esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Architectural System Design
### Feature Selection Using LASSO  
The LASSO (least absolute shrinkage and selection operator) was applied to reduce high-dimensional features to the most relevant predictors. The regularization parameter (\(\alpha\)) was selected using 5-fold cross-validation, automatically optimized via LassoCV from scikit-learn. The cross-validation procedure ensures a balance between underfitting and overfitting, enabling selection of the most predictive yet sparse feature subset.  
After extensive tuning, a value of \(\alpha=0.01\) was found to produce the best trade-off between model sparsity and performance. This value effectively shrank irrelevant coefficients to zero while preserving the most informative features for predicting pregnancy complications.  
The objective function minimized by the LASSO is  
\[\min_{\beta}\left\{\frac{1}{2n}\sum_{i=1}^{n}(y_{i}-X_{i}^{\top}\beta)^{2}+ \alpha\sum_{j=1}^{p}|\beta_{j}|\right\} \tag{1}\]  
Here, \(\alpha\) controls the regularization strength, and \(\beta_{j}\) denotes the coefficients. Features with zero coefficients are excluded.  
Figure 9 visualizes the non-zero (most impactful) features identified after regularization, reducing the dataset to 51 key features, some of which include  
Sons_died, Daughters_died, Curr_Preg, LastChild_Want, ChildFood_bottle, Child_putToBrst, Hg_levelAdjusted, B_ChildTwin, Delivery_CSection, Child_Brst, Preg_iron, ChildGiven_otherBMilk, Preg_intParDrug, ShortBreaths, ChildAge_mnths, Hg_child_adjust, Related, Hypertension, RespDisease, Thyroid, ultrasound, PostnatalChk, Resp_healthChk, DPTB, MMR, Benef_HCare, Smoke, Prenatal_care, DeliveryPlace_Private, Water_Source_Natural Source, Water_Source_Other, DPT_full, MEASLES_full, JE_full.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Without NearMiss-SMOTE** & **With NearMiss-SMOTE** \\ \hline Accuracy & 0.798 & 0.791 \\ Precision & 0.749 & 0.725 \\ Recall & 0.897 & **0.938** \\ F1-Score & 0.816 & **0.818** \\ ROC–AUC & 0.866 & 0.864 \\ \hline \hline \end{tabular} Note.: Bold values indicate the best performance between the two configurations.  
\end{table}
Table 2: Model performance comparison with and without NearMiss-SMOTE.  
Figure 8: SMOTE-Resampled train data distribution (PCA).
# Spectrum Values in Suburban/Urban Environments above 1.5 GHz
## 3 Numerical Results  
### Suburban Environment  
Figure 3 shows the ratio of the data rate at frequency \(2f\) divided by the data rate at frequency \(f\) on the y axis versus the spectrum value on the x axis, which is the data rate divided by the bandwidth. These are evaluated for the three frequency bands 500 MHz to 1.5 GHz, 1.5 GHz to 3 GHz, and 3 GHz to 11 GHz. Please note that the spectrum efficiency for each frequency range is normalized to show the same interval on the x axis. Also note that the numerical result for 500 MHz to 1.5 GHz in this paper is almost the same as the results in [3]. In Figure 3, low spectrum efficiency corresponds to the cell edge, but high spectrum efficiency indicates the cell center. At the cell edge, doubling the frequency causes a drop in the data rate of only 20% to 25%, especially for 500 MHz to 1.5 GHz and 3 GHz to 11 GHz. Meanwhile, data rates around 80% to 90% can be achieved by doubling the frequency at the cell center. Therefore, in the suburban environment, high frequencies have a much lower value at the cell edge. However, the high frequency has almost the same impact as the low frequency at the cell center. In conclusion, the result in [3] is confirmed with extensive analysis of the different frequency bands.  
\begin{table}
\begin{tabular}{c c c} \hline
**Environment** & **Frequency Ranges** & **Propagation Model** \\ \hline \multirow{3}{*}{Suburban} & 500 MHz\(\sim\)1500 MHz & Hata Model \\  & 1500 MHz\(\sim\)3000 MHz & SUI Model \\  & 3000 MHz\(\sim\)11,000 MHz & SUI Model \\ \hline Urban & 500 MHz\(\sim\)40,000 MHz & 3D Model (3 GPP) \\ \hline \end{tabular}
\end{table}
Table 4: Simulation Parameters.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **UMa** & **UMi—Street Canyon** \\ \hline TX Antenna Height (m) & 25 & 10 \\ Outdoor user RX antenna height (m) & 1.5 & 1.5 \\ Indoor user RX antenna height (m) & 9 & 9 \\ Inter-site Distance _ISD_ (m) & 500 & 200 \\ TX antenna gain, \(G_{T}\) (dBi) & 15 & 15 \\ RX antenna gain, \(G_{R}\) (dBi) & 0 & 0 \\ BS TX power, \(P_{T}\) (dBm) & 46 & 46 \\ Thermal noise density (dBm/Hz) & \(-\)174 & \(-\)174 \\ Effective environment height (m) & 1 & 1 \\ Active user number (users/km\({}^{2}\)) & 300 & 300 \\ Minimum coupling loss, _MCL_ (dB) & 70 & 70 \\ Shadow fading in LOS (dB) & 4 & 4 \\ Shadow fading in NLOS (dB) & 6 & 7.82 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters in scenarios [8; 14; 15; 18].
## 2 Bibliographic Study
### Classification Algorithms
#### 2.1.1 K-Nearest Neighbors Algorithm  
KNN is independent of data distribution [8], since it makes predictions based on the local neighbors (where \(k\) is an integer, such as 2, 3, 4... and, in the case of KNN2 and KNN3, 2 and 3 are considered neighbors) of the data points without making assumptions about the sample size and the characteristics of the data. Algorithm A1 presents the process of KNN, including parameter initialization, computation of the distance, and majority vote classification. KNN might have problems with distance calculation when working with imbalanced data because it could capture more observations from the majority class [12].  
[PERSON] and [PERSON] [13] proposed a novel ensemble algorithm for an imbalanced [PERSON]'s disease dataset, where the KNN is used as the base classifier to calculate the ensemble weights, which overcomes the challenges posed by differing severity levels given an imbalanced distribution of the data [14]. Fuzzy KNN combined with a Bonferroni mean classifier makes multiple comparisons between the input data, resulting in easier evaluation for high-dimensional datasets (microarray and COVID-19), with minimal numbers of features[15]. In Fuzzy KNN, the membership degree is calculated with fuzzy membership functions. The Bonferroni mean classifier is an aggregation operator.
## 3 Numerical Analysis of Dynamic Contact Characteristics
### Sensorless Stiffness Estimating Method Based on Contact Stiffness Model  
Through the numerical analysis of the dynamic contact model, it is clear that the axial force and the speed of the screw have major influence on the dynamic contact stiffness of the SNBSP. At the same time, they are also important parameters to solve the model. If the values of them can be obtained when the machine tool is running, we can solve the dynamic contact stiffness of SNBSP according to the dynamic model proposed in this paper. It is significant to evaluate the dynamic characteristics of  
Figure 23: The global stiffness varying with different screw speed.  
Figure 22: The global stiffness varying with different axial force.  
mass production CNC machine tools. The value of screw speed can be obtained through the CNC program. But the capture of axial force needs the help of an external sensor generally. That is why the stiffness analysis can only be carried out on components or a special test bench in references.  
The relationship between the axial force of the SNBSP and the screw torque is as follows [21; 22]  
\[F_{a}=\frac{2\pi\eta\Gamma_{\text{d}}}{L_{p}} \tag{47}\]  
where \(F_{a}\) denotes the axial force. \(T_{\text{d}}\) is the driving torque. \(L_{p}\) is the lead of the screw. \(\eta\) is the transmission efficient of SNBSP. The specific solution process can be seen in the Appendix A.  
The relationship between the torque \(T_{\text{d}}\) and torque current of servo motor (TCSM) is as follows  
\[T_{\text{d}}=k_{\text{e}}\cdot i \tag{48}\]  
where \(k_{\text{e}}\) is the servo motor torque constant; \(i\) is the TCSM.  
In this way, the problem of capturing the axial force is transformed into the problem of capturing the TCSM.  
In a previous study, we developed a collection program by using VC + +. The working data of CNC system can be collected in real time through FOCAS1 database [23]. If the same type of data is collected, the sampling frequency can reach 28 Hz. For a more detailed processing, refer to reference [23]. In this way, an Ethernet cable is used to connect the CNC system with the PC, and then the TCSM signals at any time can be captured without the help of any external sensor. (Notes: A notebook PC was used in the earlier stage of study. However, it was replaced by a PC104 BUS computer for other functions.).  
The process of estimating the dynamic contact stiffness of ball screw without a sensor can be seen in Figure 24.  
Figure 24: The flow chart of sensorless stiffness estimating method.
# A Chaotic System with Infinite Equilibria and Its S-Box Constructing Application
## 3 Circuit Realization of the Chaotic System  
In this section, we present briefly the electronic realization of the chaotic system with infinite equilibria. The schematic of the circuit is designed with common electronic component as shown in Figure 6. The circuit includes nine resistors, three capacitors, four operational amplifiers and four multipliers. It is noted that the circuit is based on operational amplifiers [46,47], which are configured as integrator amplifiers and an inverting amplifier. The circuit is powered by \(+15\) V and \(-15\) V. In order to realize the system for parameters \(a=1.5\), \(b=0.5\), and \(c=5\), we have selected \(R_{1}=400\) k\(\Omega\), \(R_{2}=266\) k\(\Omega\), \(R_{3}=800\) k\(\Omega\), \(R_{4}=R_{8}=R_{9}=40\) k\(\Omega\), \(R_{5}=R_{6}=100\) k\(\Omega\), \(R_{7}=8\) k\(\Omega\)  
Figure 5: Coexisting attractors of the system with infinite equilibria in: \(x-y\) plane (**a,b**), \(x-z\) plane (**c,d**), and \(y-z\) plane (**e,f**) for \(a=1.5\), \(b=0.5\), \(c=5.5\), and initial conditions \(\left(x\left(0\right),y\left(0\right),z\left(0\right)\right)=\left(\pm 0.5, \pm 1,0.5\right)\).
## 3 Methods
### Statistical Metrics  
In this study, the Pearson correlation coefficient (\(R\)), the Nash-Sutcliffe efficiency coefficient (\(NS\)) which are commonly used in hydrological modelling, were adopted to evaluate the simulation performance [56; 57]. Among them the \(R\) and \(NS\) will be divided into 5 categories: <0.5, 0.5-0.7, 0.7-0.8, 0.8-0.9 and 0.9-1.0. To further analyze, the normalized centered root-mean-square error (\(NCRMSE\)) and the mean relative error (\(MRE\)), which are indicators of random error and systematic error, respectively, were used to reflect the dispersion between simulated and observed runoff values [58; 59].  
\[R=\frac{\sum_{i=1}^{n}\left(Q_{o,i}-\overline{Q_{o}}\right)\left(Q_{s,i}- \overline{Q_{s}}\right)}{\sqrt{\sum_{i=1}^{n}\left(Q_{o,i}-\overline{Q_{o}} \right)^{2}}\sqrt{\sum_{i=1}^{n}\left(Q_{s,i}-\overline{Q_{s}}\right)^{2}}} \tag{11}\]  
\[NS=1-\frac{\sum_{i=1}^{n}\left(Q_{o,i}-Q_{s,i}\right)^{2}}{\sum_{i=1}^{n} \left(Q_{o,i}-\overline{Q_{o}}\right)^{2}} \tag{12}\]  
\[NCRMSE=\frac{\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left[Q_{s,i}-Q_{o,i}-\frac{1}{n} \sum_{i=1}^{n}\left(Q_{s,i}-Q_{o,i}\right)\right]^{2}}}{\sqrt{\frac{1}{n} \sum_{i=1}^{n}\left(Q_{o,i}-\overline{Q_{o}}\right)^{2}}} \tag{13}\]  
\[MRE=\frac{\sum_{i=1}^{n}\left(Q_{s,i}-Q_{o,i}\right)}{\sum_{i=1}^{n}Q_{o,i}} \tag{14}\]  
where \(\overline{Q_{o}}\) is for the observed monthly runoff, m\({}^{3}\)/s; \(\overline{Q_{s}}\) is for the simulated monthly runoff, m\({}^{3}\)/s; \(\overline{Q_{0}}\) is for the average observed runoff, m\({}^{3}\)/s; \(\overline{Q_{s}}\) is for the simulated average runoff, m\({}^{3}\)/s. \(R\) can be used to reflect the correlation between the simulated value and observed values. The range of values is 0-1. \(R\) value closer to 1 indicates that the performance  
Figure 3: Schematic diagram of the run theory used to determine drought duration (Dd) and drought severity (Ds) [55].  
of the runoff model is better. \(NS\) is commonly used to assess the predictive power of hydrological models and was calculated here also to reflect the fitting effect of the peaks. \(NS=1\) indicates a perfect match of simulation and observed data, whereas \(NS=0\) means the simulated result is as accurate as the mean of the observed value. The value range of \(NS\) is \([-\infty,-1]\). The range of both \(NCRMSE\) and \(MRE\) is \([0,\,\infty]\), when the values are equal to 0 for a perfect model.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 4 Handwritten Chinese Text Recognition
### Line-Level Text Recognition
#### 4.1.2 Segmentation-Free Recognition
Specifically, the decoder prediction obtains the recognition result by extracting the glyph and semantic embedding of Chinese characters and automatically calculating the symbol speech fusion embedding of characters according to the parametric gating fusion strategy. Furthermore, two kinds of generalized systems engineering, character-level generalized systems engineering (CGSE) and text-level generalized systems engineering (TGSE), are applied in the decoder stage to generate predictions. On the standard benchmark ICDAR-2013 HCTR competition dataset, the method achieves a character-level recognition accuracy of 96.65%, which demonstrates the effectiveness of the proposed glyph-semantic fusion embedding. Figure 10 counts the number of papers on line-level HCTR research by segmentation and segmentation-free based approaches from 2016 to 2022, as well as the highest AR achieved on the ICDAR 2013 dataset with and without LM per year. In general, segmentation-free approaches have become more mainstream in recent years due to their avoidance of expensive character segmentation annotations and character segmentation errors, combined with writer-aware, language models, etc., to improve recognition performance. Current research on segmentation-based approaches aims to achieve high-performance end-to-end recognition while providing character location information. In addition to using a single strategy, [PERSON] et al. [105] proposed an attention combination model that can combine the advantages of segmentation and segmentation-free recognition methods. Both provide character position information for more accurate recognition of characters, avoid segmentation errors, and enable end-to-end recognition. They first obtained recognition results for multiple recognition methods. Then, a combined model of  
one-dimensional gated convolutional neural networks containing multiple encoders and a decoder could infer the final recognition results. Their experimental results show the effectiveness of combining explicit and implicit segmentation methods. Table 5 summarizes the recognition accuracy of different methods on the ICDAR 2013 dataset for line-level text. Figure 10: The number of papers per year from 2016 to 2022 on line-level HCTR with different methods and the highest AR (100%-CER) achieved on the ICDAR 2013 dataset with and without LM. The references corresponding to the highest AR (without LM) per year are [75;78;79;95;101;106]. The references corresponding to the highest AR (with LM) per year are [75;76;79;95;96;99;106]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
## 3 Results and Discussion
### Influence of Dipole Orientation of ZW on the Interactions with Human Serum Proteins
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ligand** & **Protein** & \multicolumn{2}{c}{**Binding Energy**} & \multicolumn{2}{c}{**Receptor Contacts**} \\ \cline{3-5}  & **(kcal/mol)** & \multicolumn{2}{c}{**Hydrophilic**} & \multicolumn{2}{c}{**Hydrophobic**} \\ \hline MMP & HSA & \(-\)5.5 & Leu481, Val482, Ser480, Asp324 \({}^{\text{a}}\), Arg209 \({}^{\text{a}}\), Lys35 \({}^{\text{a}}\). & Leu347, Gly328, Ala350, Leu327, Gly354, Leu331, Ala210, Ala213 \\ MMP & FB & \(-\)4.9 & Arg50, Ser47, Asp61, Ala27 \({}^{\text{a}}\). & Val74, Cys49 \({}^{\text{b}}\), Cys28 \({}^{\text{b}}\), Cys76 \({}^{\text{b}}\), Ala59 \({}^{\text{b}}\),Lys29 \({}^{\text{b}}\) & Clay59 \({}^{\text{b}}\),Lys29 \({}^{\text{b}}\) \\ MMP & Tr & \(-\)4.6 & Lys291, Lys196, Leu293 \({}^{\text{a}}\), His289 \({}^{\text{a}}\), His14 \({}^{\text{a}}\), Ser189 \({}^{\text{a}}\). & Thr181 \({}^{\text{b}}\), Phe192 \({}^{\text{b}}\), Ser189 \({}^{\text{b}}\), Asn213 \({}^{\text{b}}\), Gln184 \({}^{\text{b}}\), Gly290 \({}^{\text{b}}\), Leu293 \({}^{\text{b}}\) \\ MPC & HSA & \(-\)6 & Glu153, Glu292, Lys199, Lys195, Alg22, Arg257 \({}^{\text{a}}\), His242 \({}^{\text{a}}\), Trp214 \({}^{\text{a}}\), Ser192 \({}^{\text{a}}\). & Ala291, Phe223, Ile264, Leu234, Leu260 \({}^{\text{b}}\), Leu219 \({}^{\text{b}}\), Leu238 \({}^{\text{b}}\), Trp214, Gln196 \({}^{\text{a}}\) \\ MPC & FB & \(-\)5.9 & Asp61, Arg50, Val74, Cy49 \({}^{\text{a}}\), Cys28 \({}^{\text{a}}\), Cys28 \({}^{\text{a}}\), Ser47 \({}^{\text{a}}\). & Gly73, Trp33 \\  & & Glu15, Arg124, Tr188, Ser189, His249, Lys296 \({}^{\text{a}}\), Gly190 \({}^{\text{a}}\), Gly187 \({}^{\text{a}}\), Val11 \({}^{\text{a}}\), Leu62 \({}^{\text{a}}\), Tyr45 \({}^{\text{a}}\), Leu66 \({}^{\text{a}}\), Asp63 \({}^{\text{a}}\), Lys296 \({}^{\text{a}}\), Glu354, Arg209, Lys351, Asp324 \({}^{\text{a}}\), Leu327 \({}^{\text{a}}\), Ala350 \({}^{\text{a}}\), Leu347 \({}^{\text{a}}\), Ser480 \({}^{\text{a}}\), Val482 \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON], [PERSON], [PERSON], [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\). & [PERSON], [PERSON], [PERSON], [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON], [PERSON], [PERSON], [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{\text{a}}\), [PERSON] \({}^{Despite the fact that MPC has been used in a variety of bio-related materials, choline phosphates (such as MBP) with reverse structures possess greater variability and more tunable properties than MPC. They are well-known hydrophilic molecules that have been used for antibiofouling applications across a wide range of industries [52; 53; 54].
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
## 3 Metasurfaces for OAM Generation
### Metasurface Fork Gratings  
The transmission function for a diffraction grating for OAM generation is written by  
\[t(r,\phi)=\sum_{m}A_{m}e^{j(l_{m}\phi+k_{mn}x+k_{ym}y)}, \tag{5}\]  
where \(r\) is the radial position, \(\phi\) is the azimuthal position, \(A_{m}\) is the weight of the \(m\)th beam, \(l_{m}\) is the corresponding OAM index, and \(k_{xm},k_{ym}\) are the transverse wave numbers of the \(m\)th beam. It can be considered as a hologram resulting from the interference of a plane wave and a OAM wave.  
Figure 6: Amplitude and phase distributions of the cross-circularly polarized component of electric field at a transverse plane of \(z=10\) mm calculated from (**a**) the equivalent dipole model with the aperture in Figure 5a; (**b**) the equivalent dipole model with the aperture in Figure 5b; (**c**) the full-wave simulation with the aperture in Figure 5a; (**d**) the full-wave simulation with the aperture in Figure 5b. Reproduced with permission from [85], Copyright IEEE, 2017.  
Figure 7: **(a)** Phase distribution and the scanning electron images of metasurface fork gratings with topological charge of \(q=2,3\); The plasmonic metasurfaces are fabricated on an 80-nm thick aluminum thin film by using focus ion beam method, consists of spatially variant nanoslits with a size of \(\sim\)50 nm by 210 nm. Scale bar: 3 μm. Reproduced with permission from [92], Copyright WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2016; (**b**) Schematics of off-axis incidence multi-OAM multiplexer and off-axis multi-OAM demultiplexer. Reproduced with permission from [86], Copyright WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2016.  
The transmission function is calculated based on the design requirement and the phase information is extracted and reconstructed using geometric-phase metasurfaces. We show two designs in Figure 7[86; 92]. They are formed by nanoslits with varying orientations. Figure 7a shows the phased holograms and the corresponding distributions of nanoslits for a single OAM beam generation. During the fabrication process, first, an 80-nm-think aluminum film is deposited on glass through thermal evaporation process. Then, the nanoslits array is fabricated through focus-ion-beam method. Figure 7b illustrates a general map of three-OAM-channel multiplexing and demultiplexing using metasurfaces. Two identical metasurfaces are used for the multiple OAM-beam generation and separation, respectively. In both cases of Figure 7, the metasurfaces are illuminated by a circularly polarized wave and the cross-circularly polarized component carries OAM.
###### Abstract  
With the emergence of various new Internet of Things (IoT) devices and the rapid increase in the number of users, enormous services and complex applications are growing rapidly. However, these services and applications are resource-intensive and data-hungry, requiring satisfactory quality-of-service (QoS) and network coverage density guarantees in sparsely populated areas, whereas the limited battery life and computing resources of IoT devices will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is one of the most promising solutions that ensures the stability and expansion of the network coverage area for these applications and provides them with computational capabilities. In this paper, computation offloading and resource allocation are jointly considered for multi-user multi-UAV-enabled mobile edge-cloud computing systems. First, we propose an efficient resource allocation and computation offloading model for a multi-user multi-UAV-enabled mobile edge-cloud computing system. Our proposed system is scalable and can support increases in network traffic without performance degradation. In addition, the network deploys multi-level mobile edge computing (MEC) technology to provide the computational capabilities at the edge of the radio access network (RAN). The core network is based on software-defined networking (SDN) technology to manage network traffic. Experimental results demonstrate that the proposed model can dramatically boost the system performance of the system in terms of time and energy.  
computation offloading; resource allocation; energy efficient; unmanned aerial vehicle (UAV); mobile edge computing; optimization Article