esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Regional Geo-Hazard Risk Mitigation Strategies  
Given the variation in dominant factors and disaster formation mechanisms across the eight identified zones, region-specific risk mitigation strategies are essential for enhancing hazard prevention and control in the Plateau's complex environment: Zone I (tectonic fragmentation and precipitation erosion): strengthen engineering controls along fault zones, conduct pre-monsoon inspections and dynamic monitoring, and prioritize structural mitigation such as sediment retention dams and diversion channels; Zone II (steep relief and high tectonic activity with intense precipitation): enhance slope stabilityassessments along valley edges, establish rainfall-triggered early warning systems, increase gully maintenance frequency, and implement evacuation protocols for mountain road networks and settlements; Zone III (freeze-thaw cycles and precipitation): deploy integrated temperature and humidity sensors in high-altitude areas to detect seasonal high-risk zones, and refine engineering designs through structural hazard assessments; Zone IV (highly weathered terrain and loose soils): establish disaster risk buffer zones around towns and promote targeted slope stabilization through artificial reinforcement and ecological restoration; Zone V (precipitation and freeze-thaw-driven material accumulation): develop a regional inventory of debris sources, assess the slope stability of unconsolidated deposits, and protect buffer zones along transportation corridors; Zone VI (climate and engineering geology synergy, lower hazard levels): prioritize ecological engineering measures to control freeze-thaw erosion and enhance the resilience of infrastructure to environmental disturbances; Zone VII (engineering geology-dominant, low precipitation): implement cost-effective prevention and early warning systems, including unmanned monitoring stations and pre-emptive evacuation protocols in low-frequency hazard areas; and Zone VIII (loose lithology and flat terrain): maintain ecological stability, minimize human disturbance, and promote low-impact land use strategies. These recommendations, grounded in dominant-factor identification and tailored to specific geomorphologic and environmental settings, provide a scientific foundation for targeted risk governance and precision disaster prevention [83].
# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 2 Methodology of the Multi-Classification Multi-Leak Identification (MC-MLI) Scheme
### Hydraulic Model Analysis  
A hydraulic model is a mathematical model to represent the water behavior in a given hydraulic system. The model facilitates the analysis of general WDIs. In this paper, the model revealing hydraulic behavior in the pipeline system is used to estimate the identification parameters for feature vector construction.  
Obviously, there is no ideal (frictionless) fluid in a practical situation. Energy is required to push fluid moving along a pipe against the friction due to the fluid viscosity. The energy, also known as the loss in pressure energy, is defined as the hydraulic head loss in a pipe. The factors affecting hydraulic head loss include flow rate, the friction of the inner wall related to pipe material, and pipe diameter.  
The Hazen-William equation is an empirical formulation to describe the water flow inside the pipeline with practical considerations such as pipe material, pipe diameter, pressure drop due to friction, etc. It has lower complexity compared to other hydraulic models, and therefore, it is suitable for low-cost and real-time applications. According to the Hazen-Williams equation, the water flow rate \(Q\) (m\({}^{3}\)/s) in a pipe is expressed as follows [(20)]:  
\[Q=0.278\times C\times D^{2.63}\times S^{0.54} \tag{1}\]  
where \(C\) is the Hazen-Williams friction coefficient dependent on the pipe material [(21)]. \(D\) is the pipe diameter in meters, and \(S\) represents the energy slope, which is also known as the head loss per pipe length.  
The proposed scheme considers not only the water flow rate but also pressure drop. Similarly, pressure drop \(PD\) (kPa/m) as defined in the Hazen-Williams equation is expressed as follows [(20)]:  
\[PD=1.192\times 10^{21}\times\left(\frac{Q}{C}\right)^{1.85}\times\frac{S_{G}}{ D^{4.87}} \tag{2}\]  
where \(Q\), \(C\), and \(D\) are consistent with previous definitions. \(S_{G}\) is the specific gravity of the liquid, and the \(S_{G}\) of water equals 1.  
Note that the Hazen-Williams equation is generally accurate under the conditions of \(>\)50 mm pipe diameter, 5-25 \({}^{\circ}C\) water temperature, and \(<\)1.2 MPa of inner pressure. Otherwise, a large error will occur, and a more complicated hydraulic model, namely the D'Arcy-Weisbach formula, should be considered.
## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Travel Time Prediction
Despite the need for complex data processing and significant computational resources, this model excels in handling noisy data. In conclusion, applying RNNs, LSTMs, GRUs, and other neural network models for travel time predictions has shown significant progress and promising results, especially when combined with novel data processing techniques and attention mechanisms. Nevertheless, there is still potential for further research and innovation in this area.
# 5G-MEC Testbeds for V2X Applications
## 2 Background
### V2X Applications  
For vehicular communication, the automotive and telecommunication industries have invested in the connected car market as it supports digital transformation by exploiting communication technologies [28]. V2X communications include V2I, V2P, and V2N communications, which help to enhance vehicular traffic and safe driving, as well as provide other services. The automotive industry supports acquiring MEC-based vertical segments in the 5G ecosystem. V2I communications require a low-latency environment (less than 10 ms) [29]. MEC has been proposed as a possible solution for these stringent performance requirements, and it facilitates direct communication between nodes about V2X-related information via an underlying network. MEC technologies provide an ideal setting for use cases, allowing them to be linked, communicate, and share information about traffic and maps. For V2X applications, which can also stream videos and AR, the MEC system provides cloud computing capabilities at the network's edge and its access-agnostic feature ensures the reliability of the deployment via an underlying communication network.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 2 Materials and Methods
### Materials
#### 2.1.2 Marble and Granite Waste Powder  
The granite and marble waste were acquired from the Kokeb Paint and Marble Factory, which is situated in Bahir Dar, Ethiopia, and subjected to drying at a temperature of 110 \({}^{\circ}\)C (230 \({}^{\circ}\)F), following the guidelines of ASTM D2216 to eliminate moisture. The visual appearance of cement, granite waste powder (GWP), and marble waste powder (MWP) is presented in Figure 1. The MWP exhibits a whitish color, the GWP displays a grey color, and the cement appears dark gray.  
The outcomes of the X-ray fluorescence (XRF) analysis conducted on the OPC, MWP, and GWP utilized are compared in Table 1. The results of the test revealed that the specific surface area of GWP and MWP is almost five times higher than that of OPC. Regarding their chemical compositions, OPC and MWP had a relatively high and comparable concentration of CaO, specifically 64.31% and 52.28%, respectively. On the other hand, GWP mainly consists of oxides of SiO\({}_{2}\), with the highest proportion followed by Al\({}_{2}\)O\({}_{3}\), accounting for 70.1% and 14.02%, respectively. The MWP exhibited a remarkably high loss of ignition (LOI), with a value of approximately 42% identified through analysis.  
According to the requirements outlined in ASTM C618, GWP can be categorized as a Class N natural pozzolan material, as it meets the conditions of having SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\)\(\geq\) 70%, CaO \(<\) 10%, and LOI \(<\) 10% [19]. However, according to the same standard, MWP does not fall under the classification of pozzolan material. The MgO content in GWP and MWP is 0.66% and 2.18%, respectively, and when combined, they remain under the 5% limit, signifying that they are appropriate for generating sound concrete [20].
## 3 Results
### Timing Verification 1 km SIF  
In order to verify the applicability of the downscaled 1 km SIF, a correlation analysis with eSIF was performed to verify the stability of the downscaled model. The downscaled 1 km SIF products were resampled to match the 0.05\({}^{\circ}\) spatial resolution of the original eSIF, ensuring consistency in the spatial and temporal data. Figures 11 and 12 show the scatter density plots of SIF correlations before and after downscaling, for the periods from October 2018 to May 2019 and October 2019 to May 2020. These plots demonstrate a strong correlation (R\({}^{2}>0.8\)) between the downscaled SIF products and the original eSIF data,  
Figure 10: Monthly 1 km SIF from October 2019 to May 2020.  
Figure 9: Monthly eSIF from October 2019 to May 2020.  
indicating that the downscaling model is stable and its predictions are highly accurate. At the same time, this also verifies the validity of the theoretical assumption based on the invariance of the scaling relationship. Overall, the downscaling method effectively preserves the characteristics of the original data, providing detailed SIF information at a higher spatial resolution, which provides a reliable database for further vegetation ecology research and agricultural production monitoring.
## 5 Discussion
### Limitations  
Overall, the achieved results are promising regarding the industrial application of the proposed workflow; however, they also highlight certain limitations. Currently, full automation of the sorting process, e.g., using a picking robot, cannot be achieved because this would require 100% classification accuracy. However, obtaining error-free part classification for an unlimited number of part geometries is likely impossible. To date, including a final manual step to recognize the correct part from a selection of three parts seems beneficial because this technique is still considerably faster than recognizing a single part in a batch of many. In our case study, the maximum number of parts per build job was 144, from which the printed parts had to be recognized. As the reduction in sorting time could show, the obtained top-three accuracy of 99.04% is sufficient for practical application. However, this could be improved further by addressing the classification of critical part categories, e.g., particularly large or small parts, mirrored parts or parts that only differ in scale. A first experiment was conducted to gain further information on using neural networks to distinguish scaled or mirrored parts. It included four AM parts that were scaled and mirrored versions of the same part geometry. With enhanced training times and additional training images (20 epochs, 400 synthetic images), the part classification accuracy was observed to increase. This suggests a starting point for further research, as the findings would need to be supported by a bigger test dataset. Other potential approaches are the integration of further information in the classification process, e.g., part weight, to better distinguish scaled parts, or reinitializing the deployed networks weights stemming from the pre-training on the COCO dataset on another dataset that includes mirrored objects in specific.  
In addition, the generalizability of our findings is limited because the test set only included 30 distinct AM part geometries, and the industrial case study only considered 519 AM parts and 215 distinct AM part geometries. Thus, our findings can only provide an estimate of the classification accuracy that could be obtained with an unlimited variation in part geometries. However, this estimate can be considered valid because the test set was created to resemble a typical AM production order, including parts of various sizes and geometries. Furthermore, the industrial case study included several build jobs from real-world customers and covered a wide range of part geometries. In addition, only MJF parts with changing outer geometries were included in the evaluation; however, the influence of the selected AM process on the classification accuracy is expected to be low. Note that an evaluation of the proposed workflow on additional AM parts and processes, e.g., selective laser sintering, is planned. For the recognition of parts that only differ in inner geometries, additional sensor systems are tested.
# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### DL-Based MC
The classification performance of this technique degrades to 87.3% in a minor CFO and 83.4% in a moderate CFO. However, it has a classification accuracy of 98.5% at high SNR in the absence of CFO.
## 4 Results and Discussion
### Shap Analysis across 49 Features
#### 4.2.2 Results for Quadrant 1
This optimized feature set not only improves the model's accuracy but also demonstrates that a reduced number of features can significantly enhance prediction capabilities for Quadrant 1, with F1-scores of 0.92, 0.94, and 0.95 achieved with varying subsets of these features. Notably, the model achieves peak F1-scores with 21, 37 and 46 features, suggesting optimal feature balance. Moreover, using just 13 features yields the same F1-score as using all 49 features, indicating that fewer features can train the model just as effectively. This reduction in feature count decreases the computational demand and memory usage, consequently speeding up the model's training process. Figure 5: Variations in F1-Score across Different Feature Numbers for Quadrant 1.
## 6 Experimental Validation
### Results of Distance Perception  
Figure 4 plots the mean perceived distance and standard deviation of conventional and proposed methods, respectively, for virtual sources on the horizontal plane with different target distances and azimuths. Generally, two algorithms yield similar results. The mean perceived distance increases with the target distances; therefore, both algorithms can control the perceived distance effectively.  
In most cases, when the target distance does not exceed 0.5 m, the mean perceived distance is larger than the target distance. When the target distance exceeds 0.5 m, the perceived distance is smaller than the target distance. Such biased perceived results are basic features in distance perception [31]. In addition, when the target source departs from the horizontal plane, the variation in ILD with source distance reduces; the standard deviation of perceived distance for a lateral source also increases, and thus, the accuracy of distance perception decreases, which is also consistent with previous research on distance perception for virtual sources in free field [19]. Actually, auditory distance perception in free-field is contributed by multiple cues and their interaction, including distance-dependent pressure or loudness, distance-dependent ILD, and distance-dependent spectral cues in near-field. On the median plane (such as \(\theta=0^{\circ}\) and \(180^{\circ}\)), the ILD cue vanishes due to the left-right symmetry, which accounts for the reduction in the accuracy of distance perception at these directions.  
Figure 3: The statistical graph for spherical data of conventional and proposed method.  
A multi-way ANOVA on the mean perceived distance indicates that, at a significance level of 0.05, the difference between the mean perceived distances of the proposed and conventional algorithms is insignificant. Therefore, the proposed algorithm yields a distance performance similar to that of the conventional algorithm. Moreover, a little bit larger standard deviation in the distance perception for rear sources may be probably caused by the error in reporting the perceived distances at this direction. We applied a F-test on the mean perceived distance for azimuth \(\theta=180^{\circ}\). The result \((F(1,7)=2.156,p=0.146)\) indicates that, at a significance level of 0.05, the difference between the mean perceived distances of the conventional and proposed method is insignificant, which furtherly proves the validation of the proposed method.  
The performance of perceived distances for target sources on the \(\phi=\pm 45^{\circ}\) elevation plane is similar to that on the horizontal plane. The detail results and analysis are omitted here for conciseness.
## 1 Introduction  
As for brain imaging diagnosis, both magnetic resonance imaging (MRI) and positron emission tomography (PET) are the current gold standard imaging methods for brain cancer, but the functional mapping information on the human brain has not been not fully accumulated [1]. Furthermore, experimental environments for PET and MRI studies are quite different from the daily one, and most patients find this is stressful. Therefore, a new neuroimaging technique that is completely noninvasive and does not require strict motion restriction is desirable.  
Near infrared (NIR) optical imaging, as a noninvasive optical technique, has emerged as an attractive imaging technology for brain imaging [2]. Previous studies revealed that the NIR molecule Cy5.5 were injected to rats, and the NIR imaging was perfectly companied with the size of true tumor after surgical exposure [3]. However, the NIR fluorescence of Cy5.5 is not stable in a biologically environment which is easy to bleach or dye _in vivo_. Compared with organic molecule, NIR QDs is more suitable for NIR optical _in vivo_ imaging due to their unique optical advantages such as higher photoluminescence and photostability [3]. Moreover, the size of NIR QDs is normally less than 10 nm, which is enough small to cross the BBB [3,4].  
To date, a number of NIR-emitting QDs, such as CdTe/CdS [5], CdHgTe [6], CdTeS [7] and CdTeSe [8] QDs, have been successfully synthesized. Recently, some studies reported the directly using dendrimer as a capping agent for preparing QDs could prevent QDs from further aggregation in water phrase [9,10]. Furthermore, dendrimer modified QDs enable to decrease the release of metal ions from this organic and inorganic hybrid architecture in biological environments [11].  
In this study, the one-pot water-phrase synthesis of hybrid nanocomposites constituted by CdTe/CdS NIR QDs and different generations (from 3 up to 5) of plolyamidoamine dendrimers is presented. Moreover, the feasibility of using this nanocomposite as an imaging agent for labeling brain tumor cells is also assessed _in vitro_.
## 6 Discussion
### Segmentation Network Choice  
In this system, our autonomous landing line method runs on NVIDIA Jetson NX, which supports deep learning libraries (TensorRT, cuDNN, etc.), computer vision libraries (Vision Programming Interface, OpenCV), GPU computing (CUDA), and hardware encoding and decoding of video streams to reduce the burden on the CUP. Its small size, low power consumption, and high forgetting make it an ideal edge device for robotics that offers compatibility with advanced developer tools such as ROS. To select the appropriate network model, the study tested the inference speed and accuracy of the model for different networks and resolutions. The STDC-Seg model has two backbones for the context path branch, namely, STDC1 and STDC2. Since STDC2-Seg has more network layers, it can extract richer semantic information, and the segmentation effect is better, but the inference speed is slower. The STDC1-Seg network has fewer layers, and the segmentation effect is poor, but the speed is faster. For model selection, in our study, we tested the performance of STDC1-Seg70 (input resolution 1280 \(\times\) 720), STDC1-Seg90 (input resolution 1920 \(\times\) 1080), STDC2-Seg70, and STDC2-Seg90, respectively. The same dataset and training strategy were used to train the model. The inference speed and accuracy of the model were tested on the Jetson NX, and the experimental results are shown in Table 11.  
From the experimental results in Table 8, it is evident that the difference in mIoU between different models on the test set is small, but the speed achieved by STDC1-Seg70 allowed it to operate in real time. In addition, the power line result obtained from the semantic segmentation was not used directly in this study. The segmentation result was fitted to accurately determine the position of the power line later. It is noteworthy that although Seg70 had lower segmentation accuracy compared to Seg90, the results after fitting from both models were identical, from which can be concluded that using a model with lower segmentation accuracy does not affect the final result. Therefore, in this study, the STDC1-Seg70 model was chosen for the segmentation of the power line in order to balance accuracy and speed.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 6 Discussion
### Data Filtering Approach  
In the production of SSH data, it would be convenient to skip the data validation step (as it requires derivation of DT), which means direct filtering of SSH data. However, in Section 2, it was suggested that gradients of DT and geoid might contaminate the low-pass filtering results. The Equation (20) low-pass filter was, hence, applied directly to the SSH data, to test this assumption. Subsequently, the filtered SSH data were reduced to residual values by the Equation (2) principle, and all following data processing was conducted as described in Section 5. The results were then compared to the initially derived residual values (Figure 19a). From this comparison, the directly filtered SSH resulted in slightly less biased residuals, whereby the variation of the residuals appears statistically insignificant. However, Figure 19b shows (an example from campaign C3) the actual impact of gradients on the filtering results (notice the peaks in the results when SSH was filtered directly).  
The comparison in Figure 19b demonstrates the necessity of reducing the SSH to residual values before data filtering. Otherwise, gradients of DT and geoid may result in avoidable discrepancies of over 5 cm (the primary contributor being the geoid). An alternative could be to increase the GNSS data sampling rate, which would allow a shorter filtering window (spatial) length (cf. Figure 10), since a lengthy window causes such error propagation. Nevertheless, in steeper gradient areas, this may not be sufficient to avoid errors shown in Figure 19b. At the very least, geoidal heights should be reduced from SSH before data filtering, since the gradients of the geoid contribute the most to the described errors. Furthermore, note that the squat estimation was unsuccessful from SSH data directly, resulting in nearly twice as low squat estimates, meaning more negatively biased results (for the experiment in this section, the squat correction was applied as described in Section 5.3).