esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Review
### Emerging Technologies  
The technologies identified and incorporated into this framework are tailored to address specific challenges and improve efficiencies across all lifecycle phases and were selected based on the literature review conducted [8]. These include Laser Scanning, Photogrammetry, and Reality Capture, which facilitate precise documentation of existing site conditions for accurate planning, design, and verification [41; 42]. Drones/UAVs (Unmanned Aerial Vehicles) enable aerial site inspections, progress tracking, and enhanced data collection for emergency management and large-scale project monitoring [43]. Artificial Intelligence (AI) and Machine Learning optimize cost estimation, predictive analytics, and data-driven decision making while automating scheduling, enhancing real-time quality control, and improving proactive project management [44; 45; 46; 47]. AI Predictive Analytics specifically provides insights into structural performance, energy efficiency, and other critical project parameters during design and operational phases [48; 49; 47]. Digital Twins offer virtual replicas of physical assets for real-time monitoring, simulation, and performance optimization, enabling lifecycle management [44; 45; 50; 51; 52]. The Internet of Things (IoT) and sensors collect and transmit real-time data for maintenance monitoring, system performance evaluations, and energy management [45; 46]. Big Data and Analytics analyze vast quantities of project related data to identify trends, optimize workflows, and improve decision making [53; 54; 51]. Geographic Information Systems (GISs) integrate spatial data with BIM to support site selection, environmental analysis, and resource planning [55]. Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) enhance spatial visualization, immersive design reviews, and real-time collaboration during design and construction activities [56; 57; 58]. The Common Data Environment (CDE) provides a centralized platform for managing and sharing project information, ensuring all stakeholders access the latest data. Blockchain ensures secure, immutable data sharing, transparency, and collaboration across all phases while tracking sustainability metrics [47; 54]. Robotics and Automation improve efficiency, precision, and safety in construction tasks, including material handling, repetitive processes, and onsite construction, with tools such as UAVs (Unmanned Aerial Vehicles) and AGVs (Automated Guided Vehicles) for transport and assembly [44; 59; 60; 61]. Radio Frequency Identification (RFID) enables efficient tracking and management of materials and equipment, improving logistics and resource allocation [62; 63; 16]. Finally, 3D Printing/Additive Manufacturing supports on demand fabrication, reducing material waste and lead time while enabling customized construction solutions [47; 54; 60; 64]. These Emerging Technologies complement Lean Construction tools and BIM applications, creating a cohesive and efficient framework for managing complex construction projects while addressing key industry challenges.
## 3 Results and Discussion
### Optimisation of the Processing Parameters
Our observations indicate that the cavitation flux has a smaller range in ethyl alcohol than in water, so the greater the distance of the ultrasonic probe from the bottom of the extraction vessel, the more limited the impact of the cavitation flux on the raw material, and thus the extraction is less effective. For both solvents, an increase in the diameter of the cavitation bubbles and the length of the cavitation flux is visible with the growth in the ultrasound amplitude, which partly explains the growth in extraction efficiency with the growth in ultrasound amplitude. An important factor that may affect the extraction efficiency in individual extraction vessels is also the volume of solvent remaining directly under the ultrasonic probe. The volume of solvent below the probe for the 25 mm diameter vessel is approximately 36,800 mm\({}^{3}\) and for the 35 mm diameter vessel it is 19,200 mm\({}^{3}\). Differences in solvent volume result in differences in the energy density of the ultrasonic field in individual extraction vessels. The higher ultrasonic energy density in the 35 mm diameter vessel contributes to higher polyphenol extraction yields. Interactions between the diameter of the extraction vessel and the concentration of alcohol in the solvent may result not only in a change in the efficiency of the extraction process but also affect the percentage of individual phenolic compounds in the extracts.
# An Algorithm for Finding Optimal \(k\)-Core in Attribute Networks
## 4 Experiments and Results
### Evaluating the Performance of Algorithms  
The time of the BaseLine algorithm includes core decomposition and score computation, while the time of the Advanced algorithm additionally includes building the forest. We have calculated the execution time of each part, which helps identify the efficiency bottleneck of the algorithm. Since multiple heterogeneous networks on Facebook differ significantly in scale, only the query times of a few larger heterogeneous networks are displayed here, as shown in Figure 4.  
The experimental results show that regardless of the datasets, the Advanced algorithm is always faster than the BaseLine algorithm. Upon careful observation of the time consumption at different stages of the algorithm, we find that in the BaseLine algorithm, score  
Figure 4: Runtime of algorithms on different datasets. (**a**) Runtime with Equation (1) (**b**) Runtime with Equation (2).  
Figure 3: Attribute scoring of \(k\)-cores on different datasets. (**a**) F107 (**b**) F348 (**c**) F1684 (**d**) F1912 (**e**) Twitter (**f**) Flickr.  
calculation accounts for the largest proportion of time. This is consistent with theoretical analysis since the BaseLine algorithm must calculate the score of every feasible solution, and the calculation process cannot be shared. As for the Advanced algorithm, building a hierarchical forest costs the most time. The actual query time includes two parts: locating the LCA for query vertices and extending queries. With the excellent properties of attribute tree nodes and iterative updates of primary keys, the Advanced algorithm's computation time is greatly reduced. If the forest construction time is not taken into account, the Advanced algorithm is two orders of magnitude faster than the BaseLine algorithm. By dynamically maintaining the forest, the Advanced algorithm can achieve millisecond-level online queries.
## 2 Materials and Methods  
### Field Site and Measurements  
Multi-temporal field campaign measurements, used for model calibration and evaluation, were carried out at winter wheat field experiments during two consecutive growing seasons (2016-2017 and 2017-2018). The experiments were situated approximately 4 km south of the village of East Saltoun, East Lothian, Scotland (55\({}^{\circ}\)52\({}^{\prime}\)50.5\({}^{\prime\prime}\) N, 2\({}^{\circ}\)50\({}^{\prime}\)12.2\({}^{\prime\prime}\) W; 170 m above mean sea level).
## 3 FKP-DGPS Algorithm
### RTCM Version 3.1[21]  
The development of the real-time FKP-DGPS algorithm is based on the FKP messages, which are broadcasted in the RTCM version 3.1 format through the NTRIP. The broadcasting information in the RTCM version 3.1 contains various messages regarding the GPS observations and network RTK. Among the various messages, message type 1034 contains the GPS network FKP gradient message comprising geometric and ionospheric gradients as shown in Table 2. The FKP information per satellite in the message type 1034 is approximately 66 bits except for the parity data, and it is usually broadcasted at intervals in the range of 10-15 s.
## 3 Results and Discussion  
### Characterization of the Electrodes  
A large surface area and suitable pore volume of porous carbon are key factors for high electrosorption performance in the CDI process. Figure 2 shows the SEM images of the surface of the activated carbon (AC) electrode and the activated carbon electrode coated by the cation polymer layer, referred to as the AC-CPL. The AC electrode was porous and rough, whereas the AC-CPL had a relatively uniform and smooth surface owing to the influence of a cation exchange layer. Figure 2c shows that the surface of the carbon electrode was completely covered with a cation-exchange layer that had a maximum thickness of about 3.4 \(\upmu\)m. The shape of the sturdy electrode surface was expected to mitigate the degradation of electrode performance caused by the physical adsorption of wastewater-related contaminants since there is less opportunity to come into contact with contaminants and adsorb them [12].
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 6 Discussion
### Advantages and Limitations  
The primary advantage of the proposed method lies in its ability to achieve emission reductions through the coordination of signal control and vehicle guidance when compared to the existing literature.  
Reference [12] proposed an eco-oriented signal control method that reduces ecological costs by 13.57% but requires a 4.84% increase in vehicle delay. The method proposed in this paper, when applied in scenarios with zero CBCVs, functions as a purely signal-based adaptive control approach that can reduce emissions by 5.24% to 6.70% without compromising vehicle delay benefits. Compared to reference [11], the proposed method ensures a balanced multi-objective optimization at intersections by considering delay reduction as part of the optimization goals.  
Reference [29] developed a multi-vehicle cooperative eco-driving system for signalized intersections under partial CAV environments, achieving a 10.95% reduction in carbon emissions in scenarios with fixed-timed signals. The proposed method, by utilizing the coordinated optimization of signals and vehicles, achieved a maximum emission reduction of 17.60% in experiments, outperforming the results of reference [29], thus demonstrating the necessity of coordination.  
Figure 12: Cost of carbon reduction at intersections.  
Despite the significant advantages of the proposed method in achieving emission reductions at intersections, there are certain limitations. It was observed that under highly congested traffic conditions (v/c > 0.8), the proposed controller could not significantly reduce the number of stops, leading to the failure of the emission reduction function. This is because under extremely congested conditions, the low-speed movement of CBCVs on road segments can cause traffic collapse--a phenomenon described as \"phantom jams\" on urban roads in reference [4]. Vehicles obstructed by such jams will stop on the road segment and disrupt the flow of subsequent traffic. Furthermore, the proposed control method tends to allow vehicles within the controlled area to stop at most once while passing through the intersection, leading to longer cycle lengths. This may result in optimal phase cycles exceeding the maximum cycle limit, making it difficult to handle highly congested intersections. In fact, congested intersections should not sacrifice delay and maximum throughput in pursuit of overall emission reduction, as this may lead to overflow at the intersection.
## 2 Related Work
#### Research Motivation and Identified Gaps
Another key gap in the literature involves the need for flexible mechanisms for cache eviction based on data expiration or redundancy. Traditional methods, like the LRU and probabilistic models, frequently need to accommodate the dynamic nature of IoT-generated data. Techniques like EELC by [PERSON] and [PERSON] [22] provided an early attempt at context-based cache decisions but struggled with scalability due to heavy computational requirements. In contrast, our proposed method utilizes a lightweight, freshness-based eviction strategy tailored to maximize cache utility while minimizing resource consumption, thus enhancing scalability for resource-constrained IoT networks. Finally, the limitations of predictive caching and traffic load distribution models prompted us to introduce a proactive caching approach that considers historical data patterns. While models like those by [PERSON] and [PERSON] [21] and [PERSON] and [PERSON] [23] addressed data validity, they lacked predictive insights for high-demand scenarios. Incorporating data freshness indicators with predictive cache placement enables our method to anticipate better and meet peak traffic demands. This approach optimizes cache hit ratios, reduces network delays, and lowers traffic load. This study, therefore, fills critical gaps by combining real-time freshness validation, optimized cache replacement strategies, and predictive cache management, enhancing the overall responsiveness and reliability of IoT caching systems.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior
#### 3.2.3 Information of Multiple Following Vehicles
Additionally, it is noteworthy that the single relative velocity between the object vehicle and its following vehicle may exert negative impacts on the car-following behavior. The studies incorporating the following vehicles, especially ones incorporating the following and the preceding vehicles, have a certain significance for understanding the characteristics of car-following behavior, constructing the cooperative car-following model, and the corresponding control methods for the V2X environment.
## 2 Related Work
### Information Retrieval-Based Methods  
The information retrieval-based KBQA methods typically comprise four modules [10]: (1) retrieval source construction, which involves extracting a graph from the knowledge base that is relevant to the question, consisting of both pertinent facts and a substantial amount of noisy facts; (2) question representation, which aims to comprehend the question and generate instructions to guide the reasoning process; (3) graph-based reasoning, which conducts reasoning by employing semantic matching on the graph; and (4) answer generation, which generates answers based on the reasoning status at the conclusion of the reasoning process.  
However, these methods face challenges in terms of accuracy and effectiveness, primarily due to the incompleteness of the source knowledge base and the exponential growth in subgraph size as the distance to topic entities increases, especially when dealing with complex user questions [22,23]. To address these challenges, researchers have proposed various solutions. For instance, some studies utilize sentences as nodes to expand incomplete knowledge bases [24], while others try to filter out irrelevant information in the reasoning process [25, 26, 27, 28]. To comprehend complex questions, certain studies dynamically update reasoning instructions during the reasoning process [29,30]. Moreover, aside from explicitly recording the analytical aspect of the problem using attention, other approaches suggest incorporating information retrieved throughout the reasoning process to update instructions [31]. While information retrieval-based methods have their merits, they tend to lack interpretability and do not generate SPARQL queries [10; 21], which are the focal points of this paper. As such, our research will primarily consider semantic parsing-based methods.
# Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets
## 4 Impact of Each Modality on Final Prediction  
As discussed in the previous section, integrated gradients were used to better visualize which features have the largest effect on the class-wise prediction. The network mainly trains on geometry, as demonstrated by Table 3, and the impact of the RGB color values had negligible influence on the predictions. This leads to the belief that using the current network architecture, RGB color values contribute relatively little to the prediction accuracy, they do however greatly increase the number of parameters used by the network, increasing complexity and computation time.  
A meaningful comparison between the resulting attributions from training only the coordinates and then training the coordinates and RGB values could not be made as they are calculated relative to each other. However, existing literature supports the hypothesis that the RandLA-Net architecture primarily focuses on learning geometries. This has been demonstrated by running the network on the Semantic3D dataset. When the network was trained using only coordinates, the mIoU, was 77.71. In contrast, when RGB values were included in the dataset, the mIoU score only slightly increased to 81.03. When the network was trained on only RGB values, it failed to learn distinguishing features effectively. This test was run by setting all coordinates to random values, as completely removing the coordinates makes coherently sampling the point cloud impossible.  
As displayed in Table 3, the attributions given to the coordinates vastly outweigh the RGB values. This is true for all classes; however, the differing attributions between the classes could also be interpreted as how certain the network is in predicting this class. The score might also be a measure of how unambiguous the features that represent the class are. This can be helpful when designing a class list as it allows systematic filtering out of ambiguous small classes, increasing the network's accuracy.  
Figure 12: Flow chart for obtaining feature-wise attributions.
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## 3 Results and Discussion
### Adsorption Ability and Maximum Adsorption Capacity  
To evaluate the adsorption capability of Alg-EDTA for different heavy metal ions, we conducted single metal ion adsorption and competitive adsorption experiments (Table 3). The experimental results show that Alg-EDTA has good affinity for heavy metal ions, such as Cd\({}^{2+}\), Pb\({}^{2+}\), Cu\({}^{2+}\), Co\({}^{2+}\), and Cr\({}^{3+}\), and can adsorb more than 80% of the corresponding heavy metal ions in the solution. In competitive  
Figure 8: (**A**) Plots of \(\rm{Inq_{e}/C_{e}}\) versus \(\rm{q_{e}}\) at different temperatures; (**B**) plot of \(\rm{InK_{0}}\) versus \(\rm{1/T}\).  
Figure 7: Effect of temperature on metal ion adsorption (100 mg of Alg-EDTA was added to 50 mL \(\rm{1.5}\) mM of Cd\({}^{2+}\) (or Pb\({}^{2+}\)) solution at different temperatures).  
adsorption experiments, Alg-EDTA exhibits higher selectivity for Cd\({}^{2+}\), it can adsorb more than 90% of Cd\({}^{2+}\) in the mixed metal ion solution. This may be due to the fact that the ionic radius of Cd\({}^{2+}\) (0.097 nm) is closer to that of Ca\({}^{2+}\) (0.100 nm) than Pb\({}^{2+}\) (0.132 nm) and Cu\({}^{2+}\) (0.072 nm), and thus has stronger ion exchange effect with Ca\({}^{2+}\)[16]. We further conducted experiments to determine the maximum adsorption capacity of Alg-EDTA for Cd\({}^{2+}\), and the experimental results are presented in Figure 9. As the Cd\({}^{2+}\) concentration in the solution increases from 0.1 mM to 4.0 mM, the adsorption capacity of Alg-EDTA for Cd\({}^{2+}\) increases rapidly, and then remains stable with further increase in concentration. The maximum adsorption capacity of Alg-EDTA for Cd\({}^{2+}\) is 177.3 mg/g, which is higher than most of the reported Cd\({}^{2+}\)-sorbents (Table 4) [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27].
## 2 Materials and Methods
### Experimental Program
#### 2.2.1 Air Pycnometry Method  
According to [13], the most suitable and accurate technique for obtaining reliable measures of the void ratio is the air pycnometry method. The authors also reported that existing commercial air pycnometers are costly and too small to analyze representative specimens of solid waste. Thus, in order to measure the void ratio and solid particle density of sampled waste material, a constant-volume air pycnometer was mimicked using standard geotechnical equipment. A constant-volume air pycnometer consists of pressure and specimen chambers, connected with a tube and a coupling valve. For the pressure chamber, a general-purpose fluid pressure source and volume change gauge (PSVCG) was used for the precise regulation and measurement of fluid pressure and volume change. For the specimen chamber, a toxic interface unit cell was used [14]. A photograph of the mimicked air pycnometer device is shown in Figure 1. More details about the proposed air pycnometer setup, its calibration, and its accuracy can be found in [15].  
In total, 60 waste specimens were tested in the air pycnometer device. The specimens were poured into the top of the specimen chamber in the dry state of the material, as can be observed in Figure 1. The specimens were divided into six groups, with ten specimens per group. Each group, denoted as #1, #2, #3, #4, #5, and #6, had designated constant mass values of 0.0260 kg, 0.0375 kg, 0.0486 kg, 0.0596 kg, 0.0706 kg, and 0.0817 kg, respectively. The volume of the specimen chamber was 220.683 \(\times\) 10\({}^{-3}\) m\({}^{3}\). Consequently, each designated group of specimens had constant dry density values of 118 kg/m\({}^{3}\), 170 kg/m\({}^{3}\), 220 kg/m\({}^{3}\), 270 kg/m\({}^{3}\), 320 kg/m\({}^{3}\), and 370 kg/m\({}^{3}\), respectively. For each tested specimen, corresponding triplets of values \((e,\rho_{d},\) and \(\rho_{s})\) were recorded.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
## 4 Interference Analysis of LTE-M Ground System  
Aiming at the ground interference of the LTE-M system, this paper uses the Monte Carlo method to simulate and analyze throughput, which is the main evaluation index of system performance.
## 4 Conclusions  
The conducted research confirms the effectiveness of a hybrid surface finishing approach for components made of the Inconel 939 superalloy using the LPBF technique. The method combines vibratory finishing and MAF with planetary kinematics. The proposed sequence of operations enables a significant improvement in surface topography parameters, particularly a notable reduction in Ra roughness (from 5.6 to 0.81 \(\upmu\)m), and the effective removal of typical additive manufacturing defects such as porosity and adhered powder particles.  
The study showed that eccentricity has a significant impact on process efficiency, especially concerning the Rz roughness parameter, which proved to be the most sensitive indicator of surface quality with respect to this variable. However, excessively large eccentricity values led to inhomogeneous and uncoordinated tool interactions, potentially compromising the uniformity of the finish.  
SEM analysis confirmed a marked improvement in surface morphology, including the removal of residual powder particles and the smoothing of the microtexture. Additionally, the flexible nature of the abrasive tool allowed it to adapt to local shape variations and magnetic field gradients, which enhanced the efficiency of processing irregular geometries.  
In summary, the developed solution offers a practical and competitive alternative to conventional post-processing techniques for complex additively manufactured components, ensuring high-quality surface finishes and the potential for further optimization of technological parameters.  
Conceptualization, M.M.; methodology, M.M.; software, A.P.W.; validation, D.A.M.; formal analysis, D.A.M.; investigation, A.P.W. and M.M.; resources, M.M.; data curation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, M.M.; visualization, D.A.M.; supervision, M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.