esa-sceva/satcom-chunk-collection huggingface dataset:
# Schumann Resonances and the Human Body: Questions About Interactions, Problems and Prospects
## 3 Results
### Earth's Magnetic Field and Schumann Resonance Frequencies: Origin and Essence
#### 3.4.4 Results of Studying the Interaction of Schumann Resonances with the Skin of the Human Body
A cause-and-effect relationship implies the presence of a correlation between the objects of study, but such a correlation does not imply the presence of a cause-and-effect relationship between them. Therefore, when assessing the results of studies on the interaction of SRs and the human body, it is important to correctly interpret this aspect. In modern science, one of the stages of proving the presence of a cause-and-effect relationship is to conduct a randomized controlled trial. The results of such studies of the interaction of the human body with SRs are not yet available and are a task for future research. Also, a weak point in proving the presence of a cause-and-effect relationship between SRs and the human body is the control of influencing factors. Unfortunately, the existence of a multifactorial, multilevel, complex external electromagnetic influence on the human body does not yet make it possible to carry out research taking into account all these factors. This is one of the limitations for such studies. Of course, all this gives rise to reasonable doubts and requires additional explanations. So maybe there is no cause-and-effect relationship between the human body and SRs? Maybe these are just random correlations? However, with random correlations, there is never directionality, and it cannot be said that a change in one variable causes a change in another. A systematic analysis of existing studies on the influence of SRs on the electromagnetic activity of the brain [105; 106; 107; 108; 109; 110; 111; 112; 114; 115; 118; 123; 129] and heart [99; 100; 116; 117; 118; 119; 123; 133] indicates the existence of a direction. When chronologically assessing the dynamics of changes in SRs, the established correlations indicate the existence of a time dependence. This confirms that a change in SRs as a cause leads to a change in activity indicators of the brain [105; 106; 107; 118; 112; 114; 115; 118; 123; 129] and heart [99; 100; 116; 117; 118; 119; 123; 133] as a consequence. Thus, a time direction also exists, and this is important evidence in favor of the existence of cause-and-effect relationships between the human body and SRs. Also, the presence of a cause-and-effect relationship is always supported by the existence of mechanisms for its occurrence. Modern scientific descriptions of the possible mechanisms underlying the influence of SRs on the human body exist and are described, although they continue to be discussed.
## 2 Materials and Methods
### Estimation and Projection Models for the Net Primary Productivity
#### 2.2.3 Projection of Climate Variables and Ozone Concentration  
According to the reports of National Institute of Environmental Research [71; 72], ozone concentration is reduced by 40% in scenarios, in which 60% of nitrogen oxide is removed. In this study, reverse ozone concentration increases when nitrogen oxide emissions are increased by reverse use. The researchers estimated the concentration of ozone in connection with nitrogen oxide emissions in Korea and China. The emission factor that had the greatest influence on ozone generation in Korea was identified as nitrogen oxide [71; 72]. Using the relationship between nitrogen oxide emission and ozone concentration, the following Equation (3) was derived.  
\[\begin{split}\text{O}_{3}\text{ concentration change ratio in the future = (4/6) \times\{(\text{NO}_{x}\text{ emissions in Korea \times 0.436})\] \[+ (\text{NO}_{x}\text{ emissions in China \times 0.564})\}/\text{Baseline's emissions in 2010}\end{split} \tag{3}\]  
The emission of nitrogen oxide was estimated using the data of [PERSON] et al. [73] as shown in Table 1.  
Thus, future ozone concentration changes due to NO\(x\) emissions from various scenario combinations are expected to decrease by up to 3% (0.0420 ppm) or increase by up to 21% (0.0512 ppm) when compared to their current levels in the 2050s and a change between \(-\)29% (0.0308 ppm) to +36% (0.0572 ppm) is expected in the 2090s, as seen in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**Korea**} & \multicolumn{3}{c}{**China**} \\ \cline{2-7}  & **SSP2** & **SSP3** & **SSP5** & **SSP2** & **SSP3** & **SSP5** \\ \hline
2010s & 1.06 & 1.06 & 1.06 & 21.21 & 21.21 & 21.21 \\
2050s & 1.252 & 1.174 & 1.388 & 26.244 & 17.78 & 28.25 \\
2090s & 1.23 & 0.754 & 1.868 & 26.95 & 9.638 & 28.984 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nitrogen oxide emissions of the Shared Socioeconomic Pathway (SSP) scenarios based on the Representative Concentration Pathway (RCP) 8.5 scenario (Unit: Mt).  
Figure 2: Ozone concentration change in the future according to nitrogen oxide emissions by scenarios.
###### Abstract  
El Nino events are known to be associated with climate extremes and have substantial impacts on the global carbon cycle. The drought induced by strong El Nino event occurred in the tropics during 2015 and 2016. However, it is still unclear to what extent the drought could affect photosynthetic activities of crop and forest in Southeast Asia. Here, we used the satellite solar-induced chlorophyll fluorescence (SIF), which is a proxy of actual photosynthesis, along with traditional vegetation indices (Enhanced Vegetation Index, EVI) and total water storage to investigate the impacts of El Nino-induced droughs on vegetation productivity of the forest and crop in the Southeast Asia. We found that SIF was more sensitive to the water stress than traditional vegetation indices (EVI) to monitor drought for both evergreen broadleaf forest and croplands in Southeast Asia. The higher solar radiation partly offset the negative effects of droughs on the vegetation productivity, leading to a larger decrease of SIF yield (SIF\({}_{\text{yield}}\)) than SIF. Therefore, SIF\({}_{\text{yield}}\) had a larger reduction and was more sensitive to precipitation deficit than SIF during the drought. The comparisons of retrieved column-average dry-air mole fraction of atmospheric carbon dioxide with SIF demonstrated the reduction of CO\({}_{2}\) uptake by vegetation in Southeast Asia during the drought. This study highlights that SIF is more beneficial than EVI to be an indicator to characterize and monitor the dynamics of drought in tropical vegetated regions.  
solar-induced chlorophyll fluorescence; extreme climatic events; photosynthesis; drought; El Nino events +
Footnote †: journal: Remote Sensing, 2019, 11, 910
# Maritime Supply Chain Optimization by Using Fuzzy Goal Programming
## 2 Fuzzy Goal Programming  
Goals and constraints are determined clearly in the goal programming formulation. According to [31], it is mostly hard for the decision makers to determine the access level for each objective functions. Access levels are determined based on preference priorities and relative weights of the goals and subjective evaluations of decision makers. Fuzzy logic is used as linguistic variables for the subjectivity in goal programming [32]. The decision makers might not always provide the exact values of the goals. Therefore, linguistic variables help decision makers to make an appropriate evaluations. For instance, a decision maker might evaluate the goals by some comparisons such as \"lower than \(g_{1}\)\", \"higher than \(g_{2}\)\", etc. Membership function of a fuzzy number represents the satisfaction level of the goals. If the membership function is equal to 1, it means that the goals are fully satisfied, if it is 0, the goal is not reached. If it is between 0 and 1, the goal is achieved partially. The fuzzy logic can also be employed to the parameter values and right hand side constants of constraints. The \(=,\leq,\geq\) signs provide tolerance [33]. A fuzzy constraint can be shown as \(\tilde{C}\) fuzzy set or \(\mu_{\tilde{C}}(x)\in[0,1]\) which is a subset of universe set where \(\mu_{\tilde{C}}(x)=1\), \(0<\mu_{\tilde{C}}(x)<1\) and \(\mu_{\tilde{C}}(x)=0\) represent fully satisfied, partially satisfied and not satisfied respectively. The general structure of goal programming is given below [34; 35]:  
\[\begin{array}{cc}x_{i}&i=1,2\ldots n\\ A_{j}(x_{i})\prec\overline{\Lambda_{j}}&j=1,2,\ldots J\\ A_{k}(x_{i})\succ\overline{\Lambda_{k}}&k=m+1,m+2,\ldots K\\ f_{l}(x)\leq b_{l}&l=1,2,\ldots L\\ x_{i}\geq 0&i=1,2\ldots n\end{array} \tag{1}\]where \(A_{j}(x_{i})\) is the \(j\)th objective function, \(\overline{\text{A}_{\text{j}}}\) is the target value for the \(j\)th goal. \(A_{k}(x_{i})\) is the \(k\)th constraint, \(\overline{\text{A}_{\text{k}}}\) is the target value for the \(k\)th goal. \(f_{l}(x)\) is the inequality constraint, \(b_{l}\) is the amount of probable or reachable source.  
In the literature, there exist several goal programming models [36; 37]. Ref. [38] formulates the goal programming as follows:  
\[\begin{array}{ll}\max&\lambda(x)=\gamma\lambda_{0}+(1+\gamma)\sum_{k}\theta _{k}\mu_{A_{k}}(x)\\ \text{s.t.}&\lambda_{0}\leq\mu_{A_{k}}(x)&k=1,\ldots,n\\ &x\in f(x)\\ &\lambda_{0},\gamma\in[0,1]\end{array} \tag{2}\]  
where \(\mu_{A_{k}}(x)\) is the satisfaction level of \(k\)th objective function. \(\lambda_{0}\) is the minimum satisfaction level of goals. \(\theta_{k}\) is a parameter that shows the relative importance value where \(\sum_{k}\theta_{k}=1\) and \(\theta_{k}>0\). \(\gamma\) is a compensation coefficient checks for the compromises from the target. If the \(\gamma\) is set higher, it means that \(\lambda_{0}\) is desired to be higher [39].
## 3 Results
### Types of Machine Learning Models (Ensemble vs. Single Type)  
There are many ways of training a predictive algorithm. Some examples include linear regression, decision trees, or gradient boosting. Additionally, it is possible to combine several training models into an ensemble model. Such ensemble models have been found by multiple studies to have superior accuracy when compared to single-type models. For example, [PERSON] et al. (2018) used multitask learning to create their predictive algorithms that made two predictions: the current Quick Inventory of Depressive Symptomatology (QIDS) score for all participants (healthy and depressed) and the current severity of depressive symptoms for patients. The tasks for the multitask models were based on the type of prediction, (1) QIDS score, and (2) severity, as well as the operating system of the user's smartphone, (1) iOS and (2) Android. Two-task models each did one type of prediction for the two different operating systems, and four-task models combined QIDS score and severity prediction for both operating systems. Overall, the multitask models outperformed the single-task ones, and the four-task models generally performed better than the two-task models, showing the potential of combined predictive models to enhance each other. Additionally, the models using FitBit and smartphone data performed slightly better than those using smartphone data alone, showing that the wearable data can also improve the predictive capabilities of these models.  
Another study that used a combination of different training methods was conducted by [PERSON] et al. (2018). The algorithms that were compared used logistic regression, random forest, and a weighted average ensemble that combined the two. The accuracy reported for logistic regression and for random forest was generally high, but they found the weighted average ensemble model to be the most accurate of all. As mentioned earlier, the accuracy may be inflated due to the use of some HDRS-21 data to predict HDRS-21 scores, but this was the case for all three model variations. As such, this study again indicates that ensemble models can improve predictive quality. While the accuracy of ensemble models seems better than that of singular models, they also come with some disadvantages. Firstly, more complex models require more computing power and resources, which may not be available or desirable. Secondly, the increasing complexity of a model increases the risk of overfitting, whereas simpler models are more robust. Finally, due to thecomplexity of ensemble models, it is much more difficult to rationalize and understand how predictions are made compared to single-type models. Depending on the data complexity and prediction goal, a fitting model complexity should be chosen.
# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 5 Deep Feature Extraction with Context Scene Feature Fusion and Detection
### Deep Feature Extraction and Context Scene Feature Fusion
In practice, it is found that when the network is too simple, the network classification accuracy is high in the training phase, but it is not good when using the network for detection. The possible reason is that the oversimplified network is not strong enough to abstract the features, leading to poor generalization ability. As shown in Figure 14, the final objNet network consisted of eight layers, and the first five layers were the convolution layers, which can be seen as multi-stage feature extraction. The latter three layers were the fully-connected layers, which can be seen as a classifier. The parameters of each layer are shown in Table 4. In the detection process, the candidate region was input to the network for feed forward calculation, taking the output of the fifth layer of the pool as the feature of the candidate region. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **1 st Layer** & **2 nd Layer** & **3 rd Layer** & **4 th Layer** & **5 th Layer** \\ \hline \multirow{4}{*}{convolution layer} & window size & \(5\times 5\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) \\  & number of convolution kernels & 48 & 128 & 192 & 192 & 28 \\  & stride & 1 & 1 & 1 & 1 & 1 \\  & pad & 0 & 0 & 1 & 1 & 0 \\ \hline \multirow{2}{*}{pooling layer} & window size & \(2\times 2\) & \(2\times 2\) & – & – & 2 \(\times 2\) \\  & stride & 2 & 2 & – & – & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The parameters of each layer in objNet network. Figure 14: objNet network structure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **1 st Layer** & **2 nd Layer** & **3 rd Layer** & **4 th Layer** & **5 th Layer** \\ \hline \multirow{4}{*}{convolution layer} & window size & \(11\times 11\) & \(5\times 5\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) \\  & number of convolution kernels & 96 & 256 & 384 & 384 & 256 \\  & stride & 4 & 1 & 1 & 1 & 1 \\  & pad & 0 & 2 & 1 & 1 & 1 \\ \hline \multirow{2}{*}{pooling layer} & window size & \(3\times 3\) & \(3\times 3\) & – & – & 3 \(\times 3\) \\  & stride & 2 & 2 & – & – & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parameters of each layer in the seeNet network.
## 4 Discussion
### Association between CMRJ and 505 COD Speed  
The 505 COD test examines an athlete's ability to decelerate, change direction, and subsequently re-accelerate, necessitating a complex interplay of neuromuscular coordination, strength, and power [14; 15]. Substantiating the findings of previous studies [3; 6; 14; 17; 18], the correlational analysis showed a statistical large inverse relationship between two JH values and RSImod with 505 COD times (\(r=-0.51\) to \(-0.68\), \(p\leq 0.006\)). This indicates that the ability to generate and sustain a high amount of force rapidly across the braking and propulsive phases in CMRJ is equally vital for COD tasks (maximum entry and exit velocities) [14]. In contrast to a linear sprint, the RSI showed non-significant correlations with 505 COD times, which is consistent with prior research [17]. A recent meta-analysis elucidated that when individuals perform COD manoeuvres with directional changes of >90\({}^{\circ}\), they predominantly rely on slow SSC mechanics [38]. The results from the median split analysis support this notion (Figures 4 and 7), whereby athletes who jumped higher in both jumps of the CMRJ test and scored higher RSImod and RSI values exhibited faster 505 COD times on the right and left sides (\(g\geq-0.44\), \(p\leq 0.04\)). Collectively, it seems reasonable to deduce that outcomes related to slow SSC movements (e.g., JH-1 and RSImod) obtained from the CMRJ test may offer a more accurate indication of an athlete's COD capabilities than those associated with fast SSC movements (e.g., JH-2 and RSI) [17].  
Contrary to the expectations, no significant correlation was found between CMRJ metrics and the COD deficit (Table 2). However, this finding is consistent with that of [PERSON] et al. [39] and [PERSON] et al. [14], who reported the absence of such relationships in male team sports athletes. While some CMRJ metrics are inversely associated with 505 COD times, they do not necessarily predict the efficiency of directional changes, which is what the COD deficit measures [15]. The lack of correlation with the 505 COD deficit may be due to the multidimensional nature of COD tasks, which require not only the power and force production quantified by CMRJ metrics but also specific technical skills that are not captured in the CMRJ test; these skills typically include the body lean, foot placement, and step adjustment for deceleration and acceleration [6; 40]. Finally, and although a relatively minor consideration, the median split analysis did not reveal significant differences in the COD deficit between groups with higher and lower CMRJ metrics (Figures 4 and 7). Therefore, CMRJ performance measures could confidently mirror the physical competencies relevant to a 505 COD task, but they might not be sufficient by themselves to predict COD efficiency comprehensively. For practitioners, a holistic appraisal encompassing the technical aspects of COD tasks is imperative to optimise athletic performance in sports requiring multidirectional movements [14].
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 4 System Implementation
### Process Flow  
There are three processing stages: signal-conditioning, training, and classification. Signal conditioning: The waveform sampled by the photo-diode undergoes signal conditioning prior to the identification. The signal magnitude is normalized so that the maximum value for each gesture is one. Gesture training: Data are collected for each of the 5 gestures. For each gesture, multiple frames are collected by repeating the movement over a period of two seconds. The data are then randomly split into two sets one for training and one for classification. This needs to be performed once on first use for each user, as they may have different movement styles and speed for the same gesture type. Gesture classification: The gestures are classified by ML. A practical gesture recognition system should be able to operate in real-time. Therefore a trade-off can be met between computational complexity and accuracy. We selected the LSTM algorithm as it offers a good performance to complexity ratio and is suitable for the repetitive sequential waveforms generated by hand gestures.
## 1 Introduction
The equivalent factor is determined by NLP, and the torque of the engine and motor is determined by the A-ECMS scheme using the equivalent factor. Therefore, the OCP size of the NLP can be reduced, and stable control of the vehicle is possible, even if the NLP has derived an infeasible solution. As mentioned above, it is important to obtain the reference SOC trajectory accurately and rapidly, and NLP can provide rapid and accurate calculations. To reduce NLP computation time, an optimal system model and constraints for an HEV with a P2 structure (TMED) was designed. Results of simulations showed that the proposed NLP-based ECMS closely resembled the global optimal solution produced by DP in a relatively short calculation time. The main contributions of this paper are as follows. First, a suitable required torque preprocessing method for a P2 structure is proposed. In this process, the required torque demanded by the ICE and EM according to the change in gear ratio is calculated. Secondly, a method for calculating the reference SOC trajectory using NLP is proposed. System models, constraints, and cost functions were designed to reflect the characteristics of the HEV with a P2 structure and driving conditions. To reduce the computational time, the NLP model was simplified. Finally, an A-ECMS that can efficiently follow the reference SOC trajectory is proposed as an optimization-based EMS resembling the global optimal solution of DP. This paper consists of the following structure. Section 2 presents a system model of a HEV with a P2 structure, Section 3 presents analysis result of the NLP-based ECMS, Section 4 discusses the significance of our findings, and Section 5 is discussion of the result.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 3 Accuracy and Range of Available PNT Technologies  
As discussed in the previous sections, the effective ranges and accuracies of different devices vary widely. Table 1 summarizes the approximate communication ranges of technologies discussed in this paper with corresponding references. It is important to note that several of the technologies or protocols discussed, such as Bluetooth or UHF/VHF, are both communication frequencies and methods of estimating position. As described in previous sections, position estimation using these approaches may involve initial development of a regression model to predict distance to a signal transmitter as a function of path loss (or RSSI), or other methods such as TOA and TDOA described for UWB positioning. For this reason, the effective transmission range is closely related to positioning capabilities. In some cases, frequencies or communication protocols may be used for (1) communication of data; (2) position estimation based on localization; and (3) both of the preceding applications. Additionally, although we have discussed two- and three-dimensional positioning extensively, one-dimensional (1D) proximity (distance from a point in any direction) is also commonly deployed for Bluetooth and RFID devices (e.g., phone apps and small Bluetooth devices for locating keys or tagging pieces of equipment).  
Accuracies reported in Table 1 are for static positions rather than objects, people, or equipment in motion, with the exception of GNSS-RF (as indicated). As research on PNT applications in forestry, wildland fire, and additional natural resource management concentrations emerge, it is important that researchers and practitioners develop new analytical methods, or adopt methods used in other fields, to better quantify device error and accuracy in ways that integrate both location and velocity. This is particularly important as software applications are increasingly built around equipment and human resources in motion.  
Figure 10: Relative positioning for a quadcopter unmanned aerial vehicle (UAV) with following capabilities. The device’s onboard computer navigation can be programmed by the user to maintain a fixed distance and orientation relative to a moving object, person, or animal. Infrared sensors, machine vision with pattern recognition, acoustic positioning or other methods may be used to infer the UAV position relative to the subject. In this example, the drone positions itself relative to the smartphone’s GNSS coordinates.