esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Applications and Uncertainty Quantification Analysis for Refload Tests
## 3 Results and Discussions
### Uncertainty Quantification Results  
A UQ analysis for the nine reflood tests was performed in this analysis, delivering the quenching time and cladding temperature propagation results for the SPACE simulation. As a result, the experimental data of quenching time and cladding temperature for the nine tests were covered by the propagation results from the UQ analysis, implying an appropriate selection of the uncertainty bands for the physical models.  
The quenching time and cladding temperature propagations for all reflood tests are illustrated in Figures 5 and 6, respectively. In these results, the term \"UQ\" referred to the cladding temperature or quenching time results of the UQ analysis, in which the DA method was applied; the nominal prediction referred to the prediction of the SPACEwithout any adjustment of the physical model; and the measured data referred to the actual data from the experimental work of the FLECHT SEASET refolod tests. The justification for the selected physical models and their corresponding uncertainty ranges, as outlined in Table 2, efficiently enhances the simulation of reflooding phenomena using the SPACE. This result emphasizes the UQ analysis's reliability, enhancing the code's predictive capabilities. Furthermore, observing the behaviors of the physical models during the UQ process can be valuable in further developing the physical model in the system code. However, the intricacies of this phenomenon, coupled with the nonlinear dependence of the input parameters on system states, result in a prohibitively high computational cost for the UQ analysis.  
To address this challenge, we introduced a machine learning model to predict the system behaviors and their corresponding physical models. The objective was for this machine learning model to enhance the UQ results and improve the SPACE prediction for such complex phenomena of the reflooding phase. The expectation is that leveraging machine learning can mitigate the computational demands associated with UQ and sampling algorithms in the DA process, offering a more efficient and effective approach to enhancing the predictive accuracy of the system code.  
Figure 5: The quenching time propagation for all the reflood tests.
## 2 Experimental Equipment and Methods  
### Fuel Preparation  
The effect of the initial temperature of fuels on the ignition characteristics were studied for four different compositions of composite fuels (Table 1). The following components of fuel suspensions were used: bituminous coal, coal processing waste (filter cake--FC), rapeseed oil, turbine oil, and water.  
For the preparation of suspensions, coal grade K (coking, mine \"Berezovskaya\", Kemerovo region, Russia) and a filter cake grade K were used (dry, enrichment plant \"Severnaya\", Kemerovo region, Russia). Filter cake K is a by-product of coking coal flotation after extraction of coal concentrate from it. The particle size of coal and filter cake after grinding and screening was no more than 140 \(\upmu\)m. Table 2 presents the results of the technical analysis (W\({}^{a}\)--fuel moisture content, \(A^{d}\)--ash content of fuel on dry basis, \(V^{daf}\)--volatile content, \(Q^{a}\),s,v--specific combustion heat), as well as the elemental composition. \(W^{a}\), \(A^{d}\), and \(V^{daf}\) values were determined according to international standards [26; 27; 28]. The heat of combustion at the constant volume \(Q^{a}\),s,v for filter cake and coal was measured using an IKA C 2000 calorimeter using standard procedures [29; 30].  
Fuel suspensions were prepared according to a well-tested method [18]. An AIBOTE ZNCLBS-2500 magnetic stirrer (Aibote Henan Science and Technology Development Co., Ltd., Zhengzhou, China) was used to prepare fuel suspensions. First, the liquid components (water and oil) were mixed to obtain an oil emulsion. Further, the solid components were added in portions to the emulsion with continuous stirring of the mixture. The duration of the entire mixing process was at least 15 min at a rotation speed of the magnetic stirrer armature of 1500 rpm at room temperature.  
It should be noted that fuel compositions with the addition of oil above 5% have relatively high sedimentation stability, according to the experimental results [32; 33]. The compositions based on the coal component (FC or coal) and water have a stability index of 100-WSR = 100% [32; 33] when adding turbine oil of more than 5% wt. or rapeseed oil of more than 2% wt. During the storage of the four studied fuel compositions (No. 1-No. 4) for three days (a typical time period for ensuring a fuel reserve at a thermal power plant), no separation of solid and liquid components occurred.
## 2 Materials and Methods
### Evaluation Criteria
#### 2.5.2 PCM Type  
This paper considers the careful selection of PCM types based on factors, like application, operating temperature range, thermal stability, and cost. The evaluation focuses on three commercially available PCM products pre-defined in DesignBuilder's software database: BioPCM(r), InfiniteR PCM, and WinCo EnerCiel PCM.  
BioPCM, derived from renewable biomass sources, is an environmentally friendly alternative to traditional PCMs, being non-toxic and biodegradable. InfiniteR PCM, composed of inorganic compounds like salt hydrates, exhibits a high heat of fusion, enabling significant thermal energy storage during phase transition. WinCo EnerCiel PCM, a non-toxic and environmentally friendly option, utilizes eutectic mixtures, specifically polyethylene glycol, offering suitable latent heat capacity and melting temperatures for various thermal energy storage applications. Table 5 summarizes the specifications and thermal properties of the studied PCM products.
## 4 Results and Discussion
### Balanced and Imbalanced Data Stream with Sudden Drift
From Table 4, it can be concluded that false drift detection is the main factor that causes the decrease in the performance of a base classifier. The EWMAC correctly detected all three drifts without any delay, but it also detected a high number of false drifts and detected 83 false drifts. Because of the false detection, the classifier is reset every time, and as a result, it forgets previously learned knowledge, which caused the 11.6% decrease in base classifier performance. HDDM_W and STEPD also correctly detected all three actual drifts but detected 35 and 27 false drifts, respectively. As a result, HT performance was decreased by 6.88% using HDDM_W and 6.49 using STEPD as a drift detection method. The EDDM causes a 4.07% performance decrease in HT. However, it detected one false drift, missed 2 actual drifts, and detected just one actual drift but with a delay. These factors contributed to the performance degradation of EDDM. As observed, false drift detection is the major factor that causes the performance degradation of the base classifier. The reason behind the false drift detection rate is the technique applied by the underlying method to detect the drift. For example, the EWMAC-DM exponentially decreases the weight of the old data. Hence, after a certain period, the EWMAC-DM may consider the new data belonging to a new concept even if that belongs to the current concept.
## 3 Materials and Methods
### Block Coding  
In our work, we used a block coding FEC method, which works on the macroblock of a video frame. In _block coding_, the message is divided into blocks of fixed size called _datawords_. A few redundant bits are added to each dataword to generate _codewords_. Redundancies can be added by using different scenarios. It can either be added to the start of the message, at the end of the message, or somewhere in between [19]. Figure 3 shows the basic mechanism of error detection in block coding.  
Let \(d\) be the number of bits in a dataword and \(r\) be the number of added redundant bits, the resultant block of \(c\)-bits is called a codeword, where \(c=d+r\). \(d\)-bits can represent \(2^{d}\) different datawords. Similarly, \(c\)-bits can represent \(2^{c}\) different combinations of codewords. Since \(c>d\), the number of codewords would be greater than the number of datawords. For each dataword, there exists only one codeword, so there will be \(2^{c}-2^{d}\) extra codewords that are considered as invalid. A codeword is accepted on the receiving end only if it is a valid codeword, or else it is discarded by the decoder.
## 4 Model and Calculation Principle
### Model Calibration and Validation  
Firstly, stomatal conductance, photosynthetic rate, and transpiration rate were calculated using the above three models, and then, verified using the measured values. The statistical parameters used to evaluate the fitness of the model and the measured data are the root mean square error (\(RMSE\)) and the coefficient of determination (\(R^{2}\)), calculated as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(S_{i}-O_{i})^{2}}} \tag{15}\]  
\[R^{2}=(\frac{\sum\limits_{i=1}^{n}{(O_{i}-\overline{O})(S_{i}-\overline{S})} }{\sqrt{\sum\limits_{i=1}^{n}{(O_{i}-\overline{O})^{2}\sum\limits_{i=1}^{n}{( S_{i}-\overline{S})^{2}}}}}) \tag{16}\]  
where \(O_{i}\) and \(S_{i}\) are the observed and simulated values, \(\overline{O}\) and \(\overline{S}\) are the average observed and simulated values, and n is the number of observations. The model's performance is considered satisfactory when the values of \(RMSE\) are close to 0 and those of \(R^{2}\) are close to 1.
# Applications of Digital Twin across Industries: A Review
## 4 Healthcare and Medicine
SIMULIA Living Heart by Dassault Systems is also another example of a high-fidelity model of the human heart which turns a 2D scan of the heart into a 3D model [87]. It is not just the big companies working to develop DTs of organs, researchers exploring the field such as [PERSON] et al. [88] introduced a DT architecture for detecting Ischemic Heart Diseases (IHD) by collecting the patients' data from internal and external sensors, medical records, and social networks, and helping them accordingly; they called it the 'Cardio Twin'. They were successful in implementing it by training their model using ECG data and testing its accuracy with pre-prepared datasets. The FDA (U.S. Food and Drug Administration) authorities are also considering DTs as a potential tool for clinical trials to accelerate medical innovations along with the regulatory approvals [89]. They collected data from a number of historical Alzheimer's disease clinical trials and used it to train a machine learning model to generate a DT to predict disease progression, and found out that the DTs were statistically indistinguishable from the actual control arm subjects [90]. DT technology is emerging as an exciting tool in the healthcare industry that looks promising for the future. On the one hand, DTs of healthcare infrastructures, such as hospitals, operations, and staff, can optimize patient care, cost, and performance, and on the other, DTs of patients can be a step towards personalized treatment and medicines. Both types of applications of DTs in the healthcare industry will enhance the patients' experience and success of the treatments. However, there are some concerns over certification and regulatory issues, as well as security and privacy when it comes to the implementation of the technology, especially in healthcare [91]. Figure 4: DT concept for personalized medicine. (**A**) A patient with a local sign of disease (red). (**B**) Patient’s DT is created and virtually treated with different treatments/medicines in various combinations. (**C**) Treatment showing the best results is chosen. (**D**) Personalized medicine is given to the patient to treat them (green). (Adapted from [83]).
## 2 Dataset, Models and Method  
### Dataset  
In this research, data from the National Centers for Environmental Prediction (NCEP) were used for model simulations, and AMSR-E brightness temperature data were used for soil moisture and cloud data assimilation. The other data sets listed under this section were used for the analysis and validation of the model outputs.
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 3 Materials and Methods
### Scaling Auto-Period Seasonal Length Detection Algorithm  
At first, smooth the sample KPI time series data by data pre-processing as exhibited in Figure 1. Traditional seasonal length detection of KPI time series data can be thought of as a periodic seasonal component detection. Existing detection algorithms (such as auto-correlation and periodogram) deconstruct the original data into multiple signals using power spectrum estimation methods which can find the decomposed signal with the most energy. Then, extract the seasonal components by using STL decomposition [26] as shown in Figure 2. The primary period is the reciprocal of these decomposed signals frequency with increased energy, and it is roughly equivalent to the period of the original data. However, the seasonal threshold must be manually set (e.g., auto-correlation based method), and finding long period features is difficult (e.g., periodogram based method). To compensate for the shortcomings of current methods, IBM Watson team designed the Auto-Period algorithm [27], which can discover both long and short periods in time series data without manually setting the threshold. However, under intricate KPI profiles, Auto-Period algorithm is unable to recognize all seasonal indicators.  
To solve the above problem, we design a scaling Auto-Period algorithm. In the seasonal length detection step, the scaling Auto-Period method could automatically achieve the period value of seasonal components. Firstly, down-sample the seasonal component of KPI time series data to provide a scaled sharper seasonality. Secondly, use Auto-Period algorithm to detect the seasonal length after scaling. Finally, restore the scaled seasonal length as the genuine length by using the down-sampling ratio.  
Figure 1: The sample KPI time series data after data pre-processing.  
Figure 2: Seasonal components of KPI time series data.
# Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
## 2 Materials and Methods
### Derivation of the Target Variable  
The classification problem dealt with two classes. A patient could either be 'good responder' or a 'bad responder'. This target variable was defined such that it reflected both efficacy and safety/drug toleration. For all the datasets, there were insufficient cases of death and clinical progression for these to form the basis of the target variable. Therefore, efficacy was defined in terms of PSA levels, a recognized biomarker for prostate cancer disease progression.  
A subject was a 'good responder' if he met the following conditions:  
1. PSA levels did not increase by more than 0.2 ng/mL within the initial two-year trial period (efficacy), similar to the method used in the literature [25].
2. 'PSA increase' was defined in terms of a comparison of baseline vs. 2 years. For Studies 2 and 3, 'baseline' was the day of randomization. For Study 1, 'baseline' was set at three months, to address the observation that in Study 1 (but not Studies 2 or 3) mean PSA values underwent a marked reduction (43%) from day 0 to 3 months and then, on average, stabilized. Thus, this baseline shift was an adjustment designed to ensure consistency between the three studies (i.e., the start of stable PSA values). This discrepancy between studies in the trajectory of PSA values is likely related to different entry criteria. In Study 1, subjects were required to have undergone radical prostatectomy or prostate radiation treatment (which would have caused a marked decrease in PSA levels) within the 16 weeks before randomization, a requirement not present for studies 2 or 3.
2. PSA values at 2 years after randomization was operationalized by taking the mean of PSA values within a window of 600-900 days after randomization. The width of this window was designed to balance the need to include a sufficient number of data points, with the need to confine the window, so that the time points could be considered consistent.
3. There were no related, serious adverse events (A.E.s) within the initial two-year trial period (safety) \(\bigcirc\) A related A.E. was considered serious if it:  
1. caused withdrawal from the study OR2 caused disability or incapacity OR
3. required or prolonged hospitalization OR
4. required intervention to prevent impairment OR
5. was life-threatening
3. Survived for the initial two-year trial period (safety)
4. Stayed in the trial for at least 600 days (safety)  
If a patient did not meet all four of these conditions, he was considered to be a \"bad responder\".  
Subjects were excluded from the analyses if there were insufficient data to evaluate all of these conditions.
## 4 Discussion
### The Discussion of the Algorithm Robustness
#### 4.2.3 The Axial Tilt Case  
It is also possible that the receivers are tilted in the \(y\)-\(z\) plane as shown in Figure 13b. Similarly, if the the coordinate system is rotated by \(\eta\) degrees counterclockwise, the case can be transformed into the pitch angle case. The pitch angle \(\alpha\) at this time equals to \(\eta\), the angle between the axes of dual receivers and the \(y\)-axis. The distance between the transmitter and tracking receivers, \(d_{1}\) and \(d_{2}\), and the distance between dual receivers \(r\) meet the relationship shown in (22), in which \(\gamma\) need to be replaced by \(\eta\).  
In a word, even if the dual receivers are in an unexpected case such as tilt or lateral offset, the PIG can still achieve accurate 4-D tracking by using the MFSI algorithm. Furthermore, this proves that the dual receivers tracking system and the corresponding MFSI algorithm are adaptable and robust.
## 5 Evaluation of Major Design Codes Based on Results of FE Analysis  
In this section, the critical axial force values of the arch rib according to the three codes are normalized and evaluated with respect to the FE analysis results considering the influence of certain parameters, as shown in Figures 9-11. The effects of these parameters on normalized critical axial forces for three types of arch bridges are represented in these figures by three cases with small, medium, and larger stiffness ratios. The Y axes in the first row of graphs in each figure are split to show the results more clearly. The solid black line in the figures shows where the critical axial force according to the code equals that given by FE analysis. The olive-colored dashed line shows the yield force of the cross-section normalized by the results of the FE analysis. This can be thought of as the maximum value of strength in design practice.
## 5 Simulation Result and Discussion
### Whole Process Simulation of Improved NDI Control Based on Real-time Frequency Domain Identification  
Since this paper focuses on a whole process method combining real-time aerodynamic identification and adaptive NDI control, the whole process simulation is carried out on the basis of the above verification of each part.  
The simulation process of this section includes: real-time recursive detrend, real-time recursive parameter identification, step command tracking, free flight phase, as Figure 11 shows.  
Figure 10: INDI, NDI and PID control effect comparison. subplot (**a–c**) are pitch angle, elevator deflection angle and pitch angle velocity, respectively.  
In the whole flight phase, the flight attitude of the aircraft is stable, the command response is fast, and the attitude command with large variation can also be accurately tracked to meet the design requirements.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 6 Architecture of WSN vs. Architecture of IDS
### Naive or Flat-Based WSN Architecture for Centralized IDS  
In a centralized architecture, better known as a Naive WSN architecture, a central BS collects all the information sensed by all network nodes and forwards the collected information to the cloud IoT server. Similarly, in a centralized IDSs, the BS acts as a global reference that performs computationally demanding tasks to monitor and filter data traffic to facilitate attack detection. Several studies have considered executing the IDS at both the BS level and at the remote server level connected to the IoT cloud, which is called a multi-layer IDS scenario. This approach has multiple limitations, including attack detection latency, considerable communication overhead, and high energy consumption. Latency occurs when data traffic analysis is delayed until the information reaches the BS. Communication overhead is caused by the need to transmit all sensed information to the central BS over the communication link, increasing energy consumption as the node's distance relative to the central unit increases [58]. Due to these limitations, centralized IDS architectures are typically used only in very small networks.
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 8 Conclusions  
A comparative study of the application of different TO software was conducted in this research work, and, thus, a table containing 70 existing TO software platforms was created. The table is available online and was developed in a way that could be changed and edited by readers interested in TO. In this way, an updated library of the available TO software platforms was developed, offering useful information about them, such as their availability, optimization types and methods, objective functions, constraints, and representation of the results. A search of the literature showed that the most applied TO method is the compliance optimization with the SIMP method at 80%.  
Three commercial software were chosen for further comparison--Solidworks, ANSYS Mechanical, and ABAQUS. The software was compared for its TO workflow and results using three commercial models--a bell crank lever, a pillow becket, and a small bridge. The small differences in their workflows, such as the 3D preparation, led to different optimized designs despite the fact that the same TO method (SIMP) and properties, as well as CAD designer inputs, were used in all of them. The software could reduce the models' weight in remarkable percentages, 88.3% for the bell crank lever, 85.9% for the pillow bracket, and 79% for the bridge without compromising their strength. ABAQUS gave the best designs in terms of the weight of the bell crank lever and the pillow bracket, while SolidWorks created the lightest bridge. In addition, SolidWorks was the most efficient software in terms of optimization time.  
The existing TO software provides the CAD designer with many different options for both optimization type, objective function, constraints, and representation and analysis of the results. In this way, the software supports the CAD designer in all steps that constitute the TO workflow. However, each platform has its capabilities and limitations that differentiate it from the other. Both the CAD designer's inputs and the small details in a TO software platform could affect the geometry, the weight, and the strength of the final designs. Thus, these have to be taken into account during all the steps of the TO workflow. Finally, the lack of an ideal TO platform causes CAD designers to work with different software in order to get a design that fulfills the desired requirements.