal matrix. The adsorption competition between CO\({}_{2}\) and CH\({}_{4}\) is one of the core problems in the process of CO\({}_{2}\) storage in the coal seam. Especially in the injection stage, with the injection of CO\({}_{2}\), the bottom hole and near-well reservoir contain a large pressure range. According to the Langmuir adsorption theory, different pressures determine the adsorption capacity of different gases in the coal seam (Equation (5)).  
Figure 10: 300 days, 500 days, 1000 days, 1500 days CH\({}_{4}\) content distribution cloud (**a**) \(CBM\) (**b**) \(CO_{2}\)-\(ECBM\).  
Figure 9: Variation curve of reservoir pressure at reference point B. (**a**) Comparison of \(CO_{2}\)-\(ECBM\) and CBM (**b**) \(CO_{2}\), \(CH_{4}\) partial pressure and total reservoir pressure of \(CO_{2}\)-\(ECBM\).  
Figure 11 illustrates the distribution of adsorbed CH\({}_{4}\) concentration in direct exploitation and CO\({}_{2}\) injection exploitation, as well as adsorbed CO\({}_{2}\) concentration in CO\({}_{2}\) injection exploitation. During direct exploitation, the adsorbed CH\({}_{4}\) concentration mainly changes near the production well, but the change is small. Affected by the pressure relief of the production well, the free CH\({}_{4}\) is first extracted, then the dynamic equilibrium of the gas in the pore is broken, and part of the adsorbed CH\({}_{4}\) is desorbed Figure 11a. However, the desorption of adsorbed CH\({}_{4}\) is not high during direct exploitation. When CO\({}_{2}\) is injected into the coal seam, it will preferentially adsorb in the coal matrix because the adsorption of CO\({}_{2}\) is greater than that of CH\({}_{4}\), forcing the latter to desorb. It can be obtained from Figure 11b, With the continuous injection of CO\({}_{2}\), the concentration of adsorbed CH\({}_{4}\) at the injection well and production well continues to decrease. It can also be seen from Figure 11c that as CO\({}_{2}\) is injected, the concentration of adsorbed CO\({}_{2}\) in the reservoir increased dramatically.  
In brief, CH\({}_{4}\) desorption is slow and the amount of desorption is small during direct exploitation, which often leads to a small recovery rate of CH\({}_{4}\). The CO\({}_{2}\)-ECBM technology has the dual benefits of greenhouse gas emission reduction and clean fossil energy development, and it's superiority to conventional exploration technologies.
## 4 Settings, Results and Discussion
### Experimental Settings Setup
#### 4.1.2 Experimental Details  
For LBSD, the k in generalization uncertainty is set at 3, and the KL divergence coefficient is set at 2 or 3. The basic VQA network UpDn uses a pre-trained Faster-RCNN to extract image features, a pretrained model GloVe (300 dimensions) to extract text features and a single-layer GRU to obtain question-embedded vectors (512 dimensions). Finally, the joint embedding is 2048 dimensions. In addition, the batch size is set at 512 and trained and tested on two Titan Xp GPUs. Because VQA-CP v1 and v2 lack validation datasets, VQA v2 datasets generally display results on validation datasets. In order to select the parameters of the model, the authors of this paper divide 10% of the samples from the test datasets or the validation datasets to act as the validation dataset, select the parameters of the model on the validation datasets and then test the precision of the model on the test datasets. The results of our experiments are based on the results of the original published papers, and for experiments that were not performed in the original papers, we reproduced them using the official code, and for the results that we reproduced, we put an asterisk in the upper right-hand corner. With regard to run time, the proposed method runs 30 epochs for 15 h in a 256 GB memory and two Titan XP GPUs environment. For the statistical analysis, we performed multiple experiments with a confidence of 95% for the experimental results, and for the purpose of fillability of the experimental results, we selected the median of the results of multiple experiments as the final result, the final precision and the precision of each index are filled in the table.
## 4 Experiments and Results
### Results for Redundancy, Overhead, and Delay
#### 4.5.2 Network Overhead Analysis (% Packets)  
The higher Overhead in traditional adaptive schemes (RL-PT, NFRL, and D-FL+RL) emerges due to suboptimal balance between coverage assurance and redundancy control. Specifically, the Overhead peaks for RL-PT are a consequence of rapid parameter-weighting shifts, resulting in broadcast decisions often optimized for short-term local states rather  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nodes** & **D-FL+RL** & **RL-PT** & **NFRL** & **Meth. A** & **Meth. B** \\ \hline
40 & 5.1 & 5.3 & 3.8 & 3.5 & 3.2 \\
80 & 4.7 & 4.9 & 4.5 & 3.8 & 3.4 \\
120 & 8.9 & 9.2 & 8.5 & 7.4 & 7.0 \\
200 & 15.8 & 16.1 & 14.5 & 13.2 & 12.3 \\
280 & 17.1 & 18.0 & 15.2 & 13.9 & 13.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average end-to-end delay (ms).  
than stable global performance. Consequently, frequent unnecessary rebroadcasts amplify collision probabilities, increasing overall packet overhead.  
In contrast, our Proposed Method A utilizes reinforcement learning to adjust broadcast probabilities within dynamically adjusted membership boundaries, leading to a more consistent network-wide policy, significantly reducing Overhead. Moreover, Proposed Method B achieves further Overhead minimization by adaptively switching among multiple fuzzy profiles tailored explicitly for scenarios like high density and energy constraints. Such targeted profiles (especially P1 and P2) enable nodes to systematically curtail unnecessary transmissions by actively incorporating context-specific metrics (e.g., neighbor density or Residual Energy), thus directly addressing the Overhead-causing factors of redundant transmissions and collisions.
## 3 Challenges and Opportunities of Skin Cancer Detection
### Construction of Datasets  
Another important aspect to consider is that for algorithm training, the images used are labeled (diagnosed) by dermatologists; thus, there is a high risk that the networks learn the decision-making process of dermatologists, including all possible biases. Therefore, it would be good to use only biopsy-verified images.  
Admitting that we have largely overcome the above challenges, in order to proceed with the acceptance of AI models in dermatological clinical practice, one must be able to understand and explain their decision-making processes. To do so, one must conduct interpretability and explicability analyses of the models in order to interpret and explain their rationale for making a certain decision. This would bridge the distrust--related to not fully understanding--that many physicians place in new and emerging technologies and facilitate their introduction into clinical practice. In addition, it would be good to introduce training courses in the practices of dermatologists since most of them admit that they are not familiar with the subject of AI [43]. The effort produced in addressing the many challenges associated with automated skin cancer detection is certainly repaid by the opportunities that result. In addition to those already mentioned, they include: the ability to avoid unnecessary biopsies or missed melanomas, to make skin diagnoses without the need for physical contact with the patient's skin, and to reduce the cost of diagnosis and treatment of non-melanoma skin cancer, which turns out to be considerable [44].
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 7 Evaluation
### Evaluation Environment
#### 7.2.2 Kubernetes Clusters  
We utilize two kinds of clusters to run our test applications and conduct experiments to evaluate our research questions.  
* **On-premise clusters on Openstack**: The testbed for on-premise clusters is an isolated part of a private OpenStack cloud, version 21.2.4. The OpenStack cloud consists of a master-worker architecture with two controller machines and droplets on which virtual machines can be scheduled. The droplets have 2 x Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40 GHz; i.e., 2 x (14 cores, 28 threads), and 256 GB RAM. Each droplet has two 10 Gbit network interfaces. The K8s cluster used in the evaluation was deployed using Kubeadm [55]. Kubeadm is an installer product that offers a rich yet proprietary customization interface [55]. Additionally, we use Terraform scripts to automate the testbed creation process. Our on-premise clusters have a control plane node of resource size c4m4 (4 cores and 4 GB memory) and worker nodes of size c4m4;
* **GKE cluster**: We use GKE [34] as our base line to compare the impact of our vendor-agnostic reconfiguration tactics and controller on Kubernetes of a hosted product type. GKE's default cluster uses Kubenet, so all experiments related to switching the Network Plugin are only carried out on GKE. Moreover, like on-premise clusters, GKE can support creating clusters with our selected features through its proprietary interfaces. For example, we can use NodeConfig file to partially modify the Kubelet-Configuration manifest [38], create older clusters to support deprecated APIs and enable network policy to use the CNI network plugin [56]. Our GKE clusters have a hardware setup with a vendor-managed control plane and two e2-highcpu-4 (4 cores and 4 GB memory) instances as worker nodes.  
Since the cluster we need to evaluate is a member cluster in a cluster federation, we need an additional host cluster to manage it. This host cluster only runs the Kubefed control plane and our Featureconfig API and does not run any other workloads. The cluster is deployed on the OpenStack cloud with only one c4m4 instance.
## 2 Results and Discussion
### Optimization of Extractive Alkylation Conditions  
A schematic overview of the thiol analysis method is shown in Figure A1, and steps subjected to optimization are underlined. To simplify sample preparation and reduce the risk of thiol oxidation before derivatization, we investigated the use of extractive alkylation, in which derivatization and extraction occur in a single step. Under these conditions, PFBBr derivatization of the thiol involves nucleophilic substitution by the thiolate in a heterogenous system, and the reaction typically proceeds more rapidly in the presence of phase-transfer catalysts like 18-crown-6-ether [20]. Although extractive alkylation with PFBBr is widely used [20], only one group has reported an (unsuccessful) attempt to perform extractive alkylation of thiols in wine but no details were presented as to why the procedure did not work [9].  
Figure 1: Typical chromatogram obtained in selective ion monitoring (SIM) mode: (**a**) White wine spiked with 10 ng/L 4-MMP and 540 ng/L \(d_{10}\)-4-MMP; (**b**) White wine spiked with 25 ng/L 3-MH and 440 ng/L \(d_{8}\)-3-MH; (**c**) White wine spiked with 60 ng/L 3-MHA and 400 ng/L \(d_{5}\)-3-MHA.  
As a first step, we optimized conditions for extractive alkylation. Optimization experiments are typically performed with the goal of maximizing analyte signals, for example, in determining optimal SPME extraction temperature and extraction time for analysis of 3-alkyl-2-methoxypyrazines in grape musts [21]. In our method, we observed up to tenfold variation in both analyte signal and background noise across runs for replicate analyses during optimization experiments. The reason for this variation was unclear, but because the signal intensity and noise intensity were well correlated, it may be due to variation in the drying step. To overcome this problem, we attempted to minimize the detection limit (LOD) in our optimization experiments, where LOD was defined as the minimum concentration necessary to achieve 3 times the noise limit.
## 2 Rare Earth Solution Image Feature Extraction
### Spatial Texture Feature Extraction  
The gray level co-occurrence matrix is a method to describe image texture features [19]; it can reflect the gray relationship between two pixels in the space, and is the basis of describing image texture features [20]. It is defined as follows: for a given direction \(\theta\) and distance \(d\), on a line with the direction \(\theta\), the number of occurrences of one pixel's gray scale \(i\) and another point pair whose gray scale is \(j\) and whose pixel distance is \(d\) is the value of the point \((i,j)\) of the gray level co-occurrence matrix. In order to reduce the computational complexity on the premise of retaining more information, in this paper we select eight statistical values as original texture features, which are as follows:  
\[energy=\sum_{i}\sum_{j}p(i,j) \tag{7}\]  
\[contrast=\sum_{i}\sum_{j}(i-j)^{2}p(i,j) \tag{8}\]  
\[entropy=-\sum_{i}\sum_{j}p(i,j)log(p(i,j)) \tag{9}\]  
\[homogeneity=\sum_{i}\sum_{j}\frac{P(i,j)}{1+(i-j)^{2}} \tag{10}\]  
\[autocorrelation=\sum_{i}\sum_{j}ijp(i,j) \tag{11}\]  
\[mean=\sum_{i}\sum_{j}ip(i,j) \tag{12}\]  
\[var=\sum_{i}\sum_{j}(i-Mean)^{2}p(i,j) \tag{13}\]  
\[correlation=\sum_{i}\sum_{j}\frac{(i-Mean)(j-Mean)p(i,j)}{Var^{2}} \tag{14}\]  
In these formulas, \(p(i,j)\) represents the value of point \((i,j)\) of the gray level co-occurrence matrix with the given parameters \(\theta\), \(d\). The aforementioned eight features encapsulate the grayscale texture information of the image: Energy reflects the uniformity of the grayscale distribution in the image, with a larger energy value indicating a more dispersed image; Contrast reflects the intensity of the grayscale differences in the image, with a greater contrast indicating a stronger difference in the rare earth solution; Entropy reflects the uncertainty of an image, with a higher entropy value indicating a more complex texture in the image space; Homogeneity reflects the distribution of image texture, with a smaller homogeneity value indicating a more dispersed image texture; Autocorrelation reflects the degree of correlation between grayscale values in an image and the grayscale values of surrounding pixels, with a greater autocorrelation value indicating a smoother image texture; Mean and variance describe the average of the co-occurrence matrix and the degree of deviation from the mean, respectively, reflecting the uniformity of the image; and Correlation reflects the law of change of the grayscale value of the image, with a higher correlation value indicating a more regular image.  
In the above feature extraction process, the distance \(d\) of the gray co-occurrence matrix is set as 1, and the angle \(\theta\) is taken as the mean value of each feature in the direction of 0, 45, 90, 135 to balance the influence of different directions on feature values. Therefore, eight features of the gray level co-incidence matrix are extracted as the spatial texture features of the image.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 2 Related Work
### Application of Machine Learning in Badminton  
Recent studies have begun to explore the application of advanced analytics and eye-tracking technology to study the effects of various factors on performance in racket. For instance, [PERSON] et al. [3] investigated the impact of pressure on QE depending on expertise level in basketball. While not specifically about badminton, their study highlighted the importance of integrating contextual factors like pressure and expertise level into performance models for more accurate predictions.  
In racket sports such as tennis, [PERSON] et al. [14] explored the integration of QE and biomechanical data into machine learning models to enhance their predictive power, a method that could be applied to badminton to improve performance prediction during competitive play.  
Similarly, [PERSON] and [PERSON] [15] conducted a study using neural network models to analyze player performance in tennis, demonstrating the feasibility of real-time data integration for racket sports. While their focus was on tennis, the findings suggest that machine learning can effectively capture the rapid decision-making and motor control demands of badminton.  
Incorporating QE metrics into performance models for badminton could enhance their predictive power, providing deeper insights into player focus and visual strategies during play. Future research could explore how QE training might be adapted for the fast-paced environment of badminton and how machine learning models could integrate these data with other performance metrics for more comprehensive analysis.
## 3 Results
### Accuracy of pSWE Measurements  
The pSWE mean at depths of 3 and 5 cm showed accurate measurements compared to the value reported by the manufactures (3 cm, \(p\) = 0.44; 5 cm, \(p\) = 0.05), but this is not the case at a depth of 7 cm, in which an overestimation of the pSWE mean compared to manufacture value was noted (2.7%, \(p\) = 0.006) (Figure 4).  
Figure 4: Accuracy of pSWE measurements at different depths compared to manufacture reference value. There is a significant different between pSWE measurements at depths of seven cm compared to reference value (\(p\) = 0.024). No significant differences in pSWE at depths of three cm or five cm when compared to reference value. Mean, SEM (\(\star\)\(p\) < 0.05 using one-sample \(t\)-test between pSWE measurements at different depths and reference value, number of pSWE measurements per depth = 60).  
Figure 3: Comparison between pSWE measurements at different depths. Significant differences between pSWE measurements at depths of three cm vs. seven cm (\(p\) = 0.024, (**B**)) and between pSWE at depths five cm vs. seven cm (\(p\) = 0.001, (**C**)) were noted. No significant differences in pSWE at depths of three cm vs. five cm (\(p\) = 0.44, (**A**)). Mean, SEM (\(\star\)\(p\) < 0.05 using Independent \(t\)-test between pSWE measurements at different depths, number of pSWE measurements per depth = 60).
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 5 Conclusions  
This study showcases the effectiveness of using reinforcement learning (RL) for pressure control and multi-agent systems for thrust and valve management in solid attitude and orbital control engines. The RL-based pressure control agent significantly outperforms traditional PID control, offering faster and more precise regulation. The thrust distribution agent maintains high accuracy and stability in controlling thrust forces with minimal impact on pressure, highlighting the importance of closed-loop control for valve response times. The opening closed-loop agent excels in tracking both step and continuous commands, ensuring low overshoot and rapid response times.  
Under typical operating conditions, the hierarchical multi-agent system provides comprehensive closed-loop control, achieving desired performance metrics with minimal errors and swift adjustments. These findings underscore the potential of advanced control strategies in enhancing the performance and reliability of propulsion systems. However, addressing the limitations related to training environment accuracy and thrust coordination remains a priority for future research. Developing a semi-physical simulation system and integrating global constraints into the RL framework will be critical steps toward achieving more robust and reliable control in SADPS.  
Investigation, Z.F.; resources, Z.F.; writing--original draft preparation, Z.H.; writing--review and editing, L.N.; visualization, Z.H.; supervision, L.N.; project administration, Z.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Foundation Strengthening Plan Project and the National Natural Science Foundation of China under Grant 61973210.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
The authors would like to extend their sincere gratitude to the Shanghai Space Propulsion Technology Research Institute, China, for their invaluable technical support, especially to [PERSON], [PERSON], and [PERSON]. Additionally, heartfelt appreciation is extended to the technical support provided by Shanghai Jiao Tong University, China, particularly to [PERSON] and [PERSON].  
The authors declare no conflicts of interest.