esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 2 Data and Methods
### Methods
#### 2.3.3 Trend Analysis  
This study combined the Theil-Sen median trend and the Mann-Kendall test to analyze the long-term trend of WUE in the North China Plain. For convenience, this combination is called the Sen-MK method. First, the Theil-Sen Median trend is calculated as:  
\[Sen=Median(\frac{X_{k}-X_{l}}{k-l}),2001\leq l\leq k\leq 2017 \tag{2}\]  
Figure 1: Spatial pattern of land use and ecosystem zoning locations in North China.  
where Sen is the variation slope, and if Sen \(>0\), it means that the sequence X has an increasing trend, while Sen \(<0\) suggests a decreasing trend. The Theil-Sen median trend provides no information about the significance of the sequence. Therefore, the Mann-Kendall test was used to calculate the significance of the variation trend:  
Let \(\{X_{l}\},l=2001,2002,\ldots,2017\), the Z value is:  
\[Z=\left\{\begin{array}{c}\frac{S-1}{\sqrt{s(S)}},S>0\\ 0,S=0\end{array}\right.\quad\text{Where, }S=\sum\limits_{k=1}^{n-1}\sum \limits_{l=k+1}^{n}\text{sgn}(X_{k}-X_{l}) \tag{3}\]  
\[\text{sgn}(X_{k}-X_{l})=\left\{\begin{array}{c}1,X_{k}-X_{l}>0\\ 0,X_{k}-X_{l}=0\\ -1,X_{k}-X_{l}<0\end{array}\right.,s(S)=\frac{n(n-1)(2n+5)}{18} \tag{4}\]  
When the absolute value of Z is greater than or equal to 1.96, there is a significant change in WUE variation, on the contrary, the change in WUE variation is not significant.
## 3 Results and Discussion
### Machine Learning
#### 3.3.2 Hyperparameter Optimization  
Hyperparameter optimization is a problem of finding a minimum of a certain objective function, the domain of which is the space of parameters of the examined model. The parameters can be continuous, discrete, or categorical, and additionally, they can be dependent on each other [19]. It is worth highlighting that calculating the objective function is extremely expensive: it involves the full training and evaluation of the model.  
There are different strategies of looking for optimum hyperparameters. The easiest way is \"manual\" tuning. However, it requires expert knowledge of the model and data, which does not foster generalization. The other strategy is either full or the random search of the parameters' domain, the so-called \"grid search\". Checking all combinations is usually unrealistic due to the high costs. It has been confirmed that random search can work well in the case of a model with many parameters, out of which only some play a key role in its quality [40]. The next method of searching for optimum parameters of a classifier is an SMBO (Sequential Model-Based Optimization) method. To put it simply, it consists of constructing a surrogate model approximating the objective function, the minimum of which we look for. Most frequently, GP (Gaussian Process), RFR (Random Forest Regressions), or TPE (Tree-structured Parzen Estimator) are used as surrogate models. The selection of subsequent domain points (values of hyperparameters) is calculated in order to optimize the selection function; here, we usually used the EI function (Expected Improvement). Such a strategy often provides the best results and eliminates the element of randomness [41].  
To optimize the hyperparameters of models described in this paper, SMBO with TPE model was chosen. As the objective function, the AUC metrics was applied.  
After optimizing the hyperparameters, each of the models improved its performance, especially in failure detection. This was also the main goal of the optimization that was achieved. It can be noticed that the larger the hyperparameters of the tested model, the easier it can be optimized. The neural network noted the highest progress in each metric (compare Figure 7). Significant progress was also visible in the SVM model.  
In general, most of the metrics showed progress in the range of 3-5%, which is a very good result, especially considering that the quality of the examined models before the optimization exceeded 92%.  
Figure 7: Models’ metrics after optimization.
## 3 Methods and Materials
### Data Acquisition  
Three datasets were examined here: ABIDE I [32] and ABIDE II [33], which are publicly available datasets, and a local Saudi dataset. The ABIDE datasets include anonymized sMRI and rs-fMRI scans, along with personal, behavioral, and clinical diagnostic information collected from multiple sites stored in the ABIDE repository. Each study received ethical approval for data usage and sharing. We used 220 and 418 sMRI scans from ABIDE I and II, respectively. Detailed criteria for subject inclusion/exclusion and acquisition methodology can be found on the ABIDE website [34]. The data was downloaded usingCyberduck software v8.4.2 from an Amazon Web Services S3 container in the Neuroimaging Informatics Technology Initiative (NIFTI) file format [35], which is commonly used in image processing software. The data was organized according to the Brain Imaging Data Structure (BIDS) [36; 37], facilitating script reuse and easy sharing between studies.  
The KAU dataset comprised 33 participants who were recruited from KAU Hospital. A pediatric neurologist diagnosed the subjects with ASD based on DSM-5 criteria. None of the participants had ADHD, seizure disorder, or any other significant health problems. The control group (TD individuals) who had no notable health, neurological, or developmental problems. Participant scans were acquired in DICOM format using Siemens SKYRA or VERIO machines and then converted to NIFTI format using MRIcroGL v1.2.20220720 software [38; 39]. A radiologist analyzed the MRI images visually to determine image quality, artifacts, and any gross abnormalities that needed to be excluded. The KAU Hospital Ethics Committee (approval number 386-21) approved the current study. All subjects in this work ranged from 5 to 10 years old to reduce the effect of dataset heterogeneity. T1-weighted sMRI scans were acquired in 3T scanners. Along with the sMRI data, general clinical information such as diagnosis (ASD or TD), age, and gender was collected for each participant. Investigating multiple datasets allows us to assess the robustness and generalizability of the proposed models. The demographic information of the studied participants is provided in Table 2.
## 4 Simulation Results and Discussions
### Residual Energy  
In SEEC, the factor of transmission power in the energy consumption was constant. Regardless of the distance between two communicating nodes, almost the same amount of energy we are consuming while transmitting a packet. In CTP-SEEC, the function of energy consumption varies.  
After sending the packet, the return value contributes to higher network residual capacity. Transmission power is balanced in the suggested scheme according to the distance between the source and the destination node.  
The less the distance, the less the transmission power, and vice versa. Less transmission power results in less energy tax which eventually results in higher network residual energy in CTP-SEEC. DBR considers only the depth; it does not consider the residual energy, so it has the minimum residual energy. In EEDBR, a high packet distribution ratio in the sparse area cannot be accomplished by choosing nodes and by considering residual capacity. In Figure 8, the cumulative network residual energy consumption of CPT-SEEC, SEEC, EEDBR, and DBR is observed.
###### Abstract  
Accurately estimating the carbon budgets in terrestrial ecosystems ranging from flux towers to regional or global scales is particularly crucial for diagnosing past and future climate change. This research investigated the feasibility of two comparatively advanced machine learning approaches, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), for reproducing terrestrial carbon fluxes in five different types of ecosystems. Traditional artificial neural network (ANN) and support vector machine (SVM) models were also utilized as reliable benchmarks to measure the generalization ability of these models according to the following statistical metrics: coefficient of determination (\(R^{2}\)), index of agreement (IA), root mean square error (RMSE), and mean absolute error (MAE). In addition, we attempted to explore the responses of all methods to their corresponding intrinsic parameters in terms of the generalization performance. It was found that both the newly proposed ELM and ANFIS models achieved highly satisfactory estimates and were comparable to the ANN and SVM models. The modeling ability of each approach depended upon their respective internal parameters. For example, the SVM model with the radial basis kernel function produced the most accurate estimates and performed substantially better than the SVM models with the polynomial and sigmoid functions. Furthermore, a remarkable difference was found in the estimated accuracy among different carbon fluxes. Specifically, in the forest ecosystem (CA-Obs site), the optimal ANN model obtained slightly higher performance for gross primary productivity, with \(R^{2}\) = 0.9622, IA = 0.9836, RMSE = 0.6548 g C m\({}^{-2}\) day\({}^{-1}\), and MAE = 0.4220 g C m\({}^{-2}\) day\({}^{-1}\), compared with, respectively, 0.9554, 0.9845, 0.4280 g C m\({}^{-2}\) day\({}^{-1}\), and 0.2944 g C m\({}^{-2}\) day\({}^{-1}\) for ecosystem respiration and 0.8292, 0.9306, 0.6165 g C m\({}^{-2}\) day\({}^{-1}\), and 0.4407 g C m\({}^{-2}\) day\({}^{-1}\) for net ecosystem exchange. According to the findings in this study, we concluded that the proposed ELM and ANFIS models can be effectively employed for estimating terrestrial carbon fluxes.  
terrestrial ecosystems; climate change; carbon fluxes; flux towers; machine learning techniques +
Footnote †: journal:  
0000-0001-8000-0000  
0000-0002-2000-0000][PERSON]  
0000-0002-2000-0000][PERSON]
## 2 Material and Methods
### Rheological Investigations
#### 2.7.2 Rheological Measurements  
Rheological measurements were done using a rheometer AR-2000 (TA Instrument, Great Britain, Ltd., New Castle, DE, USA) fitted with a 40 mm cone-plate geometry (54 microns gap) and equippedwith a Peltier heating system for accurate control. After loading, samples were held for 15 min before measurements to permit structure recovery and temperature equilibration. The solutions were then covered with a thin layer of hexadecane to prevent solvent evaporation during measurements [29; 33; 34]. The TA Instrument Rheology Advantage software (V5.7.0) was used to collect and analyze the rheological data. Rheological measurements were carried out in duplicate and the values of effectively overlapping traces were reported. Steady-shear flow properties for JSP solutions (from 0.25% to 2.0%) (_u/v_) were investigated at 25 \({}^{\circ}\)C over the range of shear rate (\(\dot{\gamma}\)) from 0.001 to 1000 s\({}^{-1}\). Solutions of 1.0% and 2.0% (_u/v_) were used to evaluate the effect of salts and temperature. Viscosity data were expressed using the Williamson model (Equation (1)) and the flow curves modeling were achieved using the Ostwald-de Waele (power-law) equation (Equation (2)):  
\[\eta=\frac{\eta_{0}}{\left(1+\left(\lambda.\dot{\gamma}\right)^{\left(1-n \right)}\right)}, \tag{1}\]  
\[\tau=\ k\ \dot{\gamma}^{n}, \tag{2}\]  
where \(\eta\) is the apparent viscosity (Pa.s), \(\eta_{0}\) is the zero shear viscosity (Pa.s), \(\tau\) (\(\eta\times\dot{\gamma}\)) is the shear stress (Pa), \(\lambda\) is the transition time (s), \(\dot{\gamma}\) is the shear rate (s\({}^{-1}\)) and n is the flow index (dimensionless).  
Oscillatory frequency sweep measurements of elastic modulus \(G^{\prime}\) (storage modulus), viscous modulus \(G^{\prime}\) (loss modulus) and loss tangent (tan \(\delta=G^{\prime\prime}/G^{\prime}\)) (damping factor) were carried out at 25 \({}^{\circ}\)C in a constant strain of 20% (or in linear viscoelastic range) over the set of angular frequency (\(\omega\)) ranging from 0.01 to 10 Hz (0.063-62.83 rad/s).
## 2 Related Works
### Deep-Learning-Based Methods  
With the successful applications of deep learning in some high-level computer vision tasks (such as image classification [41] and recognition [42]), more and more researchers began to explore low-level computer vision tasks such as image enhancement, which has achieved outstanding results in terms of performance indicators and visual effects.  
[PERSON] et al. [5] proposed the WaterNet model, which employed adaptive filters and deep convolutional neural networks for image enhancement and noise reduction. However, this method did not incorporate dedicated modules to address the color shift and texture loss commonly observed in degraded images. [PERSON] et al. [37] designed a domain adaptation framework based on transfer learning to transform aerial image deblurring into realistic underwater image enhancement. While it achieved notable performance indicators, it lacks dedicated modules to handle color shifts and texture loss. [PERSON] et al. [38] introduced Shallownet, a lightweight underwater enhancement framework that sacrifices accuracy for reduced model size and faster processing. Although it offers fast processing, the reduced accuracy may limit its applicability. [PERSON] [43] presented FUNIE-GAN, a conditional generative-adversarial-network-based model for real-time underwater image enhancement. Despite its excellent visual effects, it suffers from extensive network parameters and high computational requirements, which make it unsuitable for existing underwater devices. [PERSON] et al. [15] utilized wavelet-enhanced learning units to decompose hierarchical features into high-frequency and low-frequency components, enhancing them through normalization and attention mechanisms. However, it does not explicitly address the color shift and texture loss associated with degraded images. [PERSON] et al. [14] proposed Ucolor, which incorporates medium transmission-guided multicolor space embedding to enhance the network's response towards quality-degraded regions. Despite its excellent visual effects, it exhibits extensive network parameters and high computational complexity, rendering it unsuitable for existing underwater devices. [PERSON] et al. [18] introduced UHD-SFNet, an efficient two-path model that recovers color and texture in underwater blurred images through frequency and spatial domain processing. However, the specific limitations or drawbacks of this method were not explicitly mentioned. [PERSON] et al. [16] addressed underwater image enhancement by estimating the distribution and employing a consensus process using a novel probabilistic network. While it provides a unique approach, specific limitations or drawbacks were not explicitly discussed. A Hierarchical Attention Aggregation with Multi-resolution feature learning for GAN-based underwater image enhancement (HAAM-GAN) [40] was proposed to tackle color bias, underexposure, and blurring in underwater images.
## 3 Real-Time PWV Estimation  
Precipitable water vapor is the total amount of water vapor in an air column. The PWV is determined based on the zenith tropospheric delay using the following formula [34]:  
\[PWV=10^{6}\bigg{[}\ \rho_{w}R_{w}\bigg{(}\frac{k_{3}}{T_{m}}+k_{2}^{\prime} \bigg{)}\bigg{]}^{-1}\times Z_{w} \tag{8}\]  
where \(\ \rho_{w}\) refers to the liquid water density (\(\rho_{w}=997\) kg/m\({}^{3}\)); \(R_{w}\) indicates to the water vapor's gas constant (\(R_{w}=461.525\) J/K.kg); \(k_{3}\) is the troposphere refractivity coefficient (\(k_{3}=3754.63\) K\({}^{2}\)/Pa); \(T_{m}\) denotes the weighted mean temperature (K), which is calculated based on surface temperature (\(T_{s}\)) at the reference station (\(T_{m}=70.2+T_{s}\)[34]); \(k_{2}^{\prime}\) is also a tropospheric refraction coefficient and it is determined using the following equation:  
\[k_{2}^{\prime}=k_{2}-\bigg{(}k_{1}\frac{M_{w}}{M_{d}}\bigg{)} \tag{9}\]  
where \(k_{1}\) and \(k_{2}\) are equal to 0.776890 K/Pa and 0.712952 K/Pa, respectively; the wet air's molar mass \(M_{w}=15.9994\) g/mol; \(M_{d}\) represents the dry air's molar mass (\(M_{d}=8.9644\) g/mol).
## 5 Discussion
These efforts should be in line with global disaster risk reduction initiatives and backed by forward-thinking policies that encourage risk-aware development at both local and regional levels [114; 116; 117; 114; 170]. On the other hand, managing land use effectively is essential for analyzing disaster risks, particularly in light of urban growth and evolving environmental conditions [175]. Recent research [176; 177], which employs advanced data analytics and machine learning to track changes in land cover, provides valuable insights into how these shifts can affect disaster risks. These technologies allow for more accurate forecasting of land cover dynamics and their potential impact on both natural and man-made disasters [178; 179]. By integrating such cutting-edge approaches, urban planners can take a proactive stance on land use management, reducing disaster risks [180]. This study acknowledges several limitations that may impact the generalizability and accuracy of its findings: (a) while the EM-DAT database is extensive, it may contain inconsistencies or gaps, particularly for earlier periods or less documented regions, which could affect the accuracy of historical trends; (b) certain regions, especially those with less robust disaster reporting systems, may have underreported disaster occurrences and impacts, potentially introducing biases in the analysis; (c) the broad time span of the analysis, from 1900 to 2024, encompasses changes in data collection methods, reporting standards, and technological advancements, which may result in inconsistencies within the dataset; (d) categorizing disasters as either natural or man-made might oversimplify the complex interactions and overlapping nature of some events, such as those exacerbated by human activities like climate change (e.g., the influence of natural hazards/disasters on the occurrence of man-made (technological) disasters); (e) estimating economic losses from disasters involves considerable uncertainties due to varying methodologies, inflation adjustments, and the inherent difficulties in capturing indirect and long-term economic effects; (f) measuring the human impact, in terms of fatalities and injuries, is challenging due to differences in reporting standards, cultural perceptions, and the availability of reliable health and demographic data; (g) the rarity of certain events, which might occur only once every 100 or 500 years, presents significant challenges for trend analysis; due to their infrequent nature, it becomes difficult to identify clear patterns or trends within the study period. Therefore, it is essential to interpret the results with caution when considering these rare but impactful occurrences.
## 3 Related Works
### Educational Implementation and Surrogate Projects with Tello EDU  
The Tello EDU drone, created by Ryze technology, incorporates DJI flight technology and an Intel processor allowing for a low-barrier entry into programmable drone applications. Here, the key educational benefits include an easy-to-use Python Application Programming Interface (API) for basic drone functions along with the ability to leverage abstractable block-based coding for even earlier use in programs. Here, we briefly discuss its utilization in pioneering surrogate projects that simulate real-world challenges, thereby providing students with practical experience that effectively bridges theoretical learning with hands-on application.  
[PERSON] et al. showcased the strength of Tello EDUs in educational projects. Here, the challenges of autonomous searching, locating, and logging were investigated through the leveraging of cascading classifiers and real-time object detection. This enabled the drone to autonomously search for targets and interact with them in unique ways [37; 38]. In the subject of machine learning, this drone allows the capture of aerial imagery, which can be leveraged for practical experience in data-gathering and training algorithms. Here, [PERSON] et al. and [PERSON] et al. approach the detection of wind turbines and their faults in unique ways. These projects allow for team-based optimization of challenges related to real-world engineering development, enhancing students' technological literacy and fostering a deeper understanding of system integration and teamwork [39; 40].  
[PERSON] et al. and [PERSON] et al. used Tello EDU drones in group settings and competitive scenarios promoting an environment of active learning and innovation, demonstrating the drone's adaptability to diverse educational needs [41; 42]. Further studies by [PERSON] et al., [PERSON] et al., and [PERSON] et al. emphasize the potential of these drones to enhance unmanned aerial operations and interactive learning through dynamic environments and responsive tasks [43; 44; 45]. Future educational projects are expected to delve deeper into more advanced computer vision techniques and the integration of machine learning models, broadening the scope of drone applications in education [46; 47; 48].  
As has been shown, the Tello EDU drone serves as a pivotal educational platform that not only facilitates the exploration of cutting-edge technology, but also prepares students for the technological challenges of the future. Its continued evolution is anticipated to further revolutionize STEM education, enabling more complex, interactive, and comprehensive learning experiences.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### The Overall Algorithm  
1. Function Mobility_Prediction (0)
2. Initially, collection of Input data is done
3. Apply sequence mining technique
4. Collect training data set
5. Determine the next cell
6. If predicted cell is unable to satisfy requirement
7. Then
8. Apply function Multiple-attribute Access Network Selection ()
9. consider values of parameters attribute, signal strength, reliability, battery power, mobility terminal velocity and data rate
10. Apply function fuzzy logic decision () to decide the network
11. Return the best network
## 3 System Model
### Computational Model
#### 3.3.4 D2D Device Computing  
\(m_{i}\) is the size of the task and \(R_{i,c}\) represents the rate at which the requester, \(UE_{i}\), transmits tasks to the collaborator; thus, the transmission delay is  
\[t_{i,c}^{tran}=\frac{m_{i}}{R_{i,c}}. \tag{16}\]  
\(c_{i}^{collaborator}\) and \(f_{i}^{collaborator}\) represent the number of CPU cycles required by the collaborator \(UE_{i}\) to complete the computational task \(m_{i}\) of the requester \(UE_{i}\), and the current CPU working frequency, respectively. The time requirement of D2D computing is  
\[t_{i,c}^{d2d}=\frac{c_{i}^{collaborator}}{f_{i}^{collaborator}}. \tag{17}\]  
\(p_{i,c}\) denotes the transmission power that requester \(UE_{i}\) sends to the collaborator. The transmission energy loss is denoted as  
\[E_{i,c}^{tran}=p_{i,c}t_{i,c}^{tran}. \tag{18}\]  
Computation energy loss is denoted as  
\[E_{i,c}^{d2d}=p_{i,c}t_{i,c}^{d2d}. \tag{19}\]  
The total consumption provides  
\[C_{i}^{d2d}=\alpha\left(t_{i,c}^{tran}+t_{i,c}^{d2d}\right)+\beta\left(E_{i,c }^{tran}+E_{i,c}^{d2d}\right). \tag{20}\]