esa-sceva/satcom-chunk-collection huggingface dataset:
# Methods of Atmospheric Coherence Length Measurement
## 3 Coherence Length \(r_{0}\) Direct Measurement Method  
Studies have demonstrated that the measurement of \(C_{n}^{2}\) will deteriorate due to the path and accuracy of the measurement. The temperature structure measurement method can only be used on the pylons at low altitude; hence, measurements at relatively high altitude cannot be acquired. The scintillometer has a limited measurement range due to the scintillation saturation effect. As for the radar measurement method, it is appropriate for fixed-point targets only, and it involves cumbersome installation and huge cost. To circumvent those drawbacks, the direct measurement of the atmospheric coherence length \(r_{0}\) has been proposed, which includes the differential image motion monitor (DIMM), wavefront structure function method, slope difference method and wavefront phase variance method, wherein the Shack-Hartman wavefront sensor (SHWS) is applied. Note that the DIMM may use the CCD instead of the SHWS [39; 40]. The SHWS is generally considered as one of the crucial components in the adaptive optics system, as it enables accurate and rapid measurement of the wavefront shape and phase information. It includes an array of chromium-mask microlenses that image the beam onto a CCD, creating a point-array distribution of the wavefront information. The wavefront of the optical signal is usually fitted with the Zernike polynomial. The Zernike polynomial coefficients are calculated based on the positions of the dot array on the CCD, which is eventually employed for wavefront reconstruction. As such, the reconstructed wavefront provides the basis for the measurement of atmospheric coherence length.
# High Optical Feedback Tolerance of a Detuned DBR Laser for 10-Gbps Isolator-Free Operation
## 3 Results  
The OFT of the DBR laser was investigated by examining its RIN and BER performance under certain feedback levels. RIN is a well-known monitor of a free-running laser under feedback. The feedback level resulting in a sharp increase in RIN is denoted as the critical feedback value [11], a widely used quantitative indicator of OFT, beyond which the laser will be destabilized. The influence of the feedback on the directly modulated lasers can be also characterized by evaluating the signal-to-noise ratio (SNR), BER, and power penalty. Note that excess RIN due to optical feedback is the main reason for the increased power penalty during optical fiber transmission.  
Figure 1: (**a**) Schematic diagram of the three-section DBR laser; (**b**) the light current characteristics at room temperature; (**c**) optical spectrum measured from 20 mA to 65 mA.  
Figure 2: Illustration of the experimental setup. VOA: Variable Optical Attenuator; SMF: Single Mode Fiber; OSA: Optical Spectrum Analyzer.
## 3 Results
### Differences in ISQ Values According to Implant Length  
At implant placement, ISQ values progressively increased with implant length, even if no statistically significant differences were observed within the groups (9, 11 and 13 mm). After 3 months, ISQ values were statistically significantly higher than baseline (ISQ1), but exclusively for 9- and 11-mm length implants. Implants with 13 mm length showed no differences between ISQ1 and ISQ2 values (Table 3).
# Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones
###### Abstract  
Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (\(k_{l}\)). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Koppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type.  
photosynthetically active radiation; \(k_{l}\) sky classification; ANN; multilinear regression models +
Footnote †: journal: _sciences_
## 2 Methods  
### Study Area  
The water-source region of the Xiong'an New Area is located between 38\({}^{\circ}\)3\({}^{\prime}\)-40\({}^{\circ}\)4\({}^{\prime}\) N and 113\({}^{\circ}\)39\({}^{\prime}\)-116\({}^{\circ}\)12\({}^{\prime}\) E in the semi-arid region in North China, and covers an area of 3.12 \(\times\) 10\({}^{4}\) km\({}^{2}\) with the mountainous areas in the northwest and plain area in the southeast (Figure 1). Because the mountainous areas contribute to more than 75% of the water-yield generation for the Xiong'an New Area, the major six catchments of the water-source region were selected as the study area, including the Baigouhe Catchment (BGC), Jumahe Catchment (JMC), Baishahe Catchment (BSC), Tanghe Catchment (THC), Dashahe Catchment (DSC) and Ciche Catchment (CHC), ordered from the northeast to southwest. Details of the six catchments are shown in Table 1. The region has a semi-arid continental monsoon climate, with a mean annual temperature of 8.4 \({}^{\circ}\)C and it increases gradually from southwest to northeast across the mountains. The annual precipitation is about 576 mm and is mainly concentrated in the summer season. The major land-cover types in the region are forest (42.8%), grassland (39.9%), cropland (12.0%) and the rest of the land types, including water area and urban land, are relatively small, only accounting for about 5.3%. The dominant land-use type in the northeastern part is forest (83%) in the BGC and the area of forest decreases gradually to 22% in the THC and then increases again to 47% in the southwestern part (CHC), while the grassland and cropland increases firstly and then decreases from northeast to southwest. The forest is a mix of temperate coniferous and broad-leaved, mainly composing of oak, hornbeam, Chinese pie, larch and poplar. The grassland consists of white grass, yellow back grass and artemisinin, etc. The crops include grains, beans, tubers and oil crops, etc.  
Figure 1: The location of the water source area of the Xiong’an New Area in North China.
## 4 Results and Discussions
### Reservoir Pressure Characteristics  
Reservoir pressure is the specific form of formation energy, and it not only plays an important role in the content and occurrence state of CBM but also determines the migration and production power of CBM [50]. The injection/fall-off well testing results show that the reservoir pressure of coal seams in the study area varies from 1.56 to 12.62 MPa, with an average of 5.91 MPa. The reservoir pressure coefficient ranges from 0.51 and 1.26, averaging 0.88, and approximately 25% of the coal seams belong to abnormal high-pressure reservoirs. Coal seams with abnormally high pressure are conducive to CBM development. On the one hand, the abnormal high pressure can reduce the stress sensitivity of the coal seam and keep the cleats and fractures open; on the other hand, it can provide stronger migration and output power and larger pressure drop space for CBM during the production process to ensure the production potential and stable production period of the CBM well.  
Figure 10a shows that the reservoir pressure of coal seams has a good positive relationship with the depth (R\({}^{2}\) = 0.91). However, the reservoir pressure coefficients of coal seams show a fluctuation change trend (increase\(\rightarrow\)decrease\(\rightarrow\)increase) as the depth increases, and the reservoir pressure coefficient takes on a decreasing trend at the depth between 600 and 900 m (Figure 10b), which reflects that three sets of independent superposed gas-bearing systems possibly exist vertically in the P\({}_{3l}\) of the study area.
# An Assessment of Environmental RF Noise Due to IoT Deployment
## 9 Empirical Analysis  
This heuristic and empirical method was another way to test the influence of IoT radio operations on environments. The test analyzed the mean values obtained at different transmit rates for each candidate site and frequency. The exercise consisted of identifying the features or transmit rate columns containing the highest average value per site and frequency and then splitting these features into two categories after applying the \"50 percent plus one\" rule:  
* Lower transmission rate side (LTRS): grouping transmit rates Tx0 to Tx5;
* Upper transmission rate side (UTRS): grouping transmit rates Tx6 to Tx10.  
Table 6 shows the transmit rates of the maximum noise and counts how many fell under LTRS or UTRS. The analysis demonstrates that the UTRS category dominated and therefore confirmed the theory that the level of ambient radio noise is directly proportional to the volume of RF activities, even in this case of low-power IoT operations, irrespective of the frequency bands and type of environment (industrial, urban, or suburban).
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 3 Methodology
### Visualizing Geographic Data for 5G Network Deployment  
The process of visualizing geographic data is fundamental in the strategic planning and deployment of 5G networks. This involves several key steps:  
Coordinate Conversion: The transformation of X and Y coordinates into latitude and longitude is critical for accurately mapping the positions of 5G base stations. This conversion ensures that the geographic data align correctly with real-world locations, facilitating precise base station placement for optimal network coverage and capacity.  
Data Frame Creation and Storage: After coordinate conversion, the next step is to create and store these data in a structured format. A data frame is generated to hold the latitude and longitude information, typically saved as a CSV file. This file becomes a crucial repository of geographic data, supporting various analytical processes that aid in decision-making related to network planning and deployment.  
Interactive Visualization Using Folium: Folium is employed to create interactive maps that visually represent the spatial distribution of base stations. These maps are not just tools for displaying location data but are instrumental in analyzing spatial relationships and network coverage areas. By integrating features like circles and markers, Folium maps provide a clear and intuitive view of how base stations are spread across a region, helping network engineers and planners assess and optimize the deployment strategy.  
These visualization techniques play an essential role in modern telecommunications infrastructure, particularly for the efficient deployment of 5G networks. By enabling precise  
Figure 2: Block Diagram for Metaheuristic Optimization.  
geographic data handling and visualization, these tools contribute significantly to enhancing network connectivity and performance, ensuring that base stations are strategically placed to meet coverage demands effectively.
# Detection of Intelligent Intruders in Wireless Sensor Networks
## 4 Demonstration, Simulation and Discussion
### Impact of Node Density \(\lambda\)
The results also suggest that there is a saturation point of the node density, after which deploying more sensors in the field of interest does not significantly decrease the intruder escape probability. The saturation point differs when the intruder takes different intrusion strategies. It should be noted that the sensor detection avoidance strategy adopted by the pinball intruder may not be able to help improve its escape probability; however, it does help in reducing the detection probability (\(P_{det}\)) to a significant extent. As illustrated in Figure 7, the detection probability of the considered WSN in detecting the intelligent pinball intruder and the flood-fill intruder approaches zero, while the detection probability of a linear intruder keeps increasing as the node density increases. The results confirm the necessity of integrating intruder's intelligence in the WSN research for intruder detection for different applications.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
### Outline  
The remaining of the article is organized as follows: Section 2 introduces the theoretical fundamentals required to understand the subsequent sections; Section 3 describes the developed methodology to characterize the elevation angle behavior through \(f_{\Theta}(\theta)\) and \(F_{\Theta}(\theta)\); Section 4 contains the main results; and Section 5 concludes analyzing the obtained results and opportunities for future work. Figure 2 shows the main structure of the document.
## 4 Discussion
It was found that \(\text{EE}_{\text{LPM}}\) was reduced by \(-45\pm 17\%\) due to physical collisions [13]. However, physical collisions cannot be the only explanation for the systematic underestimation in \(\text{EE}_{\text{LPM}}\), because in soccer similar results were found, although the study has included no physical collisions [8,12]. Different results in \(\text{EE}_{\text{LPM}}\) compared with \(\text{EE}_{\text{Spiro}}\) were also found between game sport-specific movement patterns (\(-44\%\)), fast running (\(4.8\%\)), moderate running (\(7.8\%\)), and walking (\(43\%\)) [11]. \(\text{EE}_{\text{LPM}}\) was underestimated in sport-specific movements and overestimated in non-specific locomotion. It seems that slower locomotion increased the overestimation of \(\text{EE}_{\text{LPM}}\). However, regarding the overestimation of \(\text{EE}_{\text{LPM}}\) for walking, the \(\text{EE}_{\text{LPM}}\) calculation model was adapted for running [36]. Potential sources of errors can be summarized as vertical movements like jumps, which cannot be identified in horizontal position data, typical game sports actions like shots [12], ball control during running [7], and collisions [13]. Additional errors must be expected as they were documented also in a shuttle-run test without any of the aforementioned actions [8]. Therefore, limitations of the study included a variety of potential sources of errors in the LPM system, which have been outlined and discussed in previous sections. These were challenges observed in the current as well as previous studies in calculating the EE via position-measuring systems. Another limitation is that a comparison within the sample (e.g., sex) was not feasible due to the small sample size. However, a strength and practical implication of the study is that the results of the current study can help to advance the metabolic power model for EE, which has a huge potential because of its applicability in team sport competition as well as to prevent incorrect application of the method. In the present study, we have not found a valid regression model to predict \(\text{EE}_{\text{Spiro}}\) by \(\text{EE}_{\text{LPM}}\), to improve the accuracy of \(\text{EE}_{\text{LPM}}\). However, future studies are warranted to successfully develop more complex models by identifying and integrating the most relevant error factors (e.g., occurrence of sports-specific actions).
## 6 Conclusions  
In this paper, an uncertainty measurement method based on local component consistency is proposed for suppressing the complex background in infrared images and highlighting the target, thereby achieving the detection of small infrared targets. The local component consistency can reflect the feature difference information between the target and the background. Using the local component consistency as a confidence assignment function, the uncertainty of the image is measured using the mutation entropy function, which is highly sensitive to a wide range of compositional differences. The proposed method enhances the signal and suppresses background noise. The algorithm is implemented in hardware and deployed on the FPGA to improve the processing speed. After the experimental analysis, the algorithm proposed in this paper has strong detection capabilities and a strong processing performance.  
The methods in this paper can be applied to areas where target recognition is required in complex environments. For example, in autonomous driving, tiny targets at longer distances can be recognized to provide an early warning, thus improving safety. In addition, the method in this paper focuses on single-frame images. By introducing time-domain information, multi-frame images can be processed to dynamically analyze tiny targets, thus realizing the recognition of small video targets.  
Conceptualization, X.W. and Y.L.; methodology, X.W., Z.Z., Y.J. and K.L.; software, X.W. and Y.L.; validation; formal analysis, Z.Z., Y.J. and K.L.; investigation, Y.L., X.Y., Z.H. and W.Z.; resources, Z.Z., Y.J. and K.L.; data curation, Z.Z., Y.J., K.L., X.Y., Z.H. and W.Z.; writing--original draft preparation, X.W., Z.Z., Y.J. and K.L.; writing--review and editing, X.W., Z.Z., Y.L. and K.L.; visualization, Y.L., X.Y., Z.H. and W.Z.; supervision, F.Z. and J.Z.; project administration, F.Z. and J.Z.; funding acquisition, F.Z. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China under grant 2023 YFF07 19800.  
Not applicable.  
All data can be provided upon reasonable request to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**Symbol** & **Notation** \\ ELUM & Energy-weighted Local Uncertainty Measure \\ LUM & Local Uncertainty Measure \\ EWF & Energy-weighting function \\ PSF & Point Spread Function \\ RDR & Rank-Deficiency Rate \\ GLCM & Gray-Level Co-occurrence Matrix \\ \end{tabular}
## 2 Materials and Methods
### Statistical Analysis  
Each of the experiments were carried out on 10 samples (n = 10), where the numbers of samples were based on the previously carried out similar studies that considered application of NTAPP on different dental biomaterials [12; 20]. The data were analyzed by one-way ANOVA with Tukey test as post-hoc analysis using statistical software (PASW Statistics 18, SPSS Inc., Chicago, IL, USA). The statistical significance was declared at \(p<0.05\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Group Number** & **Surface Treatment Method** & **Surface Treatment Description** \\ \hline Group 1 & Control & No surface treatment \\ \hline Group 2 & Non-thermal atmospheric pressure plasma (NTAPP) & NTAPP for 1 min without any other surface treatment \\ \hline Group 3 & 4\% Hydrofluoric Acid & Treated with 4\% hydrofluoric acid gel (Porcelain etchant, Bisco Inc., Schaumburg, IL, USA) for 60 s and then rinsed with running water for 10 s \\ \hline Group 4 & Silane & Treated with silane (Monobond-S; Ivoclar Vivadent, Schaan, Liechtenstein) using micro-brush for 60 s \\ \hline Group 5 & 4\% Hydrofluoric Acid and Silane & Treated with hydrofluoric acid gel followed by silane, with same methods described in Group 3 and 4. \\ \hline \end{tabular}
\end{table}
Table 1: Summary of experimental groups for shear bond strength test.  
Figure 3: The photographs showing push-out shear bond strength test specimen holder and actual photograph of the test.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 3 Energy Storage System for the Marine Power System
The active materials in both electrodes possess a crystalline structure that accommodates the lithium ions through an intercalation mechanism, allowing the ions to embed within the electrode materials during charging and release during discharging. During the charging phase, lithium ions decouple from the cathode (positive electrode) and migrate through the electrolyte to intercalate into the anode (negative electrode) [106; 107; 108; 109]. Concurrently, electrons flow externally from the cathode to the anode to balance the charge. Conversely, lithium ions move from the anode back to the cathode during discharge, with electrons traveling through the external circuit in the same direction, thereby delivering power to the connected device. This bidirectional flow of ions and electrons, essential for the battery's operation, is visually detailed in Figure 10.  
Moreover, the reaction that takes place on the positive side during charging is given in the following Equation (1):  
\[LiMO_{2}\to x.Li^{+}+x.e^{-}+Li_{1-x}MO_{2} \tag{1}\]  
Equation (2) demonstrates that the reaction that takes place in the negative electrode during discharging:  
\[LiC_{6}\to Li_{1-x}C_{6}+x.Li^{+}+x.e^{-} \tag{2}\]  
The labels of Li-ion batteries in the market are created by using the chemical composition of the cathode active materials because of descriptive characteristics of the performance and behavior of the battery. Cathode active materials consist of metal oxides (mixed metal oxides) or phosphate (especially LiFePO4) in Li-ion batteries. The most suitable cathode  
Figure 10: Working mechanism of Li-ion battery. active materials for the shipping industry on the battery market are lithium nickel manganese cobalt oxide (LiNiMnyCozO2) (longer life cycle), lithium iron phosphate (reduced risk of thermal runaway), lithium nickel cobalt aluminum oxide (LiNixCoyAlzO2), lithium cobalt oxide (LiCoO2), and lithium manganese oxide (LiMn2O4) (higher charging rates and thermal stability) [27; 110; 111]. These cathode active materials have superior features in terms of energy density, power density, safety, stability, and price. Material that responds well to all requirements has not yet been developed. Whichever requirement is important for users, they should choose the battery that gives the best results in that specific feature. On the other hand, graphite, hard carbon, graphene, etc. materials made of carbon sources are generally used as anode active materials. Besides these, LiTiO2-based titanite compounds (LTO) are also anode active materials. This material is applicable for marine vessels that require high power or very large amounts of cycling.