esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
However, it should be noted that our analyses are based on the population data (i.e., demand for healthcare resources) in 2010, and future growth of demands for healthcare resources were not considered. According to relevant plans in Shenzhen, peripheral areas are expected to experience larger population growth in the future. Therefore, the dispersal strategy can work even when the future population growth is taken into account. Note that the optimized healthcare facilities in the peripheral areas are middle-to-large-sized but dispersedly distributed. This suggests that the sizes of facilities in the peripheral areas should be expanded on one hand, and more new facilities may need to be built in these areas. Despite the strengths of our study, there are also some limitations. First, the costs of the size adjustments of existing facilities are not considered in the optimization. As a result, in the optimized distribution of healthcare facilities, some facilities that need positive- or negative-size adjustments are close to each other. It is suggested that adjustments of existing facilities in such cases may be unnecessary and should not be implemented. By doing so, the costs of adjustments could be saved while the impacts on healthcare accessibility are negligible. In future study, such costs of size adjustments should be modelled into the optimization objectives or constraint conditions in a more normative way. Nevertheless, our optimization results can act as a scientific baseline for decision making. Second, although the analysis unit in this study (community) is already the smallest geographical area used by the local government, the analysis may still be faced with the modifiable area unit problem (MAUP), due to different areas and irregular shapes of the communities. More efforts are needed to examine whether and to what extent MAUP exists, as well as to figure out how to address MAUP in location-allocation modelling. Third, the accessibility to facilities and its equality was optimized independently for each level on the basis of the characteristics of the current hierarchical healthcare system in Shenzhen. However, this assumption may be inappropriate in some cases. In future works, efforts should be made to explore the interaction between various facility levels and to optimize the equality of the overall healthcare accessibility.
## 3 Methodological Background
### Model Performance Criteria  
The performance of models in this study was evaluated using the following performance measures:  
\[\text{Overall accuracy}=\frac{TP+TN}{Total\text{ }crashes} \tag{6}\]  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{7}\]  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{8}\]  
where:* _TP_ = True positive value, defined as the number of crash cases in the crash likelihood model (or fatal/injury crashes in crash severity model) that are correctly predicted.
* _TN_ = True negative value, defined as the number of non-crash cases in the crash likelihood model (or PDO crashes in the crash severity model) that are correctly predicted.
* _FP_ = False positive value, defined as the number of non-crash cases that are falsely predicted as crash cases in the crash likelihood model (PDO crashes falsely predicted as fatal/injury crashes in the crash severity model).
* _FN_ = False negative value, defined as the number of crash cases that are falsely predicted as non-crash cases in the crash likelihood model (fatal/injury crashes falsely predicted as PDO crashes in the crash severity model).  
The AUC was also used to evaluate each model. The closer the values of each of these measures are to 1, the better the prediction performance.
## 3 Experimental Results and Analysis
### Description of the Indicator Parameters  
In this paper, precision (P), recall (R), mean average precision (mAP), number of network parameters, and floating-point operations (GFLOPs) are used as evaluation metrics to assess the performance of the model. The definitions of precision, recall, and mAP are as follows:  
\[P=\frac{T_{\text{P}}}{T_{P}+F_{P}}\times 100\%, \tag{8}\]  
\[R=\frac{T_{\text{P}}}{T_{P}+F_{N}}\times 100\%, \tag{9}\]  
\[AP=\int_{0}^{1}P(r)dr, \tag{10}\]  
\[mAP=\frac{\sum\limits_{i=1}^{k}AP_{i}}{k}. \tag{11}\]  
In Equations (8) and (9), \(T_{p}\) (true positive) refers to correctly predicted positive samples, i.e., samples that are actual targets and correctly predicted as such; \(F_{P}\) (false positive) refers to false positives, i.e., non-target samples that are incorrectly predicted as targets; and \(F_{N}\) (false negative) refers to false negatives, i.e., target samples that are incorrectly predicted as non-targets. In Equation (10), \(AP\) (average precision) represents the accuracy for a single category. In Equation (11), \(mAP\) (mean average precision) is obtained by integrating the area under the precision-recall curve and represents the average accuracy across all \(k\) categories.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 4 Challenges and the Road Ahead
### SDM/OAM for FSO-Based NTN
They also highlighted using structured light beyond OAM, envisioning wider applications and potential challenges, including OAM's potential use in satellite-to-ground and satellite-to-satellite communication links.
# Characteristics of Plane Gate Vibration and Holding Force in Closing Process by Experiments
## 3 Results and Discussion  
### Characteristics of Vibration  
Characteristics of plane gate vibration are related to the constraint boundaries and external excitations. Constraint boundaries of plane gate mainly include steel cable connected to the hoists, friction contact between the slide block and track, and friction contact between the rubber seal and sidewall. Once the constraint condition is determined, gate vibration is related to the external excitations. Thus, it is necessary to clarify the relationship between gate vibration with gate opening and upstream water depth. In previous investigations, plane gate was regarded as a rigid body, and its vertical vibration can be equivalent to a single degree of freedom [26; 27]. Elastic deformation of steel cable is the main manifestation of the structural stiffness. Given the generalized stiffness theory, the equivalent stiffness of steel cable equals the generalized force dividing generalized displacement. When steel cable has the axial force \(P\), axial deformation \(\Delta\) can be expressed  
\[\Delta=\frac{PL}{EA} \tag{1}\]  
The equivalent stiffness of the steel cable \(K\) is, that is the gate vertical equivalent stiffness,  
\[K=\frac{P}{\Delta}=P/\big{(}\frac{PL}{EA}\big{)}=\frac{EA}{L} \tag{2}\]  
Thus, nature frequency of simplified system \(\omega_{n}\) can be expressed  
\[\omega_{n}=\sqrt{\frac{K}{m}}=\sqrt{\frac{EA}{Lm}} \tag{3}\]  
where \(E\) is the elastic modulus of steel cable; \(A\) is the sectional area of the steel cable; \(L\) is the steel cable length; and \(m\) is simplified system mass.  
Figure 2: Dynamic testing system applied in the physical model test.  
On basis of the equivalent stiffness theory, gate vertical equivalent stiffness \(K\) is related to the steel cable length \(L\) and simplified system mass \(m\). Through the physical model test, we mainly analyzed and compared the characteristics of gate vibration under the two operation modes.
# One-Dimensional Consolidation of Multi-Layered Unsaturated Soil with Impeded Drainage Boundaries
## 1 Introduction
Compared with the numerical methods, theoretical solutions (analytical and semi-analytical solutions) can be more accurate and simpler. Moreover, they can be also applied to verify the numerical method. To date, however, no effort has been devoted to developing a theoretical solution for assessing the 1D consolidation behavior of the common multi-layer unsaturated soil that caters to a general impeded drainage boundary condition. This study aimed to propose a general semi-analytical solution for assessing the 1D consolidation behavior of the multi-layered unsaturated soil stratum that is subjected to an impeded drainage boundary condition. The well known governing equations propounded by [PERSON] and [PERSON] [10] are adopted to simulate the consolidation process in each soil layer and formulate the consolidation system of the multi-layered unsaturated soil stratum. To obtain the final solution, the formulated consolidation system is firstly decoupled and solved in the Laplace domain. Then, the semi-analytical solutions for the excess pore-air pressure and excess pore-water pressures as well as the soil settlement are developed. The Crump method is employed to obtain their final results in the time domain. The correctness of the derived solutions is verified with the analytical and numerical solutions from the literature. Moreover, two worked examples are presented to illustrate the 1D consolidation behavior of multi-layered unsaturated soil and the influences stemming from the impeded drainage parameters are discussed.
## 4 Preliminary Background
### Verifiable Aggregator Oblivious Encryption  
Aggregator oblivious encryption cryptoscheme enables the computation of aggregated data \(M\) of \(n\) users' individual data in a privacy-preserving way by an untrusted aggregator. The main idea is that a user \(i\) encrypts a message \(m_{i}\) and sends the ciphertext \(c_{i}\) to the aggregator. Then, the aggregator computes the sum \(M=\sum_{i=1}^{n}m_{i}\) from \(\{c_{i}\}_{i\in[1,n]}\). On the other hand, verifiable aggregator oblivious encryption [38] ensures the correctness of the aggregated data. In this cryptoscheme, the user \(i\) computes a tag \(\sigma_{i}\) in addition to \(ci\), and the aggregator generates a publicly verifiable proof \(\sigma\) from \(\{\sigma_{i}\}_{i\in[1,n]}\) that proves the correctness of \(M\). Note that ensuring the correctness of the aggregated data is very critical in a voting scheme to ensure a fair ballot, and the results of the ballot reflect the voters selections. In our scheme, we employ verifiable aggregator oblivious encryption to make sure that blockchain nodes cannot manipulate the ballot results by, for example, counting multiple votes for the same voter. The verifiable aggregator oblivious encryption consists of the following algorithms:  
* \(Setup(1^{\lambda})\to\mathrm{param},sk_{A}\): Given a security parameter \(\lambda\) as an input, this algorithm generates public parameters \(PP\) and a secret key of aggregator \(sk_{A}\), a set of user secret keys \(\{sk_{i}\}_{i=1}^{n}\), and the aggregate verification key \(v_{k}\).
* \(Enc(param,t,x_{i})\to c_{i},\sigma_{i}\): Given \(param\), \(t\), a value \(x_{i}\), and \(sk_{i}\), this algorithm produces a ciphertext \(c_{i}\) and a tag \(\sigma_{i}\).
* \(AggrDec(param,\{(c_{i},\sigma_{i})\}_{i=1}^{n},sk_{A})\to Xt=\sum_{i=1}^{n}x_{i,t}\bmod M\): This algorithm is considered the aggregation and decryption algorithm and it uses the \(param\), \(t\), the ciphertexts and tags \(\{(c_{i},\sigma_{i})\}_{i=1}^{n},\sigma_{t}\) and \(sk_{A}\), to produce \(\{(c_{i},\sigma_{i})\}_{i=1}^{n}\), and the proof \(\sigma_{t}\) where \(M\) is some fixed integer contained in \(param\).
* \(VerifySum()\to\) : This verification algorithm is applied to the aggregation algorithm and it takes \(param\), \(t\), \(v_{k}\), and \((X_{t},\sigma_{t})\) as input, and outputs \(1\) or \(0\).
# Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
## 3 Materials and Methods
### Software Functionality
#### 3.2.2 Receiver Process  
In the receiver process, as it appears in Figure 4, after down-sampling and ADC in the USRP receiver block, the first step is to remove the CP. Then FFT is carried out to transform the signal from the time domain into the frequency domain, and to frame the transformation to set the sampling mode of the output signal. The magnitude of the received signal is calculated in the frequency domain. Table 3 shows the testbed parameters utilized for the generation of the OFDM signal for both the transmitter and the receiver. These parameters are also pliable and can be adjusted according to different requirements and standards. Finally, features are extracted from WCSI to ensure accurate, efficient, or meaningful analysis. Further principal component analysis (PCA) is utilized to reduce the dimensionality and the classification of WCSI data by using different machine learning algorithms.
###### Abstract  
Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan's Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for \(R^{2}\) and RMSE, while AdaBoost provides best results for MAE.  
air quality monitoring; machine learning; air quality index +
Footnote †: journal: Medical  
Recent researches focus more on advanced statistical learning algorithms for air quality evaluation and air pollution prediction. [PERSON] et al. [5], [PERSON] et al. [6], and [PERSON] et al. [7] have used neural networks to build models for predicting the prevalence of individual pollutants, e.g., particulates matter measuring less than 10 microns (PM\({}_{10}\)). [PERSON] et al. [5] used a support vector machine (SVM) and artificial neural network (ANN) to train models. Their best ANN model attained almost 79% for specificity with only a 0.82% false-positive rate, while their best SVM model at a specificity of 80% with a false positive rate of only 0.13%. [PERSON] et al. [8] proposed a random forest approach, named RAQ, for AQI category prediction. Then, [PERSON] et al. [9] applied deep neural networks for AQI category prediction. [PERSON] and [PERSON] [10] applied different settings to outperform \(k\)-nearest neighbor (\(k\)-NN), decision tree, and SVM for predicting AQI levels. Their ANN model achieved an accuracy of 92.3%, outperforming all other tested algorithms.  
The work presented in this paper focuses on the development of AQI prediction models for acute air pollution events 1, 8, and 24 h in advance. The following machine learning (ML) algorithms are investigated, i.e., random forest, adaptive boosting (AdaBoost), support vector machine, artificial neural network, and stacking ensemble methods to train models. As well, this research observes how prediction performance decays over longer time frames, and the precision is measured with three commonly used scale-dependent error indexes: mean absolute error (MAE), root mean squared error (RMSE), and R-squared (\(R^{2}\)).
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 3 Enabling Technologies for a High-Speed VLC System
### Advanced Modulation
#### CSK  
CSK is a new modulation method proposed for the visible light communication system that is composed of the red, green, and blue LED sources. As shown in Figure 8, after a scrambler and channel coding, the \(x/y\) values are transformed to \(p_{i}/p_{j}/p_{k}\) according to the Color mapping constellations (shown in Figure 9). Four color points are defined in the 4-CSK constellation, meaning that 2 bits can be sent with each symbol. Instead of using the absolute intensity of each color in a Wave Division Multiple  
Figure 7: Schematic diagram of CAP modulation. \(a(t)\) and \(b(t)\) are the original bit sequence after up-sampling. \(r(t)\) is the received bit sequence after analogue-to-digital conversion. And \(r_{I}(t)\) and \(r_{Q}(t)\) are the bit sequence after in-phase and quadrature matched filtering, respectively.  
Access (WDMA), CSK uses the intensity ratio of the red, green, and blue colors in the LED source to transmit information, thus it has a higher bandwidth efficiency and data rate, which has attracted significant attention recently [44; 45]. And CSK has been included in the IEEE 802.15.7 visible light communication standard modulation. However, according to the above analysis, CSK modulation requires accurate color separation at both the transmitter and the receiver, increasing the complexity of its implementation. And obviously, CSK is not fit for the VLC systems based on pc-LEDs.
###### Abstract
They include the use of adaptive filtering techniques [21] and a charge pump phase-locked loop architecture with dual loops [22] to suppress spuriousness to a low level. Techniques for suppressing phase-locked loop spuriousness using software methods are not yet available. Since there are different methods for dealing with the above two types of clutter, it is necessary to check the type of clutter in the system used before selecting the method. Therefore, the difference between their effects on the beat signal has become the focus of the study. In this paper, without using any approximation, the exact effect of periodic nonlinearity and the phase-locked loop spuriousness on the beat signal is established, respectively. Their characteristics can be clearly recognized in theory. To exclude environmental effects, a series of closed-loop experiments were conducted using different lengths of optical fibers. Comparing the experimental phenomena with the above theory, the clutter that appeared in the system used is confirmed to be generated by phase-lock loop spuriousness. A removal method based on long delay lines and cross-correlation is proposed which can not only identify the location of the clutter, but also remove it. After using the proposed method, the false alarms are eliminated. The effectiveness of the proposed method is verified by experiments, including the free space and through-wall situations. The rest of the article is organized as follows. In Section 2, theories of effects of periodic nonlinearity and phase-locked loop spuriousness on the spectrum of the beat signal are successively established. Section 3 introduces the fiber optic experiments used to identify the source of the clutter in the system. Section 4 presents the proposed method for removing the clutter. In Section 5, experimental results in free space and through-wall situations are shown to demonstrate the effectiveness of the proposed method. Finally, the conclusion is presented in Section 6.
## 2 Characteristics of Synchrotron X-rays at AS and ESRF  
The key characteristics of synchrotron X-rays relevant for radiobiology include:  
* The high brightness, or brilliance, which describes synchrotron radiation power. Brilliance measures the source quality and implicates the number of photons produced per second. The higher the brilliance value, the stronger the emitted beam.
* The low divergence of the synchrotron beam enables the irradiation of the target with collimated parallel microbeams, in contrast to conventional RT. The low divergence results from the fact that the target is several meters away from the permanent magnet wiggler which generates the X-ray beam. At the AS, the distance between the wiggler and the target is about 32 m, while at the ESRF it is 40 m.
* The beam current, which is the basic quantity of the beam. In this regard, the ESRF's characteristics are superior to those of the AS. The ESRF has a significantly longer periphery of 844 m, while the AS has 200 m. The brilliance values are 8 \(\times\) 10\({}^{20}\) and 4.6 \(\times\) 10\({}^{18}\) photons/(s \(\times\) 0.1% bandwidth \(\times\) mrad\({}^{2}\)) for the ESRF and AS, respectively [7]. Both have a beam current of 200 mA; however, the maximum electron energy is 6 GeV for ESRF and 3 GeV for AS.
* The intense flux (dose rate) of photons allows samples to be irradiated very quickly in the range of seconds and milliseconds. Dose rates in the AS range from 30-1000 Gy/s, while at the ESRF can be up to 16,000 Gy/s.
* The energy of the synchrotron beam is in the KeV range, which has the advantage of allowing for low secondary electron (Compton) scattering. The energy range at both the AS and ESRF is \"tunable\", meaning, the energy spectrum can be filtered to remove low energy photons and use a poly energetic X-ray beam typically between 30 and 120 KeV.  
These physical properties enable the development of new spatially fractionated RT (SFRT) such as microbeam RT (MRT) and FLASH RT, which are described below.
## 3 Results and Discussion
### Top Kerf Width
To confirm this, the Pearson correlation coefficient was calculated, which measured the strength and direction of the linear relationship between the two variables. The Pearson correlation coefficient for these data was \(-0.589\), which indicated a moderate negative correlation. A simple linear regression analysis to create a mathematical model that predicts the top kerf width based on the heat input was performed. This indicated that, as the heat input increased, the top kerf width decreased. During the process of CO\({}_{2}\) laser cutting through a \(5\) mm thick PMMA sheet, the intense heat generated by the focal point vaporizes the material, leading to the formation of a plasma plume that is expelled from the kerf. This technique induces a turbulent flow of gas that can eject some of the molten material, resulting in irregularities and deformations on the cut edges. Moreover, the high temperature of the laser generates thermal stress within the material, causing localized melting and the re-solidification of the PMMA. This phenomenon can lead to the formation of slight protrusions or bulges around the cut edges of the material's surface. Consequently, the formation of bullet-like debris after cutting PMMA is a result of the molten material being expelled by the gas flow and the localized melting and re-solidification induced by the laser's heat. The presence of these bullet pieces can have a significant impact on the precision and quality of the cut, especially when tight tolerances are necessary. The expulsion of molten PMMA during laser cutting is a multifaceted phenomenon intricately linked to various factors. While laser parameters play a pivotal role, the hydrodynamic properties of the PMMA melt and the thickness of the sample are equally critical contributors. The interaction of these elements influences the dynamics of material ejection, shaping the characteristics of the cut. The hydrodynamic parameters, including viscosity and flow behavior of the molten PMMA, intricately intertwine with the laser-induced ther  
Figure 7: The residual plot for the top kerf width (colorful squares in the figure are correspond to points of residuals normal percentage of top kerf width). mal effects. Furthermore, the thickness of the sample introduces additional complexities, influencing the distribution and dissipation of heat within the material.