esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Analysis of Influencing Factors on Changzhou Ship Lock Capacity
## 1 Introduction
(3) Existing studies predominantly focus on the normal conditions of ship locks, whereas the constraints under saturated operation are the key to breaking through the bottleneck of ship lock capacity. To quantitatively analyze the factors affecting the Changzhou ship lock capacity, utilizing the real hydrological observation data and ship lock passing data from the Changzhou ship lock, this paper proposes an analytical method based on principal component analysis (PCA). We estimated the confidence interval for normal navigation by fitting the passing time of ships to a lognormal distribution, and eliminated redundant data that do not meet navigability criteria by comparing the relationship between hydrological data and cargo load data. Then, we analyzed the primary factors influencing ship lock capacity and their respective contribution rates.
# Are Green Buildings an Indicator of Sustainable Development?
## 3 Results and Discussion
### Energy and GHG Emissions  
The relationship between the average share of renewable energy in gross final energy consumption (dependent variable) and GHG emissions (independent variable) is studied through simple linear regression with data taken from Eurostat Database.  
The ten points in Figure 6 (blue dots) represent the years between 2010 and 2019. The trendline is linear with an equation of y = \(-\)8453.7x + 5438.7, a \(p\)-value = 0.0001 and an R\({}^{2}\) = 0.8841. However, if we do not consider 2014 (highlighted with a red triangle in Figure 7 and characterised by extreme climate conditions with significantly fewer heating degree days compared to other years), the chart assumes an even more linear equation y = \(-\)8346.8x + 5432.5 with more accurate \(p\)-value = 0.0000 and R\({}^{2}\) = 0.9368.  
Figure 5: Relationship between GDP per capita and GHG emissions per capita in the EU (2010–2013 and 2015–2019).  
Figure 6: Relationship between % of renewable energy and GHG emissions in the EU (2010–2019).  
According to the assumptions stated in the Methodology, \(p\)-value and R\({}^{2}\) are consistent, and the equation that correlates the two variables is extremely accurate with a strong significance.  
As expected, more renewable energy utilisation is related to fewer GHG emissions due to less dependency on fossil fuels. This is confirmed by the literature that states that around 3/4 of the GHG emissions come from energy production [28] and that renewable energy consumption halves GHG emissions compared to fossil energy consumption in EU countries [36]. Moreover, another study affirms that renewable energy mitigates carbon emissions while non-renewable energy increases them, supporting the EKC curve [37].
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics  
Based on the measured PDPs, in this section we perform an in-depth statistical analysis by investigating both time-related and power-related characteristics of channel propagation (Sections 4.1 and 4.2, respectively). The analysis presented in the two subsections above adopts a data representation based on boxplots, generated using the MATLAB software. Boxplots are a standardized way of displaying the distribution of data based on the five-number summary (\"minimum\", first quartile (q1), median, third quartile (q3), and \"maximum\"). The boxplot draws points as outliers if they are greater than q3 + 1.5 x (q3 - q1) or less than q1 - 1.5 x (q3 - q1), where q1 (the start of the box) and q3 (the end of the box) are the 25 th and 75 th percentiles of the sample data, respectively. Outliers are represented as red plus signs in the boxplots.  
Following the analysis of time-related and power-related parameters, the relationship between all considered channel propagation parameters is then analyzed in Section 4.3, where a correlation analysis is reported and discussed.
## 5 Sensors
### Multispectral Cameras
Thermal cameras are also used in precision agriculture, particularly for detecting plant stress caused by water deficiencies. By measuring the heat emitted by plants, thermal cameras can identify areas where crops are experiencing water stress, allowing for targeted irrigation. On UGVs, thermal imaging can provide a close-up, real-time monitoring of soil moisture and plant temperature, offering a more granular approach to managing water use efficiently [31; 32].
## 2 Materials and Methods
### Evaluating Clustering Analysis
#### 2.6.3 Silhouette Coefficient  
Silhouette coefficient (SC) checks for each node's similarity with all the nodes in its own cluster and its dissimilarity from the nodes belonging to other clusters. In addition to giving a numeric value, SC can also provide a graphical representation of how well an object lies within its cluster. Equation (12) formulates silhouettes, where, \(a(i)\) is the average dissimilarity of node \(i\) from the other nodes within the cluster. The value of \(b(i)\) is the minimum average dissimilarity of node \(i\) to the nodes of all other clusters. The values from this function are in the range \(-1\) to \(1\). A value of \(-1\) indicates the worst possible clustering, while \(1\) specifies the best clustering. As follows:  
\[S(i)=\frac{b(i)-a(i)}{\max\{a(i),b(i)\}} \tag{14}\]  
In this work, data preprocessing and statistical analysis were conducted used the package library ('ggplot', 'dplyr') of R-program. And for learning and forecasting of supervised learning models, the statistics and machine learning toolbox in MATLAB (Math Works, 2019) was used.
## 4 Results
### Effectiveness and Accuracy  
Figure 11 shows the comparison of the effectiveness evaluation of reasoning using the FA and PFA approaches, where the evaluation was conducted on M.V. Yuming for the risk performance of a bauxite shipping process. It can be seen that the calculated risk rating was completely consistent with the observed risk rating, and the effectiveness of the performance reasoning was ideal. The effectiveness  
Figure 10: Comparison between calculated and observed risk performance of principal factors and the total process of M.V. Bulk Jupiter.  
Figure 9: Root-mean-square error (RMSE) and modified Nash–Sutcliffe model efficiency coefficient (MNSE) with various combinations of numbers and step sizes.  
of the risk reasoning based on FA or PFA was also quantitatively evaluated using DOB and DOD. The degree of deviation for the risk reasoning model was \(\text{DOB}\ =\ 0\), indicating no deviation in the reasoning for the four risk ratings. The degree of detection was \(\text{DOD}\ =\ 1\), showing that the reasoned risk rating was completely accurate.  
Accuracy evaluation was conducted on the calculated nine-step risk performance matrix and the actual observed risk performance matrix, as shown in Figure 12. The RMSE of reasoning based on the FA approach was 0.336 on average with a maximum of 0.516 and a minimum of 0.0, while the RMSE of the PFA approach was 0.36 on average with a maximum of 0.655 and a minimum of 0.0. The error of reasoning was small; thus, the accuracy was better evaluated. The average of the MNSE calculated using the FA approach was \(-0.459\), ranging from 1.0 to \(-2.0\). The average of the MNSE calculated using the PFA approach was \(-1.20\) with a maximum of 1.0 and a minimum of \(-4.40\). The MNSE values based on the two approaches were slightly smaller than zero; therefore, the reasoned performance was slightly conservative due to the values being lower than the actual risk performance.  
Figure 11: Effectiveness evaluation of reasoning for risk performance.  
Figure 12: Accuracy evaluation of reasoning for risk performance.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 2 Materials and Methods
### Model Training
#### 2.5.2 Model Parameter Setting  
Before training the XGBoost model, the experimental data were preprocessed to remove redundant data. The parameters were continuously adjusted during the model training process to optimize the evaluation metrics. The parameters that affected the performance of the model were max_depth, n_estimators, learning_rate, subsamples, reg_alpha, and reg_lambda. The values obtained after tuning the reference are listed in Table 1.  
The XGBoost model was mainly evaluated in terms of three evaluation indicators: coefficient of determination (\(R^{2}\)), mean absolute error (\(MAE\)), and mean square error (\(MSE\)). They are calculated as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{m}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{m}(y_{i}- \overline{y})^{2}} \tag{7}\]  
\[MAE=\frac{1}{m}\sum_{i}^{m}\lvert y_{i}-\overline{y}_{i}\rvert \tag{8}\]  
\[MSE=\frac{1}{m}\sum_{i=1}^{m}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{9}\]  
For the aforementioned indices, \(R^{2}\) is used to evaluate the degree of model fit. The closer the value of \(R^{2}\) is to one, the better the model fits. The smaller the \(MAE\) value, the smaller the prediction error. The smaller the \(MSE\) value, the higher the accuracy of the model for analyzing experimental data.  
As shown in Table 2, the evaluation metrics of the XGBoost model improved after parameter optimization. In the protein model, \(R^{2}\) increased by 6.11%, whereas \(MAE\) and \(MSE\) decreased by 81.26% and 75.51%, respectively. \(R^{2}\) also increased in the fat model, but the change was relatively small compared to the protein model at 0.80%, corresponding to decreases in mean absolute error (\(MAE\)) and mean square error (\(MSE\)) of 84.84% and 78.57%, respectively. The changes in the evaluation indices before and after tuning of the XGBoost model indicated that the tuned XGBoost model performed well.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(R^{2}\) & \(MAE\) & \(MSE\) \\ \hline Original model & Protein & 0.9251 & 0.0459 & 0.0049 \\  & Fat & 0.9899 & 0.0521 & 0.0070 \\ Optimized model & Protein & 0.9816 & 0.0086 & 0.0012 \\  & Fat & 0.9978 & 0.0079 & 0.0015 \\ Improvement & Protein & 6.11\% & 81.26\% & 75.51\% \\  & Fat & 0.80\% & 84.84\% & 78.57\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of XGBoost model before and after tuning.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Parameters** & **Protein Model** & **Fat Model** \\ \hline \multirow{4}{*}{XGBoost} & max\_depth & 6 & 8 \\  & n\_estimators & 235 & 220 \\  & learning\_rate & 0.14 & 0.20 \\  & subsample & 0.6 & 0.8 \\  & reg\_alpha & 0 & 0 \\  & reg\_lambda & 0 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter values of XGBoost model.
###### Abstract  
The deployment of the nodes in a Wireless Sensor and Actuator Network (WSAN) is typically restricted by the sensing and acting coverage. This implies that the locations of the nodes may be, and usually are, not optimal from the point of view of the radio communication. Additionally, when the transmission power is tuned for those locations, there are other unpredictable factors that can cause connectivity failures, like interferences, signal fading due to passing objects and, of course, radio irregularities. A control-based self-adaptive system is a typical solution to improve the energy consumption while keeping good connectivity. In this paper, we explore how the communication range for each node evolves along the iterations of an energy saving self-adaptive transmission power controller when using different parameter sets in an outdoor scenario, providing a WSAN that automatically adapts to surrounding changes keeping good connectivity. The results obtained in this paper show how the parameters with the best performance keep a k-connected network, where k is in the range of the desired node degree plus or minus a specified tolerance value.  
self-adaptive systems; wireless sensor networks; transmission power control; connectivity; communication range +
Footnote †: These authors contributed equally to this work.
## 3 Results  
Table 1 shows the averaged results obtained after all three participants completed three walks wearing each heel type: barefoot and 2, 6, and 10 cm heels.  
Figure 3: Location of markers on the lateral foot side (**A**) barefoot; (**C**) 2 cm heels; (**E**) 6 cm heels; (**G**) 10 cm heels) and on the medial foot side (**B**) barefoot; (**D**) 2 cm heels; (**F**) 6 cm heels; (**H**) 10 cm heels).  
Figure 4 shows the variation of the angle at the tibiotalar joint (\(\theta\)A) during the gait cycle for each heel height considered in this study.  
For \(ht=0\), the tibiotalar joint movement indicates a total range of movement (RoM) of 28\({}^{\circ}\). For \(ht=2\) cm, the total RoM slightly decreased and reached 20\({}^{\circ}\) in this case. For \(ht=6\) cm, the reduction in the total RoM was more evident, with a value of only 11\({}^{\circ}\). For \(ht=10\) cm, it further lowered and obtained a value of only 9\({}^{\circ}\).  
The metatarsophalangeal joint behavior (\(\theta\)B) was monitored. The results are displayed in Figure 5, which shows the four curves obtained with the Tracker software from the metatarsophalangeal joint movement for one full gait cycle with each heel height.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Unit**} & \multicolumn{4}{c}{**Heel Height (cm)**} \\ \cline{3-6}  & & **0** & **2** & **6** & **10** \\ \hline NC & cycles & 5 & 5 & 5 & 5 \\ T\({}_{\text{NC}}\) & seconds & 5.8 \(\pm\) 0.02 & 5.8 \(\pm\) 0.02 & 5.5 \(\pm\) 0.02 & 5.5 \(\pm\) 0.02 \\  & & & (0\%) & (\(-\)5.2\%) & (\(-\)5.2\%) \\ L\({}_{\text{NC}}\) & meters & 5.85 \(\pm\) 0.02 & 5.8 \(\pm\) 0.01 & 5.45 \(\pm\) 0.01 & 5.15 \(\pm\) 0.01 \\  & & & (\(-\)0.9\%) & (\(-\)6.8\%) & (\(-\)12\%) \\ L\({}_{\text{C}}\) & meters & 1.17 \(\pm\) 0.01 & 1.16 \(\pm\) 0.01 & 1.09 \(\pm\) & 1.03 \(\pm\) 0.01 \\  & & & (\(-\)0.9\%) & 0.01(\(-\)6.8\%) & (\(-\)12\%) \\ T\({}_{\text{C}}\) & seconds & 1.16 \(\pm\) 0.01 & 1.16 \(\pm\) 0.01 & 1.1 \(\pm\) 0.01 & 1.1 \(\pm\) 0.01 \\  & & & (0\%) & (\(-\)5.2\%) & (\(-\)5.2\%) \\ S\({}_{\text{C}}\) & m/s & 1.01 \(\pm\) 0.01 & 1 \(\pm\) 0.01 & 0.99 \(\pm\) 0.01 & 0.94 \(\pm\) 0.01 \\  & & & (\(-\)1\%) & (\(-\)2\%) & (\(-\)6.9\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average results for the spatio-temporal variables. NC: number of cycles; T\({}_{\text{NC}}\): time to complete NC cycles; L\({}_{\text{NC}}\): distance covered in NC cycles; L\({}_{\text{C}}\): gait cycle length; T\({}_{\text{C}}\): time spent doing a full cycle; S\({}_{\text{C}}\): speed.  
Figure 4: Tibiotalar joint kinematics on the sagittal plane (\({}^{\circ}\)) during a full gait cycle for heel heights 0, 2, 6 and 10 cm.  
For \(ht=0\), a total RoM of approximately \(30^{\circ}\) was seen at the joint. When the heel height was \(ht=2\) cm, the total RoM slightly decreased and obtained a value of approximately \(22^{\circ}\). For \(ht=6\) cm, the magnitude of the total RoM at the joint in the stance phase reduced and its value was \(7^{\circ}\). For \(ht=10\) cm, RoM considerably decreased and its value was slightly above \(5^{\circ}\).
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 6 Conclusion
### Experimental Verification  
To verify the proposed improvements, an experiment was conducted where selected methods were tested. In this experiment, a combination of the suggestions from the previous section was carried out. First, a series of images was taken at ca. 10 m from the facade; the camera height was 2 m above the surface in the first pass and 4 m in the second pass. Following this, without GNSS equipment, images were taken at two equal height levels (2 and 4 m) at 3 m from the facade. A comparison of this experiment with the point cloud from Faro Orbis is shown in Figure 13.
# Numerical Modeling of Single Pile Behaviors Due to Groundwater Level Rising
## 3 Numerical Modeling
### Fully Coupled Flow Deformation Analysis  
A fully coupled flow-deformation was simulated to investigate the simultaneous development of deformations and pore pressures in partially and fully saturated soils together with changes with time of the hydraulic boundary conditions [23]. The suction behavior assigned for unsaturated soil layer located above the phreatic level was also considered. The general formulation of consolidation based on total pore pressure follow [PERSON]'s theory [33]. The formulation is based on small strain theory and [PERSON]'s law for groundwater flow is assumed. Additionally, [PERSON]'s effective stress [34] is employed and is defined as Equation (1):  
\[\mathbf{\sigma}=\mathbf{\sigma^{\prime}}+\mathbf{m}(\chi p_{w}+(1-\chi)p_{a}) \tag{1}\]  
where \(\mathbf{\sigma}\) is the vector with total stresses (\(\mathbf{\sigma}=\{\sigma_{xx}\ \sigma_{yy}\ \sigma_{zz}\ \sigma_{xy}\ \sigma_{yz}\ \sigma_{zx}\}^{T}\)), \(\mathbf{\sigma^{\prime}}\) is the effective stresses, \(\mathbf{m}\) is a vector containing unity terms for normal stress components and zero terms for the shear stress components (\(\mathbf{m}=\{1\ 1\ 1\ 0\ 0\ 0\ 1\}^{T}\)), \(p_{w}\) is the pore water pressure, \(p_{a}\) is the pore air pressure, and \(\chi\) is an effective stress parameter called mat  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Weathered**} & \multirow{2}{*}{**Soft Clay**} & \multirow{2}{*}{**Medium**} & \multicolumn{2}{c}{**Stiff to**} & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \multicolumn{8}{c}{**Material property**} \\ Model & - & MC & HSsmall & HSsmall & HSsmall & MC & MC & HSsmall & LE \\ Material & - & Undrained & Drained & Drained & Drained & Drained & Drained & Drained & NP \\ \(\tau_{start}\) & kN/m\({}^{3}\) & 17 & 16.5 & 18 & 20 & 20 & 20 & 20 & - \\ \(\gamma_{must}\) & kN/m\({}^{3}\) & 16 & 15 & 17 & 18 & 19 & 19 & 18 & 24 \\ \(E_{50}\)\({}  
and varies from 0 to 1 covering the range from dry to fully saturated conditions. Usually equal to 1 in the saturated state (\(\chi=1\)) and equal to 0 in dry state (\(\chi=0\)).
###### Abstract
[23] found a seasonal dependency on GPP-SIF relationship, which indicated that the responses of GPP and SIF to the environment had seasonal variations. Several studies found the spatio-temporal variations in the GPP-SIF ratio, which also indicated the asynchronous environmental responses of GPP and SIF [24; 25; 26; 27]. Additionally, diverse responses of GPP and SIF to the environment were found under environmental stress [28; 29; 30; 31]. Some studies also extracted physiological information from SIF signals using NIRv (the product of NIR reflectance and NDVI) to explore the relationship between photosynthesis and chlorophyll fluorescence, leading to inconsistent conclusions about the GPP-SIF relationship [32; 33; 34; 35]. Although many existing studies tried to investigate the responses of GPP and SIF to various environmental conditions based on multiple methods, uncertainties and inconsistencies still persist, causing bias in SIF application. Photosynthesis is influenced by three primary factors: radiation, temperature, and water availability. Meanwhile, incident radiation, as the common driver of GPP and SIF, could cause similarities in the environmental responses of GPP and SIF, which may obscure the tiny discrepancies in the responses of GPP and SIF to other environmental variables, leading to ambiguity in the GPP-SIF relationship. In this study, we used continuous data from a corn flux tower site during two growing seasons, satellite SIF data, and AmeriFlux data to analyze the differences in the responses of GPP and SIF to the environment based on the binning method. For this, we split all environmental variables into several bins in order to exclude the dominant role of radiation on the environmental responses of GPP and SIF. We aimed to investigate the difference in the sensitivities of GPP and SIF to the environment and the impact of this difference on the GPP-SIF relationship.
### Main Contributions and Organization  
The main contributions of this work are summarized as follows: (1) We introduce a new approach for intuitive modeling and analysis of NOMA signals using _symbolic_ computations, facilitating a better understanding of complex modulation schemes with irregular constellations. The proposed approach is fundamentally different from previous approaches, which utilized MATLAB [11] or GNU Radio [12], as the operations are stated qualitatively and not quantitatively. (2) The new symbolic framework allows a more precise and much faster design and evaluation processes for NOMA systems compared to conventional numerical approaches. (3) The proposed framework is compatible with SDR platforms, enabling its direct implementation on commercially available hardware. (4) The developed model can serve as a data source for training neural networks and AI methods for optimizing the performance of NOMA systems. (5) The use of intuitive modelling facilitates maximal flexibility in the characterization of the different operations.  
The rest of the paper is structured as follows: In Section 2, we demonstrate the use of the symbolic framework through the implementation of a NOMA scheme simulation. In Section 3, we present simulation outcomes and results. Lastly, in Section 4, we summarize the results and comment on future research avenues and further steps. In the following, we use Wolfram symbolic engine to represent signal processing tasks.
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Approaches
#### 3.1.3 Segmentation  
In contrast to pixel-based threshold determination, image segmentation techniques that group connected homogeneous pixels into patches can provide information at the object level--that is, at a higher level than the pixel--and are therefore believed to be more resistant to speckle because they utilize morphological information instead of radiometric information alone. The active contour method (ACM; [60; 61]) allows a certain amount of backscattering heterogeneity within a water bodyand incorporates morphological metrics, such as curvature and tension. [PERSON] et al. [53] have applied OO segmentation [56] with SBA to reduce false alarms and speckle. [PERSON] et al. [62] compared the ACM and OO and concluded that the latter delineated the water areas more precisely than the former, which tend to overestimate their extension. [PERSON] et al. [63] developed an image segmentation method consisting of dilution and erosion operators to remove isolated groups of water pixels and small holes in water bodies, which are believed to be caused by speckle. [PERSON] et al. [49], [PERSON] et al. [51] and [PERSON] et al. [57] employed the region growing algorithm (RGA) to extend inundation areas from detected water pixels. RGA starts from seeding pixels and then keeps absorbing homogenous pixels from neighbors until no more homogenous pixels exist in neighboring areas.