esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Regulation Method and Characteristics Analysis
### Pump Bypass Model and Control Characteristics Subsection
It is assumed that the maximum linear adjustment flow rate of the TCB \(Q_{m0,max}\) is a constant value; under different pressure differences in TCB, the linear adjustable range of flow rate is related to the characteristics of the bypass valve as follows:  
It can be concluded that the operating characteristics of the bypass valve have a great influence on the control range and the control ability of the TCB; when the maximum control flow rate and control opening range of the TCB under constant pressure are determined, the larger the flow rate at the maximum opening of the valve, the wider the control range of the TCB. As shown in Figure 13a, when the constant pressure of the TCB is set at 120 kPa, the flow rate at the maximum opening of the valve is 450 L/h, the regulating range is from 350 L/h to 600 L/h and the regulating range is 250 L/h. When the flow rate of the valve is 225 L/h at the maximum opening, the limit resistance coefficient of the valve increases obviously and the minimum regulating flow rate of the TCB rises to 520 L/h;under constant pressure, the regulation range of the constant pressure region was reduced to 80 L/h and the regulation ability was obviously reduced. Under the same range of valve opening and maximum regulating flow rate, it can be seen from Figure 13b, with the increase in the working pressure difference in the TCB, the working flow rate of the drive pump decreases; the constant pressure control range of TCB increases obviously. If the flow resistance coefficient at maximum opening is maintained as a constant, it can be seen that, if the working pressure of the TCB is adjusted to 150 kPa, the minimum constant pressure regulating flow rate is 235 L/h; the regulation range under constant pressure increases to 365 L/h, which is a 46% increase from 120 kPa. After the above analysis, the pump bypass structure can effectively control the output flow and pressure difference in the TCB; the control range of the flow rate of the system under constant pressure will be increased by increasing the working pressure of the TCB; at the same time, the output flow rate of the drive pump and the power of the system will be reduced, which can lead to a higher control efficiency of the TCB. It is necessary to evaluate the power resource of the system, the demand of flow rate and the pressure drop of the  
Figure 12: The pressure difference-flow rate characteristics of drive pump and TCB. Figure 13: The relationship between the characteristics of bypass valve and the control range of TCB: (**a**) the relationship between Q\({}_{\text{smo},min}\)\(\sim\) Q\({}_{\text{smo},max}\): (**b**) the relationship between \(\Delta P_{o}\)\(\sim\) Q\({}_{\text{mr},max}\). TCB to determine the operating characteristics of the bypass valve and the suitable bus pressure parameters.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Regional Forecasting Performance Analysis  
#### 3.2.1 Daily Load Pattern Analysis for Shanghai  
Figure 3 presents a comparative visualization of predicted versus actual load for Shanghai on 1 July 2024. Several noteworthy characteristics and issues can be observed:  
1. Overnight Load Overestimation: Between 00:00 and 03:00, the model systematically overestimated the actual load, suggesting potential changes in overnight energy consumption patterns not captured by historical training data.  
Morning Valley Discrepancy: A significant forecasting error appears during the early morning hours (04:00-06:00), where the actual load reached approximately 150,000 units while the prediction remained at around 190,000 units. This 26.7% difference represents the largest proportional error in the 24 h cycle and indicates a potential structural change in morning industrial or commercial activity.
* Midday and Afternoon Peak Accuracy: Between 09:00 and 15:00, the model affirms good agreement with the actual peak load pattern but consistently underestimates the magnitude by approximately 25,000-30,000 units (6.5-7.0%). This systematic bias suggests the need for the recalibration of the peak-weighted loss function.
* Evening Transition Period: From 18:00 to 22:00, while the model correctly captures the declining trend, it again underestimates the actual load by a relatively consistent margin of approximately 20,000 units (5.0-5.5%).
## 2 Related Works  
### Traditional Topic Modeling  
Since LDA has become a commonly used topic modeling method, various extensions of LDA have been developed to solve specific tasks efficiently. For example, supervised LDA (SLDA) [16] extends LDA by incorporating various response types assigned to each document. SLDA jointly models documents and responses to find latent topics that best predict the response variables for future unlabeled documents.  
Another extension, labeled LDA (L-LDA) [17], learns from documents labeled with multiple tags. This model can perform appropriate word tagging for untagged documents, showing improved expressiveness compared with LDA. Both SLDA and L-LDA represent transitions from unsupervised to supervised learning, enhancing the applicability of topic modeling.  
Interdependent LDA (ILDA) [18] models the conditional interdependency between latent aspects and ratings, enabling the identification of aspects and prediction of ratings from online product reviews without human supervision. This approach addresses the need to accurately interpret customer feedback.  
Frequency-LDA (FLDA) and dependency-frequency-LDA (DFLDA) [19] extend LDA to address multi-label classification problems. FLDA uses the supervision of label frequencies to control document-label Dirichlet priors, whereas DFLDA considers dependencies between labels by introducing a label-shared topic level. Both models improve classification performance by incorporating label-related information.  
TopicSpam [20] leverages generative LDA-based topic modeling to detect fake reviews, outperforming existing baselines. Similarly, one study [21] conducted multilingual topic modeling of Facebook data to identify COVID-19 tracking trends by country, demonstrating the versatility of LDA in different contexts.  
Finally, time-decay LDA (T-LDA) [22] represents a novel collaborative filtering (CF) algorithm that incorporates time attributes to improve performance. This model addresses the limitations of traditional CF algorithms, which do not consider the temporal aspects of user behavior.
###### Abstract
The results are in good agreement with the corresponding RCS data in the ship target simulation; (4) In terms of practicability, the proposed MTDC method 1 and range inverse method 1 can remove the Doppler phase influence and further acquire the targets' RCS distribution without target estimation. The target peak identification results based on 33 m echo sampling can be utilized to verify target recognition. Moreover, the results were simulated and analyzed under clutter and noise backgrounds. The rest of the sections in this paper are organized as follows. Section 2 discusses the MIMO BDS signal detection model and lists existing problems. The Doppler compensation methods and range detection resolution design of the BDS signal based on MIMO multiple-target detection are discussed in Section 3. In Section 4, the simulation results and analysis are obtained. Finally, the paper is concluded in Section 5.
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood Social Sensors  
Social sensor data has already demonstrated its value in densely populated city-level flood monitoring and early warning. Citizen crowd-sourcing has been proposed as an alternative to adaptive data collection in the context of the widespread use of advanced sensor technologies and wireless sensor network for flood management [115], where individuals can provide volunteered geographic information (VGI) [116]. These data are particularly important in areas where there is no monitoring. [PERSON] [117] demonstrated that data collected from Twitter and MyCoast (a crowdsourcing application) can complement super-resolution datasets for urban flooding. [PERSON] [118] combined real-time flow sensor data, social media tweets, and remotely sensed imagery to create a near-real-time flood inundation model using the normalized difference water index (NDWI). Data from travel navigation platforms, including crowdsourced traffic and flood event data, can be used to estimate flood impacts on traffic [119]. Citizen science-based integration of flood information to estimate flood extents in areas with limited terrestrial sensors can complement physical sensor measurements. [PERSON] [120] demonstrated the usefulness of assimilating crowdsourced observations from a heterogeneous network of static physical, static social, and dynamic social sensors. During Hurricane Harvey in 2017, crowdsourced data and other information were used to predict the probability of flooding, validate model performance, and improve flood extent estimates based on 311 flood call information [121]. Although big data from different physical sensors are widely available but they cannot be used alone in many cases to meet (near) real-time data because of spatiotemporal resolution. The distributed data fusion framework proposed by [PERSON] [122] integrates remote sensing and social media data, which shows high processing efficiency and potential for  
Figure 5: Totally free and non-free flood management satellites.  
fast computation in real-world scenarios. In addition to solving the problem of data quality through data fusion, machine learning algorithms also offer new solutions for efficient information extraction from social media data [123; 124], but due to API limitations, current research mainly relies on Twitter datasets. Unfortunately, Twitter has removed the ability to freely access tweets.  
In practice, data on physical disaster attributes and social crowdsourcing are complementary, particularly in underdeveloped and disadvantaged areas, where they compensate for sparse physical sensors and unreliable data. However, these regions often have outdated mobile devices and sparse populations, raising concerns about the accuracy of social media data. Therefore, integrating physico-social sensor data is essential for effective flood disaster response.
# Phase Behavior and Rheological Properties of AES/CAPB/H\({}_{2}\)O Ternary System
## 4 Conclusions  
In this work, the phase behavior of the AES/CAPB/H\({}_{2}\)O system was investigated, and three single phases (L\({}_{1}\), H\({}_{1}\), and L\(\alpha\)) and one transition region (L\({}_{1}\)\(\rightarrow\) H\({}_{1}\)) were observed. The left half of the phase diagram was almost occupied by the L\({}_{1}\) phase, while in the right half of the phase diagram, the area occupied by the H\({}_{1}\) phase was significantly larger than the L\(\alpha\) phase. The long and narrow L\({}_{1}\)\(\rightarrow\) H\({}_{1}\) transition region was located between the L\({}_{1}\) phase and the H\({}_{1}\) phase. In terms of viscosity distribution, the H\({}_{1}\) phase had the largest viscosity (>200 Pa-s), followed by the L\(\alpha\) phases and the L\({}_{1}\)\(\rightarrow\) H\({}_{1}\) transition region (10-200 Pa-s), and the L\({}_{1}\) phase had the smallest viscosity. Additionally, the viscosity results showed that CAPB and low concentrations of salts had a synergistic effect on the thickening of the AES/CAPB/H\({}_{2}\)O system. Rheological results revealed that in a salt-containing formula, a small change in the proportion of CAPB may cause the AES/CAPB/H\({}_{2}\)O system to transform from a fluid to a gel-like state. This work not only enriches the phase diagram of surfactant systems but also has important guiding significance for the design and development of cleaning products.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14093605/s1](https://www.mdpi.com/article/10.3390/app14093605/s1)]([https://www.mdpi.com/article/10.3390/app14093605/s1](https://www.mdpi.com/article/10.3390/app14093605/s1)), Figure S1: the two-dimensional SAXS patterns of (a) sample \(\alpha\), (b) sample \(\beta\), (c) sample \(\delta\) and (d) sample \(\varepsilon\) at 25 \({}^{\circ}\)C.  
Conceptualization, G.Z.; methodology, X.W.; formal analysis, X.W. and G.Z.; investigation, X.W.; data curation, X.W. and P.W.; writing--original draft preparation, X.W.; writing--review and editing, G.Z. and P.W.; supervision, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Foundation of Hubei Provincial Key Laboratory of Green Materials for Light Industry and Collaborative Innovation Center of Green Light-weight Materials and Processing (201907B08).  
Not applicable.  
Data Availability Statement: Data is contained within the article and Supplementary Materials.  
Guangzhou KLD New Material Technology Co., Ltd. is greatly acknowledged for providing experimental materials.  
The authors declare no conflicts of interest.
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 3 Cluster-Based Channel Assignment
### Cluster-Based AIIM  
As has been mentioned already, in this paper we propose a channel assignment technique that is based on AIIM. Most of the channel assignment proposals for Wi-Fi networks in the literature ignore the STAs in the process. However, we claim that STAs must be taken into account because they represent the final customers receiving service, and their number has been increasing over the years. The proposal of this paper is a generalization of AIIM where we consider that, in addition to the interferences between APs, two clusters also interfere with each other if some of their STAs do. Another perspective of this generalization is to consider the wider concept of a cluster for considering interferences instead of just APs. In Figure 3 we show, by means of an example, a comparison of the interferences considered in AIIM and in CB-AIIM. In Figure 3 we can see five clusters with different colors for identifying each, where each cluster has an AP (large circles) and several STAs (small squares). For that setting, we show with dashed red lines the interferences that are considered in AIIM. However, in Figure 3 we show the same setting and how it would work in CB-AIIM. As can be seen, the difference is that the interferences are taken into account between STAs belonging to different clusters, as dotted red lines show.
## 3 CFD Modelling as Digital Twin
### Fluid Equations
#### 3.2.4 Thermodynamic Equations for Air-Water Interactions  
Air is a gaseous fluid composed of several gases, primarily nitrogen (approximately 78%), oxygen (21%), and trace gases such as argon and carbon dioxide. Its thermodynamic behaviour in CFD simulations is commonly represented through a relationship between air density (\(\rho_{a}\)), air pressure (\(p_{a}\)), and air temperature (\(T_{a}\)).  
In multiphase flows in closed conduits such as the filling and emptying scenarios of pressurised pipes, considerations of flow compressibility play an important role, since air is a fluid susceptible to volume changes and consequently pressure changes thus making it suitable for modelling in pipes as compressible flow. This compressible fluid condition has been widely used to model hydraulic filling and emptying scenarios with entrapped air.  
Researchers model the thermodynamic behaviour of air using the ideal gas law with a universal gas constant (\(R\)). The ideal gas equation is expressed as follows:  
\[\rho_{a}=\frac{p_{a}}{RT_{a}} \tag{7}\]
## 6 Efficiency and Security Analysis
### Security  
This subsection analyzes the security aspects of the GHZ\({}_{3}\)MQPEC protocol and proves that it is information-theoretically secure. Our proof is given in a format that is prettymuch the norm in the relevant literature. It is expedient to distinguish between _external_ and _participants_' attacks. By external attack, we refer to an attack by an outside eavesdropper, namely [PERSON], whereas by participants' attack, we mean an attack by any of the \(n+2\) players that participate in the protocol.
## 3 Simulated Results and Analysis  
### Effects of the Single Patch and the Patch Array  
The optimal parameter values of the proposed effects of the installing position antenna are presented in Table 1, and several parameters have been improved thereafter, such as the gain, the return loss, and the beam width. In this section, the various parameters of the proposed antennas were analyzed and simulated. Figure 2 illustrated the measured frequency responses of the single patch and the \(5\times 5\) square patch array at the operation frequency of \(5.0\) GHz. It could be seen from Figure 2a that the return loss of the single patch (\(d_{y}=18\) mm., \(l_{p}=11.8\) mm., \(l_{s}=8.8\) mm., \(w_{s}=0.5\) mm., \(c_{x}=c_{y}=40.6\) mm., \(c_{z}=10\) mm., and \(l_{f}=6\) mm.) was \(-15\) dB. A half-power beam width (HPBW) was \(\pm 45^{\circ}\). The bandwidth was \(100\) MHz at the return loss of \(-10\) dB. Consequently, it could be seen from Figure 2b that the return loss of the \(5\times 5\) square patch is \(-22\) dB. A half-power beam width (HPBW) was \(\pm\)\(60^{\circ}\). The same bandwidth of \(100\) MHz at the return loss of \(-10\) dB was, likewise, observed.  
Hence, the return loss reduction is improved by using the parasitic patch array antenna with respect to the single patch antenna structure. Since the presence of the parasitic patch array is around the radiated patch (the patch is over the slot at the origin), this ultimately yields a broad-beam radiation pattern.
# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
## 4 Results
### Evaluation Metrics  
We utilized 3D Intersection over Union (IoU), translation, and rotation errors as metrics to evaluate the accuracy of the model from different perspectives.  
**Three-dimensional IoU**: Three-dimensional IoU measures the discrepancy between the predicted size and the ground truth, represented as the ratio of the intersection to the union of the predicted object's three-dimensional bounding box and the ground truth. The formula is defined as follows:  
\[\text{IOU}=\frac{B_{p}\cap B_{g}}{B_{p}\cup B_{g}}\times 100\%, \tag{22}\]  
where \(B_{p}\) and \(B_{g}\) represents the predicted 3D bounding box and the ground truth of the 3D bounding box.  
We employed two types of 3D IoU metrics: 3D25 and 3D50. These indicate that the 3D IoU is greater than 25% and 50%, respectively, as the evaluation criteria.  
**Translation and Rotation Error**: The translation and rotation errors measure the discrepancy between the predicted and ground truth values in terms of translation and rotation. These errors represent the difference between the predicted translation and rotation of the target object and the ground truth. We used four specific translation and rotation error metrics: 5\({}^{\circ}\)2 cm, 5\({}^{\circ}\)5 cm, 10\({}^{\circ}\)2 cm, and 10\({}^{\circ}\)5 cm, where each denotes a rotation error of 5\({}^{\circ}\) with a translation error of 2 cm, a rotation error of 5\({}^{\circ}\) with a translation error of 5 cm, a rotation error of 10\({}^{\circ}\) with a translation error of 10\({}^{\circ}\) with a translation error of 5 cm, respectively.
## 3 Results
### Sources of Uncertainties and Errors in VPM Simulations for Sugarcane Plantations  
The sources of errors and uncertainties in the GPP estimates from light use efficiency models comprise the model structure; model parameters; and input datasets, including satellite images and climate data. [PERSON] et al. (2020) discussed VPM simulations at two sugarcane sites with a focus on the LUE parameter. The maximum LUE affects the vegetation carbon uptake at the ecosystem level [137; 138; 139; 48] and can be estimated through a linear regression between the aboveground dry biomass and total amount of radiation captured by the vegetation [140; 141] or based on the relationship between the PAR and GPP [142]. The LUE values have a large range of variability over sugarcane depending on the climate conditions, altitude, and crop management [142; 143; 144; 145]. Our results suggested a maximum LUE ranging between 0.7 and 0.9 g C mol\({}^{-1}\) PPFD for Brazil, similar to the ranges reported in other studies in the Brazilian region [147]. [PERSON] et al. (2020) [35] reported that the maximum LUE for VPM simulations was set to 0.9 g C mol\({}^{-1}\) PPFD for both sugarcane sites. Further studies are needed to evaluate the maximum LUE parameter over sugarcane plantation sites worldwide, as it could introduce a source of uncertainty for the regional and global GPP estimations.  
The comparison of VPM simulations based on MSR images and HSR images illustrates the error sources and uncertainty associated with landcover types within one image pixel and the spatial mismatch (inconsistency) between image pixels and the footprints of an eddy flux tower site [148; 149]. One MSR pixel often contains multiple landcover types, often called a mixed pixel, while one HSR pixel most likely contains one landcover type, often called a pure pixel [117; 150; 151]. The presence of different landcover types within a single pixel affects the model's representation of the fraction of PAR absorbed by chlorophyll and the vegetation water response, both strong drivers of vegetation carbon uptake. Moreover, the number of good-quality observations from satellite optical sensors would decrease if frequent clouds occurred. In addition, EC flux tower estimates are influenced by the surrounding fields as the footprint changes with the season, weather conditions, vegetation height, and vegetation cover [66; 106; 148]. The sources of uncertainty from the EC tower increase the challenges of validating GPP estimates, and the sources of error in the system increase based on the condition of the instruments and sensors. In this study, we also used the NEE and latent heat flux data from the tower sites to identify additional poor-quality data in the time series that were not removed by the site's quality assurance filters.
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
## 4 Results
### The Performance of DOA Estimation for Multiple Signal Sources  
Simulations were conducted for a scenario involving a line-of-sight signal and a multipath signal. Figure 10 illustrates the DOA estimation performance under varied \(C/N_{0}\) conditions. The incident angles for the line-of-sight and multipath signals are \(-8.98\) degrees and \(9.06\) degrees, respectively. The amplitude attenuation coefficient for the multipath signal is \(0.5\), and the relative time delay of the multipath signal compared to the line-of-sight signal is \(0.5\) chips. When \(N=8\), the DOA estimation performance of the proposed method is the best, which indicates that a better spatial resolution can be achieved when the number of array elements is small. When \(N=12\), this method can achieve similar performance to OMP.