esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Model Building Methodology  
The Ada Boost (Adaptive Boosting) algorithm, proposed by [PERSON] et al., in 1996 [PERSON] et al. (1996), is an ensemble learning technique that constructs a robust classifier by combining multiple weak classifiers to achieve higher accuracy and mitigate the risk of overfitting. One notable advantage of Ada Boost lies in its adaptive selection of iteration coefficients, which enhances convenience and usability. Moreover, this algorithm serves as a versatile framework for integrating various other algorithms, making it widely adopted in practice.  
Bagging (Bootstrap Aggregating) is an integrated learning algorithm commonly used to reduce the variance of predictive models [54]. In the Bagging method, a random sample of data is selected from the training set using the substitution method, allowing for multiple selections of a single data point. Subsequently, these weak models are individually trained and their predictions are aggregated to yield more accurate estimates based on the task type (e.g., regression or classification). Bagging integrates ridge algorithms to improve their accuracy and stability while reducing the variance of the results and avoiding overfitting.  
RF (Random Forest) is to build a forest in a random way, and the forest is composed of many decision trees, and each decision tree in the Random Forest is not related to each other. After obtaining the forest, when a new input sample comes in, let each decision tree in the forest make a judgment to see which category the sample should belong to. Then, see which category is selected the most, and predict this sample to be that category. By sampling data sets, multiple different data sets are generated, and a classification tree is trained on each data set. Finally, the prediction results of each classification tree are combined as the prediction results of Random Forest. Random Forest improves the prediction accuracy without a significant increase in computation [55].  
BPNN (Backpropagation Neural Network) can learn and store a large number of input-output mode mapping relationships without revealing the mathematical equations describing those relationships. The calculation process consists of a forward calculation process and a reverse calculation process. The forward propagation process is where the input pattern is processed layer by layer from the input layer to the hidden unit layer and shifted to the output layer, where the state of the neurons in each layer only affects the state of the neurons in the next layer. If the desired output cannot be obtained at the output layer, it is transferred to backpropagation, which returns the error signal along the original connection pathway and minimizes the error signal by modifying the weights of each neuron [56].  
In this study, the above algorithms are implemented using a machine learning library in the Python language, and the entire data set is randomly divided into a training set and a test set in a ratio of 2:1 to fit the four algorithms.
## 5 Conclusions  
This study highlights that integrating wearable IMU sensors with LSTM-RNN models in a multi-model framework significantly enhance the accuracy of KAM prediction. The Multi-model Gait-based KAM Prediction System, which classifies gait patterns and applies tailored regression models, demonstrated a superior performance, achieving an average prediction error of \(6.84\ \mathrm{N}\cdot\mathrm{m}\) and a MAPE of \(8.47\%\). This represents an improvement over the Single-model system's \(8.82\ \mathrm{N}\cdot\mathrm{m}\) average error and \(12.95\%\) MAPE. The approach leverages LSTM-RNN's capability to capture long-term dependencies in sequential data, underscoring its effectiveness in diverse gait types.  
This methodology extends beyond KAM prediction, offering the potential for other applications requiring precise regression based on unique data patterns. The approach may benefit fields where customized pattern classification can improve the predictive accuracy, broadening the impact of multi-model regression techniques.  
Future research will expand the dataset to include more diverse gait patterns and conditions, improve the model architecture for real-time KAM prediction, and enhance system practicality. These efforts will boost predictive accuracy and support cost-effective, continuous knee osteoarthritis monitoring, benefiting clinicians and patients.  
Conceptualization, D.J.; methodology, D.J.; software, D.J.; validation, D.J.; formal analysis, D.J.; investigation, D.J.; resources, H.S.J.; data curation, D.J.; writing--original draft preparation, D.J.; writing--review and editing, D.J., C.L. and H.S.J.; visualization, D.J.; supervision, H.S.J.; project administration, H.S.J.; funding acquisition, H.S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Konkuk University.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data will be made available upon request.  
This paper was supported by Konkuk University in 2023.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} KOA & Knee Osteoarthritis (OA) \\ KAM & Knee Adduction Moment \\ IMU & Inertial Measurement Unit \\ GRF & Ground Reaction Force \\ RNN & Recurrent Neural Networks \\ LSTM & Long Short-Term Memory \\ RMSE & Root Mean Square Error \\ MAPE & Mean Absolute Percentage Error \\ \end{tabular}
## 5 End-to-End Performance Estimation
### Moments of Hop Distance and Average Per-Hop Progress  
Based on the conditional PDFs of the two routing protocols \(f_{d_{FN}|K\geq 1}(x)\) and \(f_{d_{NN}|K\geq 1}(x)\), we can obtain the conditional \(m\)-th moment as  
\[\mathbb{E}\{D^{m}|K\geq 1\}=\int_{0}^{R}x^{m}\cdot f_{D|K\geq 1}(x)dx \tag{18}\]  
for \(D\in\{d_{FN},d_{NN}\}\). \(m=1\) corresponds to the conditional expected hop distance \(\mathbb{E}\{D|K\geq 1\}\). Then, as in [13], the conditional expected progress per hop, which is indicated by the effective distance travelled along the \"Relay\({}_{i}\)-Destination\" axis for Hop \(i\) for \(i\geq 2\) (or the \"Source-Destination\" axis in the first hop) in Figure 4, is  
\[\mathbb{E}\{X|K\geq 1\} =\mathbb{E}\{D\cos\phi|K\geq 1\}\] \[=\mathbb{E}\{D|K\geq 1\}\cdot\int_{-\frac{\Phi}{2}}^{\frac{\Phi}{ 2}}\cos\phi\cdot\frac{1}{\Phi}\,d\phi\] \[=\mathbb{E}\{D|K\geq 1\}\frac{2}{\Phi}\sin\left(\frac{\Phi}{2} \right). \tag{19}\]  
This per-hop progress \(\mathbb{E}\{X|K\geq 1\}\) and the \(\alpha\)-th moment of the hop distance \(\mathbb{E}\{D^{\alpha}|K\geq 1\}\), where \(\alpha\) is the path-loss exponent, determine key performance metrics in MWNs such as reliability, delay, and transmit energy consumption.  
Figure 4: An example illustration of a multi-hop route.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 6 Conclusions and Prospects  
Numerous studies have been conducted for investigating the quality and the application of the PR data. This article selected about 130 studies and performs a comprehensive review to extract the general precipitation features of tropical rainfall among the existing individual studies. We firstly looked at the accuracy of PR data by comparison studies with respectively ground-based radars and rain gauges. We then investigated the overall features of lightning, latent heat and rainfall in tropical regions by generalization and summary. In this final section, we develop our conclusions, and explore some problems and future prospects in using space-borne precipitation radar data.
# A New Method for 2D-Adapted Wavelet Construction:
## 2 Materials and Methods
### Discrete Wavelet Theory  
The space \(\ell^{2}(\mathbb{Z}_{N_{1}}\times\mathbb{Z}_{N_{2}})\) is defined as an extension of the vector space \(\ell^{2}(\mathbb{Z}_{N})\), where the elements of \(\mathbb{Z}_{N}\) are considered complex vectors with \(N\) components (see [38]):  
\[\ell^{2}(\mathbb{Z}_{N_{1}}\times\mathbb{Z}_{N_{2}})=\{z=[z(n_{1},n_{2})]:z(n_ {1},n_{2})\in\mathbb{C},\;0\leq n_{1}\leq N_{1}-1,\;0\leq n_{2}\leq N_{2}-1\},\]  
and the vector space has dimension \(N_{1}\cdot N_{2}\), where addition and multiplication by a complex scalar are similar to the 1D case. The complex inner product on \(\ell^{2}(\mathbb{Z}_{N_{1}}\times\mathbb{Z}_{N_{2}})\) is  
\[\langle z,w\rangle=\sum_{n_{1}=0}^{N_{1}-1}\sum_{n_{2}=0}^{N_{2}-1}z(n_{1},n_{ 2})\cdot\overline{w(n_{1},n_{2})},\;\forall z,w\in\ell^{2}(\mathbb{Z}_{N_{1}} \times\mathbb{Z}_{N_{2}}).\]  
The rotation operator is also useful in 2D cases.  
**Definition 1**.: _Let \(z\in\ell^{2}(\mathbb{Z}_{1}\times\mathbb{Z}_{N_{2}})\), extended by periodicity, and \(k_{1},k_{2}\in\mathbb{Z}\). The rotation operator on \(\ell^{2}(\mathbb{Z}_{N_{1}}\times\mathbb{Z}_{N_{2}})\) is defined by_  
\[R_{k_{1},k_{2}}:\ell^{2}(\mathbb{Z}_{N_{1}}\times\mathbb{Z}_{N_{ 2}}) \rightarrow \ell^{2}(\mathbb{Z}_{N_{1}}\times\mathbb{Z}_{N_{2}})\] \[z \mapsto R_{k_{1},k_{2}}z,\] \[(R_{k_{1},k_{2}}z)(n_{1},n_{2}) = z(n_{1}-k_{1},n_{2}-k_{2}).\]  
**Theorem 1**.: _Let \(M_{1},M_{2}\in\mathbb{Z},N_{1}=2M_{1},\;N_{2}=2M_{2}\). Let \(\{R_{2k}v_{1}\}_{k=0}^{N_{1}-1}\cup\{R_{2k}u_{1}\}_{k=0}^{N_{1}-1}\)be a first-stage wavelet basis for \(\ell^{2}(\mathbb{Z}_{N_{1}})\) and let \(\{R_{2k}v_{2}\}_{k=0}^{N_{2}-1}\cup\{R_{2k}u_{2}\}_{k=0}^{N_{2}-1}\) be a first-stage wavelet basis for \(\ell^{2}(\mathbb{Z}_{N_{2}})\). If we define_  
\[w_{0}(n_{1},n_{2})=v_{1}(n_{1})\cdot v_{2}(n_{2}), \tag{1}\] \[w_{1}(n_{1},n_{2})=u_{1}(n_{1})\cdot v_{2}(n_{2}),\] (2) \[w_{2}(n_{1},n_{2})=v_{1}(n_{1})\cdot u_{2}(n_{2}),\] (3) \[w_{3}(n_{1},n_{2})=u_{1}(n_{1})\cdot u_{2}(n_{2}), \tag{4}\]  
_then,_  
\[\bigcup_{i=0}^{3}\{R_{2k_{1},2k_{2}}w_{i}\}_{k_{1}\in\mathbb{Z}_{M_{2}}}^{k_ {2}\in\mathbb{Z}_{M_{2}}}\]  
_is an orthonormal basis for \(\ell^{2}\big{(}\mathbb{Z}_{N_{1}}\times\mathbb{Z}_{N_{2}}\big{)}\), where \(v_{1}\), \(u_{1}\), and \(v_{2}\), \(u_{2}\) are called generators of the wavelet basis for \(\ell^{2}\big{(}\mathbb{Z}_{N_{1}}\big{)}\) and \(\ell^{2}\big{(}\mathbb{Z}_{N_{2}}\big{)}\), respectively. The proof of the theorem can be consulted in [41]._
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Landsat Imagery  
Landsat Collection 2 Level 2 [69; 70; 71; 72; 73; 74; 75] atmospherically corrected Surface Reflectance image products (2000 to 2018) were accessed via Google Earth Engine for VI generation. Scan line corrector (SLC)-off issues associated with ETM+ data were mitigated by constraining all images for a given year's composite to a single sensor, i.e., all available Collection 2 Landsat 7 imagery from 2000 to 2002, Landsat 5 from 2004 to 2011 and Landsat 8 from 2013 to 2018 (no cloud free data available in 2003 or 2012). Summer imagery (1 June to 31 August) were used to minimize the effect of vegetation phenology on VIs.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using Jamovi (version 2.3) and SPSS (version 20) for Windows, with a \(p\)-value \(<\) 0.05 considered statistically significant. Descriptive statistics and assessment of normality using the Kolmogorov-Smirnov test were performed for the study sample categorized by sex and obesity status. The means and standard deviations were calculated for continuous variables (age, SOS, BC, and anthropometric data) and percentages for categorical variables (behavioral and lifestyle data). Differences in categorical variables between non-obese and obese individuals were tested using [PERSON]'s chi-square test in contingency tables for both sexes. Statistical comparisons between the obese and non-obese groups in normally distributed continuous data were analyzed using the independent Sample T-test, whereas non-normally distributed continuous data were assessed using the Mann-Whitney U test in both sexes. Based on the normality assessment, [PERSON]'s correlation was used to analyze the correlation between continuous variables. Forward linear regression analyses were used to examine the association between SOS and the following independent variables: age, smoking, alcohol and sweetened beverage consumption, coffee consumption, Ca and vitamin D intake, physical activity, BMI, PBF, FM (%) in the arm and trunk, and visceral FM. Only predictors with a \(p\)-value of less than 0.05 were associated with the SOS parameter.
## 5 Discussion
In the field of **product presentation**, these technologies are effective tools for displaying design ideas, as they allow the generation of multiple concepts or modifications in an interactive and immediate way. Likewise, it is confirmed that the technologies overcome the limitations of traditional methods of manual representation or CAD environments. One of the main benefits is their potential to enhance **creativity**, as they facilitate rapid idea generation with a lower cognitive load for the transition from 2D to 3D. Instant visualisation and versatile interaction favour ideation in the creative process due to the ease of exploration, immersion, and the enjoyment of an experience highly close to reality (the latter speeds up experimentation, the exchange of ideas, and enhances the search for originality). Similarly, collaborative processes also find these technologies to be useful tools. It is possible to collaborate remotely, test different options, and communicate design proposals; this is due to the fact that this type of tool minimises communication difficulties and improves the quality of the collaborative design process. VR allows teams to collaborate on an infinite 3D canvas, visualising 3D models and full-scale prototypes, being able to modify designs in real time. It enables review tasks to be completed more quickly and with greater participant satisfaction. It also facilitates more cost-effective and flexible design iterations, without sacrificing the quality of interaction or productivity in the co-design process. AR has a relevant role, improving decision-making and enriching collective creativity, thanks to the possibility of integrating virtual models developed by the design team into a real environment. Finally, the following benefits related to resource optimisation in the design process are identified: (1) **reduction in time and costs** due to the rapid creation and modification of digital models, the construction of digital prototypes, the ability to iterate designs almost instantly, and the early detection of errors and design problems; in addition, the current decrease in the cost of VR and AR hardware has made these technologies more accessible and cost-effective in the design process; (2) the **achievement of appropriate product design solutions in the early stages of the project** because of their ability to simulate and evaluate solutions through more precise and detailed feedback during the conceptual design stage than with traditional models; (3) the enrichment of technical documentation due to the **digitalisation of the design project**, designated as expanded digitalisation, which integrates all stages of the product life cycle. This is made possible by the convergence of CAD-VR/AR workflows, full-scale virtual prototyping, continuous simulation, global cloud-based collaboration, and integration with additive manufacturing.
## 3 Results and Discussion
### 3.2. _Nutritional Characteristics of Functional Bread_
#### 3.2.6 Contaminants
With regard to manganese content, a decline was noted in the functional bread samples, except for those containing 6% and 8% sea buckthorn pomace powder from the Clara variety, as well as 8% and 10% sea buckthorn pomace powder from the Mara variety. The amount of Zn detected in the current study is higher than the levels found in bread samples fortified with beetroot powder (2-15%), as reported by [PERSON] et al., 2022 [48]. On the other hand, the levels of Fe in the present study are lower than those reported by [PERSON] et al., 2022 [48]. The study demonstrated that the Fe and Zn content in bread enriched with beetroot powder was lower than those in the control sample. In contrast, the results of the current study indicate that sea buckthorn pomace powder is a viable option for enhancing bread with Fe and Cu. However, the values of Zn and Mn in the sea buckthorn-enriched bread are similar or lower compared to those in the control samples. The iron content observed in the current study is lower than the iron content found in bread fortified with fluted pumpkin, amaranth, or African eggplant leafy powders, as reported by [PERSON] et al., 2017 [35]. However, the levels of zinc obtained in this study are similar to those reported by [PERSON] et al., 2017 [35]. The authors showed that the iron and zinc content increased as the percentage of leafy powder added increased. According to [PERSON] et al., 2022 [49], the microelement content of sea buckthorn byproducts is 4.51 \(\pm\) 0.090 mg/100 g for Zn, 1.23 \(\pm\) 0.073 mg/100 g for Cu, 3.51 \(\pm\) 0.098 mg/100 g for Mn, and 3.6 \(\pm\) 0.079 mg/100 g for Fe. [PERSON] et al., 2019 [47] reported that the microelement content of sea buckthorn varies depending on the source of the raw material. They demonstrated that the tested cultivated sea buckthorn had higher Fe and Mn content compared to wild sea buckthorn. On the contrary, wild berries were found to have higher Cu, Zn, and Ni content. In terms of sodium and potassium content (Table 7), it is noticeable that the bread samples added with sea buckthorn pomace powder from the Sorana variety contain the highest amount (227.1 mg Na/100 g and 360.3 mg K/100 g, respectively). As for calcium content, the highest amount is found in the bread samples containing sea buckthorn pomace powder from the Clara variety (23.87 mg Ca/100 g), while the bread samples containing sea buckthorn pomace powder from the Mara variety has the highest magnesium content (77.94 mg Mg/100 g). The analyzed bread samples exhibited noteworthy distinctions in sodium content when compared to the control sample, ranging from 61.06 to 64.41 mg Na/100 g. Similar observations were noted for magnesium content, which varied between 10.81 and 11.19 mg Mg/100 g. Conversely, the potassium content of the functional bread was greater
## 6 Evaluation of RPL and RPAL in a Real World Environment
### Evaluation Results
#### 6.2.1 Number of Hops  
Figure 7a shows the results of average hops and last hop in the 1 st and 2 nd experiment. As nodes 3, 4 and 6 are important routers in this network, and they will encounter serious problems during the experiment, such as battery depletion and system unreliability. The augmentation of hop number is an easily observed result to evaluate whether the tested routing strategy can comply with the corresponding answers. The RPL standard protocol only uses ETX as routing metric for calculating the minimum rank with hysteresis OF. Thus, according to the measurement data, the RPL model in the 1 st test has less hop counts since it didn't change routing path before node 3 and 6 consumed all their battery energy. Nevertheless, the RPAL model has the ability to move the exceptional nodes to a lower rank position which may lead to larger rank value and more hops count in the 2 nd experiment.  
For the case depicted in Figure 7a, node 4 has more hop counts in the 2 nd test than in the 1 st test, because its NANO module counter value is advertised by its emitting DIO messages as a routing  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline \multicolumn{1}{c}{**Functions**} & \multicolumn{1}{c}{**Descriptions**} \\ \hline
**LED control** & ON and OFF switching the single LED on 1 WoCore board. \\ \hline
**Message collection** & Prepare and send a collect-view application packet immediately. \\ \hline
**RPL global repair** & Trigger global repair in the current DODAG. \\ \hline
**Collecting frequency control** & Change the frequency of sending collect-view application packet to 10 s, 15 s, 30 s, 60 s, 120 s. \\ \hline
**Remained energy control** & Modify the volume of battery +10\% and \(-\)5\%. The results can be observed in the battery indicator plot. \\ \hline
**NANO control** & Postpone the event timer of the NANO communication process. \\ \hline
**TX power control** & Modify the transmission power of the radio chip to a designated value. \\ \hline
**Power supply mode control** & Configure the targeted testbed using the below power supply modes: \\  & Mode 0: battery powered, residual energy is based on online energy estimation model \\  & Mode 1: energy harvester module (solar panel) is able to produce enough power to activate the testbed and cannot recharge the battery \\  & Mode 2: energy harvester module (solar panel) is able to produce enough power for both testbed routines and battery recharging. \\ \hline \hline \end{tabular}
\end{table}
Table 13: Remote control and fault injection functions.  
constraint and its neighbors will not choose it as preferred parent. Meanwhile, this also leads to increased hops counts for nodes 5 and 10.