esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Other Data Analysis Methods
#### 3.4.2 Feature Selection Methods  
Feature selection is the first and fundamental step in data analysis. This is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection methods aid in creating an accurate predictive model by choosing only features that are relevant. Irrelevant features in the dataset can decrease the performance of the models; redundant data can allow a greater opportunity to make decisions based on noise and increase algorithm complexity, while algorithms are trained more slowly.  
There are three general classes of feature selection algorithms: filter methods, wrapper methods, and embedded methods. Filter feature selection methods apply a statistical measure to assign a score to each feature. The features are ranked by the score and are either selected to be kept or removed from the dataset. The methods are often univariate and consider the feature independently, or with regard to the dependent variable. Examples of some filter methods include correlation coefficient scores and information gain. These methods are used to create the feature ranking [42].  
[PERSON]'s correlation coefficient is one of the methods of measuring the association between variables of interest, and it is based on the covariance method. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship [43].  
Entropy measures the amount of uncertainty in the dataset. The information gain is based on the decrease in entropy after splitting a dataset on an attribute [44]. It is used to generate a decision tree from a dataset. Constructing a decision tree comes down to finding an attribute that returns the highest information gain.  
The information gain \(IG\) is the change in information entropy \(H\) from a prior state to a state that takes some information as given:  
\[IG(d\mid a)=H(d)-H(d\mid a) \tag{8}\]  
where \(H(d\mid a)\) is the conditional entropy of decision \(d\) given attribute \(a\) and \(H(d)\) is the entropy of decision \(d\), which is equal to:  
\[H(d)=-\sum_{i=1}^{k}p(d_{i})\cdot\ln p(d_{i}) \tag{9}\]  
Information gain can be calculated for each remaining attribute. The attribute with the largest information gain is used to split the dataset on this iteration [45]:  
\[IG(d\mid a)=H(d)-\sum_{j=1}^{l}p(a_{j})\cdot H(d\mid a_{j}) \tag{10}\]
## 3 The Proposed Method
### Fundamental of Gabor Filter  
Gabor filter is calculated by a convolutional kernel function, which has been utilized in image processing applications, especially for texture classification. To represent multiscale and multi-orientation texture features, a filter bank is structured with a number of parameters in different orientations and scales. The essential variable \(g(x,y)\) of a 2-D Gabor filter is expressed by a Gaussian kernel and adjusted by a complex sinusoidal wave: [32]:  
\[g(x,y)=\left(\frac{1}{2\pi\sigma_{x}\sigma_{y}}\right)exp\left(-\frac{1}{2} \left(\frac{\bar{x}^{2}+\bar{y}^{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}}\right)+2 \pi jW\bar{x}\right) \tag{7}\]  
\[\bar{x}=xcos\theta+ysin\theta\quad\bar{y}=-xsin\theta+ycos\theta \tag{8}\]  
where \(\sigma_{x}\) and \(\sigma_{y}\) are the parameters that describe the spread of the current pixel in the neighborhood in which weighted summation occurs, \(W\) is the central frequency of the complex sinusoid, and \(\theta\in[0,\pi)\) is the orientation of the horizontal to vertical stripes in the equation above.  
A bank of Gabor filters includes multiple individuals and can be adjusted for different orientations and frequencies. Gabor filter considered here is GSSO8 (including 40 filters: 5 scales \(S\times 8\) orientations \(O\) with an initial max frequency equal to 0.2 and an initial orientation set to 0. The parameter settings of the orientations and frequencies for a bank are tuned by the following formulas [33]:  
\[Orientation(i)=\frac{(i-1)\pi}{O},where\ i=1,2,...,O \tag{9}\]  
\[Frequency(i)=\frac{f_{max}}{\sqrt{(}2)^{i-1}},where\ i=1,2,...,S \tag{10}\]  
where \(O\) and \(S\) are, respectively, the orientations and scales in total, and \(f_{max}\) is the maximum value of the frequency and is set to \(f_{max}=0.2\).
# Evaluating Collapse Fragility Curves for Existing Buildings Retrofitted Using Seismic Isolation
###### Abstract  
Few studies have investigated so far the collapse capacity of buildings with base-isolation. In such studies, preliminary considerations have been drawn based on a number of assumptions regarding: (i) the methodology used for assessing the collapse capacity, (ii) the collapse conditions and failure modes assumed for both superstructure and isolation system, and (iii) the numerical modeling assumptions. The main results pointed out that the collapse conditions of base-isolated buildings may occur for intensity levels slightly higher than those associated with the design earthquake. In this paper, further developments are made through the use of enhanced models for the description of the behavior of a rubber-based isolation system and the assumption of more rational collapse conditions. Collapse fragility functions, in terms of mean and dispersion values, are proposed for two archetypes representative of existing buildings retrofitted using the seismic isolation technique. The collapse margin ratio (median collapse capacity \(\mathrm{S_{a,C}}\), namely the spectral acceleration associated to a probability of exceedance equal to 50%, divided by the design spectral acceleration at the collapse prevention limit state) has been evaluated for each examined case-study. Values ranging from 1.10 to 1.45 were found.  
collapse fragility curves; seismic isolation; non-linear modeling; existing RC-frame buildings +
Footnote †: journal: Applied Sciences
## 2 Components of High-Speed Data Transmission for Lightweight and Small UAV Platforms
### Optical Receiving and Transmitting Systems  
The optical receiving and transmitting system for lightweight and small-sized UAV visual communication is the core component of UAV optical communication, which includes two main parts: receiving and sending optical signals. As shown in Figure 3, for the receiving part, its main task is to receive optical signals from optical communication devices and convert them into electrical signals. This process requires photodetectors with high sensitivity and low noise performance and demodulators with efficient anti-interference capability and high demodulation accuracy. Through the concerted work of these components, the received optical signals can be accurately reduced to the original electrical signals, providing stable and reliable data transmission for the UAV. For the transmitting section, its main task is to convert an electrical signal into an optical signal and send it through an optical antenna. This process requires lasers with high modulation accuracy and broad bandwidth characteristics, as well as signal modulators with efficient stability and low noise performance. Through the combination of these components, electrical signals can be converted into stable and reliable optical signals to realize long-distance wireless communication between UAVs and other devices. The optical antenna is mainly responsible for the transmission and reception of signal light and beacon light to improve the transmission and reception efficiency.  
Figure 3: Schematic diagram of transmitting and receiving antennas.  
Figure 2: Diagram of light and small UAV laser communication APT system.  
At this stage, the modulator carries out the conversion of random electrical signals into electro-optical signals by adjusting the phase and amplitude of the laser carrier. The core responsibility of the optical receiving system is to receive the signals sent from the transmitter accurately, which is usually carried out by using a spatial optical coupling device consisting of a lens or an array of lenses. However, the laser beam gradually diverges after atmospheric transmission, resulting in a more prominent spot at the receiving end. Therefore, the adequate reception of optical signals by an optical receiving system is particularly critical in the case of long-distance communications or poor channel environments.
# Complex Color Space Segmentation to Classify Objects in Urban Environments
## 3 Image Segmentation Approach  
Figure 1 shows an overview of the proposed method to segment images and identify objects. First, input color images are transformed to the CIELab space. Next, chromatic planes \(a\) and \(b\) on the CIELab space are used as real and imaginary elements to form complex image \(I\). The representative chromatic values of image \(I\) are calculated using the complex image to build a _chromatic map_. The number of thresholds per image depends on the colors of the image. The segmented areas represent those from images with similar chromatic values without a classification label. The next step consists of extracting several patches from the color image from each segmented area to build a database of images in six categories. A CNN uses the database to train, validate and test the identification of the object on the image. Note that color image patches are the input to the CNN model instead of the segmented areas. The implementation details of the method are shown in the following subsection.
# A Brief Overview of Energy Efficiency Resources in Emerging Wireless Communication Systems
## 2 Method  
Numerous innovative energy-efficient resource approaches in wireless communication networks have been presented in recent years, with an increasing emphasis on heterogeneous networks and resource allocation algorithms. The radio transmission technique has been optimized, low power circuit design has been developed, energy harvesting has been implemented, and green technology deployment has become a distinct research field. Others have also experimented with combining these approaches. A comprehensive framework for energy efficiency has been proposed in this review. This model incorporates all aspects of the wireless communication network's operation, from power generation to network infrastructure management to signal conditioning. The strategy proposes implementing renewable energy resources within the wireless network's power generating module, as well as enforcing sleep mode infrastructure management techniques and deploying energy and spectrally efficient signal conditioning algorithms. The following sections of this review go into great detail about this method.
## 5 Experimental Simulation and Result Analysis  
### Experimental Simulation  
The algorithm in this paper was implemented using Matlab programming. The data were obtained from the publicly available dataset \"2022 huawei cup China postgraduate mathematical modeling competition\", which includes two sets of data--normal data (N) and adjusted data--for testing the model and algorithm adaptability (R) after desensitization of the production data. Based on the statistical analysis, the number of car bodies in the entry sequences of both dataset N and dataset R provided in the competition is 318 vehicles each, including 106 fuel vehicles and 212 hybrid vehicles in dataset N, and 159 fuel vehicles and 159 hybrid vehicles in dataset R. In addition, there were 289 two-wheel drive vehicles and 29 four-wheel drive vehicles in both datasets. The power type of the vehicles was encoded as \"0\" for hybrid and \"1\" for fuel for ease of subsequent data processing, while the drive type was encoded as \"2\" for two-wheel drive and \"4\" for four-wheel drive. Further details are shown in Figure 6.  
Figure 5: The flowchart for solving with the state transition algorithm is based on the greedy strategy.  
Figure 6: The chart illustrating the quantity and ratio of different vehicle types in datasets N and R. (**a**) The quantity and proportion of different vehicle types in the N dataset. (**b**) The quantity and proportion of different vehicle types in the R dataset.
# Use of Total Least Squares Adjustment in Geodetic Applications
## 3 Practical Examples
### The S-Transformation
#### 3.3.1 General information about the S-Transformation
The functional model of the S-transformation is the Gauss-Markov model, which connects \"observations\" \(\mathbf{x}_{gdi}\), \"residuals\" \(\mathbf{x}_{gdj}\) and unknowns of transformation vector \(\mathbf{t}_{\mathrm{S}}\) is:  
\[\mathbf{x}_{gdj}+\mathbf{H}\mathbf{t}_{\mathrm{S}}=\mathbf{x}_{gdi}, \tag{53}\]  
where the LS solution for \(\mathbf{t}_{\mathrm{S}}\) can be:  
\[\mathbf{t}_{\mathrm{S}}=(\mathbf{H}^{\mathrm{T}}\mathbf{E}\mathbf{H})^{-1} \mathbf{H}^{\mathrm{T}}\mathbf{E}\mathbf{x}_{gdi}, \tag{54}\]  
The matrix of the S-transformation (52) and matrix \(\mathbf{H}\big{(}\mathbf{H}^{\mathrm{T}}\mathbf{E}\mathbf{H}\big{)}^{-1} \mathbf{H}^{\mathrm{T}}\mathbf{E}\) represent the orthogonal projectors, which project the observation vector \(\mathbf{x}_{gdi}\) onto two orthogonal components: \(\mathbf{t}_{\mathrm{S}}\) and \(\mathbf{x}_{gdj}\). To solve model (53), we look for a solution for \(\mathbf{t}_{\mathrm{S}}\) under the conditions of the least squares, this time for the minimum of the sum of the squares of the elements of the vector \(\mathbf{x}_{gdj}\) with the previously determined \"weight matrix\" \(\mathbf{E}\). Thus, the S-transformation model provides the solution that minimizes the Euclidean norm of the vector of the coordinate components of the points which determine the geodetic datum: \(\mathbf{x}^{\mathrm{T}}\mathbf{x}=\min\). Matrix \(\mathbf{H}\) is the datum matrix of internal constraints which includes all points of the geodetic network; or in other words, a matrix that ensures that all points of the geodetic network define a geodetic datum (example: free network). This means that matrix \(\mathbf{E}\), as a \"weight matrix\", is the one that determines which points should have a greater or lesser influence in defining the geodetic datum. So, we want to define such a weighting matrix \(\mathbf{E}\), which defines the optimal geodetic datum via the matrix \(\mathbf{S}\) or finally provides the optimal solution for the vector \(\mathbf{x}_{gdj}\).
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
## 5 Simulation of NC-OFDM Satellite Communication System
### Interference Analysis of NC-OFDM Signal to Authorized Signal  
The Spectrum Overlap Ratio (SOR) \(\Delta F\) is defined as the ratio of the spectrum overlapping range between an authorized signal and an NC-OFDM signal to the frequency bandwidth of the former. We suppose that the carrier frequency of the authorized signal is  
Figure 8: BER of data subcarriers of NC-OFDM.  
Figure 7: Spectrum of data subcarriers of NC-OFDM.  
\(f_{c1}\), the code rate is \(f_{d1}\), the carrier frequency of the adjacent NC-OFDM subcarrier is \(f_{c2}\), and the code rate is \(f_{d2}\), and they all use MPSK modulation. Therefore:  
\[\Delta F=\frac{f_{d1}+f_{d2}-|f_{c1}-f_{c2}|}{2f_{d1}} \tag{12}\]  
We assume that the NC-OFDM signal can make full use of the subchannel gain and the power spectrum densities of subcarriers approach to authorized signal, and both the modulation rate of the authorized signal and the NC-OFDM subcarrier rate are 4 MBD. When \(\Delta F\) equals 0 and 50%, the spectrum of the authorized signal and the adjacent NC-OFDM subcarrier are shown in Figure 9.  
Under the conditions of \(\Delta F\) as 0, 25%, 37.5%, and 50% respectively, the reception BER results of authorized signal with BPSK modulation and convolution coding are shown in Figure 10.  
Figure 9: Spectra of QPSK authorized signal and adjacent subcarrier of NC-OFDM: (**a**) Spectrum with \(\Delta F=0\); (**b**) Spectrum with \(\Delta F=50\)%.
# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 4 Discussion
### Comparison with Other Calibration Algorithms  
To verify the effectiveness of the proposed WOA-BPNN calibration algorithm, we compare the calibration positioning points generated by the LR, SVR, BPNN and GA-BPNN algorithms with those generated by the WOA-BPNN algorithm in real positioning points  
Figure 17: Comparison of different errors of different algorithms after calibration. (Testing mobile terminal 1).  
Figure 18: Comparison of different errors of different algorithms after calibration. (Testing mobile terminal 2).  
and obtain the positioning error of each positioning point, as shown in Figure 19. Moreover, in the tests, we set the one-sigma positioning error and two-sigma positioning error based on the ISO/IEC18305 international standard as the evaluation index and calculate the positioning error of the different mobile terminals using the different calibration algorithms, as shown in Table 1.  
It can be seen from the table that the proposed WOA-BPNN calibration algorithm can effectively reduce the indoor positioning error. In this experiment, compared with the one-sigma positioning error of different mobile terminals using the LR, SVR, BPNN and GA-BPNN calibration algorithms, the one-sigma positioning error of the mobile terminals using the proposed WOA-BPNN calibration algorithm is reduced by 41%, 42%, 44% and 36% on average. In addition, the proposed WOA-BPNN calibration algorithm reduces the two-sigma positioning error of the mobile terminals by 37%, 25%, 26% and 17% on average, compared with the four other algorithms. These results effectively show that the proposed WOA-BPNN calibration algorithm is more accurate and feasible than the other calibration algorithms. Moreover, it can be seen from the cumulative distribution function of the positioning error in Figure 20 that the proposed calibration algorithm is better than the four other algorithms. Therefore, the field test results demonstrate that the proposed algorithm can effectively reduce the positioning error caused by the heterogeneity of mobile terminals and improve indoor positioning accuracy, thereby further improving the availability of positioning algorithms between different mobile terminals.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mobile Terminal** & **Algorithms** & **LR** & **SVR** & **BPNN** & **GA-BPNN** & **WOA-BPNN** \\ \hline Test mobile & positioning error(one sigma) & 1.71 & 1.81 & 1.84 & 1.65 & 1.02 \\ terminal 1 & positioning error (two sigma) & 4.44 & 3.26 & 3.45 & 3.40 & 2.27 \\ \hline Test mobile & positioning error (one sigma) & 2.24 & 2.21 & 2.34 & 2.00 & 1.32 \\ terminal 2 & positioning error (two sigma) & 3.49 & 3.41 & 3.29 & 2.61 & 2.72 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of positioning error of five calibration algorithms (m).  
Figure 19: Comparison of positioning error of different algorithms.
## 2 Literature Review
### Comparison of IRS with Other Related Technologies  
An IRS has several distinct feature that distinguish it from other technologies. Some of them are listed below:  
* IRSs are passive metasurfaces with the ability to reflect incident signals without the use of a dedicated energy source.
* IRSs do not require analog-to-digital converters (ADC)s and digital-to-analog converters (DAC)s and power amplifiers to amplify or introduce noise when reflecting signals and thus provide an energy efficient solution.
* IRSs are easily deployed on walls, ceilings, etc., in an indoor environment due to their transverse size.
* Full band response makes it possible for an IRS to operate at any frequency, and they support full duplex transmission.  
Compared to other closely related existing technologies that are presently used in wireless networks such as mMIMO, AF and Decode and Forward (DF) relay, and backscatter communications [63; 76; 77; 78], an IRS offers a totally different solution and hence, a competitive advantage. Comparison of an IRS with closely related technologies is summarized in Table 3.  
IRS vs. mMIMO: The IRS is different from the active intelligent surface-based massive MIMO due to their different array architectures (passive versus active) and operating mechanisms (reflect versus transmit).
* IRS vs. AF Relay: AF relays play a role in source-destination transmission by amplifying and regenerating the signals whereas, an IRS reflects the incident signals as a passive array without the use of a transmitter, thus eliminating the need for transmit power consumption. An IRS is expected to function in full duplex mode while AF operates in half duplex mode as it suffers from severe interference in full duplex mode which makes it require effective interference cancellation techniques, thus making IRS more spectral efficient.
* IRS vs. DF Relay: Similar to AF relaying, DF relaying decodes and regenerates the transmitted signal from the source and transmits it to the destination. Due to the decoding operation, it has a much higher complexity and consumes high signal processing power [79]. In contrast, as mentioned previously, the IRS does not perform any decoding and only performs passive reflection. Thus, it has a lower cost and consumes negligible power.
* IRS vs. Backsient radio frequency identifier (RFID) tag to the receiver from the signal sent by the reader. The IRS improves the existing communication link performance instead of delivering its own information by simple reflection of the signal. As such, the path from reader to receiver in backscatter communication experiences undesired
interference and needs to be canceled/suppressed at the receiver. However, in IRS-aided communication, both the direct-path and the reflect-path signals carry the same useful information and can be constructively added at the receiver to maximize the total received power.
## 3 Results  
### GPP Trends  
The variations in GPP were shown to depend largely on latitude (Figure 1). Vegetation productivity in Arctic and pan-Arctic areas increased in spring and summer but decreased in autumn. The rate of variation at high latitudes was significantly higher in summer than in other seasons. GPP values also varied greatly among seasons and land cover types (Figures 2 and S1). Specifically, the values decreased in spring for all land cover types except forests. Summer GPP values were significantly different among cropland, shrubland, and tundra, varying at rates of \(-\)0.12, \(-\)0.11, and +0.08 gC/m\({}^{2}\)/decade, respectively. Positive autumn GPP trend slopes were observed for all land cover types except barren land, while winter GPP trend slope markedly varied in cropland (+0.03 gC/m\({}^{2}\)/decade) and forest (\(-\)0.08 gC/m\({}^{2}\)/decade) areas. The growth of shrubland vegetation showed a large magnitude of trend compared to that of grassland and barren land vegetation, which decreased in spring and summer and increased in autumn and winter. The GPP values of forests increased in every season except winter. In contrast, the values of vegetation in barren land declined in every season.  
Figure 1: M–K trend of GPP values for each 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) grid for the four seasons (**a–d**) and annual mean (**e**) from 1982 to 2015. The “+” symbols in each sub-figure indicate statistically significant grids at the 0.05 level; (**f**) indicates the annual mean of the GPP value and the corresponding long-term trend for six vegetation types, with the shadows showing the 95% confidence interval.