esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy MCDM Methodology for Analysis of Fibre Laser Cutting Process
## 3 Results and Discussion
### Modelling of the Aggregate Fuzzy Decision-Making Rule
Moreover, the development of the second-order prediction model and its visualization via contour plot clearly indicate that there exist a number of input parameter combinations with which one can obtain better response values. For example, the best ranked alternatives in the experiment (A10, A6) had a response value of approximately 0.6 (Figure 6). Through optimization, it can be shown that within the experimental space investigated, i.e., surface of a sphere, the minimal response value (average utility value) of 0.286 is obtained under the following combination of laser cutting parameters: focus position \(f\) = 1.81 mm, cutting speed \(v\) = 1.8 m/min and assist gas pressure \(p\) = 0.86 bar. On the other hand, the maximal response value of 0.625 is obtained under the following combination of laser cutting parameters: focus position \(f\) = 1.72 mm, cutting speed \(v\) = 3.12 m/min and assist gas pressure \(p\) = 0.75 bar. Among others, acceptable compromise solutions with higher response values than initial design points are, for example, the following: (1) \(f\) = 1.73 mm, \(v\) = 3.09 m/min, \(p\) = 0.75 bar with response value of 0.625, (2) \(f\) = 1.71 mm, \(v\) = 3.05 m/min, \(p\) = 0.75 bar with response value of 0.62 and (3) \(f\) = 1.66 mm, \(v\) = 2.95 m/min, \(p\) = 0.75 bar with response value of 0.61. With the development of an average utility value prediction model, one can identify regions with higher response values within investigated experimental space and generate various compromise solutions, as well as direct further experimental investigation. However, the selection of a particular solution may be significantly influenced by the quality, dimensional accuracy and/or productivity requirements. In that regard, the applied approach, although simpler and more resource-efficient in comparison with the traditional multi-objective optimization based on the use of multiple models for each considered response, allows the prediction of the average utility value (as an aggregate function), but lacks prediction of individual responses (criteria), which can be considered as the main drawback.
## 3 Results and Discussion
### General
#### 3.1.2. When Should COPD Patients' Weight Loss Be Assessed?  
**Recommendation 2**.: _GARIN suggests assessing weight loss in all COPD patients from diagnosis and at each visit._  
Grade of recommendation B, (100%) Strong consensus.  
Comment:  
In COPD patients, progressive weight loss occurs even with adequate caloric intake, possibly conditioned by several circumstances, such as increased work of breathing, a state of systemic inflammation and sustained oxidative stress, acute exacerbations, and infectious complications [11; 14; 15; 30; 33].  
In COPD, weight loss appears as a dynamic state that accelerates as the disease progresses [17] and is associated with increased morbidity and mortality [8; 9; 30; 34; 35; 36]. Increased mortality has been reported in patients with weight loss of \(>\)5% in the previous 6 months [15], and weight gain of 2 kg has been found to be associated with improvements in muscle and respiratory strength [32].  
\begin{table}
\begin{tabular}{c c} \hline \hline
1 & Strongly disagree \\ \hline
2 & Disagree \\ \hline
3 & Neither agree nor disagree \\ \hline
4 & Agree \\ \hline
5 & Strongly agree \\ \hline \hline \end{tabular}
\end{table}
Table 3: Likert scale [29].  
In the JO study [8], COPD-related healthcare utilization and medical costs were higher among underweight patients than in the other groups.
## 2 Preliminaries
### Motion Estimation  
The similarity between adjacent frames brings inter-frame redundancy. Using motion estimation [18], only the changing parts of adjacent video frames would be encoded to reduce the amount of data and reduce the inter frame redundancy. In motion estimation, a frame image is segmented into \(M\times N\) or more commonly used \(N\times N\) pixel block. At the matching window of \((N+2p)\times(M+2p)\) size, the current block is compared with the corresponding block in the previous frame. This '\(p\)' referring to pixels adjacent to the ROI is called the search parameter. On the basis of the matching criteria, the best match is found, and the alternative position of the current block is obtained.  
There are various matching criteria, including the mean absolute difference (_MAD_):  
\[MAD=\frac{1}{MN}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\lvert f_{k}(i,j)-f_ {k-1}(i,j)\rvert, \tag{14}\]  
mean squared error (_MSE_):  
\[MSE=\frac{1}{MN}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\left[f_{k}(i,j)-f_ {k-1}(i,j)\right]^{2}, \tag{15}\]  
and normalized cross correlation (_NCC_):  
\[NCC=\frac{\frac{1}{MN}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\Big{(}f_{k}( i,j)-\overline{f_{k}(i,j)}\Big{)}\Big{(}f_{k-1}(i,j)-\overline{f_{k-1}(i,j)} \Big{)}}{\sqrt{\frac{1}{MN}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\Big{(}f_ {k}(i,j)-\overline{f_{k}(i,j)}\Big{)}^{2}}\sqrt{\frac{1}{MN}\sum\limits_{i=1} ^{M}\sum\limits_{j=1}^{N}\Big{(}\overline{f_{k-1}(i,j)-\overline{f_{k-1}(i,j) }}\Big{)}^{2}}}, \tag{16}\]where \(f_{k}(i,j)\) are the pixels in the current \(M\times N\) block and \(f_{k-1}(i,j)\) are the pixels in the matching block in the frame \(k-1\). When \(MSE\) or \(MAD\) is the smallest, it means that the matching between the two blocks is the best. The difference is that \(NCC\) measures the similarity between the two blocks in the range of \([-1,1]\). The closer the \(NCC\) is to \(1\), the closer the two blocks are to have a linear relationship.
# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## 5 Associating Segments with the Target Component  
There are three proposed variants of assigning the target component to a query object segment based on the constructed CAG. Note that the construction of CAG is independent of the applied variant.  
_Direct Segment Association_  
The query object segment is assigned the ID of the nearest neighbor in the CAG representing a segment of a representative object.  
_Object-Constrained Association_  
The object-constrained association approach compares the query model with each representative model. Each segment of the query model is associated with every component of the representative model by a greedy search. The association with the greatest SCLM is detected, and the query segment is assigned the ID of the associated representative model component. That query segment is omitted from the further search, and the search continues until all query segments are associated with corresponding representative object segments. An object matching score between the query and the representative model is computed as the sum of SCLMs between the associated query model segments and representative model segments that represent the target component. Query model segments are finally assigned the IDs of the associated segments of the representative model with the greatest object matching score.  
_MST-Based Association_  
A root node is added to the CAG, which does not represent any segment. Then, a minimum spanning tree (MST) is constructed with the constraint that all segments of the representative objects are directly connected to the root node. Hence, each representative object segment of all representative models spreads one subtree. Segments of the query object are assigned the ID of the representative model segment in the same subtree.  
At the end of any of the three proposed procedures, each query object segment is assigned the ID of a representative object segment. Finally, the query object segments inherit the labels of the associated representative object segments. The first and the third variant associate each query object segment with the ID of a corresponding representative object segment, independent of the labels of the representative object segments. Hence, these two methods can be performed before the target component is annotated on the representative objects.
## 6 Experimental Results and Discussion
### Complexity and Comparative Discussion  
In this subsection, we analyze the complexity of the relaying protocols, in terms of hardware and arithmetic operations. From the hardware implementation, all Relay Nodes are developed with the same components (one NI-USRP 2944R and PC), which were described above. Nevertheless, from the point of the number of operations required to carry them out, as a function of the dimensions of the vectors and matrices involved present significant differences. In this sense, it can be shown in Table 2, which summarizes the arithmetic operations that the proposed protocols require to capture and forward the received signal to the UE. From Table 2 can be noticed that the strategy with less complexity is the A&F Relay Node, which \(\rm N_{T}^{R}N_{T}^{RT}\) and \(\rm N_{T}^{RT}(N_{R}^{R}-1)\), products and summations, respectively, are required. However, the A&F protocol with the pre-equalization stage increases your computational complexity in comparison with A&F conventional strategy. In this regard, both strategies,  
Figure 14: Achievable throughput comparison of the implemented RNCS with a conventional MIMO system. A&F-ZF and A&F-MMSE perform one first step of channel estimation, and after that a stage of equalization. In this sense, the A&F-MMSE presents major complexity than A&F-ZF relaying. On the other hand, the D&F scheme is the more complex between the implemented strategies, as can be observed in Table 2, due to the decoding and encoding processes, which are performed to the received signal. It should be noticed that \(\mathrm{N}_{\mathrm{T}}^{\mathrm{R}}\times\mathrm{N}_{\mathrm{R}}^{\mathrm{R}}\) is the combiner antennas in the protocols and \(\lambda\times\alpha=\mathrm{P}\) describes the total number of the pilot symbols. Besides, \(\mu\) exemplifies the total number of the data symbols, which are employed to implement the Bi-Cubic Interpolate. From this subsection, it can be seen that the computational complexity of the D&F Relay Node was higher than the rest of the developed protocols. The proposed RNCS is compared with prior works, in terms of the type of implemented strategy and developed hardware, as shown in Table 3. In general, in [11; 16; 38] works and proposed architecture, the performance of the A&F and D&F strategies have been analyzed and compared, showing that the usage of a co-operative system improves the network capacity and the D&F protocol presents higher behavior than the A&F Relay Node architecture. The main limitation found in the related works is the considered signal. In this sense, in the analyzed studies, signal models don't fulfil the requirement of the standardized by the 3 GPP [13], as have been in this paper (LTE). Besides, in this paper have also proposed
## 5 Conclusions  
In this study, biodiesel production from the new cultivar of Camelina \"Soheil\" was studied. After oil extraction, it was found that \"Soheil\" seed has 29% oil. The average yield of \"Soheil\" seed was 445-1112 kg per hectare. Therefore, the average yield of oil was 129-322 kg per hectare. As a result, the average biodiesel produced from \"Soheil\" is 129-322 kg per hectare. The process input variables, including molar ratio (methanol to oil), reaction time (min) and catalyst concentration were optimized by response surface methodology (RSM) in order to reach the highest amount of methyl ester. After optimization of the input variables (molar ratio (methanol to oil) = 10.18:1, reaction time = 7.33 min, and catalyst concentration = 1.15 wt %), the rate of biodiesel production of 98.91% was experimentally achieved. An investigation of the physical and chemical characteristics of the biodiesel produced from \"Soheil\" oil determined that the physical and chemical characteristics of the biodiesel from \"Soheil\" oil meet the requirements of the ASTM D6751 standard. The engine test showed that by using the \"Soheil\" biodiesel, brake power was increased by 17%. The improvement in engine power can be attributed to the increase of the indicated mean effective pressure for higher biodiesel concentration. Also, concentrations of the CO\({}_{2}\), and NOx emissions were increased by 7.5%, and 15.22%, respectively, because of the enhanced combustion, causing NOx and CO\({}_{2}\) to increase. However, the specific fuel consumption (SFC) was decreased by 15%. Also, the concentrations of the CO and UHC emissions were decreased by 15% and 39%, respectively, because of the enhanced combustion, causing CO and UHC to decrease.  
Conceptualization: S.S.H., G.N., B.G., and M.T.E. Formal analysis: S.S.H., G.N. and M.T.E. Funding acquisition: M.T.E. and G.N. Investigation: S.S.H., G.N., B.G., and M.T.E. Project administration: G.N. Supervision: G.N. Writing, original draft: S.S.H., Writing, review and editing: S.S.H., G.N., B.G., M.T.E. and T.Y.  
This research received no external funding.  
The authors declare no conflict of interest.  
List of Symbols and Acronyms  
B0 Biodiesel 0% blend  
B10 Biodiesel 10% blend  
B20 Biodiesel 20% blend  
BP Brake power  
CO Carbon monoxide  
CO\({}_{2}\) Carbon dioxide  
CSO\({}_{2}\) Camelina \"Soheil\" seed oil  
FAC Fatty acid composition  
GC Gas chromatography  
KOH potassium hydroxide  
NaOCH\({}_{3}\) Sodium methoxide  
NOx nitrogen oxide  
RSM response surface methodology  
SFC specific fuel consumption  
UHC Unburned hydrocarbon
## 3 Results
### The Impact of Soil Moisture Retrieval Uncertainty
For the descending orbit, SM2 RAIN-derived precipitation based on SMAP_D also performs well in capturing precipitation events, but still significantly underestimates  
Figure 8: Similar to Figure 6, but for SMAP_D (**a1,a2**) and OBS_D (**b1,b2**), respectively. Figure 9: Similar to Figure 6, but for SMAP_AD (**a1,a2**) and OBS_AD (**b1,b2**), respectively. intensive precipitation (see days around June-August 2016, June-July 2017, and Aug 2018 in Figure 8a1). In addition, there is an obvious overestimation for light rainfall events with an intensity of less than 5 mm/5 d (see days around Jun 2015, Aug 2015, Jun 2016, and Sep 2018 in Figure 8a1). With regard to the accumulated precipitation amount, it yields a value of 1215 mm, which is lower than the observed value of 1238 mm. In terms of evaluation metrics, SMAP_D yields statistical metrics of \(R\), \(RMSE\), and \(BIAS\) to be 0.72, 8.35 mm/5 d, and \(-\)0.33 mm/5 d, respectively, and categorical metrics of \(POD\), \(FAR\), and \(TS\) to be 0.73, 0.04, and 0.71, respectively (Figure 7b). The overall underestimation of SMAP_D improves when OBS_D is used as an input, but it is slightly less consistent with the rain gauge observations, as shown in scatterplots in Figure 8. Additionally, all metrics are almost comparable to those of SMAP_D. For the combined orbit, more precipitation events are detected by SMAP_AD due to more retrievals resulting from less sampling intervals. As shown in Figure 9, SM2 RAIN-derived precipitation based on SMAP_AD largely underestimates those that are intensive precipitation (see days around July 2016 and August 2018 in Figure 9(a1)) and overestimates those that are light precipitation (see days around June 2015, June 2016, and September 2017 in Figure 9a1). The estimated accumulated precipitation amount (1314 mm) is lower than that observed by rain gauge (1382 mm). The statistical metrics \(R\), \(RMSE\), and \(BIAS\) are 0.78, 8.66 mm/5 d, and \(-\)0.91 mm/5 d, respectively, while the categorical metrics \(POD\), \(FAR\), and \(TS\) are 0.78, 0.07, and 0.73, respectively (Figure 7c). When OBS_AD is used as input, a slightly degraded performance is observed as evidenced by somewhat large inconsistency with rain gauge observations (Figure 9(b2)) and slightly degraded metrics expect for \(BIAS\) (Figure 7c). From an overall point of view, the SM2 RAIN performance significantly improved by reduced uncertainty of ascending soil moisture input, with \(RMSE\) reduced by about 1.0 mm/5 d. However, it is not noticeable for the descending and combined orbits, with RMSE increased by about 0.2 mm/5 d and 0.3 mm/5 d, respectively (Figure 10).
## 4 Results and Discussion  
The methodology introduced in Section 2 was tested on the selected acquisitions reported in Table 2 so as to assess the effectiveness of the demodulation procedure (i.e., ease of detecting the defect frequency in the demodulated spectra) and the effectiveness of the different indicators in the selection of the optimal demodulation band according to the previously introduced \(dE\) and \(iE\) metrics. Note that according to [6], an order 16 FIR filter is used for the demodulation, while the \(\Delta f\) is set to 1/8 of the Nyquist frequency for all the acquisitions according to the consideration at the end of Section 2.
## 5 Conclusions
### Importance of Measurement Location  
One of the most significant findings was the superior performance of position-dependent models, particularly those utilizing NIR data collected from the skin at the stomach region of the fish. Across all classifiers, this specific location consistently yielded higher accuracy, suggesting that the stomach area may provide more relevant spectral information for detecting urea content, possibly due to the concentration or distribution of chemical compounds in that region. This insight is crucial as it indicates that, for practical applications of NIR spectroscopy in fish safety, careful consideration must be given to the anatomical site of measurement.
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 2 Methods
### Machine-Learning-Based Driving Simulation Scenarios
#### 2.3.2 Machine Learning Model Development  
Three machine learning models--multiple linear regression (MLR), random forest, and recurrent neural network (RNN)--were compared in terms of prediction accuracy. The RNN model was ultimately selected for predicting the LTR index due to its superior performance. The RNN structure, designed for processing sequential data, considers both current and past inputs, making it well-suited for time-series prediction [19]. The RNN model parameters were set as listed in Table 3, and the dataset used for training was categorized by test models as shown in Table 4.  
The RNN model used in this study consisted of an input layer, an LSTM layer, a dropout layer, and an output layer. Below is the RNN model architecture:  
* Input: curvature and lateral acceleration;
* Sequence input layer (10 time steps);
* LSTM layer with 50 units;  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Description** & **Units** \\ \hline \multirow{3}{*}{Input} & Road curvature & - \\  & Longitudinal speed & km/h \\  & Lateral acceleration & m/s\({}^{2}\) \\ \hline Output & Lateral-load transfer ratio & - \\ \hline \end{tabular}
\end{table}
Table 4: RNN input and output.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Description** & **Characteristics** \\ \hline \multirow{3}{*}{Layers} & Sequence input & 10 \\  & LSTM & 50 \\  & Fully connected & 1 \\ \hline \multirow{3}{*}{Main hyperparameters} & Optimizer & adam \\  & Epochs & 50 \\ \cline{1-1}  & Dropout rate & 0.2 \\ \cline{1-1}  & Batch size & 32 \\ \hline \end{tabular}
\end{table}
Table 3: RNN architecture.  
* Dropout layer (dropout rate = 0.2);
* Fully connected output layer;
* Optimizer: Adam;
* Loss Function: mean squared error (MSE);
* Epochs: 50;
* Batch Size: 32;
* Training-validation-testing split: 70%-15%-15%.  
Hyperparameters were tuned manually through iterative trials. Other sequence models (e.g., GRU, LSTM-Stacked) were tested but underperformed compared to RNN under identical conditions. For the reproducibility, each simulation scenario was repeated three times with different random seeds to ensure stability of results.  
Two different input layer configurations were tested: one using road curvature and driving speed, and another using road curvature and lateral acceleration. The time step length for training was set to 10. The LSTM layer had 50 units, and a dropout rate of 0.2 was applied, as determined empirically for optimal prediction accuracy. The output layer was configured to predict a single LTR value.  
The machine learning process involved using road curvature and lateral acceleration as independent variables and LTR as the dependent variable. Each simulation dataset was split into 70% for training and 30% for prediction. Model performance was evaluated by computing the mean squared error (MSE). The prediction tasks included six datasets--three variations of DLC tests at different speeds and three variations of SWD tests--each trained and tested separately.
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## Appendix A Machine Learning Models and Hyperparameters Optimization  
_Appendix A.1. Lasso and Ridge Regression_  
As a fundamental machine learning algorithm, linear regression tries to find the following function to make predictions by linearly integrating various variables.  
\[\hat{y}_{i}=X\beta\] (A1)  
where \(\hat{y}_{i}\) represents the predicted value, \(X\)= \((x_{1},x_{2},\ \cdots,\ x_{m})\) are selected feature values from the dataset, \(\beta\)= \((\beta_{0},\beta_{1},\ \cdots,\beta_{m})\) are fitted parameters, and \(m\) is the number of features. The loss function can be expressed as follows:  
\[L_{Linear}(\beta)=\sum_{i=1}^{n}{(y_{i}-\sum_{j=0}^{p}{\beta_{j}x_{ij}})^{2}}\] (A2)  
A Lasso regression is a type of regularization, which adds the absolute value sum of fitted parameters to the loss function, as shown in Equation (A3):  
\[L_{Lasso}(\beta)=\sum_{i=1}^{n}{(y_{i}-\sum_{j=0}^{p}{\beta_{j}x_{ij}})^{2}}+ \lambda_{1}\sum_{j=1}^{p}{|\beta_{j}|}\] (A3)  
Similarly, Ridge also adds a regularization term (e.g., the sum of squares of parameters) to the loss function, which can be expressed as:  
\[L_{Ridge}(\beta)=\sum_{i=1}^{n}{(y_{i}-\sum_{j=0}^{p}{\beta_{j}x_{ij}})^{2}}+ \lambda_{2}\sum_{j=1}^{p}{\beta_{j}^{2}}\] (A4)  
where \(n\) is the total number of data samples, and \(\lambda_{1}\) and \(\lambda_{2}\) are the regularization parameters in Lasso and Ridge regressions, respectively, which are two important hyperparameters that need to be optimized during modeling. In this study, the optimized \(\lambda_{1}\) and \(\lambda_{2}\) are 0.0043 and 0.24, respectively.