esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Impact of PV-Thermal and Nanofluids on the Desalination Process by Flashing
## 5 Discussion and Analysis
As observed in Figures 21 and 22 and as previously discussed elsewhere in this paper, the availability determined by Equation (15) diminished progressively along the flashing chambers and reached the lowest value at the last chamber. It also appears that the nanofluids concentrations had little impact on the availability during the process of the flashing. On the other hand, it can be observed from the results presented in these figures that the nanofluid SiO\({}_{2}\) had the highest availability among the other nanofluids, including water as the base fluid in the flashing chambers. Finally, in order to investigate the impact of the nanofluids on the brine out flow from each chamber, Figures 23 and 24 were plotted at different nanofluids under investigation where the solar radiation was at 1200 w/m\({}^{2}\). In general, the brine outflow decreased progressively across the flashing  
Figure 4:6 Availability (Btu/lb) at 1200 w/m\({}^{2}\) at 10% concentration and different nanofluids. Figure 4:7 Irreversibility (Btu/lb) at 1200 w/m\({}^{2}\) at 20% concentration and different nanofluids. chambers and was the lowest at the last chamber. This was expected since at each chamber, an amount of vapor was flashed, which reduced the brine flow progressively along the flashing chambers. It also appears from these data that the higher the concentration of the nanofluids, the higher the brine flow across the flashing chamber, which was also expected. It was also observed that the nanofluid Ai\({}_{2}\)O\({}_{3}\) produced the highest brine flow across the flashing chamber among the nanofluids under investigation, including water as the base fluid. Figure 4:8 Brine flow out (kg/s) at 1200 w/m\({}^{2}\) at 10% concentration and different nanofluids. Figure 4:9 Availability (Btu/lb) at 1200 w/m\({}^{2}\) at 20% concentration and different nanofluids. Finally, it is worth mentioning that lower nanofluids concentrations did not yield the minimum thermal capacities needed for the flashing process of the brine; therefore, 10% and 20% nanofluid concentrations were used. Their impact on the pump power was taken into consideration while calculating the availability and irreversibility during the flashing process.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 3 Cluster Tree-Based Routing Protocol
### Cluster Head Selection
The Nash equilibrium is a decision state in game theory. In each cluster, the node with the maximum payoff \"\(p\)\" value acts as a \(CH\) node in that particular round. The \(CH\) node calculation in that particular round is computed in Equation (14). \[CH_{k,l}=\underset{k=1}{max}\underset{\text{to }\text{Cl}=1\text{ to }m}{max}p(CM_{k,l}) \tag{14}\]  
where \(p(CM_{k,l})\) indicates the payoff value of the \(l\)th \(CM\) in the cluster \(k\), \(m\) indicates the total number of nodes in the cluster, and \(C\) indicates the total number of clusters in the network.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 5 Results
### Baseline
Nevertheless, in the periods in which only three satellites are available, the rover uncertainty attains values above 10 m (related to the degraded HDOP) such that the rover-induced DEM covariance increases, as shown in Figure 11b. Table 9 shows the statistics for the rover positioning with and without the usage of DEMs. It can be clearly seen that all statistics reported in Table 9 improve substantially when introducing the additional DEM observation into the positioning algorithm. The availability (e.g., percentage of the time the rover can determine its position) is increased from 52% to 82%, as the rover is able to determine its position with only 3 satellites when using the DEM. Furthermore, as also shown in Figure 11, the two periods in which 4 satellite are visible are now connected. As such, the period of continuous availability increases from slightly more than 5 h to almost 20 h. Note, as mentioned earlier, that the visibility window is expected to be better with the optimized orbital parameters under definition in the LCNS Phase A/B1 studies. Finally, the 68, 95 and 99.7 percentiles (considering the time variation of the 3-sigma horizontal covariance)of the horizontal position covariance are provided. As expected, the covariance is significantly reduced by constraining the vertical position with a very accurate measurement. Figure 11: Performance of the LCNS position solution. (**a**) shows the horizontal (2D) covariance (3-sigma) of both the LCNS-only as well as the LCNS and DEM solution, while (**b**) shows the 1-sigma uncertainty of the DEM measurement. Figure 12 shows the cumulative density function (CDF) of the time variation of the horizontal covariance (3-sigma) for both the baseline (i.e., LCNS-only) and improved configuration (LCNS + DEM). While CDF is quite similar at lower uncertainties (until a CDF of 0.5), the CDF for the improved configuration (LCNS + DEM) is shifted to the left with respect to the baseline configuration (LCNS-only) mainly related to the improvement in geometry due to the additional DEM measurement.
###### Abstract
[PERSON] designed and implemented an intelligent knowledge question-and-answer system for pig farming based on bi-GRU and SNN methods, combined with the LTSM deep learning method [30]. NER methods have found extensive applications in the agricultural domain and other specific fields [31; 32; 33; 34; 35; 36]. Nonetheless, there remains an apparent gap in current research concerning the accurate recognition of named entities within the domain of pig diseases in Chinese. Pig disease data are characterized by complex entities, fuzzy boundaries, and domain-specific vocabulary, which encompasses specialized terminologies drawn from the domains of animal husbandry and veterinary science. Furthermore, the resources in the field of pig diseases are confined and dispersed, exacerbating the scarcity of publicly available benchmark corpora and labeled datasets specific to this domain in Chinese. While considerable research has been devoted to NER systems in human medicine [37; 38; 39; 40], it remains impractical to directly transfer such models to the domain of pig diseases due to the domain-specific rules and vocabulary governing this domain. Hence, named entity recognition in the field of pig diseases needs to be further explored. A model of Pig Disease Chinese Named Entity Recognition (PDCNER) is proposed in this paper. The main contributions of the paper are as follows:1. We propose a simple yet effective NER model that integrates enhanced lexicon and contrastive learning for the complex pig disease domain, making the model more sensitive to texts in this domain and improving predictions for entities. The lexicon-enhanced BERT facilitates the direct integration of external lexicon knowledge of pig diseases into BERT layers via a Lexicon Adapter layer. 2. To enrich the semantic feature representation and improve performance under data scarcity conditions, we propose a lexicon-enhanced contrastive loss layer on top of the BERT encoder. Experimental results on small sample scenarios and common public datasets demonstrate that our model outperforms other models. 3. Given the lack of an annotated corpus for the pig disease domain, we collected and annotated a new Chinese corpus and annotated datasets consisting of 7518 entities. To address the insensitivity of word segmentation caused by the specialization of the pig disease domain, we constructed a lexicon for identifying specific terms in pig diseases using frequency statistics methods under the guidance of veterinarians. The remainder of the paper is organized as follows. Section 2 introduces the dataset and method proposed in this paper. Section 3 provides a detailed description of our experiments and analyzes the results. Finally, the conclusions are presented in Section 4.
# Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling
## 1 Introduction
This approach mitigates class imbalance by oversampling minority attack classes and downsampling overrepresented ones, ensuring robust detection across diverse network domains, from Content Delivery Network (CDN) to industrial systems. DS-FedIDS enables each client to tailor its model through personalized layers that adapt to local traffic patterns, while collaborating on a global model to capture shared attack signatures. By integrating dynamic sampling with defenses against adversarial perturbations and maintaining accurate global attack knowledge, DS-FedIDS addresses a multifaceted optimization challenge, achieving improved detection efficacy, convergence speed, and resource efficiency in a privacy-preserving paradigm suitable for heterogeneous settings. In the following sections, we present a comprehensive exploration of the DS-FedIDS framework and its contributions to federated intrusion detection. Section 2 reviews related work on personalized federated learning, focusing on frameworks like Federated Learning with Personalization Layers (FedPer) that address non-IID data. Section 3 details the DS-FedIDS architecture, its dynamic up/down sampling mechanism to mitigate class imbalance, and the experimental evaluation across diverse network environments,including CDN, IoT, and industrial systems. This section also covers the dataset preprocessing, non-IID data characteristics, feature distributions, and a comparative analysis with other personalized FL frameworks. Section 4 summarizes the key achievements of DS-FedIDS, discusses practical considerations such as computational efficiency and energy consumption in resource-constrained settings, and outlines future research directions. This research advances the integration of federated learning, network security, and dynamic data balancing, addressing the growing demand for privacy-preserving, resource-efficient solutions in modern cybersecurity landscapes.
## 3 Results
### Microbiological Evaluation
#### 3.2.2 Susceptibility of Biomaterials to Microbial Adhesion and Mixed Biofilm Formation  
Table 3 shows differences between examined microbial strains and their ability to adhere to surfaces of nanosized apatite materials. The three strains of microorganisms exhibited comparable, increasing mean levels of the attached apatite cell material, with minimal distinction between nHAp and nFAp material. In the case of _C. albicans_, over the course of 24 h, adherence eventually reaches lower levels of CFU/mL in the presence of nFAp (3.4 \(\times\) 10\({}^{6}\)) than in the presence of nHAp (1.62 \(\times\) 10\({}^{7}\)). In contrast, the _L. rhamn_ demonstrates slightly elevated values of CFU/mL over the course of 24 h when exposed to nFAp (5.61 \(\times\) 10\({}^{8}\)) in comparison to nHAp material (4.28 \(\times\) 10\({}^{8}\)). The most significant noticeable difference in the number of attached cells is _S. mutans_ at 24 h after contact with the biomaterials, where it is exceedingly higher in the case of contact with nHAp (1.31 \(\times\) 10\({}^{10}\)) than in contact with nFAp (4.19 \(\times\) 10\({}^{7}\)). Figure 5: Culture of microbial colonies. 2.3 Statistical Analysis of Susceptibility of Biomaterials to Bacterial Adhesion and Effect of nHAp and nFAp on Selected Microbial Strains  
In this scientific work's statistical analysis, the pivotal element of the script executing the specified test is as follows:  
nparLD(Abundance-Time*Material, data = data, subject = \"Sample\", time1.order = c(2, 4, 8, 24), group1.order = c(\"K\", \"nHAp\", \"nFAp\")). The analysis can be found in Tables 4 and 5. Additional information regarding statistical analysis of the effect of the materials and time on selected microorganisms can be seen in Figure S1 and S2 in the Supplementary. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strains** & **Variable** & **Statistic** & \(D_{f}\) & \(p\)**-Value** \\ \hline \multirow{4}{*}{_C. albicans_} & Time & 224.26 & 1.72 & 3428 \(\times\)\(10^{-85}\) \\ \cline{2-5}  & material & 1.71 & 1.18 & 1902 \(\times\)\(10^{-1}\) \\ \cline{2-5}  & Time \(\times\) material & 23.53 & 1.93 & 1176 \(\times\)\(10^{-10}\) \\ \hline \multirow{4}{*}{_S. mutans_} & Time & 119.30 & 1.65 & 4000 \(\times\)\(10^{-44}\) \\ \cline{2-5}  & material & 25.88 & 1.00 & 3620 \(\times\)\(10^{-7}\) \\ \cline{2-5}  & Time \(\times\) material & 41.98 & 1.83 & 1356 \(\times\)\(10^{-17}\) \\ \hline \multirow{4}{*}{_L. rhamnosus_} & Time & 82.69 & 1.03 & 3073 \(\times\)\(10^{-20}\) \\ \cline{2-5}  & material & 15.72 & 1.16 & 2607 \(\times\)\(10^{-5}\) \\ \cline{2-5}  & Time \(\times\) material & 8.04 & 1.46 & 3142 \(\times\)\(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical analysis of the effect of nHAp and nFAp on _S. mutans_, _L. rhamnosus_, and _C. albicans_ strains during the first 24 h presented in the table form. Statistics performed using the Relative Treatment Effect with the aid of the R-package “nparLD”.
# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
###### Abstract
The key contributions of this study are as follows:  
* Developed a hybrid dataset combining real and synthetic images generated by FLUX.1 to enhance AI-driven poultry monitoring;
* Evaluated the impact of synthetic data on YOLOv9-e model accuracy, demonstrating its feasibility in reducing reliance on real-world data;
* Implemented an automated annotation pipeline using Grounding DINO and SAM2, significantly reducing manual labeling effort;
* Analyzed different dataset compositions to determine the optimal balance between real and synthetic images for object detection in poultry farming;
* Provided a practical framework for integrating generative AI into smart agriculture applications, ensuring model generalization and efficiency;
* Demonstrated that a combination of real and synthetic images can achieve comparable accuracy to larger, real-only datasets, validating the use of synthetic data augmentation. The paper is structured as follows: Section 2 provides an overview of related work, Section 3 describes the materials and methods, including dataset preparation and experimental setup, Section 4 presents the results and discussion, and Section 5 concludes with key findings and future research directions.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## 3 Results  
### Simulations  
#### 3.1.1 Parabolic Flow in Vessel Phantom  
In Figure 4, the absolute bias and SD values are visualized as a function of the PRF for the parabolic flow simulations. The statistics were calculated while taking into account all simulated beam-to-flow angles. When the PRF increases, i.e., when the inter-frame displacements become smaller (see Table 2), the bias and SD for both the magnitude and angle increase. This effect is most dominant for the conventional grid, while the PSF-shape-based grid shows a more stable performance over the PRF range. Both PSF_1 Dpar and PSF_2 Dcub show high accuracy and precision, i.e., low bias and SD values, for the entire range of PRFs. For both methods, the median magnitude bias is below 10% and the angle bias remains below 0.55\({}^{\circ}\).  
Figure 5 visualizes the dependency of the performance of the methods on beam-to-flow angle. The results were obtained using a PRF of 12.5 kHz. The accuracy and precision of the velocity angle estimates show a dependency on the beam-to-flow angle, whereas the magnitude estimate is less influenced by this. When the velocity field contains larger axial velocity components, i.e.,  
Figure 4: Median and interquartile ranges of the absolute bias (**a**) and standard deviation (SD) (**c**) for the velocity magnitude, and the absolute bias (**b**) and SD (**d**) for the velocity angle as a function of PRF. Statistics were calculated taking into account all beam-to-flow angles.  
for beam-to-flow angles different from 90\({}^{\circ}\), the angle bias and SD values increase, especially for conv_1 Dpar and conv_2 Dcub. The PSF-shape-based grid combined with either 1D parabolic or 2D cubic interpolation shows a stable performance over the range of angles, with almost no increase of angle error and SD for angles other than 90\({}^{\circ}\).  
Figures 4 and 5 show a competitive performance for PSF_1 Dpar and PSF_2 Dcub. Both methods show a stable performance for all PRFs, i.e., for 0 to 600 \(\upmu\)m inter-frame displacements (see Table 2), and for all beam-to-flow angles. However, significantly lower median SD values for both velocity magnitude and angle can be appreciated for the PSF_2 Dcub method (Wilcoxon signed-rank, \(p<0.05\)). The median magnitude SD is at maximum 5.8% for PSF_1 Dpar and 4.2% for PSF_2 Dcub. Furthermore, the median angle SD is maximally 0.9\({}^{\circ}\) and 0.6\({}^{\circ}\) for PSF_1 Dpar and PSF_2 Dcub, respectively.
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 4 Experimental Results and Discussion
### Experimental Results and Discussion
#### 4.2.1 Static Test
However, the carrier tracking performance of T-GNSS under moderate scintillation (i.e., SV #12) decreases and goes on to suffer large losses under strong scintillation (i.e., SV #25). On the contrary, the carrier tracking of IF-DDC has a much better performance compared with T-GNSS. Although the carrier tracking performance decreases under strong scintillation, most of the PLIs of IF-DDC are still better than 0.5. The main reason for the performance improvement of IF-DDC is that the IF-DDC has a joint L1 C/A and L2 CM vector-tracking-based structure, which can provide more measurement information and share better tracking information to aid weak signal channels with strong signal channels. Besides this, the tracking and prefiltering of IF-DDC has adaptive process noise covariance and measurement noise covariance which can reach an optimal equivalent bandwidth during the tracking processing to improve the tracking robustness and accuracy. It is noted that the L1 C/A signal has a better tracking performance than the L2 CM signal, especially under moderate and strong scintillation, due to the different received RF signal strength and different ionospheric scintillation characters (i.e., ionospheric scintillation strength corresponds to the carrier frequency) between the L1 C/A and L2 CM signals.
# Regional Seismic Damage Simulation of Corroded RC Frame Structures: A Case Study of Shenzhen City
## 3 Corrosion Simulation of Coastal RC Buildings
### Degree of Corrosion  
Once the corrosion initiation time of reinforcements in concrete is estimated, Equations (3)-(7) can be used to determine the section loss rate of reinforcements caused by corrosion [46].  
\[\Psi(t)=\left[1-\left(\frac{D(t)}{D_{0}}\right)^{2}\right]\times 100 \tag{3}\]  
\[D(t)=D_{0}-\alpha P_{\text{ave}}(t) \tag{4}\]  
where \(\Psi(t)\) is the degree of corrosion of reinforcements at time \(t\); \(D(t)\) is the diameter of reinforcements at time \(t\); \(D_{0}\) is the initial diameter of reinforcements; \(\alpha\) is the pitting coefficient, which is 2.0 for general corrosion and 4-8 for pitting corrosion [47]; \(P_{\text{ave}}(t)\) is the average corrosion depth of mass loss per unit volume at time \(t\), which can be calculated by Equation (5).  
\[P_{\text{ave}}(t)=\kappa\int_{T_{\text{corr}}}^{t}i_{\text{corr}}(t)dt \tag{5}\]  
where \(k\) equals 0.0116, which helps to convert the unit from \(\mu\)A-cm\({}^{-2}\) to mm\(\cdot\)year\({}^{-1}\)[1]; and \(i_{\text{corr}}(t)\) is the corrosion current density at time \(t\) in \(\mu\)A-cm\({}^{-2}\). The corrosion current density \(i_{\text{corr}}(t)\) at time \(t\) can be calculated according to Equations (6) and (7), based on the study of [PERSON] and [PERSON] [1].  
\[i_{\text{corr}}(t)=0.85i_{\text{corr0}}(t-T_{\text{corr}})^{-0.29} \tag{6}\]  
\[i_{\text{corr0}}=\frac{37.8(1-w/c)^{-1.64}}{X} \tag{7}\]  
where \(i_{\text{corr0}}\) is the corrosion current density at the time of \(T_{\text{corr}}\), and \(w\)/\(c\) is the water-cement ratio of concrete. \(X\) is the cover of concrete in centimeter.
## 2 Measurement Principle
### Signal Processing Algorithm  
In the tracking local minimum (TLM) algorithm [30], the normalized chromatic output \(I_{\text{TLM}}\) is defined as the intensity ratio of the two fiber detector outputs [27; 28; 29; 30]. Denoting the chromatic output to be captured by the off-axis reference detector unit as \(I_{\text{2 ref\_m}}\), the normalized chromatic output in the newly proposed off-axis differential method can be expressed as follows:  
\[I_{\text{ITLM}}(u,\,d_{m})=\frac{I_{\text{1 mea}}(u)}{I_{\text{2 ref\_m}}(u,\,d_ {m})} \tag{6}\]  
Figure 5a shows a graphical diagram of the TLM algorithm [30]. The wavelength at a specific peak position of the normalized chromatic output \(I_{\text{ITLM}}\), which corresponds to the wavelength at the first local minimum of the confocal output \(I_{\text{2 ref\_m}}\), changes linearly with respect to the Z-directional displacement of the target surface. A displacement of the target mirror can thus be acquired by tracking the wavelength at the first local minimum of the confocal output through detecting the peak wavelength in the normalized chromatic output \(I_{\text{ITLM}}\). Figure 5b shows a schematic of  
Figure 4: Schematic of the reference fiber detector settings by the proposed off-axis differential method and conventional differential method. (**a**) Schematic diagram of the reference fiber detector setting by the conventional differential method; (**b**) schematic diagram of the reference fiber detector setting by the proposed off-axis differential method.  
the wavelength-to-displacement encoding by the signal processing algorithm. In the tracking local minimum algorithm [27], the asymmetric peak in the normalized chromatic output \(I_{\mathrm{ITLM}}\) could make it difficult under practical conditions to precisely determine the peak wavelength due to the out-of-focus noise [30]. To address the issue, a centroid wavelength of the peak is employed as the peak wavelength.
## 3 Infrared Image Segmentation
### Gray Difference Discrimination Method  
The gray difference discrimination method is used to calculate the absolute value of the gray value difference between the seed pixel and the neighborhood pixel. If it is less than a threshold T, the pixel is classified in the region where the seed is located for neighborhood extension at the seed position. Usually, four-neighborhood or eight-neighborhood expansion methods are used [20; 21; 22]. Figure 9 shows an illustration of the neighborhood expansion methods.  
Figure 8: Illustration of all initial seeds.  
Limiting Growth Range Condition  
Because, in infrared images, copper rods with a fixed width are placed vertically and equidistantly, the growth area can be limited to roughly cover a single copper rod width. Therefore, the position of pixel \(I(i,j)\) in different growth areas should match the following conditions:  
\[\left\{\begin{array}{c}0\leq i\leq EdgePos+50\\ x-18\leq j\leq x+18\end{array}\right. \tag{2}\]  
In Equation (2), \(x\) is the position of the initial seed for the copper rod on the \(x\)-axis, and _EdgePos_ is the position of the cell wall edge on the \(y\)-axis. Figure 10 shows an illustration of the limiting growth range condition.  
Figure 11 shows the result of the copper rod segmentation in different conductive states based on the region growing algorithm.