esa-sceva/satcom-chunk-collection huggingface dataset:
# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 2 Performance Evaluation of a Standard Multi-Constellation GNSS Receiver  
As we increasingly rely on GNSS-based positioning, understanding and mitigating GNSS vulnerabilities has become a critical risk management activity for manufacturers, systems and application providers as well as end-users as this can impact national security and may bring huge economic losses. The rigorous performance assessment under realistic conditions is key to understanding the different types of GNSS vulnerabilities and error contributors. In many studies, the performance of a GNSS receiver has been evaluated and characterized through field experimentation in static mode for long durations [44; 45; 46; 47; 48; 49] or under controlled environmental conditions/settings [50].  
Static tests provide good analysis of GNSS errors and can be used to establish cause and effect relationships; however, they are generally performed on pre-surveyed and known candidate sites. In addition, these tests provide little information regarding inaccuracies in positioning that occur due to movement or dynamics of the environment. On the other hand, on-road/field tests can be performed while navigating through different environmental contexts for more realistic GNSS performance assessment in a dynamic mode.  
However, these tests are demanding in nature and require a considerable skill set and amount of subject knowledge to select accurate experimental sites, route trajectories and operational modes/scenarios [24; 51; 52; 53; 54].  
In this section, dynamic performance assessment of a multi-constellation and multi-frequency standard-mode GNSS receiver is performed using fundamental quality indicators, such as the satellite availability, Position Dilution of Precision (PDOP) and Root Mean Square (RMS) error in congested city center areas. For this assessment, all four global constellations, i.e., GPS, BeiDou (BDS-3), Galileo and GLONASS, are utilized for the first time without using any augmentation or mitigation services.  
Although there exist several studies evaluating the on-road performance of GNSS and a detailed comparison of these on-road analyses can be found in Section III of [24]. However, a majority of previous studies have been conducted on dual or triple constellations with GNSS correction services (i.e., Networked RTK, Proprietary) enabled. In order to highlight the pros and cons of using an increased number of constellations for positioning estimation, the next section covers the details of the field experiments performed, route dynamics, observation periods and signal-reception characteristics.
###### Abstract  
The adoption of predictive maintenance in public transportation has gained increasing attention in the context of Industry 4.0. However, many urban bus fleets remain in early digital transformation stages, with limited historical data and fragmented infrastructures that hinder the implementation of data-driven strategies. This study proposes a reproducible Machine Learning pipeline tailored to such data-scarce conditions, integrating domain-informed feature engineering, lightweight and interpretable models (Linear Regression, Ridge Regression, Decision Trees, KNN), SMOGN for imbalance handling, and Leave-One-Out Cross-Validation for robust evaluation. A scheduled batch retraining strategy is incorporated to adapt the model as new data becomes available. The pipeline is validated using real-world data from hybrid diesel buses, focusing on the prediction of time spent in critical soot accumulation zones of the Diesel Particulate Filter (DPF). In Zone 4, the model continued to outperform the baseline during the production test, indicating its validity for an additional operational period. In contrast, model performance in Zone 3 deteriorated over time, triggering retraining. These results confirm the pipeline's ability to detect performance drift and support predictive maintenance decisions under evolving operational constraints. The proposed framework offers a scalable solution for digitally emerging fleets.  
urban bus fleets; machine learning; data scarcity; data-driven maintenance; fleet digitalization; scheduled batch retraining Article
## 2 Problem Formulation and Preliminary
### Transformation Method  
As is known to all, it is very difficult to design a controller because of the attitude tracking error constraint in Equation (11). To avoid this issue, the following transformation method is adopted to transform the constrained problem into an unconstrained one [20]:  
\[\sigma_{ei}(t)=\frac{z_{1i}(t)}{\rho_{i}(t)}\,\ \ i=1,2,3. \tag{20}\]  
In this paper, we define the equivalent unconstrained equation as  
\[e_{1i}=\tan\Bigl{(}\frac{\pi}{2}\sigma_{ei}(t)\Bigr{)}. \tag{21}\]  
Taking the first and second derivatives of \(e_{i}\), there is  
\[\begin{array}{l}\dot{e}_{1i}=A_{i}\Bigl{(}z_{2i}-\frac{\dot{\rho}_{i}}{\rho_ {i}}z_{1i}\Bigr{)}\\ \dot{e}_{1i}=P_{i}\dot{e}_{1i}+A_{i}\bigl{(}z_{2i}-\Phi_{i}\bigr{)}\end{array} i=1,2,3, \tag{22}\]  
where \(A_{i}=\frac{\pi}{2\dot{\rho}_{i}}\bigl{(}1+{e_{1i}}^{2}\bigr{)}\), \(P_{i}=\frac{2\pi}{2\dot{\rho}_{i}}\Bigl{(}z_{2i}-\frac{z_{1i}\dot{\rho}_{i}}{ \rho_{i}}\Bigr{)}e_{1i}-\frac{\dot{\rho}_{i}}{\dot{\rho}_{i}}\), and \(\Phi_{i}=\frac{z_{2i}\dot{\rho}_{i}\rho_{i}+z_{2i}\dot{\rho}_{i}\rho_{i}-z_{1 i}\dot{\rho}_{i}^{2}}{\rho_{i}^{2}}\). Equation (22) can be rewritten as  
\[\begin{array}{l}\dot{e}_{1}=A\Bigl{(}z_{2}-\operatorname{diag}\Bigl{(} \frac{\dot{\rho}_{i}}{\rho_{i}}\Bigr{)}z_{1}\Bigr{)}=e_{2}\\ \dot{e}_{2}=P\dot{e}_{1}+A\Bigl{(}\dot{z}_{2}-\Phi\Bigr{)}\\ \quad=P\dot{e}_{1}+A\Biggl{(}\begin{array}{c}(R\!R^{-1}+R\!J^{-1}\Omega J\!R^ {-1})z_{2}+R\!J^{-1}(M_{c}+M_{air})\\ +(\dot{R}R^{-1}+R\!J^{-1}\Omega J\!R^{-1})\dot{A}_{\rm C}-\dot{\Lambda}_{\rm C }-\Phi\end{array}\Biggr{)}+{\bf D}\end{array}, \tag{23}\]  
where \({\bf D}=A\!d\), \({\bf e}_{1}=[e_{11},\ e_{12},\ e_{13}]^{\rm T}\).  
**Remark 2**.: _After error transformation, the inequality-constrained problem is transformed into an equality unconstrained one. The smooth function \(\frac{2}{n}\arctan(e_{1i})\) has the properties \(\lim_{e_{1i}\to+\infty}\frac{2}{n}\arctan(e_{1i})=1\) and \(\lim_{e_{1i}\to-\infty}\frac{2}{n}\arctan(e_{1i})=-1,i=1,2,3\). The transformed error function in Equation (21) ensures that the steady-state tracking errors can converge to zero when the transformed error \({\bf e}_{1}\) approaches zero._  
Figure 3: The simulation results of PTPPF: (**a**) the curves of \(\dot{\rho}_{i}\) for five cases; (**b**) the curves of \(\widetilde{\rho}_{i}\) for five cases.
## 4 Discussions
### Microwave Extraction of Antioxidant Compounds  
As shown in Figure 1A, a significant difference in the antioxidant efficacy (\(p<0.05\)) was observed between all extraction solvents used with ethyl acetate showing the highest efficacy at 69.4%. The effectiveness of the antioxidant compounds was significantly lower when the extraction was carried out using hexane, with an efficacy rate of 4.7%. Microwave extraction for 40 s produced the highest concentration of antioxidant compounds using the ethyl acetate solvent. These results are in agreement with others [44], who demonstrated that the ethyl acetate solvent is highly efficient in extracting antioxidant compounds from _A. flavus_ when compared to other solvents. The results showed differences in the effectiveness of antioxidant compounds when using different solvents to extract antioxidants. This is the result of a range of polarities observed within various solvents tested, as well as the chemical nature of the active compounds. As a result, antioxidant compounds such as phenolic compounds are preferably dissolved into polar solvents as ethyl acetate, which has a similar polarity to the extracted antioxidant compounds [45]. The obtained results were in agreement with another research [46], in which ethyl acetate was used to extract antioxidant compounds from _Mortierella sp_. In addition, bioactive compounds such as 3.3-dihydroxyxterphenylilin, 3-hydroxyterphenylilin, and candidus were isolated from _A. candidus_ using an acetylene solvent [47]. Furthermore, the previous study demonstrates that the extraction of phenolic compounds, which also have an antioxidant activity, strongly depends on the solvent used [48].  
Figure 3: Diagram showing a summary of the proposed methodology used to extract phenolic compounds and antioxidant activity measurement.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 7 Conclusions  
This paper proposes a novel bilateral 2 DPCA algorithm named DB2 DPCA for image reconstruction and recognition, which fuses bilateral information of images in a direct manner. For image recognition, the projection result of DB2 DPCA is calculated by concatenating the projection results of two unilateral 2D algorithms, i.e., 2 DPCA and A2 DPCA. Therefore, it can extract more features, which are useful for image recognition. For image reconstruction, the reconstruction result of DB2 DPCA is calculated by averaging the reconstruction results of the two unilateral algorithms. In this way, it strengthens their common parts and weakens their differences, thus reconstructing the original image accurately. To evaluate the proposed algorithm, we compare it with four related feature extraction algorithms in the tasks of image reconstruction and recognition. Four benchmark databases are used and three different classifiers are tested in the experiments. As a result, DB2 DPCA generally outperforms competing algorithms both in image reconstruction and recognition. Additional experiments on reordered and reshaped databases further demonstrate the superiority of the proposed algorithm. In conclusion, DB2 DPCA is an effective algorithm for image reconstruction and recognition. The direct bilateral idea can be applied to other unilateral 2D algorithms to improve their performances in image analysis.  
**Author Contributions:** Conceptualization, J.W.; methodology, J.W.; validation, M.Z., X.X. and L.Z.; writing--original draft preparation, J.W., L.Z. and W.Z.; writing--review and editing, L.Z. and W.Z.; funding acquisition, J.W., L.Z. and W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the National Natural Science Foundation of China under Grant 31900710; in part by the Key Scientific Research Projects of Henan Province under Grant 22A520008 and Grant 22A520007; in part by the Natural Science Foundation of Henan Province under Grant 222300420275 and Grant 222300420274; in part by the Youth Scientific Research Fund Project of Xinyang Normal University under Grant 2021-QN-036; and in part by the Nanhu Scholars Program for Young Scholars of Xinyang Normal University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Scalable Video Coding (SVC) an Extension to H.264/Advanced Video Coding (AVC)
### Media Aware Network Elements (MANE)  
MANEs are CAN-enabled intermediate devices that implement intelligent modules, such as routing, adaptation, and so on [12; 13; 14]. Figure 3 depicts the architecture of MANE. It mainly implements Adaptation Decision Engine (ADE) and Extractor.  
The modules, such as Network Analyzer and SVC Header Analyzer, are supporting the ADE module. The Network Analyzer monitors the network conditions and availability of the network resources, and then it feeds the same to ADE, which decides the number of layers that need to be extracted. The module continuously monitors the congestion status and Packet Delivery Ratio (PDR) of the network to understand the dynamic nature of the network resources. This monitored data helps in improving the efficiency of adaptation. Along with these parameters, Bandwidth, Buffer availability, and terminal availabilities are monitored for improving the decision-making. In wireless communication, reachability is also an important parameter because it decides the performance of routing protocols and the quality of the data received. Wireless routing protocols have many overheads, such as hello packets and echo packets. Hence, studying the influence of routing protocol and bandwidth availability was the aim of this research.  
The SVC header analyzer parses the packets and then extracts the layer information from the bit-stream. The scalable video levels that are decided at ADE are fed into Extractor to extract the SVC levels accordingly. Unwanted scalable layers are removed from the fully scalable video bit-stream, and an adaptation bit-stream is delivered to the network. The adapted video bit-stream provides maximum video quality that can be achieved in the available resources and conditions.  
Figure 2: Combined scalability.
## 4 Smart5 Grid NetApp Use Cases
### UC4--Real-Time Wide Area Monitoring
#### 4.4.2 Requirements  
Phasor measurement technology is increasingly deployed in the transmission side of the power grid, aiming to improve the monitoring function of the grid, especially the state estimators. This technology is very useful, especially when inverter-based power generation is present in significant percentages (e.g., above 30-40%), leading to faster dynamics of the grid. While these advanced measurement units are able to record power system states with high granularity, the communication link needs to also satisfy requirements such as high availability of service and high communication reliability in order for this information to be available to the TSO. Specifically, this UC requires a dedicated slice for URLLC, able to transmit measurements with end-to-end latency between 20 ms and 200 ms. The communication delay between the PMU and the vPDC must be below 40 ms, which is the waiting time of the vPDC. When this time is exceeded, all data transmitted by the PMU and arriving at the vPDC will be discarded.
## 2 Materials and Methods
### Modeling Development Using Random Forest Technique
Optional parameters such as minimum leaf size and maximum tree depth were left unchanged (blank) for all models. The RF model originally considered 50 different independent variables based on five variables (CC, CDM, BH, BV, NDVI) each calculated with two different statistical measurements (mean and standard deviation) at five different neighborhood distances (50 m, 100 m, 200 m, 400 m, and 800 m). However, Building Volume (BV) calculated at all neighbor distances as standard deviation (BV_STD) showed noticeable signs of banding (straight lines across the whole area of interest) with no calculated values, and thus were excluded from the modeling. Two other variables, Canopy Cover (CC), and Canopy Density Metric (CDM) presented the same banding issue using standard deviation at distances under 400 m ((CC_STD \(<\) 400 m) and (CDM_STD \(<\) 400 m)). Their standard deviations computed at 50 m, 100 m, and 200 m were also discarded from the model for all urban areas. Therefore, only 39 different independent variables were used for the final model in all urban areas  
Although temperature data were collected in 10 cities, due to issues mentioned below, only seven had successful temperature collection for all three time periods. Temperature data were collected at three times of the day (afternoon, evening, and night) in Burlington, Cedar Falls, Cedar Rapids, Council Bluffs, Des Moines, Sioux City, and Waterloo. In three other cities, data were collected only in two periods, either due to personnel or logistical constraints (Waverly and Fort Dodge), or extreme weather conditions (thunderstorm) during the time of collection (Marshalltown).
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 3 Experiments
### Implementation Details
#### 3.2.2. Implemented Adversarial Attacks  
The attack strategies most commonly used in automated medical image diagnosis were implemented [12; 13]: the fast gradient sign method (FGSM) [5], basic iterative method (BIM) [6], and stabilized medical image attacks (SMIA) [7]. Below, an overview of the principles behind these implemented adversarial techniques is provided:  
* **FGSM:** This method computes the gradients for a given input image \(x\) and its corresponding class \(y\). These gradients are used to amplify the loss function \(J\) of the target model. By integrating this direction into the original image \(x\), the adversarial sample \(x_{adv}\) is produced. The attack formulation is \[x_{adv}=x+e\cdot\text{sign}\big{(}\
abla_{x}J(x,y)\big{)}\] where \(e\) is the step size.
* **BIM:** Unlike the one-step FGSM, BIM operates iteratively. This method repeatedly amplifies the loss, often for \(K\) iterations, and accumulates the gradients \(x^{i}_{adv}\) onto the original image. The BIM approach can be expressed as \[x^{i+1}_{adv}=\pi\bigg{(}x^{i}_{adv}+\frac{1}{K}\big{(}\
abla_{x}J(x^{i}_{ adv},y)\big{)}\bigg{)}\] where the starting point \(x^{0}_{adv}\) corresponds to the input image \(x\). The function \(\pi\) serves as a clipping mechanism to ensure pixel values remain within the range \(x-e\) to \(x+e\).
* **SMIA:** This method is tailored specifically for models in the medical domain, setting it apart from general-purpose methods like FGSM and BIM. Instead of generating noisy results like its counterparts, SMIA emphasizes noise reduction. The core concept hinges on the fact that while adversarial samples typically manifest noise, SMIA integrates a stabilization function into the loss function. This drives the noisy sample towards a blurred version obtained via a Gaussian kernel. The stabilization loss, designed for maximization in our context, is articulated as \[\mathcal{L}_{S}=\mathcal{L}\big{(}M(x_{adv}),y\big{)}-\alpha\cdot\mathcal{L} \big{(}M(x_{adv}),M(x+W*\eta)\big{)}\] where \(W\) represents the Gaussian kernel used in the convolutional operation with the perturbation noise \(\eta\) (given by \(x_{adv}-x\)), and \(\alpha\) acts as a scalar factor balancing the loss terms.
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
## 4 Result and Discussion
### Segmentation Results Visualization  
The main purpose of clustering is to distinguish the objects in traffic scenes effectively. We used the Semantic KITTI [42; 43] data set to test, which is a classic data set in automatic driving, including point cloud, image, IMU and other data. Semantic Kitti is a data set with point cloud level annotation. We compare the annotation of this dataset with the experimental results and can get the accuracy of our method.  
In Figure 8, two Semantic KITTI point clouds in city scenarios are shown in the form of a range graph: the precisely labeled data is the upper image, and the lower image is the result of the proposed method. The horizontal direction of the range graph represents the horizontal angle of the point cloud, and the vertical direction represents the vertical angle. Each pixel represents a point cloud, and different colors represent different categories. Among them, black indicates that the point is invalid, and no point is returned at this angle.  
Compared with the accurate annotation data and the results of our proposed method, the traffic objects in the road are well distinguished, which meets the requirements of automatic driving vehicles  
Figure 8: The upper image is the result of Semantic KITTI annotation, and the lower image is the result of using our clustering algorithm. Each pixel represents a point cloud, and different colors represent different categories. The traffic objects on the road are well segmented.  
for point cloud segmentation. The results show that the vehicles in Figure 8 are well differentiated from the surrounding environment. In the next subsection, we will show the test results on the entire Semantic KITTI dataset.  
We also apply our clustering algorithm to an automatic vehicle to test the method. Figure 9 is our test platform. Robosense RS-Ruby (128 channels) LiDAR sensor is installed on the top of the car. Compared with the 64 channel LiDAR, the sensor needs to process more data per frame and has higher requirements for the real-time performance of the algorithm.  
In Figure 10, we show the point cloud segmentation results of a complex traffic scene. On the road with heavy traffic, our method can well distinguish different traffic objects, which shows that the segmentation standard we adopted is very effective. The detailed perspective in Figure 10 shows that adjacent vehicles are divided into different categories. The trees outside the road are also well distinguished.  
Figure 10: Different objects are represented by different colors, and different traffic objects on the road are well distinguished, although the trees outside the road are also well distinguished (LiDAR is Robosense RS-Ruby (128 channels)).  
Figure 9: A test platform of the automatic vehicle. Robosense RS-Ruby (128 channels) LiDAR in the red box.  
The algorithm on real vehicles needs to ensure the accuracy and real-time performance of higher standards to meet the application requirements of real vehicles. So we test the accuracy and running time of the proposed method.
## 5 Numerical Simulation of Slope Overburden
### Post-Mining Roof Fracture Characteristics of Gob-Side Entry Retaining for No. 2-1 Coal Seam
#### 5.2.1 Influence of Coal Seam Thickness  
The roof fracture characteristics under different coal seam thicknesses are shown in Figure 16, and the statistical results of the main roof fracture positions are presented in Table 2. In the table, \"+\" indicates the main roof fracture position above the goaf, while \"-\" indicates the position above the solid coal. The table and figure reveal that as coal seam thickness increases, when the coal seam thickness increases from 3 m to 7 m, the fracture location migrates 8.2 m toward the solid coal side (from +0.5 m to -7.7 m, Table 2), and the main roof fracture position exhibits a clear trend of shifting toward the solid coal side. This indicates that when coal seam thickness increases, the fracture position of the main  
Figure 15: Roof Failure Location and Pattern after Mining of Coal Seam 2-1.  
roof generally moves deeper into the coal seam. Considering the influence of the main roof's lateral fracture structure on the surrounding rock stability of gob-side entry retaining and the degree of coal seam yielding, it is evident that under coal seam thicknesses of 3 m and 4 m, the surrounding rock of gob-side entry retaining can generally maintain stability. However, under thicknesses of 5 m to 7 m, maintaining stability becomes significantly more challenging.