esa-sceva/satcom-chunk-collection huggingface dataset:
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 2 Related Works
### Chebyshev Chaotic Maps  
The chaotic system is characterized by a sensitive dependence on initial conditions, pseudo-randomness, and ergodicity [35; 36; 37]. These features have the excellent properties of diffusion and confusion, which are important in cryptography [35; 36]. Researchers have proposed image encryption in chaotic maps [38; 39]. Definitions of Chebyshev chaotic maps are introduced below.  
Figure 1: A general telemedicine system with asynchronous telemedicine and synchronous telemedicine scenario.  
**Definition 1**.: _The Chebyshev polynomial \(T_{n}(x):[-1,\ 1]\rightarrow[-1,\ 1]\) is a polynomial in \(x\) of degree \(n\), defined as \(T_{n}(x)=\cos(ncos^{-1}(x))\)._  
**Definition 2**.: _The recurrent relation of \(T_{n}(x)\) is defined as \(T_{n}(x)=2 xT_{n-1}(x)-T_{n-2}(x)\) for any \(n\geq 2\), \(T_{0}(x)=1\), and \(T_{1}(x)=x\)._  
**Definition 3**.: _One of the most important properties of Chebyshev polynomials is semi-group property which establishes \(T_{r}(T_{s}(x))=T_{rs}(x)=T_{s}(T_{r}(x))\) for any \((s,\ r)\in Z\) and \(s\in[-1,\ 1]\). The interval \([-1,\ 1]\) is invariant under the action of the map \(T_{n}(x):\ [\ -1,\ 1]\rightarrow[-1,\ 1]\). Therefore, the Chebyshev polynomial restricted to the interval \([-1,\ 1]\) is a well-known chaotic map for all \(n>1\). It has a unique continuous invariant measure with positive Lyapunov exponent \(ln\ n\). For \(n=2\), Chebyshev maps reduces to well-known logistic maps._  
**Definition 4**.: _In order to enhance property of Chebyshev chaotic maps, [PERSON] [40] proved that the semi-group property holds for Chebyshev polynomials defined on interval \([-\infty,\ +\infty]\). This paper utilizes the following enhanced Chebyshev polynomials._  
\[T_{n}(x)=(2 xT_{n-1}(x)-T_{n-2}(x))\bmod N\]  
_where \(n\geq 2\), \(x\in(-\infty,\ +\infty)\), and \(N\) is a large prime number. According to the equations, the semi-group property still holds, and the enhanced Chebyshev polynomials also commute._  
\[T_{r}(T_{s}(x))\bmod N\ =\ T_{rs}(x)\bmod N=T_{s}(T_{r}(x))\bmod N\]  
**Definition 5**.: _Chaotic maps-based discrete logarithm problem (CMDLP). Given two elements \(x\) and \(y\), it is computationally infeasible to find the integer \(n\) such that \(T_{n}(x)\bmod N\ =\ y\)._  
**Definition 6**.: _Chaotic maps-based Diffie-Hellman problem (CMDHP). Given three elements \(x\), \(T_{r}(x)\bmod N\), and \(T_{s}(x)\bmod N\), it is computationally infeasible to compute \(T_{rs}(x)\bmod N\)._
# Visualizing Small Objects Using Amplitude-Modulated Laser Light at Microwave Frequencies
###### Abstract
In principle, measurement using a large synthetic aperture enhances cross-range resolution. In our system, since laser light only works as a carrier for the microwave signals, all data processing methods are identical to those of microwave radars. An important difference between microwave and laser systems is how they are scattered when they reach the target surface. Since wave scattering occurs on surfaces with a roughness scale length that is larger than the wavelength, the surfaces that mirror-reflect the microwaves may scatter the laser light, in contrast to what takes place during mirror reflection. This results in there being significant differences in the image characteristics. We have reported a LADAR experiment using a vector network analyzer (VNA) and an electro/optical (E/O) and optical/electro (O/E) transceiver, which was applied to the near-range (<10 m) small target to validate the measurement principle [7, 12, 13]. In this paper, we developed a LADAR system using a wide band microwave transmitter in place of the VNA [14]. This system change will be helpful during airborne LADAR measurements since the pulse repetition frequency (PRF) of the measurement will be over 100 time faster than the VNA. We aim to visualize a small target, and to validate the replication of the LADAR experiment using a new transmitter system. This experiment is similar to the previous experiment reported in reference [8, 9, 10, 11]. In addition to that, we also aim to visualize a small target by using spotlight-mode synthetic aperture imaging LADAR (SAIL) in lab experiments, and to validate the applicability of the developed system.
## 1 Introduction
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Type of Mix** & **Superplasticizer** & **Dosage/wt.\%** & **Slump Flow/mm** & **Observations** & **Ref.** \\ \hline SCC + limestone & Arabic gum & 2–12 & 400–800 & Optimal at 8\%, reduced compressive strength & [19] \\ \hline SCC + fly ash & Welan gum & \(\leq\)0.01 * & 600–670 & A minimum slump-flow of 600 mm & \\  & Xathan gum & \(\leq\)0.04 * & 610–670 & using binder contents of 350, 400, or & [20] \\  & Starch ether & \(\leq\)0.5 * & 590–670 & 450 kg & \\ \hline Ordinary concrete & Cassava starch & 0.4–2 & 98–18 & Viscosity-modifying agent, less susceptible to sulphate attack, no impact on compressive strength & [21] \\ \hline Ordinary concrete & Aloe vera & 2–6 & 75–6 & Increase of 41\% of compressive strength was achieved with 2 wt.\% compared to reference & [24] \\ \hline Ordinary concrete & Aloe vera & Coating & Not relevant & Corrosion inhibitor for steel rebar, \(>\) 83\% inhibition efficiency & [25] \\ \hline Porous concrete + marble waste powder & Aloe vera & 0.25 & Not relevant & Along with 30\% cement replacement with marble waste powder, an increase in the compressive strength was observed & [26] \\ \hline \hline \end{tabular}  
* Weight percentage with respect to binder content.  
\end{table} 
Table 1: Selection of the eco-friendly admixtures and their impact on the properties of SCC and concrete. This paper focuses on two main aspects: utilizing green, environmentally friendly LC\({}^{3}\) cement for the preparation of SCC and studying the effects of AVM, a cost-effective organic extract, on its fresh properties. To better understand the effects of AVM, reference samples were prepared using OPC (CEM 1 42.5 R/Type I), and the obtained results were compared with samples prepared using a commercial superplasticizer (CS), MasterGlenium 3889. A total of nine trial mixes of SCC with OPC and CS were conducted at \(w/c\) ratios of 0.8, 0.9, and 1.0 to define the reference mixture. Flow was measured using the flow table, V-funnel, and the L-box. Setting times, consistency, slump flow, yield stress, and compressive strength against flow behavior from the percentage dosage of CS and AVM were studied.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Posterior Segment Diseases  
#### 3.3.1 AI and Diabetic Retinopathy  
Roughly 600 million people could be diabetic in 2040, and almost a third of them will have diabetic retinopathy (DR) [39].  
Screening techniques like ophthalmoscopy, dilated slit-lamp biomicroscopy with a portable lens, mydriatic or non-mydriatic fundus imaging, teleretual monitoring, and retinal video recording can be utilized.  
A major obstacle to the strong implementation of DR monitoring is caused by the limited availability of human specialists and long-term financial support.  
DL has significantly improved diagnostics for the detection of DR. The main aim is to use DL applications that can be applied to different ethnic groups and are very appropriate for retinal images acquired with different video cameras.  
For example, [PERSON] et al. implemented a DL-powered application to identify DR in 52 OCT images and obtained an AUC of 0.98 [40]. It is necessary to validate their system in larger subject sets.  
A computer-aided diagnosis (CAD) application, built by means of a continuous machine learning (CML) model, uses optical coherence tomography angiography (OCTA) for automatic detection of nonproliferative DR (NPDR) has also obtained great precision and AUC [41]. The OCTA performance shows the need for further methodological analysis and robust clinical assessments.  
Applications for the detection of DR were among the first to obtain acceptance for usual clinical usage from the FDA [42].
## 3 Open-Area Environmental Interference Suppression Model
### Environmental Interference Suppression Framework  
A diagram of the environmental interference suppression framework is shown in Figure 2. Since the signal strengths of the near SA waterfall are often stronger than and similar to those of the far SA waterfall, segmentation is processed on the near SA waterfall, and a group of segmentation layers containing a signal, an environmental interference or the background in each segmentation layer can be obtained. By utilizing these segmentation layers, the waterfalls of each signal or interference in the near and far waterfalls can be captured.  
Figure 2: Diagram of \(E^{2}ISA\).  
Near and far Single-Layer Time-Varying Statistical Feature (SLTVSF) curves of each signal, including the average vs. time curve, standard deviation vs. time curve, and maximum vs. time curve, are calculated by both the near SA waterfall and far SA waterfall. Then, the similarities between near and far SLTVSF curves can be calculated by the Kullback-Leibler (KL) divergence, Jensen-Shannon (JS) divergence, and cross entropy. By using these similarity measures, we can determine the category of each signal.  
Finally, according to the classification of the segmentation layer, they are divided into three categories: the signal, environmental interference, and background. Then, we can obtain three SA waterfalls, the signal, environmental interference, and background. In other words, an SA waterfall that contains the internal signals and background can only be acquired under the condition that the environmental interferences are well suppressed.  
Note that, since the environmental interference suppression framework is based on the frequency-domain separable assumption, the interference suppression performance is determined by the separability of signals and the frequency resolution of the SA. For the typical frequency-domain separable case, a 10 dB or higher interference suppression performance can be achieved. Firstly, we need to perform the segmentation of the near-site waterfall map through the U-net network. Then, we need to separate the detected signal regions through connected domain detection to obtain multiple channel signal regions and then take the same location region in the far-site waterfall map to calculate the sliding statistics of each channel of the near-site waterfall map and far-site waterfall map, respectively.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 1 Introduction
used GOSIF and CSIF data and concluded that preseason maximum and minimum air temperatures were the primary drivers of spring phenology advancement; furthermore, fall phenology (EOS) exhibited a positive correlation with preseason precipitation, minimum air temperature, and radiation intensity, while a negative correlation was observed with the preseason maximum air temperature. However, previous studies have generally focused on a few fixed environmental variables, and the response of phenology to climate extracted from SIF data at spatial scales remains inadequately understood. To better evaluate the effect of climatic factors on phenological trends, it is essential to design experiments that evaluate the relative importance of multiple climatic factors at different spatial scales. Therefore, urgent priorities include elucidating how SIF-derived phenology tracking patterns and mechanisms vary with spatial scale, as well as addressing challenges in accurately characterizing phenology when satellite pixel resolutions differ. This includes analyzing the effects of spatial scale on phenological indicator estimation, trend detection, and response to the environment, and exploring whether phenology detected from the coarse-resolution SIF data is representative of the overall phenology captured by finer-resolution SIF products. In this study, we estimated the SOS and EOS from three different scales of solar-induced chlorophyll fluorescence datasets--1 km, 5 km, and 50 km--for deciduous forests in the middle and high latitudes of the Northern Hemisphere during 2008-2017. The main objectives of the study were to (1) compare the effectiveness of phenology tracking using SIF products at different spatial scales, (2) explore the spatial and temporal patterns of phenological events in deciduous forests in the Northern Hemisphere at different scales, and (3) examine the relative importance of climatic factors on phenological trends at different scales.
## 4 Case Study and Discussion
### Simulation Environment  
#### 4.1.1 Technical Specification of the Resources  
The technical specifications of the resources coordinated in the site are summarized in Table 4. The WPGs in the site consisted of two separate 1.5 MW WPGs which were registered in the EMS as a single aggregated resource. Data reflected in the operation related to the coordinated model were required to simulate the coordinated wind farm model with the BESS, as shown in Figure 1. The key parameters for coordinated model operation are presented in Table 4.  
Each BESS and PCS installed on the site were manufactured by LG-Chem, with a degradation period of 3 years. The SoC in the out-factory state was measured to be 2162 kWh. However, considering the degradation periods, the SoC was considered to be 1963 kWh. The reliability constraints for the range of the SoC were set according to the reliability criteria which have been in effect since February 2020. The initial SoC for the simulations were postulated to be the minimum eligible SoC for safe operation, as specified by the manufacturers, to formulate the same environment as in the real situation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resource Type** & **Component** & **Value** & **Installation Site** \\ \hline \multirow{8}{*}{PV–BESS (LG-Chem)} & PCS Capacity & 500 kW & \multirow{8}{*}{} \\  & PCS Efficiency & 98.4\% & \\  & BESS Capacity & 1963 kWh & \\  & PV Generator & 1.04 MW & \\  & BESS Charging Efficiency & 95.0\% & Outside \\  & BESS Discharging & 95.0\% & the Structure \\  & Efficiency & & \\  & Max SoC of BESS & 90\% & \\  & DoD of BESS & 10\% & \\  & Multiplier for REC & 5 & \\ \hline \multirow{8}{*}{WPG–BESS (Samsung SDI)} & PCS Capacity & 100 kW & \multirow{8}{*}{} \\  & PCS Efficiency & 98.4\% & \\  & BESS Capacity & 214.4 kWh & \\  & WPG & 3.00 MW & \\ \cline{1-1}  & BESS Charging Efficiency & 95.0\% & Outside \\ \cline{1-1}  & BESS Discharging & 95.0\% & the Structure \\ \cline{1-1}  & Efficiency & & \\ \cline{1-1}  & Max SoC of BESS & 80\% & \\ \cline{1-1}  & DoD of BESS & 20\% & \\ \cline{1-1}  & Multiplier for REC & 5 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the coordinated resources.
## 1 Introduction  
Visible light communication (VLC) has attracted much attention in recent years, because RGB-LED communication devices (such as lamps, TVs, traffic signs) are used everywhere [1]. Moreover, VLC is safer than radio frequency (RF), because of the line of sight (LOS) propagation and non-penetration of light waves through opaque surfaces. Therefore, there is the hope that a powerful offline to-online (O2O) and peer-to-peer (P2P) communication medium might be developed that receives information from multiple RGB-LEDs. In this paper, we propose a new VLC scheme different from previous modulation- and demodulation-based schemes to achieve higher accuracy and robustness for recognition of RGB-LED-ID (the unique identification of each different LED).  
There are two kinds of VLC: one uses photodiode (PD) as the receiver, and the other uses an image sensor as the receiver. VLC based on image sensors has wider application value [2]. In this paper, we propose an optical fringe code (OFC) detection and recognition based on visible light communication using a convolutional neural network (CNN). We use RGB-LED as the transmitter, and it is derived by pulse width modulation (PWM). At the receiver, a complementary metal-oxide-semiconductor (CMOS) image sensor in a smartphone is used to capture the LED projection. LED projection is a form of stripe image, because of the roll mechanism of CMOS sensors. A CNN is then used to build the classifier. After offline training is completed, online recognition of OFC can be implemented. The experimental results show that the scheme we propose can not only improve the distance and accuracy of OFC recognition, but also improve the applicability and stability of OFC recognition. In addition, the number of OFC is far more than that in our previous work in Reference [3]. Therefore, the OFC is expected to become an effective complement to traditional 2D code scanning technology in the future.  
Compared with the traditional modulation and demodulation method and our previous work, the main advantage of the proposed method is practicability. There are a lot of application scenarios of our system. For example, mobile payment is now a very popular technology, and OFC can be used as an effective complementary means of mobile payment technology. In the future field of autopilot, OFC can be used as an important means to realize navigation signs. It has the characteristic of luminescence, and has higher robustness than single pattern recognition. Last but not least, in outdoor advertising business, the use of OFC can provide new ideas for night advertising. Combined with virtual reality technology, OFC can offer attractive potential to lighting advertising boards. Overall, it could be a promising technology.
# The Condition Evaluation of Bridges Based on Fuzzy BWM and Fuzzy Comprehensive Evaluation
## 4 Case Study
### Case Study 1
#### 4.1.1 Weight Calculation for Dingjia Bridge
\begin{array}{l}\min\quad\hat{\phi}^{*}\\ \sum_{s.t.}\left\{\begin{array}{l}\left|\frac{\left(\frac{T_{1}^{s},m_{1}^{s },m_{2}^{s}}{T_{2}^{s},m_{2}^{s}}\right)}{\left(\frac{T_{1}^{s},m_{2}^{s},m_{ 2}^{s}}\right)}-\left(l_{12},m_{12},u_{12}\right)\\ \left|\frac{\left(\frac{T_{1}^{s},m_{2}^{s},m_{2}^{s}}{T_{2}^{s},m_{2}^{s}} \right)}{\left(\frac{T_{1}^{s},m_{2}^{s},m_{2}^{s}}\right)}-\left(l_{13},m_{13 },u_{13}\right)\\ \left|\frac{\left(\frac{T_{1}^{s},m_{2}^{s},m_{2}^{s}}{T_{2}^{s},m_{2}^{s}} \right)}{\left(\frac{T_{1}^{s},m_{2}^{s},m_{2}^{s}}\right)}-\left(l_{14},m_{14 },u_{14}\right)\\ \left|\frac{\left(\frac{T_{1}^{s},m_{2}^{s},m_{2}^{s}}{T_{2}^{s},m_{2}^{s}} \right)}{\left(\frac{T_{1}^{s},m_{2}^{s},m_{2}^{s}}\right)}-\left(l_{24},m_{24 },u_{24}\right)\\ \left|\frac{\left(\frac{T_{1}^{s},m_{2}^{s},m_{2}^{s}}{T_{2}^{s},m_{2}^{s}} \right)}{\left(\frac{T_{1}^{s},m_{2}^{s},m_{2}^{s}}\right)}-\left(l_{34},m_{3 4},u_{34}\right)\\ \sum_{j=1}^{4}Q(\hat{v}_{j})=1\\ I_{j}^{v}\leq m_{j}^{v}\leq u_{j}^{v}\\ I_{j}^{v}\geq 0\\ j=1,2,3,4\end{array}\right.\end{array}  \tag{21}\]  
Given that \(\hat{\beta}_{BW}=\hat{\beta}_{14}=\left(\frac{7}{2},4,\frac{9}{2}\right)\), it can be seen from Table 2 that the \(CI\) for this case is \(CI=8.04\). The Consistency index (\(CI\)) and its corresponding optimal target value \(\hat{\phi}^{*}\), derived from the FBWM, are systematically incorporated into Equation (12) to compute the consistency ratio (\(CR\)). The detailed computational procedure is mathematically formulated in Equation (22). \[CR=\frac{\hat{\varphi}^{*}}{CI}=\frac{0.2893}{8.04}=0.0360<0.1 \tag{22}\]  
The computed consistency ratio (\(CR=0.0360\)) falls below the critical threshold of 0.1, confirming satisfactory consistency in the evaluation results. Furthermore, the CRvalue closely approximates zero, demonstrating exceptional consistency in the pairwise comparison process. As in the previous process, the optimal weights of each indicator at each layer can be derived, as shown in Table 4.  
Table 4 details the optimized weight distribution across hierarchical evaluation layers. The FBWM was systematically implemented across all evaluation strata: target layer, criteria layer, primary indicator layer, and secondary indicator layer. Within the criteria layer, safety demonstrated the highest weighting (0.4201), followed sequentially by durability (0.3198), suitability (0.1570), and reinforcement economy (0.1032) in decreasing significance. The weight distribution analysis identifies safety as the critical determinant in bridge condition evaluation, while durability maintains considerable influence within the evaluation framework. These derived weights were subsequently incorporated into the fuzzy comprehensive evaluation method to systematically determine the bridge's operational condition.
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 4 Problems of Measurement and Analysis of Reflectance Indices
### Variability of Plants
On the other hand, the parameters of the variability of reflectance (e.g., variation coefficients of reflectance or RIs) can also be informative for plant remote sensing; the importance of these parameters for plant remote sensing is investigated by using the texture analysis [148], which can be used for revealing the actions of stressors, searching for ROIs, and image contrasting. Thus, the variability in plants' properties, including the content of photosynthetic pigments, anatomical structure of leaves, canopy morphological and biochemical aspects, water content, and other factors, can be important factors influencing the leaf and canopy reflectance [15; 149; 150; 154]. [PERSON] and [PERSON] [187] proposed to consider this complex of the reflectance properties of plants as \"optical type\". These optical types can be collected in the library for classification and statistical analysis [20; 187]. On the other hand, the analysis of this heterogeneity seems to be the additional tool of plant remote sensing. This tool can be used for the ecological and agricultural monitoring [10], estimation of soil quality [18], disease detection [188], and others.
## 2 Avalance Photodiodes (APDs)
It is quantified by the ratio \(\delta E/E\), where \(\delta E\) represents the full width at half maximum (FWHM) of the peak corresponding to a particular energy in the energy spectrum, and \(E\) is the centroid (mean) of that peak. A smaller \(\delta E/E\) indicates better energy resolution, meaning the detector can more precisely differentiate between energies. This metric is crucial in fields such as spectroscopy, where precise energy measurement is essential. In APDs, \(\delta E/E\) depends on factors such as the gain of the device, the noise characteristics, and the intrinsic properties of the semiconductor material [25]. Higher \(V_{bias}\) increases the gain, improving the signal-to-noise ratio, but it also increases noise, which can degrade resolution. Thus, a minimum is expected in \(\delta E/E\) when \(V_{bias}\) is changed. Temperature \(T\) also has a significant impact on energy resolution; as temperature increases, thermal noise increases, leading to poorer resolution. Conversely, cooling the APD can enhance resolution by reducing noise. Therefore, optimal \(\delta E/E\) is achieved by balancing the \(G_{APD}\) and noise through careful control of \(T\) and \(V_{bias}\).