esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Net Ecosystem Production  
The mean NEP of intact forests was 10.8 Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\) (2.94 Mg C ha\({}^{-1}\) yr\({}^{-1}\)). In contrast, oil palm estates were significant sources of greenhouse gas emissions. The NEP of oil palm was \(-25.1\) Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\) (\(-6.85\) Mg C ha\({}^{-1}\) yr\({}^{-1}\)). Logged forests were also sources of greenhouse gas emissions with a slightly negative NEP (\(-0.1\) Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\)). The difference in NEP between intact forest and oil palm was 35.9 Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\), and between intact and logged forest was 10.9 Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\).  
The NEP of logged forests and oil palm estates was significantly lower than PSF (Table 4) (\(p\) = 0.056 and 0.001), but not ES (\(p\) = 0.8). NEP was significantly correlated with the NPP (r = 0.95), but not with the heterotrophic respiration (r = 0.08; Figure 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **Total NPP** &
\begin{tabular}{c} **Hetetotrophic** \\ **Respiration** \\ \end{tabular} & **NEP** \\ \hline  & \multicolumn{3}{c}{**\(\mathbf{-Mg}\) CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\)**} & \multicolumn{1}{c}{\(\mathbf{-}\)} \\ \hline Peat swamp forest 1 & 50.4 & 40.0 \(\pm\) 5.8 & 10.4 \\ Peat swamp forest 2 & 42.4 & 40.1 \(\pm\) 5.3 & 2.3 \\ Peat swamp forest 3 & 52.8 & 32.9 \(\pm\) 4.5 & 19.9 \\ Peat swamp forest mean & 48.5 \(\pm\) 2.8 & 37.7 \(\pm\) 2.4 & 10.8 \(\pm\) 5.1 \\ \hline Logged peat swamp forest 1 & 38.8 & 43.0 \(\pm\) 5.4 & \(-\)4.2 \\ Logged peat swamp forest 2 & 40.7 & 39.6 \(\pm\) 5.1 & 1.1 \\ Logged peat swamp forest 3 & 42.2 & 39.4 \(\pm\) 4.3 & 2.8 \\ Logged peat swamp forest mean & 40.6 \(\pm\) 1.0 & 40.7 \(\pm\) 1.2 & \(-\)0.1 \(\pm\) 2.1 \\ \hline Early seral 1 & 31.3 \(\pm\) 12.8 & 26 \(\pm\) 2.5 & 5.3 \\ Early seral 2 & 48.1 \(\pm\) 19.7 & 31.3 \(\pm\) 4.0 & 16.8 \\ Early seral 3 & 39.9 \(\pm\) 16.3 & 34.6 \(\pm\) 2.0 & 5.3 \\ Early seral mean & 39.8 \(\pm\) 4.9 & 30.7 \(\pm\) 2.5 & 9.1 \(\pm\) 3.8 \\ \hline Oil palm estate 1 & 14.1 \(\pm\) 1.4 & 42.2 \(\pm\) 7.0 & \(-\)28.1 \\ Oil palm estate 2 & 13.4 \(\pm\) 3.5 & 38.5 \(\pm\) 3.3 & \(-\)25.1 \\ Oil palm estate 3 & 13.4 \(\pm\) 1.0 & 35.4 \(\pm\) 3.6 & \(-\)22.0 \\ Oil palm estate mean & 13.6 \(\pm\) 0.2 & 38.7 \(\pm\) 2.0 & \(-\)25.1 \(\pm\) 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Net primary production (NPP), net ecosystem production (NEP), and heterotrophic respiration in intact and logged peat forest, early seral, and oil palm estate were reported as mean \(\pm\) SE whenever possible.  
Figure 5: The net primary production and heterotrophic respiration rates in intact (PSF), logged peat forests (LPSF), early seral communities (ES), and oil palm estates (OP). Vertical bars are one standard error of NPP and respiration.
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
## 4 Experiments
### Counting Performance
#### 4.5.3 Confusion Matrix Analysis  
Figure 4 provides a comparative view of counting predictions via confusion matrix visualization. Traditional captioning models such as BLIP, GIT, and ExpansionNet v2 exhibit a clear tendency to omit numeric tokens as object counts increase. This behavior suggests a limited ability to generalize beyond low-count scenarios, often defaulting to generic descriptions that lack explicit numerical information.  
VLMs like mPLUG-Owl3 and BLIP (large) demonstrate relatively better numerical reasoning; however, their predictions remain concentrated around smaller numbers, and they frequently fail to generate captions with counts greater than five. This indicates a persistent limitation in modeling structured numerical relationships.  
In contrast, NuCap demonstrates improved numerical consistency across a wider count range. The confusion matrix reveals that NuCap's predictions are more closely aligned with ground-truth counts, particularly in the mid-range (4-8), where existing models struggle the most. Furthermore, NuCap minimizes large-count errors, with predictions densely concentrated along the diagonal. These findings highlight NuCap's effectiveness in mitigating number generation failures and in incorporating numerical information into captions.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Accuracy (\%)** & **F1 Score** \\ \hline mPLUG-Owl3 (2B) [21] & 11.39 & 0.062 \\ mPLUG-Owl3 (7B) [21] & 13.36 & 0.077 \\ BLIP (base) [20] & 17.9 & 0.099 \\ BLIP (large) [20] & 28.29 & 0.177 \\ GIT (base) [37] & 4.72 & 0.025 \\ GIT (large) [37] & 13.56 & 0.076 \\ \(\text{MeaCap}_{TF}\)[12] & 1.77 & 0.009 \\ ExpansionNet v2 [11] & 9.04 & 0.050 \\ GRIT [26] & 11.59 & 0.068 \\ NuCap & 34.18 & 0.342 \\ \hline \end{tabular}
\end{table}
Table 4: Counting performance across different models.
## 4 Experimental Set-Up
For the measurement of air temperature in the cavity and on the back surface of PV panels, sensors with a lower accuracy (of \(\pm\)0.3 K in a range from 10 to 90 \({}^{\circ}\)C) but wider measurement range (\(-\)50 to 150 \({}^{\circ}\)C) were applied. Moreover, a middleware electronic board with an advanced RISC machine (ARM) microprocessor that communicates the sensors, and a raspberry pi device, was custom-developed for the purpose of the experimental set-up. Software for this purpose was developed in C/C++ and Python using GPL licensed compilers and libraries. The provided solution automated the process of periodical collecting data from the sensors and enabled reliable and secure data storage. The readouts were made with a 5-min time step. Figure 3: Cross section through the facade equipped with a PCM composite layer, dimensions in millimeters. PV: photovoltaic. The thermal conditions inside the adjacent room reflected the indoor climate of a typical office. Moreover, external environment parameters such as dry bulb temperature, and direct and diffuse solar radiation were monitored by the weather station located on the roof above the experimental set-up. This allowed to verification for any incorrect readouts not consistent with the external boundary conditions that were made.
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
## 6 ODI vs. MRR Qualitative Comparison
### Tunability:  
The tunability of each type of filter can be assessed against bandwidth tunability and wavelength tunability [89]. For the first ability, we can calculate its minimum, which can be done in a straightforward manner by using the analytic formulas that quantify it. These are given from [52]\(FWHM_{ODI}=FSR_{ODI}/2\) for the ODI and \(FWHM_{MRR}=\lambda_{amp}^{2}(1-rl)/(2\pi^{2}n_{eff}R\sqrt{rl})\)[75]. Recall now that in order to prevent the filtering action that suppresses the SOA pattern effect on the data pulses from distorting the latter, the ODI delay should not exceed 10 ps [31], while the MRR radius should not be larger than 42 um [33]. Then, substituting these maximum values together with \(\lambda_{amp}=1550\) nm, \(r=l=0.95\) and \(n_{eff}=1.41\) in the above expressions of the FWHM, it can be found that \(FWHM_{ODI}\geq 0.4\) nm and \(FWHM_{MRR}\geq 21\) nm. For the second ability, on the other hand, we have shown from Figure 10 that the wavelength offset of the MRR is allowed to be larger than that of the ODI while maintaining an acceptable \(Q^{2}\)-factor. Thus, in both cases, the numerically-obtainednumbers suggest that the MRR filter is better tunable for the target application of SOA pattern effect compensation. Table 2 summarizes the qualitative comparison of the MRR and ODI key characteristics. Note that this comparison is not absolute in the sense that each filter has its own special features, which may render it more suitable than the other for exploitation in SOA direct amplification applications with a suppressed pattern effect.
# Weak Multiple Fault Detection Based on Weighted
## 5 Experimental Verification
### Application to a Real Signal Mixture
The analyzed results are shown in Figures 22-24, and the weak, multiple faults are not more obvious than the results in Figure 21. Similarly, the energy ratio and CPU running time are used to compare and analyze the above algorithms, and the results are shown in Table 5. It can be seen from the table that the energy ratio of the proposed algorithm is obviously larger than in other algorithms. The conclusions drawn are basically consistent with the simulation analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bearing Type** & \(\mathbf{f_{i}}\) & \(\mathbf{f_{o}}\) & \(\mathbf{f_{b}}\) & \(\mathbf{f_{c}}\) \\ \hline HRB 6205 & 94.76 (Hz) & 62.74 (Hz) & 41.2 (Hz) & 6.95 (Hz) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The theoretical calculation result of characteristic frequencies of rolling bearing 6205. Figure 19: Experimental bearing with multiple faults on the surfaces of the inner race and outer race. Figure 21: Cont. Figure 20: Experimental signal: (**a**) Time domain waveform. (**b**) Frequency spectrum. (**c**) Envelope spectrum. Figure 21: Results of the proposed WOGS algorithm: (**a**) Time domain waveform. (**b**) Frequency spectrum. (**c**) Envelope spectrum. Figure 22: Cont. Figure 23: Results of OGS: (**a**) Time domain waveform. (**b**) Envelope spectrum. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & \(\mathbf{R_{f}}\) & \(\mathbf{T_{f}(s)}\) \\ \hline TQWT & 0.0185 & 2.26 \\ OGS & 0.023 & 1.43 \\ Soft threshold & 0.0252 & 0.19 \\ WOGS & 0.135 & 1.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of \(R_{f}\) and \(T_{f}\) of four algorithms. Figure 22: Results of TQWT: (**a**) High oscillatory component. (**b**) Low oscillatory component. (**c**) Residual component.
# Weight Reduction of a Ship Crane Truss Structure Made of Composites
## 4 Constraints and Relationships of the Optimization Problem Parameters
### Results  
Application of the proposed technique reduced the mass of the CM boom by 18%. At the same time, the number of calculation iterations using the screening and Jaya algorithm together was 250. The proposed optimization technique was compared with multi-objective genetic algorithm (MOGA), adaptive multi-objective algorithm and Jaya algorithm. Comparison of the results of applying the algorithms was based on the reduction in the mass of the arrow and the value of the objective function. The optimization calculation for each of the algorithms was limited to 24 h. Proposed technique showed a more efficient solution by 7-10% in terms of weight reduction. The total calculation time was about 16 h on a standard desktop with an Intel Core i7 10700K processor, which is quite acceptable for engineering use at the design stage. The weight reduction per iteration of the algorithm is shown in Figure 7.  
The comparison of the original and optimized design is shown in Figure 8.  
The direct stresses of rovings for one of the design cases are shown in Figure 9.  
Figure 8: The comparison of the designs.  
Figure 7: Weight reduction.  
Numerical results of applying the proposed optimization technique at each step are shown in Table 5.  
Considering rather large number of parameters, finding the absolute minimum of the objective function may be extremely time-consuming and is not usually set as a practical goal. In engineering practice, a weight reduction of 18% is usually considered as a satisfactory one. Further weight reduction in the studied case leads to a dramatic increase in the required number of iterations, and is not thought reasonable.
## 5 Conclusions  
The aim of this research was to predict the PPV using a hybrid ML model enhanced with both boosted-CHAID and SVM techniques with different Kernels. The boosted-CHAID model required only four out of a total of five input variables (distance from blast-face, stemming length, powder factor, and maximum charge per delay). Based on these input variables, different SVM kernels, i.e., SVMRBF, SVMPOL, SVMSIG, and SVMLIN, were designed to predict PPV values. Among these four SVM kernels, SVMRBF and SVMSIG were selected as the best and worst models, respectively, in predicting the PPV. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases respectively, confirm the high capability of this SVM kernel in predicting PPV values. With regards to the importance of PPV predictors, \"distance\" had the greatest importance, which is in line with the boosted-CHAID model results. All models also identified this input parameter as an influential predictor, which implies the importance of this predictor for PPV forecasting. The results of model importance are in line with the published intelligence and empirical studies in the area of PPV prediction.  
This investigation intends to emphasize that this study's modeling method can be utilized in other disciplines to add a different problem-solving perspective. The performance of the SVM models is extensively impacted by the choice of the right values for \"\(\gamma\)\" and \"\(d\)\". In the present study, we employed the grid-search technique for determining the optimal value for \"\(\gamma\)\" and \"\(d\)\". Hence, the performance of the SVM models can be improved if the process of choosing \"\(\gamma\)\" and \"\(d\)\" is conducted by novel optimization techniques. Therefore, future studies on the employment of SVMs for PPV prediction should concentrate on adopting innovative soft computing optimization techniques to optimize values of kernel parameters.  
While the present study selected the RBF kernel as the best kernel, it should be mentioned that the proper kernel function is problem specific. Thus, it can be an interesting topic for future studies to determine the practical process for picking appropriate kernel functions and their corresponding parameters' values consistent with the given problem.  
Future investigations aiming at utilizing the SVM models may apply single and hybrid forms with various kernels in other environmental issues of blasting. It may also be of interest to apply the model to a wider database and enhance the model's prediction accuracy.  
Formal analysis, J.Z. and D.J.A.; Conceptualization, D.J.A. and S.A.F.; Supervision, P.G.A. and A.S.M.; Writing--review & editing, D.J.A., S.A.F., A.S.M., P.C.R., C.M. and P.G.A. All authors have read and agreed to the published version of the manuscript.  
Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant NO. KJQN201804305, KJQN201904307).  
The authors declare no conflict of interest.
## 4 Discussion
### Total Content of Carotenoids  
The presence of carotenoids in apple-peach juices can also affect their color and appearance and enhance the juice's nutritional value and antioxidant activity, as confirmed by [PERSON] et al. [86]. The studies identified and quantified 19 free carotenoids, including 11 xanthophylls in 'Cara Cara' fruits.  
However, over time, carotenoid levels may begin to decline. In our studies, a decrease in the level of carotenoids was observed during 12 months of storage. This phenomenon may be related to thermal processing and the juice's pH, as demonstrated in a study by [PERSON] (2018) [87]. It showed how epoxy-carotenoid esters were sensitive to the low pH of Cara juice, and esterified \(\beta\)-cryptoxanthin was the only ester group that survived thermal processing [87].  
[PERSON] et al., 2010, demonstrated pasteurization temperature's effect on reducing the carotenoid content in orange juice (_Citrus sinensis Osbeck_) [88]. They also found that increasing the microwave pasteurization temperature from 70 to 85 \({}^{\circ}\)C for 60 s causes significant degradation of carotenoids, a decrease of about 50% [88].  
Thermal food processing usually leads to the degradation of carotenoids, primarily through oxidation reactions. Some plant enzymes, such as lipoxygenase, also accelerate this degradation [89; 90]. During pasteurization, in both the TP and MFP methods, the heat generated by the process affects the degradation of carotenoids. Additionally, carotenoids are compounds sensitive to oxidation, and during pasteurization, access to oxygen is inevitable. Our study noted that the innovative venting method during MFP had a favorable effect on carotenoid content. Generally, a higher proportion of carotenoids is evident in MFP-processed juice than in TP.
# Body Area Networks in Healthcare: A Brief State of the Art
## 3 Physical Layer Requirements
### Wearable Sensors in Healthcare
For example, gait detector, temperature, heart rate, and respiration sensors are used together to improve the performance of a professional athlete or alert about heart failures from patients with heart diseases. Lastly, let us highlight that wearable sensors capturing physiological data are used for applications beyond healthcare as such. For example, a biometric authentication system using electrocardiograms provided by a single mobile chip has been proposed [43]. The authentication follows the next scheme: (1) get the electrocardiograms from the mobile sensor, (2) apply a canceling noise filter to the data, (3) extract the features of the heartbeats, and (4) authenticate the user. The research team tests the solution in 175 subjects. The result of those tests showed that around 5% of authentication using a single heartbeat experiments failed and around 2% of authentication with 15 seconds of heartbeat data tests failed. With these results, the authentication system proves to be a viable solution. Wearable sensors are divided into four groups [27]: mechanical, chemical, electrical, and optical. The use of each group depends on the characteristics of the application. Each type of sensor has its own requirements. In the case of chemical sensors, they must be able to detect chemical concentration rapidly with short responses. The major issue with chemical sensors is their stability. The continuous exposure to biofluids can lead to false positives. Mechanical sensors can suffer from deformation, mechanical degradation or failures by continuous use. Usually, optical and electrical sensors are more reliable and robust as there are fewer moving parts. Each type of sensor presents setbacks. For this reason, teams around the world are investigating ways for improving the stability of these sensors. The following subsections describe each type of sensor, focusing on the advantages and weaknesses of every technology.
## 4 Proposed GLLCSA-KELM-FS Model  
In this section, the proposed GLLCSA-KELM-FS model is introduced in detail, as shown in Figure 2. First of all, the input data needs to be preprocessed; the first is the normalization operation and feature selection to eliminate the redundant partial features. The ten-fold cross-validation was used to avoid overfitting. For the KELM technique, the input data was mapped into the hidden layer space through the RBF kernel, including the hyperparameters \(C\) and \(\gamma\) to be optimized and the \(n\) features. The proposed GLLCSA was adopted to optimize the two hyperparameters involved. Specifically, the dataset was divided into 10 parts, with 9 parts selected as training data and 1 part used as the test data, in turn. Further, the nine pieces of data were divided into five pieces again, and four pieces of data were selected, in turn, as training data, which were combined with GLLCSA and used to train the KELM model. We evaluated the trained KELM model with one copy as  
Figure 1: Flowchart of GLLCSA.  
the test data and obtained an optimal KELM model. Finally, for this model, the original reserved one test data was used to evaluate the performance of the resulting KELM model.
## 3 Development of a Non-Intrusive, Modular Formulation for Structural Reliability Assessment
### Multivariate Regression  
Regression analysis is a statistical process for establishing the relationship between a dependent variable and one or more independent variables. Taking the ultimate limit state (ULS) stress constraint as an example, the dependent variable (i.e., maximum von Mises stress, \(\sigma_{VM,\ max}\)) and independent  
Figure 1: Flow chart of the non-intrusive formulation method [2].  
variables (i.e., thrust load \(x_{1}\), tilting moment \(x_{2}\), torsional moment \(x_{3}\), total hydrodynamic load (wave and current) \(x_{4}\), soil 1 Young's modulus \(x_{5}\), soil 2 Young's modulus \(x_{6}\), soil 3 Young's modulus \(x_{7}\), and weight of rotor-nacelle assembly (RNA) \(x_{8}\)) are assumed to have the following functional relationship:  
\[\sigma_{VM,\ max}=[a_{0},a_{1},\ \ldots,a_{16}]\left[\begin{array}{c}1\\ x_{1}\\ x_{1}^{2}\\ \cdot\\ \cdot\\ x_{8}\\ x_{8}^{2}\end{array}\right], \tag{5}\]  
where \((a_{0},a_{1},\ \ldots,a_{16})\) are 17 regression coefficients for a quadratic regression. For other types of constraints, i.e., deformation, buckling, vibration, and fatigue, expressions similar to Equation (5) can be derived. Multivariate regression can be used to obtain the regression coefficients for different limit states [49].  
Taking the stress constraint case as an example, 300 stochastic FEA simulations were performed, obtaining 300 samples. The \(R\)-square value obtained in the regression analysis performed in this case was consistently higher than 0.95, which is relatively high and indicates a satisfactory multivariate regression to be used in this study.
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
### Brief Overview of Related Work
**Notation**: In this paper, matrices are denoted by capital letters, vectors are denoted by boldface, \(Re\{\cdot\}\) means the real part of a complex signal, \(\circ\) is the Hadamard product, \(\mathbf{A}_{[i]}\) means matrix \(\mathbf{A}\) for the \(i\)th transmitter and receiver, \(\mathbf{A}_{[ij]}\) means matrix \(\mathbf{A}\) with respect to the \(i\)th radio transmitter and \(j\)th radio receiver, \(\mathbf{A}_{[K]}\) means matrix \(\mathbf{A}\) with respect to the radar only, \(\mathbf{A}_{N\times M}\) denotes an \(N\times M\)-dimensional matrix, \(\lfloor\cdot\rfloor\) is the floor function,is the Hermitian transpose of matrix \(\mathbf{A}\), and \(\overline{\mathbf{A}}\) is matrix \(\mathbf{A}\) on the reciprocal interference channel. Moreover, the math symbols are summarized in Abbreviations.