esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ground-Truth Data and Methods for Satellite-Sunphotometer Comparison
### Sunphotometer Data
It is noted that Beijing and XiangHe are part of AERONET stations, and their aerosol data during 2005 were used in the cluster analysis for the VIIRS_ML aerosol model assignment, but the Xinglong site was not used [(23)]. Moreover, the retrieved aerosol properties in recent years may change from those used in 2005 and 2010 due to the rapid development in the past few years over the NCP region [(11; 12)]. Therefore, using aerosol property data from more ground sites during recent years over this region can be used to help evaluate whether those past analyses from shorter periods (e.g., only one year) and fewer sites are still representative. Notably, none of these three sites were used in the analysis by [PERSON] et al. [(27)], which means they do not characterize typical aerosol properties of smoke, dust, and urban particles that have been adopted in the VIIRS_EDR algorithm. Thus, these three sites are better suited to evaluate the aerosol model type used in both the VIIRS_EDR and VIIRS_ML algorithms over the NCP region.
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 3 Antenna Selection Schemes for FGSM
### TAS Based on ML for FGSM  
In this work, TAS in FGSM is carried out with the use of ML-based methods. An ML-based classification architecture is depicted in Figure 2. At first, source data are normalized using the mean normalization technique. Pre-processed data are then used to extract features. Several ML methods are then used to train the model using the chosen features and class labels. A trained model can be used to predict the class labels for new data using data that have not previously been fed into the system. The process for the execution of ML-based TAS for FGSM is shown in Figure 3. If CQI is present at the UE, the class labels obtained by the ML algorithms are utilized to choose \(N_{tx}\) antennas from \(N_{T}\). Algorithm 1 shows the processes that must be followed for ML-based TAS. This section also includes details on the dataset development process and the supervised algorithms that are employed.  
Figure 3: ML-supported TAS for FGSM.  
Figure 2: Workflow of supervised learning algorithms during the training and testing process.  
```
1. Establish a mapping between the antenna subsets and class labels.
2. Pre-process the input data using the mean normalization technique.
3. Identify the key performance indicators (KPI) for each input sample. Find out the class label \(c\) on the basis of the KPI.
4. Employ the normalized feature matrix \(\hat{\mathbf{H}}_{T}\) and the class label \(c\) to create a learning system utilizing SVM, NB, KNN, and DT algorithms.
5. The learning system converts the test channel \(\mathbf{H}_{T}^{new}\) to a normalized feature vector \((\mathbf{f}^{new})\) and determines the class label \(c\).
6. Compare the class label acquired in Step 5 with the map created in Step 1 to identify the antenna subset.
```  
**Algorithm 1** Primary steps involved in ML-based TAS
## 4 Quasi-Static Tests of NLD Devices  
Quasi-static tests have been performed to characterize the force-displacement behavior of HD and UFP steel dampers, as representative of the devices installed at each story of the SBF and TBF models. Figure 5a,b show the controlled-displacement testing apparatus available at University of Basilicata to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Vibration Periods of SBF**} & \multicolumn{2}{c}{**Vibration Periods of TBF**} \\ \cline{2-5}  & \(T_{exp}\) **(s)** & \(T_{mm}\) **(s)** & \(T_{exp}\) **(s)** & \(T_{mm}\) **(s)** \\ \hline
1 st & 0.28 & 0.28 & 0.43 & 0.41 \\ \hline
2 nd & 0.08 & 0.08 & 0.12 & 0.12 \\ \hline
3 rd & & & 0.06 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic properties of the SBF and TBF braced model.  
Figure 4: Idealized non-linear response from displacement-based design of (**a**) SBF and (**b**) TBF.  
test the HD [14] and UFP devices [44], respectively. A load cell attached to the loading actuator and linear potentiometers were used to record forces and displacements of dampers. The number and relative amplitudes of the excursions should represent, on average, the history of the loading at each damage state that is to be quantified. This is not possible because lower damage states are associated with smaller earthquake intensities and higher damage states are associated with larger earthquake intensities. A loading history complete for all damage states was used for the single specimen.  
The experimental force-displacement responses of HDs and UFPs are shown in Figure 6. normalized considering the experimental yielding force (\(F_{y}\)) and displacement (\(d_{y}\)) obtained from experimental tests of each single device (see also yield point indicated in Section 5.2). The reference design ductility values were \(\mu_{d}\) = 5 for HDs and \(\mu_{d}\) = 4 for the UFPs. The quasi-static testing procedure used for HDs (Figure 6a) followed the loading protocol required for TT by EN15129 [36] (see Figure 1) with five cycles at \(\mu\) = 1.25 and \(\mu\) = 2.5 and ten cycles at \(\mu\) = 5. A monotonic ramp test up the amplitude of \(\mu\) > 5 was also performed. Different loading testing criterion was considered for UFPs (Figure 6b) following the interim protocol for quasi-static cyclic testing proposed by FEMA 461 [38] (see Figure 1). In this case the loading protocol was composed by two cycles at increasing amplitudes with five steps in the elastic range and five steps at ductility values of \(\mu\) = 1, \(\mu\) = 1.8, \(\mu\) = 2.5, \(\mu\) = 3.5, and \(\mu\) = 4. A monotonic ramp test up the amplitude of \(\mu\) > 4 was applied also in this case. As can be observed in Figure 6, the force-displacement results obtained by quasi-static tests, exhibited a stable hysteretic cyclic behavior without failure over the design displacement for both HDs and UFPs.  
Figure 5: Quasi-static testing set-up of (**a**) HD and (**b**) U-shaped flexural plates (UFPs).  
Figure 6: Experimental force-displacement response from quasi-static testing of (**a**) HD and (**b**) UFPs.
## 7 Conclusions  
We have gathered several AQI prediction studies as well as FL research in this survey. We discovered through research and synthesis that, prior to 2020, the majority of AQI forecasting mechanisms relied on single ML techniques such as statistics, ANN, DNN, hybrid, and ensemble. Although commonly used, ANN and DNN were prone to failure in local optimum conditions. The anticipated outcomes could be enhanced by using some hybrid and ensemble approaches.  
Since 2000, various works have integrated FL into AQI processing. We have identified the three different FL architectures: centralized FL, decentralized FL, and hierarchical FL as well as their corresponding algorithms. The system mostly used centralized FL, and the typical process model was DNNs. There has been a trend in the use of UAVs in this field. Additionally, we mentioned several benchmarks, challenges and solutions, and domains of FL.  
Reviews demonstrate that multi-model FL improves the accuracy of other domains, thus it could be applied to enhance AQI predictions. Multi-model FL is a study area that needs to be focused on in future works. DNN should be incorporated into client models, since it is more responsive than conventional ML architectures. Additionally, DNN should be designed to adopt both partial and temporal data.  
More importantly, to have a common baseline to assess new solutions, the scientific community should also be interested in developing a dedicated open dataset on AQI.  
**Author Contributions:** Project administration and conceptualization, M.-S.D., A.-K.T.; writing--review and editing, D.-D.L., A.-K.T., M.-S.D., K.-C.N.-L., H.-S.L., X.-D.N.-T, T.-Q.P., V.-L.N., B.-Y.N.-T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is funded by the University of Economic Ho Chi Minh City (UEH) Vietnam grant number 2022-09-19-1160.  
**Data Availability Statement:** This is a survey, we did not generate any data.  
**Acknowledgments:** We acknowledge the University of Economic Ho Chi Minh City (UEH) for funding this research.  
Figure 5: Multi-model centralized FL.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### Tumor Segmentation and Feature Extraction  
An abdominal radiologist who was blinded to the patients' clinical information segmented the renal lesions on each scan. A MATLAB-based (MathWorks, Natick, Massachusetts) application was used to visualize and segment the tumors on CT scan. The application is based on a semi-automated algorithm, which combines the region-based active contours and a level set approach, in a slice-by-slice manner. The images were then isotropically resampled to 0.5 \(\times\) 0.5 \(\times\) 0.5 mm\({}^{3}\). A total of 1160 RFs were extracted from each segmented tumor via the Columbia Image Feature Extractor (CIFE) [30].
## 3 Results  
### Analysis  
#### 3.1.1 Multidimensional Scalogram Analysis (MSA)  
A multidimensional scalogram analysis (MSA) [38; 39; 93; 96; 97] was carried out to see whether there were similarities or differences amongst the places sorted by different individuals. The software HUDAP (1997) [103] was used to compute information from the data matrix produced from the sorting tasks using \"MSA-1\", though there is other software available for this type of analysis such as the NewMDSX suite [93]. In this non-metric multivariate statistical procedure, each participant's grouping of the photographs is assigned as a \"profile\" which can be compared to the profiles of all the photographs. The overview of the MSA result for _Sample One_ can be seen in Table 2. It specifically treats each response as a categorical one comparing the categories with each other. The various categories assigned by different individuals are unique themselves, however their order and meaning are not incorporated into the MSA.  
Each picture is given a coordinate by MSA and can be plotted in a Euclidean space as a point, which makes a two-dimensional representation possible. MSA measures the distance between given objects (points in space) [38; 93; 97]. The closer the points are to each other, the more similar the photographs are considered to be. The photographs that were more frequently grouped together are closer together and therefore can be regarded as conceptually more similar to each other. Points which are further away from each other represent places are that conceptually different to participants.  
Further, the two-dimensional output of MSA can be divided into regions with lines drawn to best represent the categorizations of the participants. Points in one region share important common aspects. Points in other regions show difference between perceptions of participants. Points in adjacent areas to each other but divided by a line can have some similar aspects. The overall structure of the output provides a representation of the aggregated data and each data's reference has a much stronger influence on where to draw the line to define the boundary of a region. In other words, places close to the boundaries are ambiguous. The detailed accounts of these places lie in the profile report. The coordinates, frequency of distribution for the data and the projection for each participant is included in the MSA report. The descriptors participants used for grouping are essential for the interpretation of the regions.
# Effective Heart Disease Prediction Using Machine Learning Techniques
## 4 Results  
This research utilized Google Colab on a Ryzen 7 computer with a 4800-H processor and 16 GB of RAM. The dataset consisted of 70,000 rows and 12 attributes, but after cleaning and preprocessing, it was reduced to approximately 59,000 rows and 11 attributes. Since all attributes were categorical, outliers were removed to improve the model efficiency. The algorithms used in this study were random forest, decision tree, multilayer perception, and XGBoost classifier. This study used several measures of performance, namely, precision, recall, accuracy, F1 score, and area under the ROC curve. The dataset was split into two parts: 80% of the data used to train model and 20% used to test the model.  
We employed an automated approach for hyperparameter tuning by utilizing the GridSearchCV method. GridSearchCV takes in an estimator, a set of hyperparameters to be searched over, and a scoring method, and returns the best set of hyperparameters that maximizes the scoring method. This method is implemented in the scikit-learn library, and it uses k-fold cross-validation to evaluate the performance of different sets of hyperparameters.  
As shown in Table 5, various machine learning classifiers, such as MLP, RF, decision tree, and XGBoost, were applied on the cardiovascular disease dataset to identify the presence of cardiovascular disease after hyperparameter tuning. The results indicate that the multilayer perceptron (MLP) algorithm obtained the highest cross-validation accuracy of 87.28%, along with high recall, precision, F1 score, and AUC scores of 84.85, 88.70, 86.71, and 0.95, respectively. All classifiers had an accuracy above 86.5%. The random forest algorithm's accuracy was increased by 0.5% from 86.48% to 86.90% through hyperparameter tuning with GridSearchCV. Similarly, the accuracy of the XGBoost algorithm increased by 0.6% from 86.4% to 87.02% through hyperparameter tuning.  
A binary classifier's performance is graphically represented by the receiver operating characteristic (ROC) curve. At different categorization criteria, it shows the true positive rate (TPR) vs. the false positive rate (FPR). The area under the ROC curve (AUC) is a scalar metric that measures both the classifier's sensitivity and specificity while also reflecting the classifier's overall performance. As depicted in Figure 5(a)-d, all models exhibit a high AUC of above 0.9. The multilayer perceptron (MLP), random forest (RF), and XGBoost models have a joint highest AUC of 0.95.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} & \multirow{2}{*}{**AUC**} \\ \cline{2-2} \cline{5-9}  & **Without CV** & & & & & & & & \\ \hline MLP & 86.94 & 87.28 & 89.03 & 88.70 & 82.95 & 84.85 & 85.88 & 86.71 & 0.95 \\ \hline RF & 86.92 & 87.05 & 88.52 & 89.42 & 83.46 & 83.43 & 85.91 & 86.32 & 0.95 \\ \hline DT & 86.53 & 86.37 & 90.10 & 89.58 & 81.17 &
# Dynamic Cost-Aware Routing of Web Requests
## 2 Literature Review
### Load Balancing by Spatial Pricing  
The U.S. electricity market has been analyzed in detail with prices from 30 locations [7] and it has been proved that 40% savings are possible with dynamic price-aware routing through a simulation study using Akamai's content delivery network (CDN) traffic traces. A two-level technique to reduce the electricity cost in multi location DC has been proposed [8]. The spatial variations in electricity prices were exploited at the first level and scheduling strategies were proposed at the second level to distribute the requests among heterogeneous servers available at the selected DC.  
The stratus system proposed in [9] used a Voronoi graph partition algorithm to select a DC for servicing the requests. The service time, cost of the energy consumption and carbon emission were considered as metrics to be optimized in selecting a DC. A distributed cloud framework to reduce power consumption in the cloud based on dynamic speed scaling of processors was proposed [10].  
Temporal and spatial variations in electricity prices were exploited to balance the load across the clusters and to reduce the energy consumption.  
The total electricity cost reduction is formulated as a constrained mixed-integer problem and solved using [PERSON]'s polynomial algorithm [PERSON] (1976) with a simulation study using prices from three locations of Google Internet datacenters. The energy consumption was optimized using a global load balancing policy and at a server level management by switching _on/off_ using a mathematical analysis study [PERSON] (1976). A corrective marginal cost algorithm was proposed to solve the total energy cost by using a nonlinear integer programming optimization model [PERSON] (1976). A Big Data analytic framework was proposed to minimize the total energy cost and delay for running big data analytic jobs on geographically distributed big data sets using a stochastic optimization method [PERSON] (1976). A recent work [PERSON] (2016), proposes a solution to the cloud service providers who procure electricity from deregulated electricity market to optimize total electricity cost and bandwidth cost by dynamically routing the requests to DCs where electricity is cheaper.
# Precise Calculation of Inverse Kinematics of the Center of Gravity for Bipedal Walking Robots
## 2 Materials and Methods
### Simulation Model  
A simulation model is used to avoid mechanical rigidity, gearbox backlash, and signal delay issues and to obtain the effectiveness of the CCG algorithm. A dynamic model in which the limb angles calculated using this algorithm are used as inputs is obtained as in Figure 6 using MATLAB R2023b SimScape MultiBody [37]. The foot positions in the gait of the robot are predetermined. To define contact with the floor, the \"Spatial Contact Force\" [38] block of SimScape MultiBody that defines contact between spherical and planar surfaces is used. A total of 4 contact spheres are added at the corners on both feet of the robot with a radius of 0.01 m. The \"Inertia Sensor\" component in MATLAB Multibody [39] is used to measure the actual CoG of the mechanism in the simulation. The parameters that are used in this study are presented in Table 4.  
Figure 5: Graphical presentation of CCG algorithm.  
The joint angles of the left and right legs are input to the model. The model applies trajectories to the robot. ZMP and CoG are calculated in the model. Since the floor is flat and the CoG height of the robot is constant in the simulation, only the \(x\) and \(y\) components of the ZMP and CoG are plotted.
## 2 Materials and Methods  
Bi:RIG single-layer and HD multilayer media were deposited on nonmagnetic substituted gadolinium gallium garnet (SGGG) substrates through the radio-frequency magnetron sputtering or ion beam sputtering. For Bi:RIG deposition, we used a target with the composition Bi:Dy:Y:Fe:Al = 1.5:1.0:1.0:3.8:1.2, and Tb\({}_{3}\)Ga\({}_{5}\)O\({}_{12}\) (TGG) layers were also deposited as HDLs for the HD multilayer medium. The total thickness of the Bi:RIG layer(s) was approximately 3 \(\upmu\)m. The deposited Bi:RIG was crystallized by rapid thermal annealing at 750 \({}^{\circ}\)C for 15 min in air because the as-deposited Bi:RIG was not crystallized.  
Figure 2 illustrates the experimental setup of the two-beam interferometer to evaluate the diffraction efficiency and collinear interferometer to record and reconstruct the 2D data [27]. A pulsed laser with a wavelength of 532 nm and pulse width of 50 ps/pulse was used to record and reconstruct the magnetic holograms. The diffraction efficiency, \(\eta\), was evaluated using:  
\[\eta=\frac{I_{1}}{I_{0}+I_{1}}, \tag{2}\]  
where \(I_{0}\) and \(I_{1}\) are intensities of the zero-order transparent light and first-order diffracted light, respectively. The periods of the interference fringe were set to range from 500 line-pairs/mm to 1500 line-pairs/mm. A Helmholtz coil was used to apply an assist magnetic field in the direction opposite to the initial magnetization for enhancing the magnetization reversal. For data recording in the collinear system, \(48\times 48\) pixels with the 3:16 encoding method for the signal were used [26]. The reconstruction images were obtained by irradiating only the reference parts, and the pixel error ratio was evaluated. The recorded magnetic interference fringes with spatial frequencies of 500 line-pairs/mm and 1500 line-pairs/mm in the Bi:RIG medium were observed in the SPring-8 X-ray radiation facility. X-ray magnetic circular dichroism (XMCD) spectroscopy mapping using circularly polarized X-rays was performed at the BL39 XU beam line at SPring-8. The X-ray energy was set to 7.11 keV, which is the Fe K absorption edge, and XMCD measurements were performed with the focused spot size of \(100\times 300\) nm\({}^{2}\). Numerical analysis using COMSOL Multiphysics was also performed to clarify the formation mechanism of the magnetic fringe by thermomagnetic recording and the relationship between the fringe shape and the diffraction efficiency (see Appendix A in detail).
## 1 Introduction  
Widespread acceptance of electromobility is highly dependent on the performance and service life of the battery technology used. Lithium-ion batteries (LIB) are currently the most promising energy storage technology for mobile applications available, but cell performance and capacity are greatly affected by environmental conditions, especially temperature [1]. Winter temperatures reduce the energy efficiency and practical energy availability of lithium-ion battery packs by up to 30%. The charging characteristics of many lithium-ion technologies generally do not allow discharging at excessively high temperatures. In addition, freezing temperatures often prevent charging. By maintaining a defined temperature range of at least 15 \({}^{\circ}\)C up to 30 \({}^{\circ}\)C, these drawbacks can be greatly reduced and even eliminated [1]. With this technology, battery temperature becomes a key parameter for battery performance. Also, batteries generate a lot of heat during charging and discharging and must be dissipated by proper cooling. Heating is also required when the vehicle is operated in cold temperatures. The possibility of storing thermal energy in battery packs is being explored for more efficient use of energy. The advantage of storing waste heat in the battery system lies in user behaviour and the system-related fact that the vehicle is not always connected to a charging station in order to generate the energy to heat the battery pack. Maintaining a traction battery temperature window not only optimizes performance and storage capacity but also reduces the load on the battery cells, which has a positive effect on service life. This reduces the total energy consumption of electric vehicles in their lifecycle from production to recycling. This alleviates the weaknesses of LIB, which are reduced performance and storage capacity at low temperatures. Phase change materials (PCM) offer the potential to store thermal energy with high energy densities. Our goal was to integrate such a PCM into the battery box. The manufacturing options examined are metal 3D printing. This field of additive manufacturing technology makes it possible to produce parts that could not be manufactured in the past. This allows to optimally implement an innovative heat transfer system inside the battery pack for the first time. The new system can keep all battery cells at a uniform temperature throughout the battery pack. The purpose of thermal management is to keep the inside of the box within a defined temperature window to ensure the safe, long-lasting, and efficient operation of the battery cells.