esa-sceva/satcom-chunk-collection huggingface dataset:
# Tradeoffs When Building and Running Cohort and Patient-Level Markov Simulation Models
## 2 Materials and Methods
### Model Structure and Assumptions  
We developed a model for a hypothetical neurological disease with eight health states to capture both severity and symptom types, using a two-year time horizon to account for expected frequent therapy changes. The model followed a Markov process divided into two phases: the first six weeks and the subsequent period. Given the typical titration periods of several such therapies, transitions between health states occurred weekly during the initial 6 weeks, with 12-week cycles for the remainder of the analyzed period. The model allowed for switching from first-line to second-line treatment options, but did not permit switching back to first-line therapy. The model compared two first-line options, A and B, with the possibility of switching to the same second-line treatment. Medication adherence and persistence, as well as disease-specific mortality, were not included, although general mortality was accounted for.  
The utility values for the eight health states ranged from 0.4 to 0.9. We designed the hypothetical transition probability matrix to reflect typical progression patterns seen in common mental health conditions. The matrix indicates a higher likelihood of patients remaining in the same health state, with some transitioning to less favorable states. As the severity of the condition increases, transitions between health states become more frequent, highlighting the dynamic nature of these conditions, especially in the more severe stages. To capture the full spectrum of possible outcomes, we allowed transitions between all health states. The selected model parameters for costs are presented in Appendix A. The disease model was developed using both CH and PL techniques, with differences confined to technical aspects. Essentially, the CH model relied on average cohort characteristics (e.g., age, gender ratio), while the PL model incorporated individual patient characteristics derived from probability distributions similar to the CH model's central estimates. Both models were constructed in the same Microsoft Excel spreadsheet, using identical data and structure frameworks.  
The PL approach is typically used in two scenarios: (i) patient heterogeneity, where individual characteristics significantly impact prognosis, making it impractical or overly complex to model multiple subgroups within a CL model; and (ii) complex patient pathways, where a patient's prognosis involves intricate factors, such as multiple reasons for treatment switching, mortality, and adverse events, rendering cohort modeling less suitable. In our study, the complexity arises from frequent treatment switches (e.g., due to adverse events or lack of efficacy), necessitating the use of a PL approach. Although a CH model would have been feasible, it would not have effectively captured the nuanced pathways.
# Behavior of Two-Chord Steel-Concrete Composite Columns under Axial Compression
## 5 Numerical Modeling
### Verification Study  
The results of the numerical analysis were compared with the results obtained from the conducted experiments to validate the accuracy of the FEM model, similarly as in [44]. The factors explored in the analysis included normal, shear, and equivalent strains and stresses; deformations in global axis directions; and reaction forces. The columns' bearing capacities obtained from the numerical analysis (\(N_{u,FEM}\)) are presented in Table 5. The FEM result matched the peak load from the experimental study with the ratio \(N_{u,FEM}/N_{u,\mu}=1.01\). It has to be noted that the forces from the FEM analysis must be doubled since only half of the column was modeled. A comparison between the curves for load (\(N\)) vs. axial shortening (\(\Delta\ell\)) and load vs. lateral displacements (\(U_{nn}\)) is presented in Figure 19a. The numerical results showed a good agreement with the experimental results. The initial stiffness of the FEM model was to some extent higher because the ANSYS model did not consider the column's geometric imperfections, an effect that is often present in experimental results [45]. The total deformations of the finite element model presented in Figure 19b are obtained from the FEM analysis at the last converged load step. Figure 19b compares the predicted and experimental failure modes of the column. The FEM model was found to have good predictions in terms of overall strength and buckling.  
In order to compare the failure mode between the FEM and experimental results, von Mises stresses were developed, and they are presented in Figure 20a. The maximal value of stress in steel is equal to the ultimate stress \(f_{u}\), while in large areas on both ends and the middle of the chord, stresses are higher than the yield stress value. From these results, it could be concluded that failure of the steel occurred. The compressive normal stresses in the concrete infill are shown in Figure 20b. The maximal value of the stress at the peak load was lower than the enhanced compressive strength of the concrete \(f_{cc}^{\prime}\). This observation could indicate that the strength of the concrete was not entirely exploited. However, because the yielding of the steel started, the concrete could not be efficiently confined anymore, and it was reduced to its regular strength \(f_{c}^{\prime}\). After that, the crushing of the concrete started, and the column failed. The collapse of the TCCFST column model occurred as a combination of steel yielding, global buckling, and concrete crushing.
# Effect of Heat-Treatment Remediation on the Mechanical Behavior of Oil-Contaminated Soil
## 2 Experimental Section
### Testing of Soil Mechanics
#### 2.3.4 Direct Shear Test  
The shear strength of soil is commonly defined by the Mohr-Coulomb failure criterion:  
\[\tau=c+\sigma\tan(\phi) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pollutant** & **Concentration (ppm)** & **Temperature (\({}^{\circ}\)C)** & **Time (min)** & **Set of Specimens** \\ \hline \multirow{4}{*}{None} & \multirow{4}{*}{None} & None & None & C-0 \\  & & 320 & \(10,30,60\) & CL-1, CL-3, CL-6 \\  & & 560 & \(10,30,60\) & CM-1, CM-3, CM-6 \\  & & 900 & \(10,30,60\) & CH-1, CH-3, CH-6 \\ \hline \multirow{4}{*}{Gasoline} & \multirow{4}{*}{5000} & 320 & \(10,30,60\) & GFL-1, GL-3, \\  & & 5000 & \(10,30,60\) & GFL-6 \\  & & 560 & \(10,30,60\) & GFM-1, GFM-3, \\  & & 900 & \(10,30,60\) & GFM-1, GFM-3, \\  & & & 900 & \(10,30,60\) & GFL-6 \\ \hline \multirow{4}{*}{Diesel} & \multirow{4}{*}{10,000} & 320 & \(10,30,60\) & GTL-1, GTL-3, \\  & & 560 & \(10,30,60\) & GTL-6 \\  & & 900 & \(10,30,60\) & GTM-1, GTM-3, \\  & & 900 & \(10,30,60\) & GTM-1, GTM-3, \\  & & 900 & \(10,30,60\) & GTM-1, GTM-3, \\  & & & 900 & \(10,30,60\) &where \(c\) represents the cohesion of the soil and \(\phi\) is the friction angle. Generally, a higher friction angle corresponds to better resistance of a material to sliding and subsidence, and a higher bearing capacity [14; 15].  
To obtain the shear strength of soil, the direct shear test is effective and convenient. It could be performed according to ASTM D3080-90. The size of the direct shear box was \(100\times 90\times 20\) mm, and the loads applied to the specimens were 31, 62, 124, and 187 kPa. Horizontal shear stress was applied to the direct shear box at 1 mm/min. Under various normal stresses, the shear stress was recorded. From the shear stress-displacement diagram, the highest shear stress under different normal stresses was obtained. From the failure envelope of the specimen, the friction angle (\(\phi\)) was determined.
# Large-Scale Aircraft Pose Estimation System Based on Depth Cameras
## 4 Conclusions  
In this paper, we propose a contactless attitude estimation system for large aircraft. In our experiment, we used several depth cameras to measure the attitude of an aircraft model on a biaxial turntable, achieving an attitude measurement accuracy of approximately 0.05\({}^{\circ}\). The experimental results reveal that the proposed system has good application prospects in high-speed and high-precision model pose measurement scenarios. Obtaining accurate attitude information for the model is the prerequisite for spacecraft connection and docking, the capture of faulty spacecraft, and removal of debris in space. In the design of aircraft, the accurate attitude measurement of aircraft models also provides important basic information for the aerodynamic analysis of aircraft. The limitation of this system is that the measurement speed is restricted by the computing power of the hardware system. In addition, the model attitude measurement method proposed in this paper is suitable primarily for large-scale models. For small-sized models, due to the limited ranging accuracy of ToF cameras, the attitude-solving accuracy of models will be decreased to a certain extent. Under the hardware configuration utilized in this paper, the realized system measurement frequency is about 10 Hz. The number of threads in the CPU and the number of CUDA cores in the GPU are the main factors affecting the measurement frequency of the system. In the follow-up work, the algorithm flow should be further optimized without affecting the accuracy, and the frequency of the measurement system should be improved. Considering the obvious angular and convex features on the surface of the aircraft model, local feature descriptors are used in this paper. Moreover, there are also global feature descriptors and feature descriptors based on deep learning. In future work, the construction of such feature descriptors should be attempted in order to carry out a more detailed and comprehensive description of the target features, in order to improve the accuracy of high-point cloud registration.  
Conceptualization, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, J.H. and S.S.; project administration, K.L. and T.H.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFF0204901, and the Zhejiang Provincial Natural Science Foundation of China, grant number Y19F050039.  
Not applicable.  
Data Availability Statement: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Processing
#### 2.1.8 Solar Food Drying
Thus, good systems also should be rather closed, and the food should not be exposed to direct sunlight. Several reviews on solar drying using PCM for thermal energy storage exist: [21; 23; 24]. They show that research on this topic is quite widespread. In summary, they mention the option of using a thermal energy storage (TES) as backup heat source, the TES being charged by solar heat in daytime, and specifically using Phase Change materials (PCM). The use of PCM in a food drying process is here not just for heat storage in general, the phase change of the PCM additionally allows stabilizing the temperature in the range that gives the best food quality. Further on, if heat from a solar collector is used, thus solar process heat, the high storage density in a small temperature range limits the rise of the temperature of the storage and therefore leads to an increase in the efficiency of the solar collector. While the reviews cover many aspects, like different dryer types, ways to integrate PCM, and PCM choice, [25] report on the development of PCM specifically for solar drying systems. Ref. [26] report on the experimental investigation of a PCM Assisted Greenhouse Solar Dryer explicitly for Hibiscus Leaves and improvements in drying by PCM. For a few years now, there is also a commercial solar food dryer using PCM, which was developed by PLUSS Advanced Technologies Ltd., Mumbai, India. The dryer, with the name Aagun(r) (Figure 5), allows drying of food after sunset, even during night, by heat stored in PCM. In addition, the PCM buffers the drying temperature such that too high drying temperatures are avoided and the resulting food quality is optimized. Details are published in [27] and the product website [28].
## 3 Rotor-Side External Retrofit Techniques
### Series Dynamic Resistor  
The SDR is a configuration where a dynamic resistor is put in series between rotor windings and the RSC to limit rotor overcurrent during grid voltage dips, as shown in Figure 7. The operation of the SDR is controlled by a power electronic switch: (1) in steady-state conditions, the switch is closed and the series resistor is bypassed; (2) during fault conditions, the switch turns open and a series resistor is connected between the RSC and rotor windings, where rotor current flows through the resistor [28]. Due to the specific topology, the SDR can directly control the magnitude of current. Furthermore, the limitation of rotor current can reduce the charging current to a DC-link capacitor; thus, DC-link overvoltage can be avoided [29; 30]. During voltage dips, the high rotor voltage is shared by the series resistor, and the continuous operation of converter can be guaranteed. By maximizing the operation time of the RSC, more reactive power can be generated from DFIG-WTs to support the terminal voltage.  
A coordinated control approach which consists of a series rotor-side crowbar (SRSC), DC-link capacitor crowbar (DCCC), and circuit breaking (CB) was proposed in Reference [31]. In this scheme, the SRSC is in parallel with CB, and it connects in series with rotor windings and the RSC. The DCCC is connected in parallel with the DC-link capacitor with the function of restricting DC-link overvoltage. Figure 8 shows the configuration of the coordinated control approach. In steady-state conditions, the rotor is connected with the RSC through CB, and the normal operation of the generator can be ensured. During grid voltage dips, the SRSC and DCCC operate in coordination to protect the RSC and DC-link capacitor. Furthermore, the RSC can be connected with rotor windings through the SRSC and maintain operation to provide control efforts.
## 2 Materials and Methods
### Theoretical Backgrou
#### 2.1.8 Collaborative Filtering  
Collaborative Filtering is a method that provides recommendations by identifying other users or items that exhibit similar patterns to a user's previous behavior. There are two main types of collaborative filtering: User-Based Collaborative Filtering and Item-Based Collaborative Filtering.  
User-Based Collaborative Filtering finds other users with tastes similar to a specific user and recommends items that these similar users liked. It operates by calculating the similarity between users and recommending items favored by similar users to a given user.  
Item-Based Collaborative Filtering, on the other hand, recommends items to a user by finding items similar to those the user has previously liked, thereby recommending items akin to those previously favored by the user.  
The quality and quantity of data are critical in collaborative filtering since it calculates the similarity between users and items and bases recommendations on this. Furthermore, recommending for new users or items can be challenging. Collaborative filtering is used in various applications such as online shopping sites, movie and music streaming platforms, social media feeds, and book recommendations.
## 6 Simulation Results and Discussion  
The proposed minigrid system depicted in Figure 7 is optimized using HOMER Pro based on LCOE and NPC. Once the optimal minigrid system was determined, various sensitivity variables were considered to check the effect on the optimal system. According to the simulation result, the possible configurations for the selected site are discussed as follows:
## 3 Materials and Methods
### Implemented SOTA Baseline ML Models  
In the evaluation of the newly curated dataset, six baseline ML models were utilised to assess their correctness. These models were Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), XGBoost (XGB), and K-Nearest Neighbour (KNN). Each of these models offers a unique approach to classification and prediction, making them suitable for a comprehensive analysis of the dataset's accuracy and reliability. The diversity of these models ensured a thorough evaluation, covering various aspects of ML from simple linear approaches to more complex ensemble-based methods.  
Logistic Regression was used to create a decision boundary to separate malware and goodware based on input features. Equation (3) illustrates the formula, where \(\beta_{0},\beta_{1}\) are the model coefficients, and \(e\) is the base of the natural logarithm.  
\[f(x)=\frac{1}{1+e^{-(\beta_{0}+\beta_{1}x)}} \tag{3}\]  
Random Forest was used to build multiple decision trees and combine their predictions to classify input (Equation (4)), where \(N\) is the number of trees, and \(DT_{i}(x)\) is the prediction of the \(i\)-th decision tree.  
\[RF(x)=\frac{1}{N}\sum_{i=1}^{N}DT_{i}(x) \tag{4}\]  
Support Vector Machine was used to create a hyperplane to classify a given program as malicious or benign (Equation (5)), where \(\beta_{0}\) is the bias and \(\beta_{1}\) is the weight vector.  
\[f(x)=\beta_{0}+\beta_{1}x \tag{5}\]Naive Bayes was used to calculate the probabilities for each feature belonging to malware or goodware to predict a given input. Equation (6) calculates probabilities using [PERSON] theorem, where \(P(A|B)\) is the posterior probability, \(P(B|A)\) is the likelihood, \(P(A)\) is the class prior probability, and \(P(B)\) is the predictor prior probability.  
\[P(A|B)=\frac{P(B|A)P(A)}{P(B)} \tag{6}\]  
XGBoost is a gradient-boosting algorithm used for classification and regression tasks, known for its high performance and scalability. In Equation (7), \(L\) is the loss function, \(y\) is the true label, and \(f(x)\) is the predicted label.  
\[L(y,f(x)) \tag{7}\]  
K-Nearest Neighbour is a lazy learning algorithm used for classification and regression, where predictions are based on the majority vote of the nearest neighbours in the training data. Equation (8) represents the formula, where \(k\) is the number of nearest neighbours and \(y_{i}\) are the labels of the \(k\) nearest neighbours.  
\[KNN(x)=\frac{1}{k}\sum_{i=1}^{k}y_{i} \tag{8}\]
###### Abstract  
This paper contains the development of a training service for foreigners to help them increase their ability to speak Korean. The service developed in this paper is implemented in the form of a mobile application that shows specific Korean sentences to the user for them to record themselves speaking the sentence. The objective is to generate the score automatically based on how similar the recorded voice with the actual sentence using Speech-To-Text (STT) engines and Sentence Transformers. The application is developed by selecting the four most commonly known STT engines with similar features, which are Google API, Microsoft Azure, Naver Clova, and IBM Watson, which are put into a Rest API along with the Sentence Transformer. The mobile application will record the user's voice and send it to the Rest API. The STT engines will transcribe the file into a text and then feed it into a Sentence Transformer to generate the score based on their similarity. After measuring the response time and consistency as the performance evaluation by simulating a scenario using an Android emulator, Microsoft Azure with 1.13 s is found to be the fastest STT engine and Naver Clova is found to be the least consistent engine with nine different transcribe results.  
Keywords:Korean speaking proficiency; Speech-To-Text engine; sentence transformer; similarity check; automatic scoring
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 3 Methods
### Multi-Scale Feature Fusion Module  
Our model incorporates the MFFM into the feature fusion network in order to distinguish the importance of different features. The channel attention map is generated using the relationship between feature channels, and it is multiplied with the original input feature element to obtain a new feature element. To efficiently compute features at different scales, the spatial dimension of the input feature map is compressed in our model. First, this module aggregates the spatial information of the input features \(\mathrm{X}\in\mathrm{R^{(N\times C)}}\) by global average pooling and maximum pooling. Additionally, the features, \(\mathrm{X_{avg}}\)\(\in\)\(\mathrm{R^{(1\times C)}}\) and \(\mathrm{X_{max}}\)\(\in\)\(\mathrm{R^{(1\times C)}}\), are obtained by the above pooling layers. \(\mathrm{X_{avg}}\) and \(\mathrm{X_{max}}\) are formulated as follows:  
\[\mathrm{X_{avg}} =\mathrm{AvgPool(X)} \tag{7}\] \[\mathrm{X_{max}} =\mathrm{MaxPool(X)} \tag{8}\]  
Then, they are fed separately into a two-layer multilayer perceptron (MLP). To reduce the parameters and computational overheads, the number of channels in the first layer of the MLP is set to \(\mathrm{C}/\mathrm{r}\), where \(\mathrm{r}\) is the reduction rate. In addition, the activation function is ReLU, and the second layer channel number is \(\mathrm{C}\). Afterwards, the outputs of the features from the two pooling layers through the MLP are summed by element-wise multiplication and afterwards by the sigmoid activation layer to generate the final attention feature map \(\mathrm{M_{c}}\). \(\mathrm{M_{c}}\) is calculated as follows:  
\[\mathrm{M_{c}} =\sigma\big{(}\mathrm{MLP}\big{(}\mathrm{X_{avg}}\big{)}+\mathrm{ MLP}\big{(}\mathrm{X_{max}}\big{)}\big{)} \tag{9}\]  
where the activation function sigmoid is denoted by \(\sigma\), and the two input features, \(\mathrm{X_{avg}}\) and \(\mathrm{X_{max}}\), share the weights of the MLP. Finally, the output feature \(\mathrm{X_{out}}\) is obtained by multiplying the attentional feature map \(\mathrm{M_{c}}\) and the input features \(\mathrm{X}\). \(\mathrm{X_{out}}\) is formulated as follows:  
\[\mathrm{X_{out}} =\mathrm{M_{c}}\cdot\mathrm{X} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Params/M** & **FLOPs/G** \\ \hline Sampling model & 0.2 & 0.94 \\ No sampling model & 0.2 & 1.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of calculation volumes for the lightweight design of a global feature-learning module.