esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Multiple Linear Regression  
MLR is the most commonly used linear regression model. It is a multivariate statistical technique used to elucidate the relationship between multiple independent variables (\(X_{1}\), \(X_{2}\),..., \(X_{k}\)) and a dependent variable (\(Y\)) with an explanation and prediction as objectives:  
* The explanation objective examines the regression coefficients and their magnitude, sign, and statistical inference for each predictor variable;
* The forecast objective examines the extent to which the explanatory variables can estimate the explicative variable [55].  
MLR prediction models are shown as follows:  
\[Y_{t}=X_{t}\beta+\varepsilon_{t} \tag{1}\]  
where \(Y_{t}\) is the estimated value at time \(t\), \(\beta\) = (\(\beta_{0}\), \(\beta_{1}\),..., \(\beta_{k}\)) is an indication of the relationship between the independent and dependent variables, and \(X_{t}\) = (1, \(x_{1t}\), \(x_{2t}\),..., \(x_{jt}\)) is a vector of \(j\)-dependent variables at time \(t\). \(\varepsilon_{t}\) is a random error term at time \(t\), \(t\) = 1,..., \(N\). The error terms should be independent and show a Gaussian distribution behavior. The presumptions of the MLR model could be examined by the Kolmogorov-Smirnov test and the Q* Ljung-Box statistic, as in the timeseries.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 5 Discussion
### Bit Error Rate (BER) for Integrated Signals  
It is necessary to obtain the data transmission signal from the integrated signal via filtering. In the separation process, the filter will reduce the CNR of the signal and eventually increase the BER.  
The BER of the integrated signal and pure signal are shown in Figure 8. If the integrated signal needs to achieve the same BER, the CNR of the integrated signal will be 4-5 dB higher than that of a single signal.  
For BOC signal, the BER will be less than 1e-4 when success is acquired. Therefore, Figures 5 and 8 show the BER of TT&C signal is higher than the data transmission signal at the same CNR. Thus, the data transmission signal power can be kept 10-30 dB higher than the TT&C signal. If the power is higher than this level, the subpeak of the data transmission signal will interfere with the measurement of the BOC signal. If it is lower than 10 dB, the power of the BOC signal should keep a high level, which is not necessary for TT&C.
## 4 Experimental Results and Analysis  
In this section, the performance of the proposed LM-RRDH scheme against some of the well-known spatial-domain-based RRDH schemes, such as [PERSON] et al. [28], SBDE [29], and TLE [30], is comparatively analyzed to avoid such problems. For experimental pur  
Figure 4: The proposed LM-RRDH extraction and recovery example.  
poses, six standard grey-scale test images, each of size 512 \(\times\) 512 pixels including Lena, Baboon, Airplane, Peppers, Elaine and Boat, were used, as shown in Figure 5. Further, the secret information stream was generated using the random function of MATLAB, as implementation was carried out using the same platform. The comparison of the performance was performed based on embedding capacity and image quality using peak signal-to-noise ratio (PSNR). The experimental results for robustness are not included for comparative performance analysis, as the LM-RRDH embeds the secret information into the MSB planes such as SBDE [29] and TLE [30], so it provides the same degree of robustness.  
Two-dimensional (2D) line graphs have been used, as shown in Figure 6, to represent the embedding capacity (EC) versus PSNR experimental results. The 2D graphs present the comprehensive and thorough comparison of experimental results of the proposed LM-RRDH scheme against [PERSON] et al. [28], SBDE [29], and TLE [30]. It can be easily observed from Figure 6 that the proposed scheme has superior performance than all the other aforementioned spatial domain-based RRDH schemes. The main reasons behind the superior performance are (1) the more correlated grouping of the pixels based on their local moments, (2) adaptive selection of embedding strategy based on the cardinality of the group, and (3) usage of enhanced pairwise prediction-error-expansion-based embedding strategies. The more correlated groups of pixels help in generating sharper prediction error histograms, which lead to lesser shifting of pixels while increasing the expansion. Further, embedding the secret information using the best embedding strategy based on the type of group further helps in managing the trade-off between the EC and PSNR.  
Furthermore, the enhanced-pairwise-embedding-strategies-based data hiding techniques assist in optimally exploiting the correlation in the form of prediction errors as well. This further helps in achieving superior performance. Therefore, it can be clearly stated that the LM-RRDH scheme provides improved embedding capacity and PSNR trade-off than the existing spatial-domain-based RRDH schemes, such as those of [PERSON] et al. [28], SBDE [29], and TLE [30], while maintaining the same degree of robustness.  
Figure 5: Cover images, each of size 512\(\times\) 512 pixels.
# External Field Response and Applications of Metal Coated Hemispherical Janus Particles
###### Abstract  
Hemispherical Janus particles that were coated with silver or nickel on the equatorial plane of hemispherical polymer microparticles were prepared and dispersed in water and the responses to AC electric and stationary magnetic fields applied were investigated. Both of the particles are so oriented that the equatorial plane is parallel to the AC electric field, owing to electric-field induced dipole orientation, which is the response proportional to the quadratic electric field. The nickel coated particles are self-assembled to make a chain-like structure aligned in the direction of the stationary magnetic field. In addition, when both AC electric and stationary magnetic fields are applied, the orientation of a nickel-coated hemispherical particle is uniquely determined in such a way that the equatorial plane is parallel to both electric and magnetic fields. Because the particle is magnetized on the plane, its direction is reversed when the magnetic field is reversed, which is the response that is proportional to the magnetic field. Utilizing these features, mirrors are fabricated that can switch the transmittance and reflectance with electric and magnetic fields. Such features of the Janus particles as to be controlled by an electric and magnetic fields will find wide applications in the fields of microoptics and microfluidics.  
Janus particle; microparticle; hetero-shaped particle; heteromorphic particle; silver; nickel; micromirror; electric field; magnetic field; self-organization Article
## 2 Beidou-Binocular Cooperative Navigation Model
### Intelligent Collaborative Fusion and Path Planning Module  
The Beidou and binocular vision modules in the BBCN model collaborate and fuse with each other. First, when Beidou signals are lost, the binocular vision system generates a local map using visual SLAM and performs short-term position estimation using odometry. Second, the binocular vision system provides real-time obstacle information to Beidou, enabling dynamic path adjustment when combined with global path planning. Finally, through the Unscented Kalman Filter (UKF), data from Beidou and binocular vision are fused to achieve global state estimation for the robot.  
The intelligent collaborative fusion and path planning module is a core component of the Beidou-Binocular Cooperative Navigation (BBCN) model. The main task of this module is to fuse the global position information from the Beidou navigation positioning module with the local environmental information from the binocular vision recognition module, and to implement data fusion and dynamic path planning through intelligent algorithms. In complex environments such as substations, this module can effectively coordinate multi-source information, overcome the limitations of a single data source, adjust the robot's path in real-time, avoid obstacles, and ensure the efficiency and safety of navigation. This module consists of three core parts: multi-sensor data fusion, global-local path planning coordination, and dynamic obstacle avoidance and optimization control. This section will detail the theoretical foundations and algorithm implementations.
## 3 Methodology
### Acoustic Features
It is calculated with Equation (5). \[u_{spread}(t)=\left(\sum_{k=1}^{K}(f(k)-u_{cnt}(t))^{2}\;p_{k}(t)\right)^{1/2} \tag{5}\]  
Spectral slope is how the energy of the spectral amplitude of a signal changes in different frequencies. Linear regression is applied to the spectral amplitude of a signal to get the spectral slope value, as in Equation (6). \(a_{k}(t)\) is the amplitude value of the magnitude STFT at frequency \(k\) and time \(t\). \[u_{slop}(t)=\frac{K\sum_{k=1}^{K}f(k)\;a_{k}(t)-\sum_{k=1}^{K}f(k)\cdot\sum_{k =1}^{K}a_{k}(t)}{K\sum_{k=1}^{K}f(k)^{2}-\left(\sum_{k=1}^{K}f(k)\right)^{2}} \tag{6}\]  
Figure 3: Complete MFCC feature extraction process. Spectral flatness_ is a measure of how noisy the signal is. We can measure it by dividing the geometrical mean by the arithmetical mean of the spectrum. If spectral flatness value is close to 1, the signal is considered noisy. It is computed with Equation (7). \[u_{flat}(t)=\frac{\left(\prod_{k=1}^{K}a_{k}(t)\right)^{1/K}}{\frac{1}{K}\sum_{ k=1}^{K}a_{k}(t)} \tag{7}\]  
We grouped the acoustic features derived from sinusoidal harmonic partials of the speech signal as harmonic. They are calculated using a sinusoidal harmonic model. We indicate this with \(a_{h}(t)\) and \(f_{h}(t)\), which are the amplitude and frequency of partial \(h\) at time \(t\). \(H\) is the total number of partials. Harmonic energy is the energy of the harmonic partials. It is calculated like computing the normal signal energy. However, this time, it is calculated from harmonic partials at a time \(t\) (8). \[E_{H}(t)=\sum_{h=1}^{H}\left(a_{h}(t)\right)^{2} \tag{8}\]  
Noise energy is the difference between the total energy and harmonic energy (9). \[E_{N}(t)=E_{T}(t)-E_{H}(t) \tag{9}\]  
Noiseness is the measure of the degree to which the signal is harmonic or non-harmonic. When the value of noiseness is high, a signal is mainly non-harmonic. It is calculated using Equation (10). Figure 3.0  
Tristimulus is a timbral equivalent to color attributes in vision. It consists of three different types of energy ratios. It helps to describe the first harmonics of the spectrum. It is calculated via Equation (11). Figure 3.1  
Inharmonicity measures the departure of the frequencies of the partials \(f_{h}\) from purely harmonic frequencies \(hf_{0}\). It is calculated as the weighted sum of deviation of each partial from harmonicity. It is computed with Equation (12). Figure 3.2
## 4 Tuning Parameters and Results Assessment
### Parameter Optimization and Accuracy Assessment
#### 4.2.4 The RF Algorithm  
Like the DT algorithm, the RF algorithm is capable of operating under classification and regression functions. RF is made of a bunch of decision trees using a random subset of data [34]. The number of trees is the most important parameter to optimize with RF algorithms [38]. Based on the data shown  
Figure 17: Mean score vs. max-depth.  
Figure 18: Mean and standard deviation of positioning errors vs. max-depth by: (**a**) whole floor, (**b**) center area, and (**c**) edge area.  
in Figures 19 and 20, the selected trees for both cases (i.e., classification and regression) are shown in Table 2.
## 5 Simulation Results and Discussion  
The effect of the rotation angles on the BER performance of the MA-SM system is evaluated for several system configurations. We adapted in this paper the same transmitter design in [20] in terms of the number of transmit antennas, the number of active transmit antennas at each channel use, the modulation schemes used for bit-to-symbol mapping, and the channel and noise models. The elements of the channel matrix and the noise vector are follow a cyclically symmetric Gaussian distribution with zero mean. The channel state information (CSI) is assumed to be perfectly known for the study of this paper. This is a general assumption in the literature when the channel estimation is not addressed in the provided work. This paper and [20] developed a baseband implementation of the communication system. We also assume that all signal symbols are drawn from the same modulation set.  
\begin{table}
\begin{tabular}{c c}  
Figure 4 depicts the performance of MA-SM with \(N_{T}=4\), \(N_{P}=2\) and several values of \(n_{R}\). QPSK and 16-QAM are used to generate the results depicted in the left and right sub-figure, respectively. For both cases, we remarked that the performances of MA-SM using the optimal angles, the angles proposed in [20], and with the angles set to zero (without rotation) are identical for \(n_{R}=3\) and \(4\). In the case of \(n_{R}=6\), the BER performance of MA-SM using the optimal angles slightly outperforms the performance of the other two angle sets, especially in the case of QPSK. However, this improvement is marginal and barely noticeable.  
Figure 5 depicts the performance of MA-SM with \(N_{T}=5\), \(N_{P}=3\) and several values of \(n_{R}\). The performance of MA-SM using the three angle sets coincides for \(n_{R}=3\). In the case of \(n_{R}=4\), a slight improvement in the BER performance is remarked in the case of QPSK. At \(n_{R}=6\), the performance of MA-SM with QPSK is improved due to the optimal rotation angle, and a slight improvement is also observed when 16-QAM is used. Again, the improvements in the BER using the 3-dimensional constellation are marginal and the MA-SM system can be used without applying any rotation to of the combinations while keeping the BER performance identical to that when the optimal rotation angles are applied. This paper developed a baseband implementation of the communication system. As a future work, we would like to implement the system using software-defined radio. This will permit the evaluation of the system under more realistic environment and taking into consideration system impairments.  
Figure 4: BER of MA-SM using the optimal rotation angles, the angles derived in [20], and the all-zero angle set, for \(N_{T}=4\), \(N_{P}=2\), \(n_{R}=\)3, 4, and 6, using QPSK (**left sub-figure**) and 16-QAM (**right-sub-figure**).
## 3 Results
### Spatial and Temporal Variations of CI in the Sanjiang Plain from 2000 to 2015
#### 3.2.3 Spatial Variations  
A slight overall variation of the clumping effects in the Sanjiang Plain during the study period was found, and the total area proportion of the areas with slight variations (light improvement and light degradation) was 88.43%. Most of the significantly improved areas were distributed in Fuyuan County, Tongjiang City, Suibin County, and eastern Luobei County. Areas with significant degradation were mainly distributed in northwestern Luobei County, northwestern Hegang City, Qiaihe City, west Mishan City, Jidong County, Jixi City, and Muling City (Figure 8).  
Figure 6: Interannual variations of growing season mean CI (CI\({}_{\text{GS}}\)): (**a**) point plot of CI\({}_{\text{GS}}\) and (**b**) area proportions of the variation trend of CI\({}_{\text{GS}}\).  
Figure 7: Seasonal variation of CI.  
The multiyear mean CI of each season was calculated by compositing a mean CI map for each season over fifteen years based on the mean composition method. The spatial distribution of the multiyear mean CI of each season was related to the spatial distribution of land use type (Figure 9). The main land use types in the Sanjiang Plain are cultivated land and forestland. In 2015, for example, 50.61% and 32.10% of the study area were occupied by cultivated land and forestland, respectively. Most of the cultivated land areas were distributed in the northern study area, and areas with forestland were mainly located in the southern, central, eastern, and northwestern regions of the study area. The multiyear mean CI of each season for cultivated land was greater than that for forestland; in other words, the clumping effects of the woody plants were more significant than those of herbaceous plants. Moreover, the multiyear mean CI values of cultivated land and forestland reached their maximum values in winter and decreased gradually in spring. The clumping effects of cultivated land and forestland in summer were the most intensive, and foliage later became sparse in autumn.  
The CI variation trend with elevation in each season was explored based on the pixel-level statistics of the seasonal multiyear mean CI and elevation. As shown in Figure 10, the multiyear mean CI of each season exhibited a slight downward trend with elevation. The variation range of the multiyear mean CI in winter was the largest, while that of the multiyear mean CI in summer was the smallest. As the elevation increased by 100 m, the multiyear mean CI in winter and summer decreased by 0.06 and 0.01, respectively.  
Figure 8: Spatial variation of CI\({}_{\text{GS}}\).  
Figure 9: Spatial distribution of the multiyear mean CI for each season.
## 1 Introduction
For example, the WiMAP [22] and DWiMAP [23] mainly focus on reducing the selection of unstable reference points, but they ignore narrowing sampling regions, which increases the sampling workload and leads to the redundancy of reference point selection. The localization methods based on PSO [24] and GWO [25] are slow in convergence speed and easy to fall into local optimization, resulting in low localization precision. To address the above problems, this paper proposes a novel localization method of wireless covert communication entity for post-steganalysis, which is based on hybrid particle swarm optimization and gray wolf optimization to improve localization precision (ILP-PSOGWO). In this method, the relationship model between RSS and distance is firstly constructed for the indoor environment where the target node is located. Secondly, dichotomy is used to narrow the region where the target node is located. Then, the weighted distance strategy is used to select the reference point locations with strong and stable RSS. Finally, the intersection region of reference points is taken as the target node region, and then the hybrid PSOGWO is used to optimize the target node location. The main contributions of this paper are as follows:  
* We propose to use the Gaussian filter model and the weighted distance strategy to select the reference point location. These two strategies can effectively reduce the influence of weak RSS and unstable reference points on the localization results and improve the localization precision. * Through multiple measurements, we find that the measured RSS at the same location will fluctuate up and down within a certain range. Compared with the circle used in the traditional method, this paper uses the ring as the region where the target node exists. Therefore, it will effectively narrow the region where the target node may appear, and then improve localization precision and the stability of localization results. * The hybrid PSOGWO algorithm is first used in the field of wireless sensor node localization. The algorithm not only improves the localization precision of target node but also promotes localization efficiency. The rest of this paper is organized as follows. In Section 2, the related work and the basic principle of the hybrid PSOGWO algorithm are discussed. Section 3 describesthe basic principle of ILP-PSOGWO and gives the basic idea, main steps and complexity analysis of the algorithm. Section 4 verifies the performance of ILP-PSOGWO by comparing it with existing methods. Section 5 summarizes the paper and discusses the direction of further research.
## 5 Simulation Experiments and Analysis
### Simulation Setup  
IoT services include sensor data, RFID data, image and video, etc. And the simulation parameters settings are listed in Table 2.  
The simulations compare the performance of our proposed policy with that of the LCE [24; 25], the OPP [27], the LCD [24; 25], and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) [28]. The request pattern follows a Zipf distribution, and the request process for content in the network exhibits a poison distribution. The contents lifetime is uniformly spread between one second and one minute. It is assumed that each content cache occupies one cache unit and no content is cached at each satellite node in the initial state. And the default broadcast routing policy is used.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 3 Methods
### Brightness Temperature Difference (BTD)  
BTD (11-12 \(\upmu\)m) was first developed primarily for the detection of volcanic aerosols and used for dust detection due to the similarity between mineral dust aerosols and volcanic aerosols [53]. The basic principle is that in the TIR bands, the difference in the imaginary part of dust aerosol refraction affects the atmospheric radiation of dust weather, resulting in BT12 \(\upmu\)m greater than 11 \(\upmu\)m [54]. Negative BTD (11-12 \(\upmu\)m) values typically indicate the presence of dust [27].  
The central wavelengths of the NOMChannel12 and NOMChannel13 from FY-4A are 10.8 \(\upmu\)m and 12 \(\upmu\)m, respectively. In this study, the BTD was calculated as follows:  
\[\text{BTD}=\text{BT}_{\text{NOMChannel12}}-\text{BT}_{\text{NOMChannel13}} \tag{1}\]  
Studies have revealed that the BTD thresholds for dust vary depending on the sensors used and the location. [PERSON] and [PERSON] found BTD thresholds of \(-1.0\), \(-0.4\), \(0.5\), and \(-0.2\) for the Gobi/Taklamakan, Australian, Nubian, and Thar Deserts, respectively [26], indicating that there is not a universally applicable threshold. In this study, the dust identification threshold of BTD was set to less than \(-0.4\). In order to determine an appropriate threshold to distinguish dust pixels from other pixels in this area, the true color image was classified into dust, cloud, and bare ground pixels with visual interpretation. Figure 2a shows the frequency distribution of the BTD (11-12 \(\upmu\)m) values. A total of 51.1% of the BTD values were located around the peak from \(-1.8\) to \(-3.3\). There was only a very small portion of dust pixels with BTD exceeding \(-0.4\). Thus, a BTD value less than \(-0.4\) was identified as dust.