esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Composition and Characteristics of CSA-BRB  
The CSA-BRB, formed by two steel tubes restrained limbs connected by webs, has the role of each component, as shown in Figure 1. Lightweight concrete can be filled in steel tubes, and the core can flexibly adapt to various cross-section types, such as a flat plate or a cross-shaped welding section. As is well known, the external restraint member only bears the bending moment, not the axial force. When considering the filler concrete material, the lightweight concrete's contribution can be eliminated and just considered filling material. In this approach, the split steel tube's section can be increased, and the spacing between the two steel tubes can be flexibly set, both of which can improve the external restraining members' bending rigidity. At the same time, cold-formed thin-walled steel tubes can be used for steel tube sections, which are easy to obtain in the market. Therefore, CSA-BRB is a core-separated BRB with higher carrying efficiency and more practicality.  
When the ends of a core-separated BRB design are extended, the dual cores lose the constraint of the external components, and how their dual cores are effectively integrated to make a whole becomes a critical component of the core-separated BRB design. This section is outside the scope of this work; therefore, please refer to [22].  
Obviously, by adjusting the gap between the CSA-BRB limbs, the inertia moment and bending stiffness of the external restraint section around the \(x\)-axis can be effectively increased. This paper focuses on the elastic buckling load, bearing capacity, and hysteretic energy dissipation performance of CSA-BRB hinged at both ends around the \(x\)-axis, corresponding to the design method around the \(y\)-axis, which is the same as that of a single solid web section BRB and is therefore not discussed further.
## Materials and Methods
### Heterotrophic Microorganisms  
The biofilm was fully characterised by culture-dependent and independent techniques. The cultivable portion of the heterotrophic community was first studied, to isolate the microorganisms and use them for subsequent in vitro investigation for individual sensitivity to the tested biocides. To characterise the cultivable portion of the heterotrophic community inhabiting the biofilm, 20 g of biomass was sampled from random points in a selected area of \(2\times 2\) m by gently scraping off the biofilm with sterile scalpels; 5 g of the mixed sampled biofilms were then suspended in a 50 mL solution of sodium pyrophosphate and Tween 80 (0.05% and 0.001% _v/v_ respectively). The suspension was then kept in an orbital shaking incubator (New Brunswick(tm) Innova 4430--New Brunswick Scientific Co Inc, Edison, NJ, USA) at 28 \({}^{\circ}\)C and 150 rpm for 1 h. The number of cultivable heterotrophic microorganisms was obtained by plating 100 \(\mu\)L of suspension and following serial dilutions in three different agar media: Trypic Soy Agar (TSA, Conda, Spain) and Mineral Medium ([PERSON], [PERSON], 1989) added with 0.5% (_v/v_) sodium gluconate and 1.5% agar (Agar, Conda, Spain) for bacteria and Potato Dextrose Agar (PDA, Conda, Spain) for fungi. The plates were then incubated at room temperature until the complete growth of colonies (up to one month).  
The isolation of bacterial and fungal strains was based on colony morphologies. Colonies were streaked three times on the same agarized medium from which they were isolated, in order to obtain pure cultures. The plates were then incubated at room temperature until there was complete colony development (24-72 h). Bacterial strains were identified by 16S r-DNA sequencing. Genomic DNA was extracted by resuspending two fresh colonies of each isolate in 20 \(\mu\)L of lysis buffer (NaOH 0.5 M and SDS 0.5% _w/v_), heating at 95 \({}^{\circ}\)C for 15 min and then transferring to an ice bath for 15 min. The 16S r-DNA complete gene was PCR amplified by using 9 bfm as a forward and 1512 uR as a reverse primer [31]. The reaction mixture contained 1\(\times\) PCR Master Mix (Thermo Scientific, Waltham, MA, USA), 2 \(\mu\)L of template DNA and 20 pmol of each primer in the total reaction volume of 25 \(\mu\)L. The PCR products were sequenced by a commercial facility. Sequence identities were analysed using the National Center for Biotechnology Information (NCBI) BLAST program ([[https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi)]([https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi))) and the GenBank database. Each isolate was compared against known taxa present in the database. A preliminary identification of the fungal isolates was done according to typical colonial and conidial morphology using a stereo microscope.
## 2 Materials and Methods
### Field Campaigns and Measurements
#### 2.1.2 Ground Measurements  
Ground measurements were recorded on three dates for each farm (Table 1) at regular points that were within the field areas of interest and were positioned using a standard handheld Global Navigation Satellite System (GNSS). Across the farms and growing seasons, a total of 80 sets of measurements were recorded and included LAI and growth stage observations in accordance with the Zadoks decimal code [44]. For each of the 80 measurement points, three replicate LAI measurements were made within a two-metre square using a SunScan device (Delta-T Devices, Cambridge), which were then combined to provide an average. SunScan LAI measurements typically include all plant components, including the wheat, stem, and ears. We, therefore, apply a bias correction to the LAI values detailed in [PERSON] et al. [26], which was based on regression analysis between SunScan and leaf area meter measurement carried out by destructive sampling.  
Figure 1: Locations of two farms (red dots) and map insets highlighting the field sites (red border) and within-field areas of interest (black cross-hatching) where field campaign data, including ground measurement and UAV surveys, were carried out.
# Short-Term Load Forecasting Based on Spiking Neural P Systems
## 3 Experiments
### Evaluation Metrics  
To quantitatively test and verify the predictive behavior of the exploited LF-NSNP model, three evaluation metrics were used in experiments.  
The first indicator is mean absolute error (MAE), which is expressed as below:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|\hat{y}_{i}-y_{i}| \tag{16}\]  
The second indicator is mean absolute percentage error (MAPE), which is described by Formula (17).  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}|\frac{\hat{y}_{i}-y_{i}}{y_{i}}| \tag{17}\]  
The third indicator is root mean square error (RMSE), which is presented in Formula (18).  
\[RMSE=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2} \tag{18}\]  
where, \(\hat{y}_{i}\) is the forecast load value, and \(y_{i}\) is the real load value; \(n\) is the quantity of time points for predicting the load. Generally, the smaller the metric value, the more precise and effective the predicted result of the model is.
# Effects of Oxidized Pyrenes on the Biological Responses in the Human Bronchial Epithelial Cells
## 3 Results  
### Effect of Pyrene and Its Quinone Forms on the Cellular Viability of Bronchial Epithelial Cells  
BEAS-2B cells were treated with varying concentrations of pyrene, 4,5-PyQ, and 1,8-PyQ + 1,6-PyQ (1, 3, and 10 \(\upmu\)M), and their viability was evaluated. No significant difference in cellular viability was observed after treatment with pyrene at all concentrations compared with that in the control cells. Increased viability was observed after treatment with 1 \(\upmu\)M 4,5-PyQ and 1,8-PyQ + 1,6-PyQ (\(p\) < 0.01 vs. control). Notably, at a concentration of 3 \(\upmu\)M, cellular viability decreased after 4,5-PyQ exposure but increased after 1,8-PyQ + 1,6-PyQ exposure compared with that in the control (\(p\) < 0.01 vs. control). After exposure to 10 \(\upmu\)M 4,5-PyQ and 1,8-PyQ + 1,6-PyQ, cellular viability decreased (\(p\) < 0.01 vs. control).  
A relative comparison between compounds at 1 \(\upmu\)M revealed higher cellular viability after 4,5-PyQ and 1,8-PyQ + 1,6-PyQ exposure than that after pyrene exposure (\(p\) < 0.01). When the effects of 4,5-PyQ and 1,8-PyQ + 1,6-PyQ were compared, cellular viability was found to be relatively lower after 4,5-PyQ exposure (\(p\) < 0.01). At 3 \(\upmu\)M, cellular viability decreased after exposure to 4,5-PyQ and increased after exposure to 1,8-PyQ + 1,6-PyQ compared with that after pyrene exposure (\(p\) < 0.01). A comparison between 4,5-PyQ and 1,8-PyQ + 1,6-PyQ exposure revealed decreased cellular viability after 4,5-PyQ exposure (\(p\) < 0.01). At 10 \(\upmu\)M, the relative cellular viability was lower after exposure to 4,5-PyQ and 1,8-PyQ + 1,6-PyQ than that after pyrene exposure (\(p\) < 0.01). In contrast, when exposures at 4,5-PyQ and 1,8-PyQ + 1,6-PyQ were compared, cellular viability decreased after exposure to 4,5-PyQ (\(p\) < 0.01) (Figure 1).  
In summary, fluctuations in cellular viability were observed after exposure to 4,5-PyQ and 1,8-PyQ + 1,6-PyQ, with relatively higher viability observed after treatment with 1 \(\upmu\)M 4,5-PyQ and 1,8-PyQ + 1,6-PyQ. However, cellular viability did not change significantly after exposure to pyrene.
## 6 Simulation Results
### Network Performance Parameters: Definitions
#### 6.2.4 Network Throughput  
Figure 13a-c shows the throughput of AURP, AEERP, AEDG and AEDG without GN rotation in different node densities and different network areas. In AEDG, the maximum number of nodes is alive for a long duration, as the restriction on the GNs enhances the stability period, and more nodes are available to relay the data of far end nodes; thus, Figure 14a-c shows that the packet transmission rate of the AEDG is better than the AURP and the AEERP, respectively. The AEDG has enhanced network throughput as compared to the AURP and the AEERP, because nodes transmit packets for a longer duration. In this way, the chances of packet dropping decrease, and throughput increases. Figures 13a-c and 14a-c show the impact that node density and network area have on throughput and the packet transmission rate, respectively; a high node density means a relatively high packet transmission rate, which ultimately leads to a relatively high network throughput. Moreover, these figures clearly illustrate the impact of the GNs' rotation.  
Figure 12: End-to-end delay. (**a**) 10 nodes deployed in 100 m \(\times\) 100 m; (**b**) 100 nodes deployed in 100 m \(\times\) 100 m; (**c**) 100 nodes deployed in 10 km \(\times\) 10 km.  
Figure 13: Network throughput. (**a**) 10 nodes deployed in 100 m \(\times\) 100 m; (**b**) 100 nodes deployed in 100 m \(\times\) 100 m; (**c**) 100 nodes deployed in 10 km \(\times\) 10 km.
## 4 Analysis of CFD Simulation Results
### Analysis of Simulation Results of Blockage Conditions  
#### 4.2.1 The Influence of the Degree of Blockage on the Distribution of Pipeline Characteristics  
To investigate the relationship between blockage degree and pipeline pressure drop, experimental data under three different blockage shapes were selected. Each set of data corresponds to three levels of blockage: 25%, 50%, and 75%. The pressure along the pipeline for each condition was recorded and compared, as shown in Figure 11.  
Figure 11: _Cont._As observed in Figure 11, under the three types of blockage, blockage will cause the overall pressure in the pipe to increase. It is obvious that after the blockage of type B, the pressure is too high, but overall, the pressure-drop values under different blockage degrees increase with the increase in the blockage degree. On the other hand, when the blockage degree is relatively low (i.e., less than 50%), the variation in pressure along the pipeline is minimal, with the maximum pressure drop being only about 3% of the maximum pressure. However, when the blockage degree exceeds 75%, the pressure drop along the pipeline increases sharply, with the minimum pressure drop being over 17% of the maximum pressure. This indicates that the degree of blockage is a significant factor influencing the water flow pressure in the pipeline.
## 4 Experiment and Result Analysis  
In order to verify the feasibility of the proposed interferometer, an experimental setup was constructed, as shown in Figure 3. A stabilized He-Ne laser (HRS150, Thorlabs, Newton, NJ, USA) was used as the laser source with a wavelength of \(\lambda\) = 632.992 nm, and a rotation stage (M-038.DG1, Physik Instrumente, Singapore) was used to generate the rotational angle with a resolution of 3.5 \(\times\) 10\({}^{-5\circ}\); a linear stage (M531.DD1, Physik Instrumente) was used to provide a linear movement with a motion range of 306 mm and unidirectional repeatability of \(\pm\)0.1 \(\upmu\)m, and APD was a self-designed 2 \(\times\) 2 array photodetector. For comparison, a commercial interferometer (XL-80, Renishaw, Wotton-under-Edge, UK) was also used to measure the displacement with a resolution of 0.001 \(\upmu\)m and the angle with a resolution of 2.78 \(\times\) 10\({}^{-6\circ}\), respectively. Three experiments--namely, the differential displacement stability test, the rotational angle comparison experiment, and the differential displacement measurement with rotational angle compensation--were carried out.  
Figure 3: Constructed experimental setup.  
Figure 2: Schematic diagram of OPD change caused by rotation angle.
## 3 Results and Discussion  
### FTIR Spectroscopy  
FTIR spectra of the fluorinated silica materials, obtained as powdered materials, are shown in Figure 2. The bands at \(\sim\)940 and \(\sim\)1050 cm\({}^{-1}\), which appear in both spectra of samples F1 and F2, may be assigned to Si-O-Si bonds (asymmetric stretching vibration or bending vibrations) [33]. The bands observed at \(\sim\)1630 and \(\sim\)3420 cm\({}^{-1}\) are attributed to OH bending and stretching vibrations, respectively [34]. The peak that appears at \(\sim\)1272 cm\({}^{-1}\) corresponds to the stretching vibrations of Si-CH\({}_{3}\) bonds [35]. The band localized at \(\sim\)1410 cm\({}^{-1}\) is related to C-H bonds (asymmetric deformation) and \(-\)CH\({}_{2}\)-groups (scissors vibration) [36]. Moreover, in both samples, a peak at \(\sim\)775 cm\({}^{-1}\) was detected that belongs to C-H bonds (out-of-plane vibration) [37]. The peak that appeared at \(\sim\)1380 cm\({}^{-1}\) is assigned to C-H asymmetric stretching in \(-\)CH\({}_{3}\) aliphatic group [25]. In addition, the characteristic band at 658 cm\({}^{-1}\) is attributed to the characteristic of \(-\)CF\({}_{2}\)[38]. Thus, it was demonstrated that the organic alkyl chains derived from silane precursors are covalently attached to the PFOA.  
In the FTIR spectrum of sample F1, a peak at 2974 cm\({}^{-1}\) was detected which corresponds to the stretching vibration of the \(-\)CH\({}_{3}\) groups [39]. The characteristic band of \(-\)CH\({}_{2}\) (scissoring vibration) is present at 1455 cm\({}^{-1}\)[40]. In the FTIR spectrum of sample F2, other peaks were observed in the range of 2925-2853 cm\({}^{-1}\) and are attributed to C-H bonds originating from stretching vibrations of the \(-\)CH\({}_{3}\) and \(-\)CH\({}_{2}\)\(-\) groups [41; 42]. The intensity of these peaks was found to be increased as compared to that of those noted for sample F1. This result is consistent with the assumption that the incorporation of HDTMES in the solution leads to the replacement of surface \(-\)OH groups by more \(-\)CH\({}_{3}\) groups [37]. Another peak at 1468 cm\({}^{-1}\) was detected and is assigned to the aliphatic CH- groups originating from the organic groups (scissoring vibration) [43].  
Figure 2: FTIR spectra of fluorinated silica materials (as powders): TEOS/MTES/FS (F1) and TEOS/MTES/HDTMES/FS (F2).  
By analyzing Figure 2, it can be concluded that organo-silicate networks were created that contained siloxane groups, alkyl chains, and fluoroalkyl chains derived from the silane precursors and from perfluorooctanoic acid (see Scheme 1).
## 4 Case Study
### Performance Evaluation of Information Extraction Compared to Other Models  
#### 4.3.1 Dataset Partitioning and Annotation  
To validate the effectiveness of the proposed model, which integrates GPT-4 with an information extraction template, in information extraction tasks, we compared its performance with traditional pre-trained deep learning models widely used in information extraction tasks, such as BERT, RoBERTa, and the generative pre-trained model GPT-3.5-turbo, to evaluate its advantages in requirement extraction accuracy. Pre-trained learning models typically require data annotation. Considering the varying proportions of different requirement categories within the overall dataset, we adopted a random sampling strategy to partition the data, allocating 80% as the training set and 20% as the test set. Specifically, the training set comprises 411 data entries, while the test set consists of 105 entries. The test data encompass five requirement categories: 40 functional requirements, 10 interface requirements, 12 performance requirements, 22 physical requirements, and 21 design constraints. This balanced distribution across categories within the test set ensures a comprehensive evaluation of each model's extraction performance across different requirement types.  
Based on the elements outlined in the information extraction template in Section 3.2, we adopted the \"BIO\" annotation strategy, where \"B\" denotes the beginning of an entity, \"I\" represents the inside of an entity, and \"O\" indicates non-entity segments. Following the information extraction template, we established a corresponding labeling system, which is shown in Table 7. The annotated dataset has been uploaded to the Hugging Face platform for further research use (dataset access link: [[https://huggingface.co/datasets/OrangeSofty/Metro_Code_Chapters_18_to_22_Data/tree/main/out_bio_txt](https://huggingface.co/datasets/OrangeSofty/Metro_Code_Chapters_18_to_22_Data/tree/main/out_bio_txt)]([https://huggingface.co/datasets/OrangeSofty/Metro_Code_Chapters_18_to_22_Data/tree/main/out_bio_txt](https://huggingface.co/datasets/OrangeSofty/Metro_Code_Chapters_18_to_22_Data/tree/main/out_bio_txt)), accessed on 12 February 2025).
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 4 Results
### Validation of Three Radiometric Calibration Coefficients

Table 7: The relative difference between the TOA predicted and measured radiance (in units of \(W\cdot m^{-2}\cdot sr^{-1}\cdot\mu m^{-1}\)). Figure 13: The relative difference between the TOA predicted and measured radiance with different coefficients.
## 4 Discussion
### Comparative Analysis  
The pilot point placement results expose the applicability of the pilot point calibration methodology in the small-scale heterogeneous area focused on the estimation of hydraulic conductivity field similarly investigated in other literature [10; 16; 17]. The structural and stratigraphic traits and trends in these research are identical. Literature [10] emphasizes the importance of pilot point methodology for estimating transmissivity in limited field data. In contrast, the others use pilot points in surface groundwater interaction problems regarding data scarcity and combine the zonation and the pilot point method with exploring the effect of pilot point location in Karst. In addition, These works of literature emphasize the validation of the steady-state model conducted using the PEST by the database approach according to the review of the groundwater flow and calibration evaluation [4]. It has been illustrated that groundwater model calibration should be evaluated statistically prior to sufficient observation data.  
Moreover, despite several methods associated with groundwater modeling, such as the artificial neural network (ANN), or the surrogate model using a genetic algorithm, the MODFLOW model takes advantage of the dynamic response of the aquifer and the application of automatic calibration [49]. The modeling using an ANN often requires extra data, such as pumping data and climate data [50]. Therefore, the MODFLOW model remains the vastly used model chosen for the present problem. These models often present an uncertainty associated with the model input parameters that could significantly impact the simulation outcome. It can be evaluated by the sensibility analysis when using the PEST for automatic calibration while needing specific technics such as full Bayesian technics [51] or Markov Chain Monte Carlo-based uncertainty analysis method [52]. However, the findings of this study could aid in focusing on areas of high uncertainty where more field data is needed to improve model calibration. It also aids in positioning pilot points for a more reliable calibration. Admittedly, the uncertainty problem could be the future development of the present situation assessed with machine learning techniques.
# MAC Address Anonymization for Crowd Counting
## 2 Results
### Requirement 4: A Collision Rate of Less Than \(10^{-9}\) for \(10^{7}\) MAC Addresses
#### 2.4.1 Mathematical Foundations  
Variable \(m\) denotes a number of possible outputs, such that \(\log_{2}(m)\in\mathbb{N}\), and \(\{0,1\}^{\gamma}\) denotes the set of all binary sequences of \(\gamma\) bits. We consider a function \(h:\mathcal{X}\rightarrow\{0,1\}^{\log_{2}(m)}\) (with \(n:=\text{card}(\mathcal{X})\)). Hereafter, \(h\) is a hash function, whose output is approximately uniformly distributed in \(\{0,1\}^{\log_{2}(m)}\) ([21], Section 9.7.1). It could also be a token generator, in which case the uniform distribution assumption is exactly satisfied.  
We follow the standard terminology in the study of hash tables and refer to \(m\) and \(n\) as the _number of buckets_ and the _number of inserts_, respectively. Similarly, \(\alpha:=n/m\) is called the _load factor_. Finally, \(Y^{(n,m)}\) denotes the (random) number of collisions when inserting \(n\) values into \(m\) buckets (with the uniform distribution assumption). Theorem 1 provides an exact--yet numerically unstable--formula of \(\mathbb{E}\Big{[}Y^{(n,m)}\Big{]}\).  
**Theorem 1**.: _For \(n\) inserts into \(m\) buckets, the collision rate, \(\mathbb{E}\big{[}Y^{(n,m)}\big{]}/n\), is_  
\[\frac{\mathbb{E}\Big{[}Y^{(n,m)}\Big{]}}{n}=1-\frac{m}{n}\bigg{(}1-\bigg{(} \frac{m-1}{m}\bigg{)}^{n}\bigg{)}, \tag{1}\]  
_where the uniform distribution assumption has been used._  
Proof.: See the Appendix A.  
As shown in Figure 3, (1) suffers from numerical instabilities for sufficiently low values of the load factor. Therefore, for systems whose load factors are too low for (1) to provide accurate estimates, approximations are needed. In particular, to ensure such approximations are accurate enough, they should have proven analytical error bounds. Theorem 2 proposes three approximations of \(\mathbb{E}[Y^{(n,m)}]/n\), with proven error bounds. Only the penultimate and last inequalities of Theorem 2 are numerically stable.  
**Theorem 2**.: _For a degree of approximation \(K\geq 2\), a number of inserts \(n\geq 2\), and a load factor \(\alpha\leq 1\), there exist error terms \(\delta(\alpha,n)\) and \(R_{K-1}(\alpha)\) such that_  
\[\frac{\mathbb{E}\left[Y^{(n,m)}\right]}{n} =1-\alpha^{-1}(1-\exp(-\alpha))+\delta(\alpha,n) \tag{2}\] \[=\sum_{k=1}^{K-1}\frac{\alpha^{k}(-1)^{k+1}}{(k+1)!}+\delta( \alpha,n)+R_{K-1}(\alpha)\] (3) \[=\frac{\alpha}{2}+\delta(\alpha,n)+R_{1}(\alpha), \tag{4}\]  
_where_  
\[-\sqrt{\frac{\alpha^{2}}{n^{2}-\alpha^{2}}\left(\frac{\pi^{2}}{6}-1\right)} \leq\delta(\alpha,n)\leq 0, \tag{5}\]  
\[|R_{K-1}(\alpha)|\leq\frac{\alpha^{K}}{(K+1)!}, \tag{6}\]  
_and, in particular,_  
\[\frac{|R_{1}(\alpha)|}{\alpha/2}\leq\frac{\alpha}{3}. \tag{7}\]  
Proof.: See the Appendix A.