esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fusion Multi-Strategy Marine Predator Algorithm for Mobile Robot Path Planning
## 3 Fusion Multi-Strategy Marine Predator Algorithm
### Golden Sine Position Update Strategy  
The golden sine algorithm (Golden-SA) is a new intelligent algorithm proposed by [PERSON] et al. in 2017 [PERSON] et al. (2017), which is inspired by the periodic variation of the sine function in trigonometric functions. This algorithm has the advantages of fast optimization speed and strong robustness of the Golden-SA search according to the relationship between the sine function and the unit circle combined with the golden partition coefficient. It uses the sine function to scan the search space that is simulated by the unit circle, and introduces the golden ratio coefficient to narrow the search range while traversing the optimal solution region, which enhances the local optimization and accelerates the convergence accuracy [PERSON] et al. (2017). The core of Golden-SA is the location update method, and its location update mathematical model is shown in Equation (20):  
\[P_{novel}^{t+1}=P_{i}^{t}\cdot|sin(R_{1})|+R_{2}\cdot sin(R_{1})\cdot\left|x_{ 1}\cdot X_{i}^{t}-x_{2}\cdot P_{i}^{t}\right| \tag{20}\]  
\[\left\{\begin{array}{l}x_{1}=-\pi+2\pi\cdot(1-\tau)\\ x_{2}=-\pi+2\pi\tau\\ \tau=\left(\sqrt{5}-1\right)/2\end{array}\right. \tag{21}\]  
where \(P_{novel}^{t+1}\) is the position after agent \(P_{i}\) completes a search. \(P_{i}^{t}\) is the current position of the agent, \(R_{1}\) is a random number between \([0,2\pi]\), and \(R_{2}\) is a random number between \([0,\pi]\). \(X_{i}^{t}\) is the target position and is the global optimal position in Golden-SA. \(x_{1}\) and \(x_{2}\) are coefficients obtained by the golden section method, which can reduce the search space of the agents and improve the search efficiency of the agents. The coefficients allow the agents to move from the current position to the target position. \(\tau\) is the golden ratio coefficient.  
Although the Gold-SA strategy can improve the search accuracy of the algorithm, it cannot directly determine whether the resulting new individual position is superior to the original. Therefore, the greedy strategy is proposed to compare the old and new individual fitness values before deciding whether to update the individual positions. The search performance the of algorithm is improved by obtaining better solutions continuously [PERSON] et al. (2017). The mathematical model of the greedy strategy is shown in Equation (22)  
\[P_{new}^{t+1}=\left\{\begin{array}{l}P_{novel}^{t+1}\ \ \ f(P_{i}^{t+1})>f(P_{novel}^{t+1})\\ P_{i}^{t+1}\ \ \ f(P_{i}^{t+1})\leq f(P_{novel}^{t+1})\end{array}\right. \tag{22}\]  
At the global position update of the MPA, the Gold-SA strategy causes the top predator to navigate its neighborhood to avoid falling into a local optimum. At the same time, the top predator occupies the current fitness position, which guides other predators to the optimal solution, so that the optimal location information is transferred rapidly between the predators, and accelerates the convergence speed.
# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
## 5 Conclusions  
Although numerous machine learning models have been used and have achieved good results for predicting the monthly or daily ST, hourly ST predictions have been scarce.  
This paper presented a novel scheme for forecasting the hourly ST at five different soil depths in 30 sites, located under five common climates in the United States. Differing from other studies that consider meteorological parameters as the input of the model and the ST as the output, our proposed method includes two submodels for the daily average ST prediction and ST amplitude prediction. The hourly ST was obtained by superposing the prediction results of the two submodels. Through an input-output correlation analysis, we selected nine meteorological parameters and two temporal parameters as the inputs of the daily average ST prediction model, and seven meteorological parameters and one temporal parameter as the inputs of the ST amplitude prediction model. The experimental results show that the proposed model produced the best results for 30 stations (100%) for RMSE, 27 stations (90%) for MAE, and 30 stations (100%) for R\({}^{2}\), respectively. We compared the prediction results of each algorithm for the ST at different depths, and demonstrated that the integrated BiLSTM developed in this study is better than the benchmark algorithm in terms of ST prediction at the five soil depths involved in the experiment, and has greater potential than the benchmark algorithm in terms of a deeper ST prediction. By comparing the prediction results according to the climate type, it was found that the algorithm proposed in this study performs better than the benchmark algorithm under all climate types involved in the experiment, especially in warm or dry areas.  
Although we verified the effectiveness of the model with hourly ST predictions, our method is also applicable to the sequence learning of other data, such as the wind speed, humidity, or temperature.  
In future work, we will improve the prediction accuracy of the model for ST by enriching data sets, and adjusting the structure of neural network.  
Conceptualization, C.L. and Y.Z.; methodology, C.L.; software, C.L.; original draft preparation, C.L.; review and editing of manuscript, C.L.; visualization, C.L. and X.R.; and data curation, X.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Data Sharing Fundamental Program for the Construction of the National Science and Technology Infrastructure Platform [grant no. Y719H71006].  
The authors declare no conflict of interest.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 3 Existing Frequency Contour-Line Method with Limitations in Portraying Damage  
### Eigen-Frequency Panorama  
For a crack in a beam, the possible variation of crack depth at a crack location that varies throughout the beam length can evoke an overall set of crack scenarios. This overall set collectively characterizes all crack situations of the beam, which can be analytically depicted by the solutions of Equation (8) with crack location and severity as independent variables and eigen-frequency as dependent variable. In theory, for a given crack scenario of a continuous beam with specific \(x\) and \(y\), Equation (8) can produce a series of eigen-frequencies from the 1 st order to the infinite order due to the infinite degrees of freedom (DOFs) for a continuous beam. All eigen-frequencies arising from Equation (8) can constitute an eigen-frequency panorama that comprehensively reflects the effect of all possible crack scenarios on the dynamic property of the beam in the situation of unlimited computer resource. In reality, the finite instead of infinite DOFs are usually considered for a beam, leading to finite eigen-frequencies. To this end, the eigen-frequency panorama for a cracked beam involves the first \(k\) eigen-frequencies out of the overall set of crack scenarios. Figure 2 illustrates the eigen-frequency panorama involving the first three eigen-frequencies for a certain cantilever beam bearing a crack. The eigen-frequency panorama comprises three eigen-frequency surfaces labeled by \(\omega_{1}\),\(\omega_{2}\),\(\omega_{3}\), each consisting of eigen-frequencies of the same order as functions of crack locations and severities. This eigen-frequency panorama reflects the effect of all possible cracks in the beam on its first three eigen-frequencies.
# RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System
## 5 Results and Discussions
### Nonlinear Effect  
To evaluate the RCLED nonlinear effect, the DMT transmission with a total bit rate of 750 Mbps over a 50 m SI-POF link is conducted under the following two conditions: (a) \(P_{Eave}=6\) dBm and \(I_{DC}=20\) mA; (b) \(P_{Eave}=8\) dBm and \(I_{DC}=22\) mA. The Chow bit loading algorithm [20] is adopted with the objective BER at \(P_{e}=1\times 10^{-3}\), which is favorable for the reception with forward error correction (FEC). After POF transmission, the received symbols are demodulated in the demodulator. Figure 8 compares the measured subcarrier BERs with both the absence and the presence of the nonlinear effect. It can be observed that the excess noise, which appeared in Figure 8b, increases the noise standard by a factor of 6, approximately. In addition, the nonlinearity affects the signals significantly at low frequencies more than at high frequencies, mainly because the high frequency parts are more attenuated by the low-pass characteristics of the RCLED and therefore suffer less from the nonlinearity [21], which indicates that the low frequency signals are more sensitive to the nonlinear distortion.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**DMT Parameters** & **Values** \\ \hline Sampling frequency(GHz) & 1 \\ Fast fourier transform (FFT) length & 2048 \\ Subcarrier spacing (MHz) & 0.488 \\ Subcarrier number \(N\) & 256 \\ Cyclic prefix (CP) ratio (\(\mathrm{\SIUnitSymbolMicro s}\)) & 0.032 \\ DMT symbol duration (\(\mathrm{\SIUnitSymbolMicro s}\)) & 2.08 \\ Signal bandwidth (MHz) & 125 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The modulation parameters.  
Figure 7: DMT modem with bit-loading scheme.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 3 Experimental Results and Analysis
### Real Shooting Experiment  
Most of the measured images in the above experiments are from the LIVE database. In order to verify that the sharpness evaluation method proposed in this paper is also effective for real shooting images, we conducted a real shooting experiment by HUAWEI P40 Pro, taking a total of six images with different contents, as shown in Figure 23. The blurring degrees of these six images are increasing sequentially; the sharpness indexes after applying the proposed evaluation method to these images are shown in Figure 24. The experimental result shows that the evaluation method proposed in this paper has the irrelevance of image content and subjective-objective consistency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Proposed** & **Reference**[14] & **Tenegrad** \\ \hline Time (s) & **0.00697** & 0.0207 & 0.0247 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Running time comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RMSE** & **CC** & **MAE** & **ROCC** & **OR** \\ \hline Proposed & **5.78** & **0.9346** & **4.9383** & **0.9373** & **0** \\ Reference [14] & 7.393 & 0.8906 & 5.9560 & 0.8754 & 0.0276 \\ Tenegrad & 8.297 & 0.8599 & 6.7808 & 0.8301 & 0.0456 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation of the three methods.
# Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach
## 3 Materials and Methods
### Images  
#### 3.2.1 Daily PAR Images  
Daily PAR images were obtained from daily irradiation images over Spain. The latter were calculated using two different procedures, previously inter-compared to ensure their homogeneity. Only the first procedure uses a satellite product, the down-welling surface short-wave radiation  
Figure 1: Average annual precipitation (**a**); and average annual PAR (**b**) for the study area using the eight years analyzed in the study (2005–2012); and (**c**) annual reference GPP for 2011.  
flux (DSSF) from SEVIRI/MSG images [39]. Therefore, it is preferred from an operational point of view. In this procedure, the DSSF product was integrated along a day to obtain the daily irradiation. The DSSF data are, in the native geostationary projection, centered at 0\({}^{\circ}\) longitude and with a sampling distance of 3 km at the sub-satellite point. DSSF data were downscaled using the spatial disaggregation of the irradiance with a digital elevation model, DEM [40]. First, these data were re-projected and spatially smoothed by means of a linear interpolation to obtain 1-km lat/lon global irradiance images. This procedure removed the sharp borders or discontinuities between adjacent pixels. When no surrounding data were available (e.g., in the coastline), a fast technique to interpolate the images was developed. A further topographic correction was applied, which included the elevation correction and slope and aspect effects by means of a DEM. This procedure was used from 2007 to 2012. The resulting PAR images at 1-km spatial resolution present a mean absolute error (MAE) ranging from 0.5 MJ\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) to 0.9 MJ\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) in rugged terrain. Although the DSSF product is preferred from an operational point of view, a second procedure was employed when DSSF images were not available. This second procedure [41] applies artificial neural networks to temperature and precipitation maps generated by ordinary kriging from in situ data. Unlike temperature and precipitation, solar radiation data are recorded only at a limited number of weather stations in Spain and thus solar radiation maps cannot be obtained directly interpolating those data. However, they can be estimated using extraterrestrial irradiation and air temperature and precipitation, which serve to characterize atmospheric transmittance. The MAE for PAR was also rather low in this case, about 1.2 MJ\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\). A relationship between both series was found and used to fill the gaps in the first series.  
According to climate records (www.aemet.es), the study period (2005-2012) presents remarkable weather heterogeneity. It comprises hot and dry years as well as hot and humid ones. However, these general features depend on each particular region, and the drought events are non-uniform and affect different regions on different years.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results
### Comparative Analysis  
The existing methods, such as SA-SPGD [27], AMFSPGD [28], and OBAFS [29], are used to analyze the BDCOA used in the FSO. SA-SPGD [27] and OBAFS [29] are analyzed for Scenario 1 while the AMFSPGD [28] considers the wavelength of 632 nm i.e., Scenario 2. The analysis between the two different wavelengths emphasizes the sensitivity of various wavelengths to atmospheric turbulence i.e., a time-varying phenomenon. Scenario 1 is generally utilized in FSO communication due to its lower attenuation in the atmosphere and higher tolerance to turbulence. Scenario 2 is highly vulnerable to scattering effects, specifically adverse weather conditions. The analysis of two distinct scenarios is used to justify the effectiveness of BDCOA in practical settings, i.e., real-world wavelength options for FSO systems.  
Tables 3 and 4 display the comparative analysis of BDCOA for Scenarios 1 and 2, respectively. This comparison confirms that BDCOA has superior performance when compared to SA-SPGD [21], AMFSPGD [28], and OBAFS [29]. The incorporation of BM and DMS in BDCOA made the FSO communication robust against the turbulence effects. Specifically, BM and DMS are used to avoid the risk of suboptimal solutions and improve the search space efficiency while searching for an optimum control signal for SLAO. From the outcomes, it is confirmed that the BDCOA has enhanced convergence when compared to the existing algorithms, specifically in reducing the RMS and increasing SR. This demonstrates that the BDCOA requires fewer iterations to achieve enhanced performance. The selection of dynamic control signals using BDCOA is used to compensate for the wavefront distortions in both the time-varying phenomenon used to improve the quality of the received
## 4 Experimental Results and Discussion
### Results and Analyses
#### 4.3.1 Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**BERT+BiLSTM+CRF**} & \multicolumn{3}{c}{**BERT+FLAT+CRF**} & \multicolumn{3}{c}{**PSLF-CTR**} \\
**TYPE** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline AIST & 92.88 & 95.83 & 94.33 & 93.81 & 96.54 & **95.15** & 93.84 (0.96) & 95.81 (\(-\)0.02) & 94.82 (0.49) \\ RIST & 85.13 & 85.67 & 85.40 & 85.53 & 86.90 & 86.21 & 89.14 (4.01) & 89.14 (3.47) & **89.14 (3.74)** \\ FIST & 81.86 & 77.67 & 79.71 & 80.65 & 79.91 & 80.28 & 81.17 (\(-\)0.69) & 82.65 (4.98) & **81.90 (2.19)** \\ \hline AITL & 86.40 & 85.71 & 86.06 & 85.83 & 86.51 & 86.17 & 88.00 (1.60) & 87.30 (1.59) & **87.65 (1.59)** \\ RITL & 75.89 & 79.55 & 77.68 & 76.83 & 80.11 & 78.43 & 78.75 (2.86) & 81.97 (2.42) & **80.33 (2.62)** \\ FITL & 68.60 & 81.38 & 74.45 & 69.19 & 81.51 & 74.84 & 76.92 (8.32) & 82.19 (0.81) & **79.47 (5.02)** \\ IITLC & 78.57 & 91.67 & 84.62 & 77.27 & 91.89 & 83.95 & 79.76 (1.19) & 90.54 (\(-\)1.13) & **84.81 (0.19)** \\ \hline PISTC & 60.33 & 68.46 & 64.14 & 62.13 & 48.98 & 48.98 & 64.41 (4.08) & 70.95 (2.49) & **67.52 (3.38)** \\ IISTC & 47.37 & 69.23 & 56.25 & 52.94 & 69.23 & 60.00 & 64.29 (16.92) & 69.23 (0.00) & **66.67 (10.42)** \\ PITLC & 44.44 & 52.17 & 48.00 & 51.85 & 60.87 & 56.00 & 53.85 (9.41) & 60.87 (8.70) & **57.14 (9.14)** \\ IITLC & 71.43 & 71.43 & 71.43 & 77.78 & 77.78 & **77.78** & 77.78 (6.35) & 77.78 (6.35) & **77.78 (6.35)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The result of private dataset time recognition. are shown in Table 4. For the AIST and RIST, the identification results of all three models were better, reaching more than 90%. Analyzing the data shows that the reason is a large number of samples and more obvious features. For other types of time, although the BERT-FLAT-CRF model was better than the baseline model for most of the time types, it even had the highest _F1_ value for RITL and IITL. However, it was less effective than the PSLF-CTR model in recognizing complex temporal entities with longer text lengths and larger feature spans (e.g., IITLC and IISTC). Among them, the PITLC had a special recognition effect, marked as 50% in all three models. The analysis shows that the reason is that there are fewer temporal entities of this type, so the experimental results obtained are subject to chance. From Tables 3 and 4, PSLF-CTR significantly improves recognition of complex temporal entities compared to the baseline model BERT+BiLSTM+CRF and performs worse in recognition of some types of entities compared to the BERT-FLAT-CRF model. However, compared to the overall, PSLF-CTR performs better in recognizing all types of temporal entities, especially complex ones.
# _Sci_
###### Abstract  
The use of Light Amplification by Stimulated Emission of Radiation (i.e., LASERs or lasers) by the U.S. Department of Defense is not new and includes laser weapons guidance, laser-aided measurements, and even lasers as weapons (e.g., Airborne Laser). Lasers in the support of telecommunications is also not new. The use of laser light in fiber optics has shattered thoughts on communications bandwidth and throughput. Even the use of lasers in space is no longer new. Lasers are being used for satellite-to-satellite crosslinking. Laser communication can transmit orders-of-magnitude more data using orders-of-magnitude less power and can do so with minimal risk of exposure to the sending and receiving terminals. What is new is using lasers as the uplink and downlink between the terrestrial segment and the space segment of satellite systems. More so, the use of lasers to transmit and receive data between moving terrestrial segments (e.g., ships at sea, airplanes in flight) and geosynchronous satellites is burgeoning. This manuscript examines the technological maturation of employing lasers as the signal carrier for satellite communications linking terrestrial and space systems. _The purpose of the manuscript is to develop key performance parameters (KPPs) to inform the U.S. Department of Defense initial capabilities documents (ICDs) for near-future satellite acquisition and development._ By appreciating the history and technological challenges of employing lasers, rather than traditional radio frequency sources for satellite uplink and downlink signal carriers, this manuscript recommends ways for the U.S. Department of Defense to employ lasers to transmit and receive high bandwidth, and large-throughput data from moving platforms that need to retain low probabilities of detection, intercept, and exploit (e.g., carrier battle group transiting to a hostile area of operations, unmanned aerial vehicle collecting over adversary areas). The manuscript also intends to identify commercial sector early-adopter fields and those fields likely to adapt to laser employment for transmission and receipt.  
telescopes; lightweight telescope mirrors; adaptive optics; better resolution; increased accuracy; more bandwidth; cluster of satellites; innovative platform; more capabilities into smaller packages; far-shorter time from click to customer +
Footnote †: journal: Computer Science and Technology  
1
Footnote 1: [[https://www.mdpi.com/journal/sci](https://www.mdpi.com/journal/sci)]([https://www.mdpi.com/journal/sci](https://www.mdpi.com/journal/sci))
## 3 Results and Discussion
### Health Risk Assessment  
The incremental lifetime cancer risk (_ILCR_) associated with inhalation exposure was estimated based on the concentrations of PM\({}_{10}\)-bound PAHs and PM\({}_{10}\)-bound metals in the atmospheric air. The analysis covered eight Polish agglomerations, with risk estimates calculated separately for adult men and women over a five-year period (2018-2022). The results are presented in Table 2.  
The calculations considered a number of parameters, including the average life expectancy of males and females (Table S2). On average, women live longer than men, with the difference in Poland amounting to around seven years. Between 2018 and 2022, average life expectancy ranged from 70.4 to 75.9 years for men and from 78.7 to 83 years for women. During this period, particularly in 2020 and 2021, life expectancy decreased for both sexes (Table S2), primarily due to the impact of the COVID-19 pandemic. In 2021 alone, the pandemic accounted for an additional 48,805 male and 42,389 female deaths in Poland, with nearly 18% of all deaths that year being attributed to the virus [109]. Cancer remains the second leading cause of death in Poland, after cardiovascular diseases, accounting for around a quarter of all fatalities [60,110]. Historically, cancer incidence has been higher in men; however, since 2021, the number of newly diagnosed cases of cancer in women has surpassed that in men [111]. Lung cancer remains the leading cause of cancer-related death, accounting for 22% of fatalities. In 2021, it caused 20,841 deaths nationwide [110,111]. Air pollution, both outdoor and indoor, is estimated to contribute to around 2% of all cancer-related deaths in Europe. For lung cancer specifically, the proportion is considerably higher, with conservative estimates at 9%, and more comprehensive assessments suggesting up to 17% [112,113]. In our study, people living in Upper Silesia, Cracow, and Wroclaw were the groups most affected by air pollutants that can cause cancer or might cause cancer (such as PM\({}_{10}\)-bound PAHs and PM\({}_{10}\)-bound HM) through breathing, among Polish urban agglomerations. As shown in Table 2, the highest _ILCR_ values for adult men in 2018 were recorded