esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Study in Short Delay Regime
### Linewidth Narrowing  
The measured linewidth of the 3 mm device under different coupling current is shown in Figure 8a. Injection current at the coupling region adjusts the coupling phase and compensates waveguide absorption simultaneously. When the current is below 50 mA, the absorption loss in the coupling region is too severe that coupling between the two sub-DFB lasers is negligible. Upon increasing the current further, stable mutual injection locking state is established at the discrete coupling current with appropriate coupling phase. Narrowed linewidth together with enhanced output power are obtained with increased coupling strength. The corresponding self-heterodyne signal obtained with 20-km-long  
Figure 7: Laser behavior in a 3 mm long integrated device. (**a**) Optical spectra and intensity noise spectra of mutually injection locked state (MIL), four wave mixing (F), periodic oscillation with relaxation oscillation frequency (R) and chaos (C); (**b**) left part: evolution of measured optical spectrum with coupling current; right part: simulated lasing frequency of sub-DFB lasers with different coupling phases \(\varphi_{p}\).  
fiber is shown in Figure 8b, revealing that the linewidth is narrowed by a factor of 8.2 at a coupling current of 103 mA. This technique is interesting since the linewidth performance can be improved without changing the epitaxial structure.  
The heating effect of the coupling current induces a red shift of the gain spectrum, causing mismatch between the gain peak and the Bragg wavelength of the grating. Consequently, as is shown in the inset of Figure 8a, FP modes due to cleaved facets emerge at high currents, preventing an increase of the coupling current further. However, this proves that strong coupling between the two sub-DFB occurs. In addition, threshold reduction by 5 mA is also observed in the experiment due to optical injection, which also indicates that lasers are upon strong coupling. Future work on heat dissipation or optimization on epitaxial layers may improve device performance further. The linewidth-power product is reduced from 52.9 to 8.9 MHz\(\cdot\)mW, indicating that the linewidth narrowing is more than a result of increased output power.  
As was analyzed in Section 2, longer coupling region can lead to better noise suppression. This prediction is confirmed in Figure 8c. More pronounced linewidth suppression tends to be achieved in lasers with longer coupling regions except for the device with 7 mm cavity length. The reason for the decreased linewidth narrowing factor of the 7 mm device is attributed to heat effect: It requires the highest current to compensate the absorption in the coupling region and suffers from heat induced gain peak shift most severely. The largest linewidth narrowing factor of \(\sim\)13 is achieved in the 5 mm-long device, and the delayed self-heterodyne signals are shown in the inset of Figure 8c. Laser linewidth is narrowed to 0.74 MHz with 6.1 mW output power, corresponding to a linewidth-power product of 4.5 MHz\(\cdot\)mW.
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
## 2 OAI-Based 5G mmWave Network
### Positioning Reference Signal  
The 5G NR PRS is specified in the 3 GPP standard since Release 16 as a reference signal dedicated for positioning purposes, with enhancements introduced as of the current standard in [5]. The PRS can be flexibly configured to adapt to the positioning application, by using tailored configurations of the PRS sequence and its resource allocation, such as based on the number of consecutive orthogonal frequency-division multiplexing (OFDM) symbols per slot, bandwidth or beams. In OAI, the PRS is explicitly implemented for 30 kHz and 120 kHz subcarrier spacing, corresponding to numerology 1 and 3, respectively. Furthermore, the implementation of PRS in OAI also supports the flexible resource allocation in terms of frequency, time and beams.  
The resource allocation of the PRS implementation in OAI mmWave configuration is here defined by a subcarrier spacing of 120 kHz, 64 physical resource blocks (PRBs) occupying a bandwidth of 92.16 MHz, 6 consecutive OFDM symbols, comb-size of 4 (i.e., one every four subcarriers used for PRS resource elements), and allocated for 8 different beams. The resulting PRS resource allocation within the 5G NR radioframe and within every PRS slot is shown in Figure 1.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 2 Preliminaries
### The Logistic-Fraction Hybrid Chaotic Map
#### 2.1.4 NIST Test  
The NIST (National Institute of Standards and Technology) SP800-22 Revla test consists of 15 sub-items that are used to detect the randomness of the sequences [28]. The method of detection is by comparing the calculated \(p\)-value of each sub-item to a prescribed significant level 0.01, and the generated values are all expected to greater than 0.01 to pass the test. We iterated Equation (1) with different initial values to generate 100 binary streams with 1,000,000 bits, then calculated their average values. The results are shown in Table 3. It is obvious that the calculated \(p\)-value of each sub-item falls into \([0.01,1]\), that is, the outputs generated by LFHCM can pass all the tests of NIST.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 3 Methods  
### Overall Framework  
As depicted in Figure 1, we present a novel framework aimed at enhancing the performance of remote sensing scene classification. Our approach, known as the Two-stage Fusion-based Audiovisual Classification Network (TFAVCNet), serves as the foundation of this method. In the first step, the audio module extracts audio embeddings from the audio input, represented by an audio spectrogram computed from the raw audio file. Simultaneously, the image module extracts the visual embedding from the image input, consisting of a remote sensing aerial image. Subsequently, in the feature-level fusion stage, the adaptive distribution weighted fusion module combines the audio and visual embeddings. Importantly, in the decision-level fusion stage, we preserve the results of single-mode decisions to effectively retain the original information from each modality. In the final step, the results of audio-, visual-, and feature-level fusion undergo a second-stage weighted fusion process, culminating in classification via the Softmax function. The subsequent subsections provide a more-detailed exploration of each module.
## 3 System Design and Implementation
### ECG Monitoring System Implementation
#### 3.2.2 Data Extraction and Manipulation Process  
Upon receiving the data packet from the ECG device, the packet is processed and useful data is extracted. An appropriate way to handle and manipulate the ECG data is vital because this is intimately related to data quality. Figure 8 shows the flow chart of the data manipulation process. A sensor node's node ID is identified first when the data is received to ensure that the aggregated data is from the correct sensor source. Then, the received data is scanned through to ensure the data packet is a complete packet. The 20-byte of ECG data are extracted. The ECG data is converted to a decimal value and presented in waveform format on mobile phone. The ECG data presented in waveform is for better visualization and better understanding of the heart condition, a beneficial feature for both doctors and patients.  
Figure 7: Communication between the sensor node and the ECG device.  
To perform precise ECG data analysis on a resource-limited mobile phone at real time, an accurate and simple technique is required instead of complicated techniques which involve more processing memory. In our approach, the QRS detection algorithm by [PERSON] [11] is adopted for the detection of the QRS peak in the ECG waveform which can be used for further analysis. Information such as QRS interval time, QT interval time and RR interval time can be obtained from the detection of QRS peaks. This information is useful for pathophysiological indications of ECG. As an example, a shortened QT interval indicates hypercalcemia where as a prolonged QT interval indicates hypocalcemia.
## 2 EO CEP Shifter  
### Theory  
Due to the wavelength dispersion of the refractive index in dispersive media, phase and group velocities have, in general, different values. In a crystal with a non-vanishing EO effect, the refractive index can be linearly modulated by an external electric field, \(E\), leading to a variation of the CEP. We consider a laser pulse whose carrier angular frequency is \(\omega_{0}\), propagating in a homogeneous dispersive medium of length, \(L\), which is non-centrosymmetric and exhibits a Pockels effect. This is, for example, the case [17,20] in LiNbO\({}_{3}\)--uniaxial crystal, crystalline class 3 m or RTP-biaxial crystal orthorhombic crystalline class mm2--when choosing appropriate directions for the linearly polarized laser field and the applied static electric field. Figure 1 illustrates how the direction of propagation and the polarization of light are to be chosen in practice and how the voltage is to be applied on the crystal in two interesting cases, LiNbO\({}_{3}\) and RTP. \(X\), \(Y\) and \(Z\) are the principal dielectric axes, which are parallel to the crystallographic axes. With these choices, the variation of the corresponding electric field-dependant refractive index, \(\Delta n\), is given as a linear function of the EO coefficient, \(r\), and of the electric field, \(E\). We only consider here the case where one can neglect the change in the crystal length, \(\Delta L\), due to the inverse piezoelectric effect, as is the case in LiNbO\({}_{3}\) and RTP [20, 21, 22].  
The CEP shift, \(\Delta\phi_{CEP}\), after propagation in the EO crystal when applying the electric field is defined by:  
\[\Delta\phi_{CEP}=\frac{2\pi}{\lambda}\big{(}(\tau_{g}-\tau_{\varphi})[E]-( \tau_{g}-\tau_{\varphi})[0]\big{)} \tag{1}\]  
For the sake of simplicity, we only consider the case when the change of the refractive index due to the linear EO effect depends on a unique \(r(\lambda_{0})\) coefficient (unclamped EO coefficient at wavelength \(\lambda_{0}\)) and can be written in a scalar form as:  
Figure 1: Geometry of the interaction for LiNbO\({}_{3}\) and simple rubidium titanyl phosphate (RTP). \(X\), \(Y\) and \(Z\) are the principal dielectric axes (parallel to the crystallographic axes).  
\[n(E,\lambda_{0})=n_{0}\left(\lambda_{0}\right)-\frac{1}{2}n_{0}^{3}\left(\lambda_{0 }\right)r\left(\lambda_{0}\right)E \tag{2}\]  
where \(n_{0}\left(\lambda_{0}\right)=n\left(E=0,\lambda_{0}\right)\) is the refractive index without applied field.  
Neglecting the inverse piezoelectric effect, one obtains:  
\[\Delta\phi_{CEP}\approx 2\pi\Bigg{[}\frac{3}{2}n_{0}^{2}\left(\lambda_{0} \right)r(\lambda_{0})\frac{\partial n_{0}}{\partial\lambda}\Bigg{]}_{\lambda_ {0}}+\frac{n_{0}^{3}\left(\lambda_{0}\right)}{2}\frac{\partial r}{\partial \lambda}\Bigg{]}LE \tag{3}\]  
This phase change is proportional to the length, \(L\), of the crystal and to the electric field, \(E\), applied. It can be used to make an active correction of the CEP and maintain its value constant with a control loop, despite environmental fluctuations.
## 3 Results
### Maximum Cloud Cover  
Beyond the lowest cloud height, the properties of the cloud cover were also evaluated in terms of the maximum cloud cover. The observations carried out by the AWOS, LO and RO approaches were classified on the basis of the standard METAR coding:  
* FEW (few)--1 or 2 oktas (eights) of sky are covered by clouds;
* SCT (scattered)--3 or 4 oktas of sky are covered by clouds;
* BKN (broken)--5, 6 or 7 oktas of sky are covered by clouds;
* OVC (overcast)--8 oktas of sky are covered by clouds, i.e., full cloud coverage.  
The inter-comparison of the AWOS vs. LO data (RO vs. LO data) is presented in Table 2 (Table 3). Note that the total sum of cases is different in both tables: 181 (227) cases are reported in Table 2 (Table 3). The difference (46) stems in the inability of the standard AWOS to estimate the cloud cover on the basis of a single measurement of the ceilometer, and by means of the associated interpolation methods defined by the manufacturer.  
These summaries reveal several characteristic features of the observation methods:  
* The AWOS method generally overestimates the maximum cloud cover, by indicating non-empty bins with higher numbers above the diagonal of the observation matrix.
* The AWOS data often differ from the LO data by two or three categories, for instance indicating overcast, whereas the true circumstances are few or scattered. This kind of deviation is rare in the case of the RO data; it only appeared once during the analyzed period of time.  
Figure 8: The same as Figure 7, but with the lowest cloud base determined by the local observers (LO) and the remote observers (RO).  
In line with the previous statements, the percentage of the exact matches (i.e., the sum of figures along the main diagonal of the observation matrix) is much higher in the case of the RO data set. The RO method indicates perfect matches in 80% of all the cases, which is a considerable improvement in comparison with the AWOS observations, with perfect matches only reaching up to 31%.
* If we do not insist on perfect matches, but we allow for a mismatch by one category, the dominance of the RO methodology is still clear: the RO mimics the LO method in 99.55% of all cases, whereas the same statistic is 83% for the standard AWOS.  
The validation experiment indicated that the RO method noticeably outperformed the measurements of the standard AWOS, when evaluating both of the perfect or imperfect matches (i.e., a mismatch of one category allowed) in the cloud coverage categories.
## 3 Results and Discussion
### Response Surface Optimization ANOVA for the Ultrasound-Assisted Enzymatic Extraction of SDF
#### 3.2.4 Determination of Optimal Ratios and Verification of Regression Model  
According to the results of the design analysis, under the conditions of an enzyme addition amount of 5.7%, a material-liquid ratio of 1/20.6 g/mL, and a reaction time of 31.0 min, the extraction rate of SDF from SSR was predicted to be 77.3%. Based on the operability and convenience of industrial production, the conditions of the enzyme addition amount, solid-liquid ratio, and reaction time were adjusted to 5.7%, 1/20 g/mL, and 30 min, respectively. To verify the operability of the model, three parallel experiments were conducted under the adjusted conditions, and the extraction rate of SDF from SSR was 76.8 \(\pm\) 0.8%. Compared with the predicted value in the model, the error was within 1%, indicating that the model was credible. The optimal process of SDF extraction from SSR, obtained through response surface test, had certain theoretical reference and practical application value. Moreover, the SDFs obtained under different conditions in the response  
Figure 2: Response surface plot and contour plot of interaction of various factors.  
surface test showed no significant differences in quality, with almost the same appearance, so the SDF extracted under the optimum conditions with the highest extraction rate was prepared, in order to test the following physicochemical properties.
## 3 Controller Design
### Path Tracking Control Layer
The output value of the output layer neuron corresponds to three adjustable parameters \(k_{p}\), \(k_{i}\), \(k_{d}\) of the PID controller. Through self-learning of neural network and weighted coefficient adjustment, the neural network outputs PID controller parameters corresponding to some optimal control law. The BP neural network is designed to have three layers with nodes of three, five, and one. The PID control algorithm can be expressed as:  
\[ \begin{array}{c}u(k)=u(k-1)+k_{p}(error(k)-error(k-1))+\\ k_{i}error(k)+k_{d}((error(k)-2 error(k-1)+error(k-2))\end{array}  \tag{34}\]  
where \(k_{p}\) is the proportion coefficient, \(k_{i}\) is the Integral coefficient, and \(k_{d}\) is the differential coefficient. The structure of three-layer BP neural network is proposed in this paper. The neural network consists of one input variable and three output variables. There are five neurons in the hidden layer. The input of the neural network input layer is:  
\[{O_{1}}^{(1)}=e \tag{35}\]  
where \(e\) is the control error. Figure 4: NNPID controller structure. The input and output of the hidden layer are:  
\[net_{i}{}^{(2)}(k)=w_{i1}{}^{(2)}O_{1}{}^{(1)}\ \ \ \ \ (i=1,2,3,4,5) \tag{36}\]  
\[O_{i}{}^{(2)}=f(net_{i}{}^{(2)}(k))\ \ \ \ \ (i=1,2,3,4,5) \tag{37}\]  
where \(w_{i1}{}^{(2)}\) is the weighting coefficient of the hidden layer. The activation function of neurons in the hidden layer selects the sigmoid function of positive and negative symmetry:  
\[f(x)=\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{38}\]  
The input and output of the network output layer is:  
\[net_{l}{}^{(3)}(k)=\sum\limits_{i=0}^{5}w_{l}{}^{(3)}O_{i}{}^{(2)}(k)\ \ \ \ \ \ \ (l=1,2,3) \tag{39}\] \[O_{l}{}^{(3)}(k)=f(net^{3}{}_{l}(k))\ \ \ \ \ \ \ \ (l=1,2,3)\] \[O_{1}{}^{(3)}(k)=k_{p}\] \[O_{2}{}^{(3)}(k)=k_{i}\] \[O_{3}{}^{(3)}(k)=k_{d}\]  
The output nodes in the output layer correspond to three adjustable parameters \(k_{p}\), \(k_{i}\), \(k_{d}\). The performance indicator function is determined as:  
Figure 4:0  
The weight coefficient of the network is modified according to the gradient descent method, that is, the negative gradient direction of the weighted coefficient is searched and adjusted according to \(E(k)\), and an inertial term is added to make the search rapidly converge to the global minimum. Figure 4:1  
The learning algorithm of weight coefficient of network output layer can be expressed as:  
Figure 4:2  
Figure 4:3  
The learning algorithm of the weight coefficient of a network hidden layer can be expressed as:  
Figure 4:4  
Figure 4:5  
where \(f^{\prime}(\bullet)=(1-f^{2}(x))/2\). The NNPID control algorithm is designed, as shown in Algorithm 1. The block diagram of the path tracking control layer is shown in Figure 4:6 The inputs of the system are the desired vehicle speed and desired trajectory function. The outputs are the vehicle yaw rate.
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 4 Results
### Analysis of GDP Modeling Results
#### 4.1.2 Time Regression Analysis  
Figure 3 shows the optimal function fitting results with the NTL index of mainland China as the independent variable and the GDP or per capita GDP of mainland China as the dependent variable. The study found that the \(R^{2}\) between the GDP and MNL, TNL, and CNLI is higher than the \(R^{2}\) between the per capita GDP and MNL, TNL, and CNLI, and the function obtained by fitting the GDP is convenient for constructing a spatial model of the GDP. The \(R^{2}\) strength of the regression model of each NTL index is ranked as follows--MNL > TNL > CNLI, and the coefficient values of the linear model and the quadratic regression model are \(\geq\)0.97.  
After comprehensive consideration, the linear regression model of the MNL and GDP is the optimal model. The regression equation is y = 558592.81x \(-\) 53715.7, \(R^{2}\) = 0.972, and the fitting effect is very good (Figure 4). Comparing the two regression methods, it is known that the optimal fitting model of the spatial regression method is the quadratic function model obtained by regressing the per capita GDP and MNL, and the optimal model obtained by the time regression method is the linear model obtained by regressing the GDP and MNL. The \(R^{2}\) value of the optimal spatial regression model is between 0.722 and 0.874, while the \(R^{2}\) value of the time regression model is 0.972. The correlation coefficient of the time regression model is higher than that of the spatial regression model. Therefore, the linear model fitted by the time regression model is selected to invert the GDP data through the MNL value.
## 1 Introduction
use Bluetooth low energy (BLE) with constrained extended Kalman filter to realize a low-cost indoor positioning system [22], and [PERSON] et al. also use passive UHF-radio frequency identification (RFID) to aid the localizing accuracy of SLAM [23]. Moreover, several proposed methods focus on how to locate and find the patrol path in a known environment. [PERSON] et al. proposed the SIFT method to define the features of images by which to guide a robot while patrolling [24]. [PERSON] et al. used multiple transient radio sources to locate the mobile robot and plan a path, and using the Monte Carlo method to solve the problem of capturing and analyzing the radio signals [25]. [PERSON], [PERSON] et al. propose a sound source based landmark for SLAM [26]. A robot with a built-in audio source is used in the research to interact with microphone arrays which are at known coordinates in the environment to locate the robot itself. [PERSON], [PERSON] et al. propose an IMU-Assisted SLAM method to improve the localization accuracy of 2D LiDAR by using extended Kalman filter [27]. [PERSON] et al. propose a path planning method that a patrol robot can re-plan a patrol path from sufficient number of observation points while meets dynamic obstacles [28]. However, the expense of setting up and maintaining numerous signal emitting sources led us to select the image analysis method to locate a path in this study. Moreover, we referenced the work of [PERSON] et al. [29] and [PERSON] et al. [30] to realize the functions of patrolling check-ins and intruder detection. In the research of [PERSON] et al., the RFID indoor positioning system was visualized using geographic coordinates. They also used the architecture of an RFID indoor positioning system for the installation of checkpoints. In the research of [PERSON] et al., they used an infrared sensor array to enable mobile robots to track and follow humans. Finally, besides functions of SLAM for patrolling, the system outlined by [PERSON] et al. [31] is used in our research for the backing up of patrolling data and making internet queries. Though web service is not the latest technology, it remains useful for regional network communication. To reduce data transmission, [PERSON] et al. propose an embedded spatial coding method, which assigns weights to objects of interest [32]. In this study, images of intruders are encoded and then transmitted for backing up. The remainder of this paper is organized as follows. In Section 2, we introduce the system architecture. In Section 3, the algorithm used to direct all patrolling functions is explained. Experiment results and a brief discussion are presented in Section 4. Conclusions are made in Section 5.