esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cross-Set Accuracies for the Proposed Mapping  
Additionally, the cross-set validation was performed for evaluation of generalizability following the procedure described in Section 2.4. The results are presented in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dimension**} & \multicolumn{3}{c}{**Distance-Based Metrics**} & \multicolumn{6}{c}{**Threshold-Based Accuracy Metrics**} \\ \cline{2-9}  & **MAE/PAD\({}_{\text{ABS}}\)** & **RMSE** & **R\({}^{2}\)** & **M\({}_{\text{10}}\)/PAD\({}_{\text{10}}\)** & **M\({}_{\text{20}}\)/PAD\({}_{\text{20}}\)** & **M\({}_{\text{SD}}\)/PAD\({}_{\text{SD}}\)** & **DD\({}_{\text{10}}\)** & **DD\({}_{\text{20}}\)** \\ \hline P & 0.174 & 0.297 & 0.45 & 33.3\% & 59.9\% & 62.5\% & - & - \\ A & 0.097 & 0.122 & 0.197 & 58.7\% & 90.6\% & 96.8\% & - & - \\ D & 0.090 & 0.113 & 0.330 & 61.6\% & 92.1\% & 98.3\% & - & - \\ PAD & 0.239 & & & 17.8\% & 54.9\% & 59.9\% & 32.0\% & 80.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy metrics for the new mapping for the SYNE-ANEW dataset (cross-validation results).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dimension**} & \multicolumn{3}{c}{**Distance-Based Metrics**} & \multicolumn{6}{c}{**Threshold-Based Accuracy Metrics**} \\ \cline{2-9}  & **MAE/PAD\({}_{\text{ABS}}\)** & **RMSE** & **R\({}^{2}\)** & **M\({}_{\text{10}}\)/PAD\({}_{\text{10}}\)** & **M\({}_{\text{20}}\)/PAD\({}_{\text{20}}\)** & **M\({}_{\text{SD}}\)/PAD\({}_{\text{SD}}\)** & **DD\({}_{\text{10}}\)** & **DD\({}_{\text{20}}\)** \\ \hline P & 0.045 & 0.056 & 0.918 & 92.4\% & 99.9\% & 99.3\% & - & - \\ A & 0.070 & 0.088 & 0.574 & 74.9\% & 97.6\% & 99.1\% & - & - \\ D & na & na & na & na & na & - & - \\ PA \({}^{*}\) & 0.090 & & & 70.5\% & 97.6\% & 98.4\% & 83.7\% & 99.8\% \\ \hline \hline \end{tabular} Note: \({}^{*}\) dominance dimension is not available for the set.  
\end{table}
Table 9: Accuracy metrics for the new mapping for the NAWL-NAWL dataset (cross-validation results).  
The cross-set validation results on the NAWL-NAWL dataset are moderate: higher than reference mapping, but lower than in cross-validation on the same dataset. This result was expected--usually cross-validation procedures provide higher accuracies. For the dimension of valence: over 56% accuracy was obtained for the 10% precision threshold, while, for 20% and SD-based precision thresholds, the accuracy exceeded 87%. For the dimension of arousal: over 50% accuracy was obtained for the 10% precision threshold, while, for 20% and SD-based precision thresholds, the accuracy exceeded 84%. Single-dimension results are lower than in cross-validation, but still satisfactory for lower precision requirements. The joint dimension accuracies are lower. Setting a 10% precision threshold resulted in 30% accuracy, while the 20% and SD-based thresholds resulted in about 81% accuracies. The observed accuracies are higher than for the reference mapping for the same dataset; however, for high precision requirements this might still be insufficient.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 4 Implementation and Verification
### Power and Performance
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operation** & **Logic (mW)** & **ADC (mW)** & **Flash (mW)** & **Total (mW)** \\ \hline SHA-1 Calculation & 0.98 & 6 & 7.6 & 14.58 \\ AES Decryption & 1.18 & 6 & 7.6 & 14.78 \\ AES Encryption & 1.25 & 6 & 7.6 & 14.85 \\ ECC Point Multiplication & 2.68 & 6 & 7.6 & 16.28 \\ ECC First Point Inversion & 2.11 & 6 & 7.6 & 15.71 \\ ECC Second Point Inversion & 2.06 & 6 & 7.6 & 15.66 \\ Transmit Mode & 0.69 & 6 & 7.6 & 14.29 \\ Receive Mode & 0.71 & 6 & 7.6 & 14.31 \\ SPI & 0.98 & 6 & 7.6 & 14.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Post-layout power estimation of TNODE5 microcontroller. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test @ 1 MHz** & **PVDDCORE (mW)** & **PVDDPAD (mW)** & **PVDDA (mW)** & **PVDDM (mW)** & **PTotal (mW)** \\ \hline Rx & 7.5954 & 3.3426 & 1.7194 & 2.2422 & 14.8996 \\ Tx & 7.2630 & 3.0922 & 1.7236 & 2.2540 & 14.3328 \\ Crypto & 7.6204 & 2.8366 & 1.7292 & 2.2747 & 14.4609 \\ SPI & 6.0176 & 3.2306 & 1.7163 & 2.2625 & 13.2270 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Post-production power measurements of TNODE5 chip at a frequency of 1 MHz. access to low power cells, analog components and memory devices. If scaled to an advanced 130 nm low power process, the power of TNODE5 could be reduced at least 6-8 times. If fabricated in 90 nm, the active power consumption of TNODE5 would be expected to drop under 1 mW. The application of advanced low power techniques for leakage reduction could additionally improve the power efficiency of the microcontroller. Nevertheless, the energy efficiency of the current design when executing implemented crypto algorithms is still superior to equivalent software implementations on commercially available 16-bit microcontrollers.
## 3 Simulation Results and Analysis
### Comparison of Improved PSO and Traditional PSO  
Table 3 shows a comparison of improved PSO and traditional PSO. In Table 3, \(v_{\text{max}}\) represents the maximum velocity, and \(v_{\text{min}}\) represents the minimum velocity. For the convenience of comparison, some parameters of improved PSO and traditional PSO were set as the same: the number of particles \(D=30\) and the number of iterations \(I=100\). In Table 3, the data from rows 2 to 5 are the traditional PSO with different velocity ranges and without threshold T. The data in row 6 are the improved PSO when cancelling the setting of velocity ranges and using threshold \(\text{T}=0.15\).  
It can be seen from Table 3 that when using traditional PSO, the range of velocity has a great influence on the optimal solution. A large or small range of velocity will cause PSO to fall into the local optimal solution. Therefore, it is necessary to repeatedly try different velocity value ranges to obtain a more appropriate cubic phase mask parameter. However, the improved PSO does not need to set the velocity ranges, and furthermore, a threshold is introduced to ensure the quality of image restoration and improve the stability of optimization.  
The improved PSO avoids the disadvantages of the traditional PSO, which requires multiple tests to obtain the appropriate range of velocity. Therefore, the improved PSO avoids the problem of falling into the local optimal solution in the optimization process, and its robustness is significantly improved.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 1 Introduction
This study takes the original \(IQ\) audio signals as input and models the DOA estimation task as a regression problem so that the model can output continuous angle values between 0 and 360\({}^{\circ}\), avoiding the error problem caused by angle discretization in traditional classification methods. In addition, the regression method model is lighter and has a higher angular resolution than the classification model. In terms of network structure design, the advantages of Transformer and CNN are combined, and a squeeze-and-excitation (SE) module is introduced to enhance the modeling abilities of inter-channel information [34], thereby further improving the accuracy of DOA estimation. Pyroomacoustics Python 3.12.9 package [35] is used to construct a realistic acoustic simulation environment for synthesizing training datasets, thereby enhancing the credibility of the experimental results. Meanwhile, this article systematically analyzes the performance of the proposed network under different SNRs, sampling frequencies, and numbers of elements, and the results show that the proposed method has a stronger generalization ability and smaller estimation error than traditional CNN and ResNet trained based on the same input mode. The main contributions of this study are as follows:  
* A novel DOA estimation neural network architecture based on original \(IQ\) input, named TSNetIQ, is proposed. The architecture combines the structural advantages of Transformer and CNN, and combines an SE module to enhance the feature extraction ability, which can significantly improve the accuracy of DOA estimation. By reformulating the DOA problem as a regression task, the proposed algorithm directly predicts continuous angle values between 0 and 360\({}^{\circ}\), avoiding boundary classification errors. This strategy can offer higher resolution and a smaller model size. * A dynamic simulation environment is constructed by rotating a microphone array in an anechoic chamber to simulate sound source reception at various angles. This procedure generates diverse datasets with random angles, enhancing the validity and interpretability of the training process.
# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
## 3 Research Methodology  
### Two Combination Orders for Feature Selection and Over-Sampling  
In this paper, two orders of combining the feature selection and over-sampling steps are compared by being given a training set, denoted as \(TR\), which is composed of \(M\) and majority and minority class data samples, respectively, and each data sample is represented by \(k\) dimensional features. For the first order, i.e., performing feature selection first and over-sampling second, a chosen feature selection algorithm is employed to select some representative features from the \(TR\). As a result, a reduced feature subset of \(TR\) is produced, denoted as \(TR_{reduced}\), where each data sample is represented by \(o\) dimensional features (\(k>o\)). Next, the over-sampling algorithm is used to generate \(M\)-\(N\) synthetic data samples for the minority class, leading to a balanced training set, denoted as \(TR_{reduced\_balanced}\), which is composed of \(2M\) data samples. That is, the number of data samples in the majority and minority classes are the same.  
On the other hand, for the second combination order, the over-sampling algorithm is used first to re-balance the training set, i.e., \(TR\), which results in a balanced training set, denoted as \(TR_{balanced}\). \(TR_{balanced}\) is composed of \(2M\) data samples, and each data sample is represented by \(k\) dimensional features. Next, the chosen feature selection algorithm is performed over \(TR_{balanced}\), leading to a reduced feature subset of \(TR_{balanced}\), denoted as \(TR_{balanced\_reduced}\). In \(TR_{balanced\_reduced}\), each data sample is represented by \(p\) dimensional features (\(k>p\)). Note that the number of features in \(TR_{reduced\_balanced}\) by the first combination order and \(TR_{balanced\_reduced}\) by the second combination order are not necessarily the same, i.e., \(o\
eq p\).  
Therefore, the performances of the classifiers trained by \(TR_{reduced\_balanced}\) and \(TR_{balanced\_reduced}\) can be compared individually based on the same testing set. Moreover, other classifiers trained by \(TR_{reduced}\) through performing feature selection alone and \(TR_{balanced}\) through performing over-sampling alone are regarded as the baseline approaches for further performance comparison.
## 3 Dataset Evaluation
### Evaluation Metrics  
As shown in Figure 4, each of the 17 sets containing samples related to a specific activity is different in size. To cope with the class imbalance problem of the dataset we used as metric the macro average accuracy [57].  
Given \(E\) the set of all the activities types, \(a\in E\), \(NP_{a}\) the number of times \(a\) occurs in the dataset, and \(TP_{a}\) the number of times the activity \(a\) is recognized, MAA (Macro Average Accuracy) is defined by Equation (1).  
\[MAA=\frac{1}{|E|}\sum_{a=1}^{|E|}Acc_{a}=\frac{1}{|E|}\sum_{a=1}^{|E|}\frac{TP_ {a}}{NP_{a}}. \tag{1}\]  
MAA is the arithmetic average of the accuracy \(Acc_{a}\) of each activity. It allows each partial accuracy to contribute equally to the evaluation.
# Product Demand Prediction with Spatial Graph Neural Networks
## 3 Methodology  
To tackle the complexities of online retail and to accurately predict product demand, our methodology adopts a novel approach based on the model foundation of the Spatial Graph Neural Network (SGNN). This section unfolds our methodological framework, meticulously designed to employ the intricate spatial relationships and rich attribute data inherent in retail locations. We start with the _attributed graph prediction problem formulation_,setting the stage by defining the graph structure that encapsulates the multifaceted nature of retail locations and their interconnections. Following this, we delve into _constructing graph with spatial adjacency_, a critical step that operationalizes the notion that geographic proximity significantly influences demand patterns among retail nodes. The \"Attention-Aware Message Propagation\" subsection introduces a sophisticated mechanism to dynamically weigh and integrate information from neighboring nodes, ensuring that the most relevant spatial and contextual signals are emphasized in predicting demand. In _node-Level product demand prediction_,we articulate how the model synthesizes the aggregated information to predict demand at individual locations, highlighting the model's capacity to distill both local and global insights. Lastly, the \"training objective\" outlines our strategy for refining the model's predictions, emphasizing the optimization of a loss function that aligns predicted demands with actual demands, thus encapsulating our comprehensive approach to address the challenge of demand predicting in the digital marketplace. Through this methodological journey, we aim to offer a robust framework that not only enhances the accuracy of demand predictions but also provides actionable insights for retailers operating in the dynamic online marketplace.
## 4 Discussion
### Main Findings
A satisfying outcome of the MEU mode also depends on the premise that sufficient ablation time [30] and RF power are permitted, as plenty of power and energy are consumed in areas other than the myocardium. By contrast, the MEB mode is not influenced by external factors in any meaningful way, which causes faster temperature growth and lesion formation even under a lower applied RF power. For a single active electrode in the MEB mode, the generated current is guided by the adjacent grounding electrodes. Consequently, the current density converges around each electrode, and the lesion has a stronger impact in the region close to the surface of the cardiac tissue. In this study, the application of the RF power of 10 W in the MEB mode reached the temperature threshold in about 12 s, and the subsequent increase in the applied power in the MEB mode would easily allow the temperature to exceed 100 \({}^{\circ}\)C if the control algorism was not activated. Therefore, the maximum power for the MEB mode must be restrained to a lower level than that for the MEU mode. This idea accords with the power configuration of nMARQ in which the maximum power set for the MEU mode is 25 W and 15 W for the MEB mode [31], almost half of the general power selection for single-tip catheters [32; 33; 34]. Discontinuous lesions on the tissue surface and limited lesion depth or volume are two notable deficiencies of the MEU and MEB modes, respectively. A practicable means to offset these issues is to combine the two modes [35; 36]. Note that the two modes can yield contiguous lesions, while there is an innate gap between electrode 1 and electrode 10 if the electrodes are not configured and positioned sufficiently close or if the applied RF power is insufficient. Thus, the operator may need to reposition the whole catheter to accomplish circumferential PVI. Electrodes 1 and 10 are two special electrodes positioned at the two ends of the catheter body. They only have one neighbor electrode; thus, the corresponding production of RF power is deposited unilaterally, yielding a relatively small effective lesion depth. In particular, the confined effective lesion depth under electrode 1 is largely ascribed to its round tip. The contacting volume of electrode 1 inside the myocardium, namely the ablation field of electrode 1, is always the smallest in both ablation modes. In Figure 7, the series of maximum lesion depth is symmetric and corresponds with a pair of electrodes counting from the two ends of the catheter (i.e., electrode 1 and electrode 10, electrode 2 and electrode 9, etc.). This phenomenon agrees with the results of the simulation study by [PERSON] et al., who focused on a multielectrode linear catheter [19; 20]. As long as the electrical boundary condition of the electrodes is uniform, the lesion shape will tend to be regular and symmetric, despite the ablation modes after a sufficient RFCA duration.
# Bioactivity and Delivery Strategies of Phytochemical Compounds in Bone Tissue Regeneration
## 4 Bioactive Phytochemicals and Bone Signaling Pathways
### Antioxidants Activity  
Phytochemicals that operate as direct antioxidant substances are able to activate and regulate antioxidant enzymes, with simultaneous action on the inhibition of oxidases, cyclooxygenase, and other enzymes such as iNOS, involved in radical generation [80]. In general, polyphenols, thanks to their B-ring hydroxyl configuration, display a significant antioxidant action that enhances with the increasing of the total number of OH groups and the attendance of the 3,4-catechol structure [81]. By decreasing the oxidative state, bioactive molecules provide for osteoblasts proliferation, activity, and differentiation through the involvement of crucial molecular signaling pathways.  
The ROS-scavenging activity is particularly visible in icaritin (a flavonoid isolated from _Epimedium pubescens_), which can reduce superoxide generation in osteoclasts by indirect action on NFATc1 [82] and in naringin, a flavanone with effects on lipid peroxidation, glutathione (GSH) oxidation, and DNA cleavage [83]. Through the same mechanism of action, curcumin and resveratrol (a polyphenic compound found in grapes and wine) upregulate in the osteoclast the antioxidant enzymes like glutathione peroxidase (Gpx)-1 and superoxide dismutase (SOD), thus modulating ROS levels [84; 85]. Curcumin acts on osteoclastogenesis contributing to mitigate bone loss during osteoclast formation and function, preventing ROS and cytokine production. Myricitrin, a glycoside from myricetin, is able to inhibit bone-resorbing cytokines production under oxidative conditions, displaying protective effects against osteoblast cytotoxicity [86]. On the other hand, polyphenols can exert their antioxidant activity through a mechanism of chelation interacting with metals, in particular, Fe and Zn [87; 88]. The EGCG shows cytotoxic properties on osteoclasts thanks to its reductive action on Fe (III) catalyzed by the Fenton reaction, leading to hydroxyl radical's generation [89; 90].
## 1 Introduction
Machine learning models have been applied to various aspects of scientific research, including space exploration, mainly for predictive and forecasting purposes. Given the growing volume of data from satellite missions and ground-based instruments, machine learning provides a good means to extract valuable information from the data. In this work, we developed two machine learning models, namely, the Random Forest (RF) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm for forecasting ionospheric scintillations by leveraging solar wind parameters and Pc5 wave power. The Random Forest (RF) algorithm is a well-liked ensemble learning technique for classification and regression problems [17]. The RF algorithm works by segmenting the sample data into a cluster of decision trees for voting, with the majority vote being used to make the final decision. XGBoost is a type of ensemble learning technique based on the gradient boosting framework, known for its exceptional efficiency and performance on large datasets, making it more effective than other boosting methods in terms of speed and predictive accuracy. To achieve high performance, the XGBoost builds the models in a sequential form, with new sub-models derived from previous results. It employs parallel decision trees for a faster computational time even when the dataset is large. Here,we investigate the different benefits of these machine learning (ML) models for efficient ionospheric scintillation prediction when ULF wave power (typically Pc5 waves) is infused. The structure of the current paper is as follows: the data utilized to generate the models are described in Section 2; the models themselves are described in Section 3; the results and discussions are presented in Section 4; and the study's summary and conclusion are provided in the last section.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 4 Mobile LiDAR Sensing Circuit and Chip Design
### CORDIC Angle Calculator
#### 4.3.3 Angle Normalization  
Using characteristics of SIN and COS in four quadrants, all input angles are first standardized from 0 to 90 degrees. When the input angle is 0 to 90 degrees, the output is positive COS and positive SIN. When the input angle is 90 to 180 degrees, the input angle is subtracted by 180 degrees, and the output is negative COS and positive SIN. When the input angle is 180 to 270 degrees, the input angle is used to subtract 180 degrees, and the output is negative COS and negative SIN. When the input angle is 270 to 360 degrees, the input angle is subtracted by 360 degrees, and the output is positive COS and negative SIN. To normalize the angle to 0 to 90 degrees, the final result of the outputted COS and SIN must be plus or minus. Therefore, the output angle information is originally 16 bits, and one bit is added to the front of the 16-bit number. The output information is a total of 17 bits.
## 2 Related Work
### Location-Based Protocol  
A location-based protocol uses the location information of the sensor node and BS to set the transmission path of the collected data. Therefore, a location-based protocol does not require separate energy consumption for path setting or maintenance. Geographic and Energy-Aware Routing (GEAR) [9] is a representative example that can minimize bandwidth consumption.  
GEAR is divided into a step that transmits a packet from a BS to a target region and another step to propagate a packet within a target region. When transmitting packets from the BS to the target area, gradual routing is used so that neighboring sensor nodes use energy evenly by applying the concept of learning cost and prediction cost. In the  
Figure 4: Energy-Efficient Uneven Clustering (EEUC) operation process.  
packet propagation stage within the region, a recursive transmission method using location information is applied instead of flooding, which consumes much energy. The recursive transmission technique is a method of dividing a target region into sub-regions and then copying and propagating packets to each sub-region. However, when the density of the sensor node in the target area is low and the communication range of the sensor node does not reach the entire sub-area, packet propagation is performed inside the area using limited flooding, which limits the hop count of packets to 1 or 2.