esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Prediction Performance  
This study retrospectively collected 1248 records from five cancer registries. These cases were included if the patient was diagnosed with skin carcinoma and treated accordingly. The study group was composed of survivors with SPSC, and the control group comprised survivors with non-SPSC. Incomplete fields, incomplete histology data, and incomplete variable data were excluded. This study was approved by the Institutional Review Board of the Chung Shan Medical University Hospital (IRB no. CS2-20114) without patient consent.  
A balanced dataset and 10-fold cross-validation were used for the performance analysis. Table 2 lists the comparisons among algorithms, including MLP, C4.5, AdaBoost with C4.5, bagging with C4.5, and random forest. Random forest exhibited a metric outperformance in F1 of 90.7%, ROC area of 93.9%, PRC of 92.6%, and accuracy of 90.2% for the SPSC category. In our case, MLP demonstrated worse metric performance than C4.5 as a benchmark between the numerical-base algorithm and the categorical-base decision tree algorithm. Based on the evidence that random forest surpassed the C4.5 and bagging sets, we believe that the main improvement is based on the randomness design of bootstrap subsampling and the choice of the feature for splitting nodes. Compared to AdaBoost, the independent trees of random forest showed better ensemble synergy than the boosting ensemble that arranges trees in series.  
Based on the comparison of the ROC curves illustrated in Figure 2(a), random forest covers the largest area of 0.939, which implies that the trade-off between TPR and FPR based on threshold setting is relatively better than the others, while MLP is the worst.  
As listed in Table 3, the recall rate increases (i.e., from 95.2% to 95.9%) if we increase the penalty cost of the FN error of the SPCs category but maintain the FP error cost at one, whereas the precision rate (i.e., 86.6-77.9%) and overall accuracy (i.e., 90.2-84.3%) are compromised accordingly. This policy scenario implies that we hope that the potential patient with SPC would be labeled as much as possible, considering the acceptable misclassification FP results of the patient. As shown in Figure 2(b), there is no variation in the costs.
## 2 Technical Design of the Device  
### Device Anatomy  
The device construction and main components of the ATMID are described in this section in addition to its mechanism of action. The considered ATMID, as shown in Figure 1, is fabricated by a parallel layout consisting of a variable inertance, small viscous damping, and stiffness. Later this layout is serially linked into an auxiliary mass. In this device, a terminal is used to affix the parallel constituents to an additional mass while the second terminal is fixed to the structure where the vibration is to be reduced.  
The details of the configuration of the proposed variable inertance section called the adaptive inertance damper (AID) are shown in Figure 2. The figure shows a ball-screw inerter and a CVT core such that the outer rotor and ball-screw system are encompassed by the core of the CVT. The conversion of the nut spin to a screw translation, that is, bidirectional movement of the primary structure, occurs due to the interaction between the steel balls and the thread of the screw. The nut's spinning can be transmitted to the input ring of the CVT via the firm connection between the nut and the input ring (see Figure 2). By tuning the CVT, an ideal rotation can be transmitted and obtained in the output ring. Afterward, the gyration of the output ring is transmitted to the flywheel through the stiff contact between the output ring and the flywheel.  
The reciprocating deformation of the structure causes the screw shaft to accelerate translationally. Subsequently, the screw's acceleration (_a_) leads the nut to spin, and later the flywheel rapidly swirls by utilizing CVT. The flywheel rotation gives rise to a moment of inertia (I), and afterward an amplified rotational torque. The torque is converted and amplified into a translational inertial force of F\({}_{\text{I}}=ba\), in the opposite direction to the structure's motion. The physical light mass of the flywheel (m\({}_{\text{I}}\)) is magnified multiple times, causing the generation of a translational amplified mass known as inertance (_b_) having kilograms (kg) as its unit. The generated inertance (_b_) is derived as follows:  
Figure 1: Perspective view of the adaptive tuned mass inertance damper (ATMID).  
\[b=\mathrm{I}\left(\frac{2\pi}{\mathrm{L}}\right)^{2}, \tag{1}\]  
where the screw lead is denoted as \(\mathrm{L}\), and CVT performs in neutral mode.  
Substantially, a large inertial resistance force is provided using the inerter's magnification mechanism while the physical mass of the flywheel is small. This generated inertial force can be attained in a seamless and variable way as CVT operates in underdrive or overdrive mode. Consequently, a controllable inertance (\(b\)) and its associated inertial force are yielded. This will be further described in Section 2.2.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Security and Privacy
#### 4.5.1 Privacy  
Privacy is an important aspect of knowledge-based networking. Sensitive information must be protected in knowledge dissemination such that the intellectual properties of the users are secured while at the same time enabling smooth network functions [320].  
For knowledge-based IoT smart towns, an architecture known as the Privacy-Preserving Secure Framework (PPSF) was originally put forward. It is made up of a two-tier privacy arrangement with a blockchain section that sends information privately, as well as a principal component analysis module to transform raw data and an anomaly detector using gradient boosting [321]. Additionally, anonymity-critical apps at the network's edge may guarantee that privacy is preserved by utilizing edge machine learning to evaluate information at the edge network and the Ethereum blockchain for maintaining details of users who access result analysis [322]. Furthermore, a framework for Privacy-Preserving Big Data (PPBD) transfer uses graph modeling and extracts subsets of nodes using artificial intelligence, while blockchain-based resources may be communicated with privately and secretly utilizing symmetric-based digital tokens [323]. Moreover, in Controller Area Networks (CAN) that are utilized in vehicular networks, the vehicle owners' and manufacturers' data are sensitive. Thus, in [324], a framework that protects the sensitivity of manufacturer's and owner's data for training a Federated Forest KDN Intrusion Detection System (FFIDS) by using blockchain, which stores only the hash digest of the trained machine learning algorithm and a pointer to its location, allowing individuals to provide partially trained models in a privacy-protecting manner, has been presented. In contrast, for a Smart Watering System (SWS), an integrated approach of fuzzy logic and blockchain is used, where fuzzy logic has been proposed to make intelligent watering decisions while blockchain is utilized to provide privacy for the IoT network [325]. Similarly, a framework known as FDEMATEL makes decisions based on knowledge generated using fuzzy reasoning by identifying factors for security issues where security criteria classification is realized using blockchain technology [326].
# Numerical Simulation of Flapping Airfoil Aerodynamic Characteristics
## 3 Results and Analysis
### Effects of Motion Parameters on the Aerodynamic Characteristics of Flapping Airfoils
#### 3.1.2 Impact of Flapping Frequency on the Aerodynamic Characteristics of Flapping Airfoils
Based on Equations (9)-(11), the lifting efficiencies are calculated when the flutter frequencies are 25 Hz, 50 Hz, 100 Hz and 150 Hz, respectively, and the results are shown in Table 1. It can be seen that the flutter frequency has an inverse relationship with the lifting efficiency, i.e., as the frequency increases, the lifting efficiency gradually decreases. Although increasing the flutter frequency can significantly improve the lift coefficient, it will also bring greater energy consumption, which leads to a reduction in lifting efficiency. And the decrease is more obvious, the lifting efficiency at 25 Hz is 102.3% higher than that at 150 Hz.
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 4 Discussion
The photosynthetic area of cotton reaches its maximum in the flowering and boll period, and the accumulation of photosynthetic substances at this stage may be a key factor in determining cotton yield [69]. Compared with SIF, SIFy can better estimate the cotton yield. In the estimation of cotton yield by SIFy, the RF model had the best effect (R\({}^{2}\) = 0.9612, RMSE = 66.27 kg\(\cdot\)ha\({}^{-1}\), RPD = 4.264), followed by Ada Boost, whereas BPNN had the worst effect. The model was verified with the data of 2023, and the applicability of the model was proved. [PERSON] et al. [70] estimated the yield of a major crop (wheat) in Australia using satellite SIF data based on machine learning (ML) methods such as RF and SVM, and the Random Forest algorithm achieved high-performance yield prediction (R\({}^{2}\) = 0.75). Many studies have shown that RF outperforms other machine learning methods for crop yield estimation [71]. [PERSON] et al. [72] estimated rice yield using three methods: linear, ML, and deep learning. Among these, the best results were obtained using a Random Forest model with R\({}^{2}\) = 0.76-0.82 and RMSE = 366.0-723.3 kg\(\cdot\)ha\({}^{-1}\). PAR data can eliminate the effect of SIF due to light instability. This method can extract a more accurate and reliable SIF data set from long-term field observations for studying the relationship between SIF and vegetation photosynthesis [73]. SIF can better estimate yield after PAR normalization [74; 75]. The relationship between SIF and yield is complicated. In this study, SIF was used to directly estimate cotton yield, and although AGB and LAI, which are significantly correlated with cotton yield and canopy SIF, were utilized for interpretation, other indicators, such as chlorophyll content, canopy leaf structure, and other biometric parameters, also influenced SIF production. Therefore, it is necessary to analyze the interaction process between incident light and vegetation in detail, to identify the reasons affecting SIF and photosynthesis, and better explain the relationship between SIF and yield from the mechanism. In this study, the PAR-normalized SIF was used to eliminate the effect of PAR on SIF, resulting in improved accuracy of yield estimation models for cotton. In this study, the normalization of SIF was used to eliminate the effect of PAR on SIF so that the accuracy of the yield estimation model for cotton was improved. However, environmental factors such as solar zenith angle, moisture, air temperature, and vapor pressure difference (VPD) affect the process of SIF generation, and further study is required to eliminate the effects of environmental factors on SIF and thus improve the quality of SIF data. In this study, cotton yield was estimated based on the hourly scale of cotton in the ground canopy. Different time and spatial scales create uncertainty in the relationship between SIF and cotton yield, where the optimal observation requires further investigation.
# On Ad Hoc Communication in Industrial Environments
## 1 Introduction
Additional topics regard the method of observing the topology, the applicability of different routing strategies to the industrial use case, and the ability to emulate a complex network without simulating it in detail. The contribution of this work is four-fold:  
1. The applicability of an existing acquisition method for time-variant network topologies to the industrial use case is analyzed, and the method is found to not be applicable. 2. A new method in the form of a custom protocol for recording time-variant network topologies is described and published. This includes the tools and methods for processing the acquired the data. 3. NEPs describing the time-variant network topology of mobile industrial ad hoc networks are recorded and published for use by other researchers in further examinations and industrial ad hoc network simulations. 4. The acquired NEPs are analyzed. Connection characteristics are extracted from these NEPs, and recommendations for suitable ad hoc networking solutions are given. Wireless communication in the industrial context and related nodal encounter patterns are surveyed in Section 2. In Section 3 two different methods for NEP acquisition are compared in terms of their ability to represent the true network characteristics. Additionally, metrics are introduced, which characterize a wireless network and can be extracted from a NEP. Section 4 introduces the proposed and utilized tracing protocol and the process of encounter pattern generation from the recordings. The performed measurements and examined network characteristics are described in Section 5. Afterwards, Section 6 concludes this work with a discussion on methods and presented observations.
## 1 Introduction  
The extremely high frequencies above 30 GHz, known as millimeter waves, cover a wide range of the electromagnetic spectrum. Many applications, such as mobile wireless communications [1; 2], satellite communications [3] mobile communication, road traffic safety radars [4; 5], and remote (imaging and non-imaging) sensing, as well as wireless power transfer (WPT), are all being considered to use this relatively free frequency regime. Wide frequency bands in millimeter waves allows a high bit rate in digital communication links and high distance resolution in radar systems to be obtained [6].  
However, when millimeter-wave radiation propagates through the atmosphere, it suffers from molecular absorption and refraction [7; 8; 9]. The gas composition and meteorological conditions of the atmosphere have frequency-dependent effects on the millimeter wave propagation [10; 11; 12]. In particular, the presence of suspended water droplets, like in fog and clouds, may be one of the major factors for attenuation and dispersion effects on millimeter wave signals [13; 14; 15]. In radar systems that operate in millimeter and sub-millimeter wavelengths, atmospheric dispersion plays an important role in the accuracy of distance measurements, as discussed in [16].  
The atmospheric frequency response is being studied intensively both theoretically and experimentally. However, vast majority of the works refer to the attenuation effect only. The signal phase shift and time delay effects are usually ignored, although they can be significant in extreme conditions. Furthermore, works on the propagation in dielectric media sometimes using equivalent but different terms to describe the problem, such as medium permittivity, refractivity, and susceptibility.  
In the present work, the effect of suspended droplets was studied theoretically and experimentally to demonstrate the effect of extremely low visibility conditions on the signal strength and time delay. First, the propagation factors are presented as analytic expressions of the permittivity and refractivity, and the relation between them. Second, a modified millimeter-wave propagation model (MPM) is employed for the prediction of the suspended water droplets effect. Finally, an experimental verification of the effect on an MMW radar signal strength and time delay is presented. We used a dense artificial fog, created by a fog machine, to demonstrate the effects quantitatively. The results are compared to the analytical model predictions. In order to set the definitions employed along the paper, we start from a short review of field propagation in dielectrics in general and then focus on the atmospheric medium.
# Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale
## 2 Satellites Sensors and Data
### Land Cover Type Data  
In this study, a global static classification of ten broad land cover types [2] derived from 32 different land cover types [27] has been used. These ten land covers include (1) tropical/sub-tropical evergreen broad-leaved forest, (2) deciduous forest, (3) evergreen broad-leaved and needle-leaved forest, (4) Deciduous woodland, (5) sclerophyllous woodland and forest, (6) wooded and non-wooded grassland, (7) tundra and mossy bog, (8) boreal and xeromorphic shrubland, (9) non-vegetated desert, and (10) ice. These ten land cover types are termed LC01 to LC10 hereafter and their percentages of global land coverage are shown in Figure 1[28]. These static ten broad land cover types have been used in several studies.
# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
## 2 Time-Frequency Characteristic Analysis of Microseismic Waveform Signal  
### Time Domain Analysis of Microseismic Signals  
From the data collected by underground microseismic monitoring equipment, 50 groups of rock blasting and rock fracture signals were randomly extracted for statistical analysis. It can be observed from Figure 2a,b that there is a significant difference in the main duration ratio between the two types of signals: the duration of the rock fracture signals is mainly distributed in the range of 0-100 ms, accounting for 96% of the total sample, whereas the duration of the rock blasting signal is mainly distributed in intervals greater than 150 ms, accounting for 78% of the total samples. By comparing Figure 2a,b, it can be deduced that the durations of the rock rupture and rock blasting signals are both distributed in the range of 50-150 ms.  
Figure 3a,b show the distribution and proportion of the maximum amplitude values in 50 groups of data of the two types of signals. By comparing Figure 3a,b, it can be deduced that the maximum amplitude of rock fracture signal is mainly distributed in the range of 0-500 MV, accounting for 94% of the total number of samples of rock fracture signal maximum amplitudes. The maximum amplitudes of the rock blasting signals are mainly distributed in the range above 3000 mv. However, 32% of these values of rock blasting signals are distributed in the range of 0-500 MV.  
Therefore, using the signal duration and maximum amplitude value to distinguish the rock blasting and rock fracture signals can cause misjudgment. It is necessary to analyze the two types of signals to find a more accurate method to distinguish them.  
Figure 1: Technology roadmap.  
Figure 3: _Cont._  
Figure 2: (a) Duration distribution of the two signals; (b) histogram of time ratio of two types of signals.
# Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm
## 2 Problem Formulations
\begin{array}{l}CV=C_{d}+C_{s}+C_{b}\\ \\ CV=\sum_{j}^{N_{j}}CV_{\delta}^{j}+\sum_{j}^{N_{k}}CV_{\sigma}^{j}+\sum_{j}^{N _{\text{m}}}CV_{b}^{j}\\ \\ C_{\text{s}}=\left\{\begin{matrix}\left|\frac{\delta_{j}-\delta_{all}}{\delta _{all}}\right|&if&\delta_{j}>\left|\delta_{all}\right|\\ 0&else&\delta_{j}\leq\left|\delta_{all}\right|\end{matrix}\right\}\end{array} \]\[C_{d}=\left\{ \begin{vmatrix}\frac{\sigma_{j}-\sigma_{all}}{\sigma_{all}}\Big{|}& if&\sigma_{j}>\left|\sigma_{all}\right|\\ 0&else&\sigma_{j}\leq\left|\sigma_{all}\right|\end{vmatrix} \right\}\]  
\[C_{b}=\left\{ \begin{vmatrix}\frac{\sigma_{j}-\sigma_{all}}{\sigma_{all}}\Big{|} &if&\sigma_{j}<0\;and\;\sigma_{j}<-\frac{kEA_{d}}{L_{j}^{2}}\\ 0&else&\sigma_{j}>0\;or\;\sigma_{j}>-\frac{kEA_{d}}{L_{j}^{2}}\end{vmatrix} \right\}\]  
where  
CV represents the total constraint violation in the optimization problem, which is decomposed into three components: displacement constraint violation (C\({}_{\text{d}}\)), stress constraint violation (C\({}_{\text{s}}\)), and buckling constraint violation (C\({}_{\text{b}}\)). For displacement constraint violation, Cd, it is calculated as the sum of the absolute differences between individual displacement values (sj) and the allowable displacement limit (s\({}_{\text{all}}\)) divided by the allowable displacement limit. If the individual displacement (sj) exceeds the allowable limit (s\({}_{\text{all}}\)), the constraint violation is penalized; otherwise, it is considered to be zero. C\({}_{\text{s}}\), the stress constraint violation, is computed similarly to C\({}_{\text{d}}\). It involves the sum of the absolute differences between individual stress values (\(\sigma_{\text{j}}\)) and the allowable stress limit (\(\sigma_{\text{all}}\)), divided by the allowable stress limit. If the individual stress (\(\sigma_{\text{j}}\)) surpasses the allowable limit (\(\sigma_{\text{all}}\)), the constraint violation is accounted for; otherwise, it remains zero. C\({}_{\text{b}}\), representing the buckling constraint violation, is determined based on the individual stress values (\(\sigma_{\text{j}}\)). If the stress is negative (\(\sigma_{\text{j}}<0\)) and it falls below a critical buckling threshold \(\frac{KEA_{d}}{L_{j}^{2}}\), then the constraint is violated and penalized accordingly. Otherwise, if the stress is either positive or above the critical threshold, the constraint violation is considered to be zero. In summary, the total constraint violation (CV) is the sum of the constraint violations for displacement, stress, and buckling, each computed according to the specified equations and conditions.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 1 Introduction
### Machine Learning and Deep Learning in Acute Aortic Syndrome
#### 1.1.2 Clinical Variable and Results Analysis  
* Risk Prediction Models: Models integrate clinical variables and imaging results to predict AAS risk, leveraging data patterns for accurate risk estimation [13].
* Integration of Multimodal Data: Machine learning integrates information from diverse sources for comprehensive AAS detection and classification [13].