esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Determination of the ELM Structure Frameworks  
After determining the input variables of the VMFs, we need to build an ELM-based, data-driven prediction model for each subsequence. Due to the stochastic nature of the ELM models, the ELM prediction results in this paper are based on the average of ten calculations with the removal of four outliers. During the prediction process, a corresponding ELM model is established for each subsequence, so that the number of the ELMs and the decomposition modulus are consistent. The output of each ELM represents the predicted results of the corresponding VMF, and the final prediction result is obtained by summing the predictions of all ELMs.  
Since the prediction accuracy of ELMs will directly affect the prediction accuracy of the model, the selection of appropriate ELM parameters is crucial. The ELM model has two adjustable parameters, \(g(x)\) and \(l\), mentioned in Section 2.4. Sigmoid function is chosen as the \(g(x)\) in this study. Meanwhile, the performance of an ELM model also depends on \(l\), which can be obtained by Equation (24) as the ELM belongs to SLFNs.  
\[l=round\Big{(}\sqrt{n+m}+rand(\,1\sim 10)\Big{)} \tag{24}\]  
In this study, \(n\) and \(m\) represent the number of input and output variables determined in Section 4.3, respectively. Since the original displacement sequence is decomposed into five VMFs, correspondingly, five ELM prediction models will be constructed. Equation (24) has given the method of determining the number of hidden layer neurons, and thus the structural framework of each ELM can be determined, expressed as '\(n-l-m^{\prime}\). Take VMF\({}_{1}\) as an example to illustrate the determination process of the ELM structural framework: the initial ELM structural framework of VMF1 is '5-l-1'. According to Equation (24), the best \(l\) of the model is located at interval [3; 12] or its neighboring values, and ten operations are performed for different hidden layer neurons to calculate each performance indicators. The results show that when \(l=10\), the learning performance of the ELM is the best, so that the structure framework of the ELM model corresponding to VMF1 can be determined as '5-10-1'. The ELM structure frameworks of VMF2-VMF5 can be obtained by the same method, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Prediction Model** & \(n\)**\(-\)**\(l\)**\(-\)**\(m\)** \\ \hline ELM\({}_{\text{VMF1}}\) & 5-10-1 \\ ELM\({}_{\text{VMF2}}\) & 5-12-1 \\ ELM\({}_{\text{VMF3}}\) & 9-15-1 \\ ELM\({}_{\text{VMF4}}\) & 4-10-1 \\ ELM\({}_{\text{VMF5}}\) & 6-12-1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ELM structure framework of each VMF.  
Figure 11: The process of determining the input and output variables for VMF\({}_{1}\).
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 3 Schedule Overview
### Problem Definition
This is mostly applied to nurses but the ideas are applicable to any type of staff. * Emergency Room Scheduling: This is very similar to medical staff scheduling but has the added caveat that physicians must be scheduled with the knowledge that the workload is variable. The emergency room occupancy has stochastic elements regarding the amount of work the emergency department is predicted to have. This domain looks into both online and offline schedules depending on their use case. * Operating Room Scheduling: This type of scheduling is very broad and can fall under both types of schedules. On the end hand, operating rooms and surgeons can be treated as resources and must be allocated as they need them. This would be deterministic in nature and similar to emergency room scheduling. However, operating rooms also have to deal with scheduling patients within the operating room and likewise, in emergency rooms, it is impossible to know the demand prior to creation. As demand for the operating room can fluctuate and different priorities exist it is difficult to plan a schedule for it upfront. * Outpatient Scheduling: Outpatients are patients that do not stay at the hospital overnight. These patients typically arrive either through the emergency room or call ahead to book an appointment. This leads to a dynamic schedule as cancellations and arrivals can happen at any time. Often scheduling is designed such that there is an ongoing schedule that is consistently updated. Since outpatients are a primarily stochastic resource they fall comfortably into type two.
## 4 Results and Analysis
### Characteristics of Sand-Moving Wind
#### 4.2.3 Characteristics of Monthly Sand-Moving Wind  
There were differences among various months in the variation characteristics of sand-moving wind frequency and velocity. As shown in Figure 6a, the variation trends of sand-moving wind frequency in Qiemo and Ruoqiang were consistent. Specifically, sand-moving wind frequency gradually increased from January to April, peaked in April and May, and presented a declining trend from April and May to December. For the two counties, the extreme values of monthly average sand-moving wind velocity and monthly sand-moving wind frequency occurred in the same periods. The monthly average sand-moving wind velocity of Qiemo fell within the range of 5.33-6.28 m/s and reached its maximum and minimum values in May and December, respectively. The monthly average sand-moving wind velocity of Ruoqiang fluctuated between 5.35 and 6.47 m/s and reached its maximum and minimum values in April and December, respectively (Figure 6b). Figure 6c,d indicates that the variation trends of monthly sand-moving wind directions were consistent with annual and seasonal sand-moving wind directions in the two counties. The prevailing direction of the monthly sand-moving wind in Qiemo was NE, and the prevailing directions of monthly sand-moving wind in Ruoqiang were ENE and NE. Prevailing directions accounted for large proportions of monthly sand-moving wind frequency in March, April, and May. Specifically, in Qiemo, NE accounted for 69.02, 63.72, and 52.55% of monthly sand-moving wind frequency in March, April, and May, respectively. In Ruoqiang, ENE and NE accounted for 90.63, 89.73, and 84.78% of monthly sand-moving wind frequency in March, April, and May, respectively. Sand-moving wind direction presented a blunt bimodal pattern in June in Qiemo and Ruoqiang. June is the beginning month of the summer. The variation trend of its sand-moving wind frequency in various directions was consistent with the overall sand-moving wind frequency in the summer. Among various summer months, June accounted for the largest proportion of sand-moving wind frequency and had a variable sand-moving wind direction and a harsh aeolian sand environment.  
Figure 6: Monthly sand-moving wind frequency and velocity: (**a**) sand-moving wind frequency; (**b**) average sand-moving wind velocity; (**c**) sand-moving wind frequencies in various directions in Qiemo; (**d**) sand-moving wind frequencies in various directions in Ruoqiang.
## 2 Literature Review
[PERSON] et al. [2] presented a perspective paper on the opportunities and challenges of adopting digital agricultural technologies in the United States. They summarized several economic and noneconomic factors expected to influence adoption decisions based on a nonsystematic review of the literature on the subject. However, their work only focused on the issue in the US, which is a relatively more experienced country in applying innovative technologies. Since the drivers of digital technology adoption differ greatly from country to country, it is not meaningful to draw conclusions from other countries' experiences [23]. The main components absent in existing literature are the integration and categorization of factors that influence technology adoption by farmers in China. This study will be valuable to digital transformation researchers and educators, agribusiness firms involved in selling digital tools, as well as policymakers concerned about sustainable agricultural production and farmers' welfare. Within research, several methods have been applied to support the rigorous investigation of digital agricultural technology adoption. Existing studies analyzing farm-level adoption have typically used regression-based analysis (e.g., logit, probit, Poisson models) [12]. Due to the quantitative nature of these methods, they fail to capture qualitative factors, such as feedback from users in the form of opinions and suggestions. Some other studies adopt qualitative descriptive approaches accounting for less measurable factors such as material contingencies and cultural dimensions of knowledge [24]. Recently, some newly developed models placing greater emphasis on both quantitative and qualitative analysis have been applied to studying the adoption and diffusion of digital farming technologies, such as the Theoretical Framework of Acceptability [25]. In this study, we do not intend to review the existing methods used in the literature. Instead, we briefly overview modeling approaches for examining the determinants of farmers' adoption decisions in existing studies in China and discuss the limitations and possible improvements of these approaches. The remainder of this paper is structured as follows. In Section 3, the literature search methodology is discussed. The results and discussion on identified factors and their implications are elaborated in Section 4. Section 5 concludes the paper.
## 3 Research on Sunshine Shadow Occlusion Algorithm  
### Three-Dimensional Light Occlusion Theory  
Shadow occlusion caused by sunlight can be divided into the following four types based on the existing research: no occlusion, self-occlusion, mutual occlusion, and permanent occlusion [35], as shown in Figure 5.  
If the incident angle of the sunlight projected onto a certain area of the surface of the structure is less than 90\({}^{\circ}\) and no other surface is projected onto the area, the area is said to be unshielded at this moment. The surface area in a no occlusion state is not affected by direct solar radiation.  
If the incident angle of the sunlight projected onto a certain area of the structural surface is greater than 90\({}^{\circ}\), the sunlight cannot illuminate the area due to the occlusion of the area itself. The area is said to be in a self-occlusion state at this moment. An area in the self-occlusion state does not apply a direct solar radiation load.  
If the incident angle of the sunlight projected onto a certain area of the structural surface is less than 90\({}^{\circ}\) but the area is in the shadow area projected by other surfaces, it is considered to be in a state of mutual occlusion at this moment and cannot be directly radiated by the sun. The most commonly used method to determine whether an area is in a state of mutual occlusion is to determine whether the connection between the node and the sun in the area has an intersection with other surfaces at this moment. If it exists, it is determined that the node is in a state of mutual occlusion at this moment.  
If the line between the node and the sun on the surface of the structure (generally refers to the internal surface) passes through other surfaces of the structure at any time, the surface is in a permanent occlusion state. Surfaces in permanent occlusion are generally not in direct contact with the external environment, so such surfaces are generally not directly affected by solar radiation.  
Figure 5: Shadow occlusion classification.
## 2 Key Steps in a FRB Search Pipeline
### Candidate Classification  
All real-life FRB detection pipelines produce many spurious candidates due to various terrestrial sources. These so-called 'false positives' make an actual detection extremely challenging in high RFI environments. Ever-proliferating cellular network towers, radar stations and satellite constellations emitting intense radiation across the radio-band can generate millions of false FRB candidates in any telescope even after proper RFI excision. Hence, one has to use clever techniques to identify a real astrophysical source from the noise.
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 4 Proposed Spoken Language Identification System
### Preprocessing  
Data preprocessing is critical for the success of deep learning systems [37; 38]. In this article, data processing was done by detecting the boundaries of speech. In particular, speech signals were segmented and silent parts removed depending on speech boundaries using R2020b-MATLAB's detect speech function [39]. This was done to prevent the silent parts from influencing the classification task. Figure 2 shows plots of the detected speech boundaries.
# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
###### Abstract
Furthermore, automated and iterative optimization is made possible by its adaptable Component Object Model (COM) interface, which is essential for the suggested methodology. In the current study, a microsimulation model was created for a highway segment calibrated as a case study to test the framework. During the calibration process, vehicle trajectory data from the Wejo dataset, a comprehensive collection of connected vehicle data, were utilized alongside Remote Traffic Microwave Sensor (RTMS) data to create a traffic volume dataset used as an input in the simulation model. Using the two mentioned datasets, vehicles in the simulation were divided into two groups: connected vehicles from the Wejo dataset and non-connected vehicles from the RTMS dataset. The trajectory data from connected vehicles were essential for the remainder of the study. After the simulation period, these trajectories were extracted and compared to real-world traffic trajectories to identify discrepancies. In subsequent iterations, driving behavior parameters were adjusted to make the trajectories more similar. By employing a Genetic Algorithm (GA) and repeating this process, the differences between the trajectories were minimized, and optimal values for driving behavior parameters were determined. The absence of high-resolution data, the difficulty of parameter adjustments, and the computational burden of iterative simulations are some of the major issues in traffic simulation calibration that are addressed in this study. The work maintains computational efficiency while achieving notable reductions in calibration errors through the use of GA and distributed computing. The findings show how including cutting-edge data sources, such as connected vehicle trajectories, can improve traffic models' precision and dependability, making them a more effective tool for traffic management and infrastructure development. The study's findings show promise for greatly increasing the accuracy of traffic simulations, which directly affects advanced traffic management and infrastructure design. The primary objective of this research is to improve the fidelity of microsimulation models by creating a novel calibration framework that makes use of traffic volume measurements and connected vehicle trajectory data. The study aimed to improve model fidelity by minimizing the differences between simulated and real vehicle trajectories using a GA. This section provides a brief introduction to the study. After a quick review of the literature, the specifics of the suggested methodology are presented in the next section. The outcomes of this study are thoroughly explored in the results and discussion sections, which are presented last.
The automated diagnosis allows doctors to act quickly upon lifesaving decisions [19]. The aim of this research is to assist doctors in making clinical decisions and provide an early diagnosis of COVID-19. In this research a customized convolutional neural network (CNN) model is proposed to diagnose COVID-19 from X-ray images. The model takes chest X-ray images as inputs and predicts whether the provided X-ray image is of a COVID-19 patient or a normal person. The model successfully detects COVID-19 with high accuracy in less time and using fewer resources. The model consists of nine layers and a total of 5,668,097 parameters. Two publicly available datasets were used for the development of the model. First, the images from the dataset that were used for the training and validation of the model were preprocessed to generate accurate results. The novelty of this study is that we propose a model to detect COVID-19 early. The model contains a few numbers of layers and parameters to provide prediction in less time with accurate and efficient results. Two different datasets are used for training and validation which allows us to test cross dataset robustness of the model. We compared our classifier to a variety of advanced DL classifiers and found that ours outperformed them in terms of both computational cost and classification efficiency. The proposed model can be deployed in the most remote areas and can be used for early diagnosis of COVID-19 before the patient develops serious symptoms. The rest of the paper is further divided into five sections. Section 2 presents the work related to our research. The dataset, techniques used for developing the model in our research, and details of the designed layers and preprocessing are discussed in Section 3. Section 4 describes the experimental setup and results of the research followed by a comparison with similar models. The accuracy, performance and datasets are explained in detail in Section 4. Section 5 represents limitations and future work which should be considered for this research. The last section, i.e., Section 6 provides the conclusion of the study.
## 3 Results and Discussion
### Comparison of Emulsion Characteristics  
Figure 1 shows the creaming index changes of emulsions prepared in different MD:GA ratios. Creaming is the most commonly encountered type of emulsion instability [56]. As seen in Figure 1, increasing the amount of GA in the emulsion has a positive effect on emulsion stability. At the end of 7 days of storage, the emulsion prepared in the ratio of 25:75 MD:GA was the most stable one with a 5% creaming index.  
Figure 2 shows the light microscopy images of emulsions prepared in different MD:GA ratios. The results support the creaming index results. The size and distribution of the droplets in the emulsion prepared with an MD:GA ratio of 25:75 was more uniform.  
The mean particle size (D[4; 3]) values of emulsions prepared in MD:GA/25:75, MDGA/50:50, and MD:GA/75:25 were 3.04 \(\pm\) 0.03, 3.81 \(\pm\) 0.02 and 13.52 \(\pm\) 0.38, respectively. The volume distribution of droplets in emulsions prepared in different MD:GA ratios are given in Figure 3. The particle size distributions of the emulsion prepared by using MD:GA/25:75 and MD:GA/50:50 exhibited a monomodal distribution with a sharp peak, indicating a more uniform distribution, while MD:GA/75:25 showed a monomodal distribution and bell curve peak. The particle size of emulsions containing more MD shifted to larger particle sizes, resulting in lower stability. Particle size analysis and droplet size distribution results also show that MD:GA prepared in 75:25 has the smallest particle size and uniform distribution. One of the primary causes of emulsion stability loss is the increase in droplet size [57].  
In encapsulation, an emulsion with reduced droplet size exhibits higher oil retention [27]. A stable emulsion is needed for increased encapsulation efficiency, relatively low surface oil, and higher volatile component retention [58]. For this consideration, the emulsion prepared in MD:GA ratio of 75:25 has been chosen for further encapsulation experiments.  
Figure 1: Creaming index of emulsions prepared in different MD:GA ratio.  
Figure 3: Particle size distribution graph of emulsions prepared in different MD:GA ratios.  
Figure 2: Microscopic images of emulsions obtained at 40\(\times\) magnification **(A)** MD:GA/25:75 (**B**) MD:GA/50:50 (**C**) MD:GA/75:25.
## 4 Deployment of Information Systems
### Versioning Strategies  
Version control strategies and branching strategies offer structure and guidance on how code changes are managed, integrated, and deployed within a development team or organization. The choice of the method depends on factors such as team size, project complexity, implementation frequency, and collaboration requirements. The management of repositories can be conducted in the following way:  
* Version control systems like Git, Mercurial, and Subversion are tools that manage changes in source code over time. They enable multiple developers to collaborate on a project by tracking changes, providing version history, branching, merging, and more.
* Hosting platforms are online platforms that offer hosting for version-controlled repositories. Some popular options include GitHub, GitLab, Bitbucket, and Azure DevOps. These platforms provide features like repository management, collaboration tools, issue tracking, and continuous integration.  
* Access control managing a repository often involves controlling who has access to the repository and what they can do with it. This includes defining user roles (read, write, and administer) and establishing permissions at different levels.
* Branching strategies refer to how branches are organized and used in a repository and are an important aspect of management. Different branching strategies, such as GitHub or trunk-based development, dictate how code is developed, integrated, and deployed.
* Code review is a process for code review which is essential for maintaining code quality. This involves reviewing and approving code changes before they are merged into the main codebase.
* Documentation--the proper documentation of the repository, including project purpose, setup instructions, coding standards, and contribution guidelines, helps in the efficient management of the repository.
* Keeping track of issues and bugs is crucial for monitoring and addressing problems. Many hosting platforms offer integrated issue-tracking systems.
* How and when code changes are released to users is another aspect of repository management. Proper release management ensures that the stable and tested versions of the software are made available to users.
* Implementing backups and having disaster recovery plans are important for protecting the repository and its data.
* Git hooks are scripts that automatically run in response to specific events, such as push or commit. They can be used to enforce coding standards, run tests, and perform other actions [33; 34].