esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation  
The performance of the SA-PSO hybrid algorithm is compared with the traditional GS algorithm. For the diffraction process from DOEs to the target plane, the Fresnel diffraction equation or the Fraunhofer diffraction equation [35] can be used to calculate the intensity distribution on the target plane. The goal of the simulation is to shape a plane wave into a Gaussian beam. The diffraction model used in the process is Fresnel diffraction and the numerical algorithm used in the simulation is a single fast Fourier transform (SFFT). The intensity distribution on the target plane is shown in Figure 2a. The specific parameters used in the simulation are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline The size of the DOE & 8.64 mm \\ Diffraction distance & 300 mm \\ Sampling points & \(256\times 256\) \\ Wavelength & 532 nm \\ C\({}_{1}\), C\({}_{2}\) & 1.6, 1.9 \\ \(\alpha\), \(\beta\) & \(5\times 10^{5}\), 1 \\ \(\omega\) & 0.9 \\ \(T_{s}\) & \(10^{-4}\) \\ \(T_{f}\) & \(10^{-7}\) \\ \hline \end{tabular}
\end{table}
Table 1: Parameters in the simulation  
Figure 1: The flow chart of the simulated annealing–particle swarm optimization (SA–PSO) hybrid algorithm.  
Two DOEs are calculated by using the GS algorithm and the SA-PSO hybrid algorithm, respectively. The diffracted intensity distributions on the target plane are calculated, as shown in Figure 2b,c. The shaping effects are compared in Figure 2d. The deviation to the target by the GS algorithm and the SA-PSO hybrid algorithm are shown in Figure 2e.f. It can be seen that the SA-PSO algorithm has a better shaping effect than the GS algorithm, especially in the central high-light intensity area. The cost function values of the light intensity by the GS algorithm and the SA-PSO hybrid algorithm are 9.30 and 8.09, respectively. So, the relative reduction by the SA-PSO hybrid algorithm is 13.00%.  
Figure 2: The beam shaping target (**a**), the simulated beam shaping results by (**b**) the Gerchberg–Saxton (GS) algorithm and (**c**) the SA–PSO hybrid algorithm. (**d**) The normalized intensity along the red-dotted lines in (**a**), (**b**) and (**c**). The deviation to the target by the GS algorithm (**e**) and the SA–PSO hybrid algorithm (**f**).
## 3 Results and Discussion
### Structural Characterization of Glasses  
The XRD patterns of both 100 Si and 70 Si30 CaO glasses are shown in Figure 2a. Both diffraction patterns indicate the amorphous phase of the prepared materials.  
FTIR spectroscopy was used to characterize the structure of the derived glasses. The FTIR spectra of pure silica and 70 Si30 CaO glasses are depicted in Figure 2b. The spectrum of pure silica shows a peak at 461 cm\({}^{-1}\) assigned to rocking vibrations of bridged oxygen atoms vertical to the Si-O-Si plane. The next bands at 803 and 1100 cm\({}^{-1}\) are assigned to transverse optical modes of the Si-O-Si groups. The band at 803 cm\({}^{-1}\) is due to Si-O-Si bending vibrations. The high-intensity peak at 1100 cm\({}^{-1}\) is attributed to the asymmetric stretching mode of Si-O-Si groups and is related to oxygen motion parallel to the Si-Si direction [19; 27].  
The FTIR spectrum of glass containing 30% Ca differs significantly from the spectrum of pure silica, indicating structural modifications. The band at 803 cm\({}^{-1}\) shows a lower intensity, while its energy remains practically unchanged. A new absorption band appears at around 930 cm\({}^{-1}\), which is attributed to non-bridging oxygens. In addition, a shift in the peak at 1100 cm\({}^{-1}\) is observed, corresponding to vibrations of the silica tetrahedron to higher wavenumbers (lower energy), which indicates a relaxation of the Si-O bonds. The above shows depolymerization of the silica network in the presence of Ca.  
Figure 1: Rheological characterization of the prepared hydrogels: (**a**) mechanical spectra of all samples recorded at 25 \({}^{\circ}\)C (0.5% strain), and (**b**) steady-state flow curves of the hydrogels.  
The Raman spectra of pure silica glass are characterized by the bands at 602 and 492 cm\({}^{-1}\), which are assigned to vibration of oxygen atoms in four- and three-membered rings, respectively (Figure 2c). The band at 800-810 cm\({}^{-1}\) is due to Si-O stretching vibrations. Upon the addition of Ca, which plays the role of network modifier, significant differences in the Raman spectrum are present. The existence of bands at around 858, 920, 950-1000, and 1074 cm\({}^{-1}\) arises from Si-O symmetric stretching vibrations of SiO\({}_{4}\) tetrahedral units that contain non-bridging oxygen atoms [19].
## 4 Results
### Extended Fairness and Sensitivity Analysis  
As shown in Table 10, demographic parity differences are relatively low for most individual sensitive attributes, but the intersectional group (sex and Medu) exhibits a substantially larger DP gap (0.350). This underscores the importance of intersectional fairness analysis, as group-level disparities may remain hidden when only single attributes are considered. Figure 3 further visualizes these differences, revealing that socioeconomic and gender-based disadvantages can combine to create amplified bias in model predictions.  
In response to reviewer feedback, we expanded our fairness evaluation to include additional sensitive attributes related to socioeconomic status. These include parental education levels (Medu and Fedu) and family structure (famsize and Pstatus), which may encode latent socioeconomic signals.  
Table 11 presents the demographic parity (DP) and Equalized Odds (EO) metrics for all considered attributes. As expected, gender and school type still show the highest disparities, but several newly evaluated attributes--such as parental education--also  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Sensitive Attribute** & **DP** \\ \hline sex & 0.020 \\ school & 0.125 \\ Medu & 0.196 \\ Pstatus & 0.034 \\ famsize & 0.044 \\ Intersectional (Sex and Medu) & 0.350 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Demographic parity (DP) differences across sensitive attributes and their intersection. Higher DP values indicate greater group-level disparity in model outcomes.  
Figure 3: Demographic parity (DP) differences for each sensitive attribute and for the intersection of sex and Medu (parental education). Intersectional bias is substantially higher, highlighting compounded disparities in specific subgroups.  
exhibit non-trivial bias levels. These results are also visualized in Figure 4, which displays DP and EO disparities across all sensitive features, including socioeconomic variables.  
These results indicate that socioeconomic features, though often overlooked, can introduce meaningful biases in model outcomes. This supports the importance of multi-attribute fairness monitoring in socially sensitive domains, such as education.  
In line with the subgroup fairness literature, we also report DP and EO metrics for all sensitive features as assessed with the XGBoost model (see Table 13). These allow for direct benchmarking with recent related studies.
###### Abstract  
Tilt-to-length coupling, a non-constant systematic error source caused by satellite attitude variations, has been observed in the laser ranging signals of the GRACE Follow-On mission. This error can be corrected by certain calibration maneuvers performed regularly in orbit. In this paper, we introduce an alternative approach to tilt-to-length coupling estimation for a laser ranging interferometer in future gravity missions, using the ranging signals without any specific calibration maneuvers, which allows daily estimation. An analytical model of laser ranging signals is derived. The tilt-to-length estimation is performed under different conditions using the least squares method as well as the simulated data. The pointing angle noise is found to be the most significant limiting factor. When the pointing angle noise is below \(0.3\,\mathrm{\SIUnitSymbolMicro rad}/\mathrm{Hz}^{1/2}\), the RMS of the estimation error is below \(4\,\mathrm{nm}\), much better than the tilt-to-length error of GRACE Follow-On. In the case of low pointing angle noise, the estimation error of an under \(1.5\,\mathrm{m}\) offset between the center of mass and the interferometer reference point is not obviously different from the case with only a \(0.5\,\mathrm{mm}\) offset, which provides installation flexibility for the laser ranging interferometer.  
tilt-to-length coupling; laser ranging interferometer; satellite gravimetry Article
## 2 Materials and Methods  
### Experimental Materials  
Soil bulk samples and _X. strumarium_ plants at the reproductive stage were collected from fifteen different abandoned habitats in Khyber Pakhtunkhwa, Pakistan (34\({}^{\circ}\)26\({}^{\prime}\)59.42\({}^{\prime}\) N and 72\({}^{\circ}\)0\({}^{\prime}\)45.65\({}^{\prime}\) E; Figure 1) from July to September 2019. Pollutants in the experimental sites discharged from automobiles, and its workshops derivatives or industrial effluent predominately include plastics residues, pesticides, and marble factories beside domestic sewages in residential areas. The geological features of the study area revealed 13 land-cover classes extracted from 37 sub-categories. The relatively high mean annual temperature (34.96 \(\pm\) 1.36 \({}^{\circ}\)C to 0.67 \(\pm\) 0.97 \({}^{\circ}\)C) and low precipitation (384 mm to 639 mm) contributed to heavy metals contamination in addition, the road, urban, and industrial sources. At each site, five cores of soils were sampled at a depth of 0-20 cm (at rooting zone) by stainless steel trowel to avoid contamination and were mixed thoroughly to achieve composite samples [30]. Soil samples were air-dried and then passed through a 2 mm sieve, oven-dried, and was placed in sealed polythene bags in the laboratory. Forty-five plants (3 plants/site) healthy with sound structural characteristics were randomly harvested using a destructive method for robust results and to minimize the risks of invasive species spread and extinction of rare and threatened species within the natural plant communities [31]. The harvested plants were washed immediately with fresh water and subsequently oven-dried at 80 \({}^{\circ}\)C for 24 h. The plant's functional parts were separated (i.e., roots, stems, leaves, and fruits), converted into a fine powder using a commercial grinder, and sieved through a 1.5 mm mechanical shaker to remove ample residues [32]. The powdered samples were stored in precisely labeled polythene bags for acid-digestion and metal analysis.  
Figure 1: Study area map along with sampling point for collection of plant and soil samples in abandoned sites of Khyber Pakhtunkhwa, Pakistan. Note: KTS (Kanju Town Junction), GTM (GT Road Mingora), SKR (Skhakot Road), SMR (Shamozu Road), PC (Peshawar), HIZ (Hayatabad Industrial Zone), MKI (Mardan Kid Industry), BKP (Barikot Crush Plant), CUA (Charsadda Urban), MSM (Mardan Shiekh Malton), TFN (Tarnab Form), TBM (Takht Bhai), GCD (Gulabad College Degree), AMS (Amankot), CBS (Charbagh street).
# Study on Fire Smoke Distribution and Safety Evacuation of Subway Station Based on BIM
## 3 Subway Station Layout Optimization and BIM Model Construction
### Layout Optimization of Subway Station and Construction of BIM Model  
With the same evacuation facilities, increasing the number of effective entrances can effectively balance the waiting passenger flow on the platform [25]. Based on this, this study takes the layout scheme of the original subway station as the basis, by adding an ascending escalator, the evacuation facilities in the station are rearranged to optimize the layout of the station and its BIM model is constructed.  
In the original layout scheme of the station payment area, three effective entrances are set and numbered as entrances 1, 2 and 3. At the same time, all evacuation facilities (stairs, escalators, gates, etc.) in the station are numbered and their BIM three-dimensional model is established. The corresponding layout of the station hall floor is shown in Figure 2(a), and the specific combination form of evacuation facilities is shown in Table 2.  
Figure 3: Layout schematic diagrams of station hall floor before and after optimization: (**a**) layout diagram of the hall floor of the original scheme; (**b**) layout diagram of hall floor of optimized station.  
In the original layout scheme, the T-shaped stairs are located in the middle of the pay area, so the evacuation path is relatively long when evacuating people in the station. In order to shorten the evacuation distance and improve the evacuation efficiency of the station, this study rearranges the vertical traffic facilities in the station by adding an escalator at the same time, and rearranges them in the form of alternate distribution, increasing the number of entrances in the pay area from three to four. The numbers correspond to 1, 2, 3 and 4 entrance. The corresponding layout of the station hall floor in the BIM model is shown in Figure 3b. The specific combination form of evacuation facilities is shown in Table 3.  
The three-dimensional visualization model of the subway station established by BIM technology is shown in Figure 4.
## 2 Materials and Methods
### HPLC-DAD Analysis  
The quantification of Artepillin-C (3,5-diphenyl-4-hydroxycinnamic acid) was performed using a high-performance liquid chromatography (HPLC) system (Agilent 1100, Model G1311A, Santa Clara, CA, USA) equipped with a quaternary pump and auto-sampler. A Nucleosil C\({}_{18}\) column (150 \(\times\) 4.6 mm, 5 \(\upmu\)m) (Supelco, Lot: 608010560) was used for separation. The mobile phase consisted of (A) ultrapure water with 5% formic acid (Merck, 99%) and (B) 100% methanol, with a flow rate set at 1 mL/min and a linear gradient. The total analysis time was set at 50 min, with detection at 280 and 320 nm using a diode array detector (DAD). Artepillin-C was quantified by comparison with an authentic standard, following a calibration curve.
## 4 Discussion
### Drivers of Lake Ecosystem Metabolism  
GPP and R rates are not only controlled by environmental and biological factors which determine the biomass and physiological state of lake phytoplankton, but also by factors which determine the daily production rates for a certain biomass and species composition [5]. In present study, GPP and R rates were strongly positively related to water temperature and irradiance. Specifically, water temperature was the strongest factor when the entire data was analyzed (Table 1). These results are in agreement with [PERSON] and [PERSON] [5]. The apparent temperature effect also was coupled with available irradiance. High summer temperature and irradiance were associated with formation of blue-green algal blooms and consequently high rates of GPP and R, making the lake net autotrophic during the summer.  
In present study, a negative relationship between GPP and wind speed in both buoy stations is found (Table 1). It is because that high wind speed accelerates deeper vertical mixing and lower light availability in the water column, resulting in the reduction of GPP. A positive relationship is found between R and turbidity at the SSC station, which is consistent with the result of [PERSON] _et al._[42]. This maybe explained, in part, by the relationship between turbidity and phytoplankton concentrations.  
Over longer timescales of weeks and months, the biomass and physiological state of phytoplankton are markedly influenced by inorganic nutrient availability, mixing regime and food web structure in addition to the impact of temperature and irradiance [43]. In accordance with previous studies [5], high pH and ORP can also constrain photosynthetic production (Table 1), because more dissolved inorganic carbon is changed into CaCO\({}_{3}\) that precipitates at high pH and ORP.  
There are similar dynamic metabolism patterns between the two buoy stations (JS and SSC), which are net autotrophic. From August to October, the maximum GPP values of JS and SSC stations are 10.31 and 6.77 g O\({}_{2}\cdot\)m\({}^{-3}\cdot\)d\({}^{-1}\), mean GPP rates of JS station and SSC station are 3.66 \(\pm\) 1.67 and 2.52 \(\pm\) 1.28 g O\({}_{2}\cdot\)m\({}^{-3}\cdot\)d\({}^{-1}\), respectively, while no significant differences in GPP, R, and NEP between the JS station and SSC station (\(p\) = 0.183, 0.437, and 0.355) were found. Possible explanations for the differences of GPP rates at the two buoy stations include: (1) there are more abundant macrophytes (species and biomass) at the JS station, such as \(P\). _maackianus_, \(P\). _maalanus_, \(M\). _spicatum_, _Elodea nutallii_, _Vallisneria natans_, _Ceratophyllum demersum_, _Nymphoides peltata_, and _Trapa natans_. (2) Compared to the JS station, the site of the SSC station is close to the main lake, where underwater light may be limited because of the higher turbidity, resulting in an adverse effect on macrophyte photosynthesis.
## 4 Si Transporters
### Brief Introduction of Si Transporters in Some Other Plants  
#### 4.2.1 Barley and Maize  
Various plants accumulate Si, which helps improve resistance against all sorts of abiotic and biotic stresses. The Si-influx and efflux transporters HvLsi1 [20], HvLsi2 [51] and HvLsi6 [51] are found in barley. Bombardment of rice epidermal cells with _HbLsi1_-GFP complexes demonstrated that HvLsi1 is localized to the plasma membrane. HvLsi1 is a Si import transporter involved in the radial transport of Si through the epidermis and cortex of basal region of seminal roots [20]. Moreover, HvLsi2 localizes at the parenchyma cell layer adjacent to the transfer cells (Figure 2). The Si translocated via the enlarged vascular bundles is unloaded to the transfer cells by HvLsi6, then Si is reloaded into the diffuse vascular bundles connecting the upper part of the plant by HvLsi2.  
The high accumulation of Si also occurs in maize and is responsible for these three transporter genes, _ZmLsi1_, _ZmLsi2_ and _ZmLsi6_[50]. _ZmLsi1_ is expressed in roots, whereas _ZmLsi6_ is expressed in leaves and leaf sheaths. In contrast to rice influx transporters, their expressions are thought to be unaffected by the exogenous supply of Si (Figure 2).  
The Lsi1 and Lsi2 proteins of maize and barley reveal that their Si uptake systems are different from that of rice. This difference is due to the different localization patterns of Si-influx and -efflux transporters on different cells. HvLsi2/ZmLsi2 only localizes to endothelial cells at the base of radicle and lateral roots, and it has not been found to have a polarity distribution similar to that of OsLsi2 [21,50].
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 5 Performance Evaluation
### Scenario 1: String Topology, \(N=3\)  
In Scenario 1, there are three networks which are installed linearly. Figure 4(a)-c show the network performance as a function of the offered load. It is seen from Figure 4(a) that the throughput increases in proportion to the offered load in the range of \(0\leq O\leq 13.3\) Mbps. This is because all the networks are in the non-saturation state. The observation is confirmed from Figure 4(b), where we can see that frame-existence probabilities of all the nodes are less than one in the range. The frame-existence probability of Network 2 reaches one first at \(O=13.3\) Mbps. For \(O\geq 13.3\) Mbps, the frame-existence probabilities of networks except Network 2 still increase. It leads to the increment of the carrier-sensing probabilities, which are confirmed from Figure 4(c). As a result, the throughput of Network 2 decreases as the offered load increases in the saturation condition. It is also seen from Figure 4(b) that all networks are saturated when \(O=28.1\) Mbps. Therefore, the network throughput with respect to each network keeps the constant value in the range of \(O\geq 28.1\) Mbps. From Figure 4(c), it is confirmed that the degradation of throughput in Network 2 is caused by the bias of carrier-sensing.  
\begin{table}
\begin{tabular}{c c} \hline \hline Data payload & 1500 bytes \\ PHY header & 24 bytes \\ MAC header & 24 bytes \\ ACK size & 10 bytes \\ \hline Data rate & 54 Mbps \\ ACK bit rate & 24 Mbps \\ \hline DATA & 252 μs \\ DIFS & 34 μs \\ SIFS & 16 μs \\ Slot time (\(\sigma\)) & 9 μs \\ \hline \(CW_{min}\) & 15 \\ \(CW_{max}\) & 1023 \\ Retry limit (\(K\)) & 7 \\ \hline Inter-network distance & 30 m \\ Transmission range & 40 m \\ Carrier-sensing range & 40 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: System Parameters.
# AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection
## 4 Experiments  
### Datasets and Evaluation  
We evaluated the proposed approach on two widely used scene text datasets--ICDAR2013 for horizontal text detection and MSRA-TD500 for multi-oriented text detection.  
The ICDAR2013 dataset is released for ICDAR (International Conference on Document Analysis and Recognition) competitions [33; 34], containing 562 color images with a variety of text patterns, and well captured from the outdoor scene. The texts are horizontal and focused on images center. There are 229 images prepared for the training stage, while 233 images are used for the testing stage. All the images provide word-level annotations. Competition organizers adopt the evaluation algorithm introduced by [PERSON] et al. [35].  
The MSRA-TD500 dataset includes 500 color images, captured from indoor and outdoor scenes with line-level annotation. The texts in images are multi-orientation; with various scales; and consist of two language characters, Chinese and English. Thus, the dataset is very challenging for text detection. There are 300 images used for training and 200 images for testing. The dataset and the evaluation protocol are introduced by [PERSON] et al. [2].  
Both two datasets are evaluated by Precision, Recall, and F-measure metrics. The final results are derived from the mean of all the images. Specifically, F-measure, representing the balance of precision and recall, is considered the most important metric of text detection.