esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In past, the issue was usually solved by reducing the data processing window to 1-6 h and by combining normal equations for the tropospheric parameter estimation [11; 28]. It was also reported, that ZTD values at the edges of the processing interval are about 20% less accurate compared to those in the middle of the interval and that the main impact is actually observed during the first and last hours of the interval [29]. The E-GVAP expects estimated ZTDs within the last (production) hour, thus a trade-off between the accuracy and the latency is important. A piece-wise linear function for modeling the tropospheric parameters within each hour is used by a majority of E-GVAP contributors using the Bernese GNSS Software V52 [30]. In addition, horizontal tropospheric gradients are usually not estimated in operational solutions because of two reasons. First, there is presently no operational assimilation of gradients into NWM. Second, the estimation of high-resolution horizontal gradients reflecting a high spatio-temporal variability of local humidity increases the number of estimated parameters in the network and, consequently, the computation time by a factor of 2-3 at least. The PPP with an epoch-wise filtering supported by the IGS RTS products is an optimal strategy for generating advanced GNSS tropospheric products for future meteorological applications [20; 31] such as high-resolution ZTDs, horizontal tropospheric gradients, and slant tropospheric delays. In this paper, we present a new processing strategy adaptable in the operational mode when prioritizing product latency (RT) or product accuracy (NRT). Additionally, all the advanced tropospheric parameters for RT and NRT products are derived within a single continuously operating RT PPP engine supported by the IGS RTS orbit and clock corrections. We believe that such unified processing system will replace soon our existing NRT contribution to E-GVAP. The strategy exploits all abovementioned advantages including benefits of full multi-constellation in near future. The paper is organized as follows: after the introduction we assess real-time precise orbit and clock products available from the IGS RTS, evaluate an operational prototype of GNSS ZTD real-time production, both as pre-requisites for the new strategy. In Section 3, we introduce the new strategy for an adaptable PPP processing solution for estimating ZTDs, horizontal tropospheric gradients and tropospheric slant delays using precise real-time products. In Sections 4 and 5, we assess the results of estimated tropospheric parameters driven by the backward data smoothing including study of impacts of IGS real-time products and multiple constellations. In the last section, we close the paper with conclusions.
## 3 Results  
### Characteristics of the Study Subjects  
Table 1 shows the descriptive characteristics of the study subjects. There were no significant differences in age, height, weight, or body mass index (BMI) between the LST and control groups. Table 2 shows the results of OGTT in the LST and control groups. No significant interaction was observed in time \(\times\) group. Table 3 shows the CGMS of the primary endpoints and physical activity data of the secondary endpoint for the study subjects. No significant differences were observed between the groups. In addition, the 75 g OGTT showed that all subjects had normal glucose tolerance. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **(Index)** & **LST Group** & **Control Group** & **\(p\)-Value** \\ \hline Participants & & 10 & 10 & & \\ Males & & 5 & 5 & & \\ Females & & 5 & 5 & & \\ Age \({}^{\dagger}\) & (year) & 28.6 \(\pm\) 3.9 & 27.2 \(\pm\) 4.21 & 0.449 & \\  & (range) & 22–34 & 23–36 & & \\ Height \({}^{\dagger}\) & (cm) & 163.3 \(\pm\) 12.3 & 167.4 \(\pm\) 8.9 & 0.408 & \\  & (range) & 149.4–177.5 & 156.9–181.0 & & \\ Weight \({}^{\dagger}\) & (kg) & 60.8 \(\pm\) 15.6 & 59.6 \(\pm\) 10.8 & 0.841 & \\  & (range) & 44.5–93.0 & 41.0–79.0 & & \\ BMI \({}^{\ddagger}\) & (kg/m\({}^{2}\)) & 22.5 \(\pm\) 3.3 & 21.1 \(\pm\) 2.6 & 0.331 & \\  & (range) & 19.0–31.1 & 16.0–24.1 & & \\ \hline \end{tabular} Data are displayed as the mean \(\pm\) standard deviation (SD). \({}^{\dagger}\)\(t\)-test. LST, low-intensity resistance exercises with slow movements and tonic force generation; BMI, body mass index.  
\end{table} 
Table 1: Descriptive characteristics of the participants in the LST and control groups. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **(Index)** & **LST Group** & **Control Group** & **Group \(\times\) Time** & **Group** & **Time** \\ \cline{3-6}  & & & \(F\) & _p-Value_ & \(F\) & _p-Value_ \\ \hline Glucose before & & 0.152 & 0.860 & 0.053 & 0.820 & 17.477 & \textless{}0.001 \\ measurement & (mg/dL) & 94.7 \(\pm\) 6.2 & 96.5 \(\pm\) 12.1 & & & \\ OGTT 1 h & (mg/dL) & 140.7 \(\pm\) 50.8 & 134.7 \(\pm\) 28.7 & & & \\ OGTT 2 h & (mg/dL) & 117.2 \(\pm\) 30.6 & 114.3 \(\pm\) 25.4 & & & \\ \hline \end{tabular} Data are displayed as mean \(\pm\) standard deviation (SD). LST, low-intensity resistance exercises with slow movements and tonic force generation.  
\end{table} 
Table 2: The results of the 75 g oral glucose tolerance test (OGTT) in the LST and control groups.
## 3 User Study
### Stimuli and Tasks  
For the purpose of this study, a realistic visualization of a rural mountain range was created (Figure 2). This environment deliberately included key visual components according to spatial landmark theories such as trails, districts, environmental borders/edges, key landmark objects [50; 51], etc. This included detailed and unique items scattered across the environment. The user interface consisted of head-mounted display, keyboard and mouse. A small, purposefully bland tropical island (Figure 1b, map B) was created for a trial task to familiarize participants with the control devices and VR technology. The overall visual complexity of the trial island was much lower than the one of the experimental environment, as the emphasis in there was on the user interface. To learn the principles of the interface, participants were asked to walk around the island in a first-person perspective and collect five objects (virtual coconuts) scattered across the terrain. Since some of the objects were harder to find than the others, it took the participants about 2-5 min to find them. Individual differences in the trial task can be attributed to participants' visual attention and their initial mastery of the interface. In spite of a brief exposure, this task ensured the participants with no prior VR/3D experience grasped the necessary skills to navigate in a VGE.  
Figure 1: Isometric, elevated overview of map A (**a**), map B (**b**), and map C (**c**).  
After completing the trial task, participants were brought to the experimental mountain range environment (Figure 1a, map A; Figure 2). This environment was much bigger, spanning half a square kilometer. Here, participants were instructed to freely explore the features of the environment. An undisclosed time limit (10 min) applied to exploration (if they exceeded this limit, participants were then asked to finish exploring); this limit was not disclosed to participants in order not to induce any feelings of constraint and to keep the process of exploration natural to reflect the participants' specific visual cognitive styles. As they explored, data was collected on participants' movement patterns, stops/ times spent pausing, and use of the interface.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 3 Background
### Support Vector Regression  
Support vector regression (SVR) [54] is based on the structural risk minimization, characterized by a convex optimization problem, generating a single global minimum. The objective is to find a function in the form of 2:  
\[\left\{f\middle|f(x)=w^{T}x+b,\ w\ \in\Re^{d},\ b\ \in\Re\right\} \tag{2}\]  
where \(w\) is a weight vector with components determined by the regularized risk function, \(x\) is the input vector, and \(b\) is the bias.  
In regression problems, the model provides continuous outputs to the training data, considering a maximum deviation of \(\varepsilon\) of the expected value [55]. However, the cost function is not computed for values inside the region limited by \(\varepsilon\). In addition, the use of kernel functions makes it possible to perform nonlinear mappings in the data to a feature space in which a linear function is found.
## 2 Problem Analysis and Theory
### Principles of PIRNN Method
#### 2.4.1 PIRNN Forward Modeling
We integrated the PIRNN-based forward modeling process into an SGFD RNN Operator (Staggered Grid Finite Difference RNN Operator), with the complete forward modeling and loss calculation process shown in Figure 4. Here, \(\text{Input}(i)\) represents the wavefield information stored in the RNN cell at time step \(i\), while \(\text{Output}(i)\) represents the simulated shot gather record \(\mathbf{d}_{\text{cal}}(t_{i},\mathbf{m})\) generated at time step \(i-1\). Starting with the initialized wavefield variables as input at time step 0, forward modeling is performed step by step through the SGFD RNN operator. During this process, velocity parameters are embedded into the RNN structure as trainable network parameters. Consequently, the resulting \(\mathbf{d}_{\text{cal}}(t,\mathbf{m})\) contains the chain relationships of the entire spatiotemporal field with respect to velocity parameters. This information enables the calculation of gradients of the current objective function with respect to velocity parameters during subsequent loss calculation and neural network backpropagation, thereby updating the velocity parameters.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 5 The Prototype
### Results and Discussion
#### 5.4.2 Compared Classifiers Regarding Accuracy  
Table 7 shows the comparison of the precision and recall of the proposed approach using non-contextual classifiers (DT, KNN and SVM). The proposed contextual Bayesian classifier based on Multi-OSCM ontology model attains a promising precision as compared to other non-contextual classifiers with 95.23% on multi-smart domain rules repository. Besides, the proposed approach demonstrates a higher recall of 95.82%. From these results, we can conclude that the proposed method is more effective than traditional classifiers in which ontology-based contextual Bayesian technique is applied.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Accuracy** & **Recall** \\ \hline KNN & 92.06\% & 93.25\% \\ DT & 80.95\% & 86.39\% \\ SVM & 87.30\% & 86.39\% \\ Proposed approach & 95.23\% & 95.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the proposed approach with other classifiers.  
Figure 14: Comparison of accuracy for different criteria by the proposed approach.
###### Abstract  
The aim is to assess the influence of fabrication techniques--conventional (CN), CAD-CAM (CC), and 3D printing (3D)--on the marginal fit, adaptation, surface roughness, and wear of interim restorations of crowns. Thirty interim crowns were fabricated using CN, CC, and 3D techniques. Sixty discs were fabricated to evaluate surface wear and surface roughness properties, with 10 disc samples in each group (\(n=10\)). Internal adaptation and marginal integrity of interim crowns were analyzed with micro CT to detect microgaps at selected points. Average surface micro-roughness (Ra) was calculated in micrometers (\(\mu\)m) using an optical non-contact surface microscope under cyclic loading. Surface wear was evaluated by surface area measurements (mm\({}^{3}\)) using a micro CT. Analysis of variance (ANOVA) and [PERSON]'s book born comparison tests with Pearson correlation were performed for data analysis. The highest adaptation means were for CN (269.94 \(\pm\) 64 \(\mu\)m), and the lowest mean value was observed for 3D (197.82 \(\pm\) 11.72 \(\mu\)m) crowns. CN and CC specimens showed comparable adaptation (\(p>0.05\)), which were significantly higher (\(p<0.05\)) than 3D crowns. CN crowns showed significantly higher marginal misfit compared to 3D (\(p<0.05\)) and CC (\(p<0.05\)) crowns. The highest mean surface roughness was observed in the 3D crowns (5.61 \(\pm\) 0.33 \(\mu\)m), whereas the lowest was found in CC crowns (3.28 \(\pm\) 0.34 \(\mu\)m). Better restoration Ra was observed using the CC method followed by CN. CN had the highest wear (17.79 \(\pm\) 2.78 mm\({}^{3}\)), and the lowest wear was observed in the 3D (10.81 \(\pm\) 2.00 mm\({}^{3}\)) specimen. Low surface wear was observed using 3D printing, followed by CN and CC techniques. The printed specimen showed comparable outcomes to CAD-CAM restoration; however, they were better than CN restoration. A positive correlation between adaptation and surface roughness was observed, indicating an effect of the fabrication technique on material physical property.  
interim restoration; CAD-CAM; 3D printing; micro CT; surface wear; marginal misfit +
Footnote †: journal: Applied Sciences
# Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM
## 4 Experiments  
### Data Acquisition and Processing  
On 6 April 2022, we collected images in the coal conveyor belt of Hebei University of Engineering and made a foreign object data set. The image was captured by HIKVISION's MV-CA013-20 GC industrial camera, which was fixed before shooting and set to a manual shooting mode to adjust the angle and supplement the light source. This camera had 1.3 million pixels, a high frame rate with a resolution of 1280 \(\times\) 1024 and 90 FPS, a global shutter, multiple exposure modes, and 1 Gbps bandwidth with a Gigabit Ethernet interface, which could adapt to a long-term operation in harsh environments. Unqualified photos, such as blurred backgrounds and the too-high and too-low light intensity of the pictures, were removed, and 2031 qualified images were finally screened. The foreign object data set was divided according to the ratio of 8:1:1, 1626 images were randomly selected as the training set, 203 were randomly selected as the validation set, and 203 images were randomly selected as the test set. To enhance the generalization ability of the model, seven kinds of non-coal foreign objects in this experimental dataset included gangue, screw, nut, angle iron, wooden stick, U-shaped iron, and quadruped. The region and category information of the foreign objects were labeled using labeling software. The number of images for non-coal foreign objects is shown in Table 1.  
In order to evaluate the algorithm proposed in this paper more accurately, we used the HIKVISION MV-CA013-20 GC industrial camera to collect 800 coal images transported by the Xiaotun Coal Mine in Fengfeng Mining Area of Handan on 15 March 2023 and made a data set of coal gangue on a conveyor belt. The gangue dataset was divided into the training set, validation set, and test set according to the ratio of 8:1:1. The large amount of gangue in this dataset was much higher than the amount of coal, using the labeling software to label the coal areas and categories of information. Due to the complex environment of the underground, the high-speed movement of the conveyor belt, and the high amount of coal gangue, the inspection task was extremely challenging.  
This paper also used VOC2007 [47] and VOC2012 [48] data set to verify the algorithm proposed in this paper. The VOC2007 and VOC2012 data set consisted of twenty different categories, including airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, Motorbike, Person, Potted plant, Sheep, Sofa, Train, tv monitor. According to the benchmark of the PASCAL data set, we selected 1000 pieces from the train + val of VOC2007 and the train + val of VOC2012 for training. Finally, the test of VOC2007 was used to verify the algorithm's performance.
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 4 Experimental Environment and Results
### Experimental Results
#### 4.3.1 RMSE Results  
The amount of change in the point-cloud rearrangement result is shown based on the representative voxel size. We measured the RMSE and RC (rearrangement cost) values to show the difference. The following Figure 3 visually displays the rearrangement results.  
Since it is difficult to see the difference in visual quality in Figure 3, we will compare the quality in more detail in the next experiment.  
Table 3 shows the results of the proposed rearrangement process using the dynamic point-cloud data set, which was measured for both RMSE and RC (rearrangement cost) values.  
When the voxel size was 2.0, the experimental result RMSE was measured with an average value of about 1443. This result is equivalent to about 721.8 times the voxel size. The RMSE occurs in the range of about 6.5% (47.45) on average. The average value of RC was about 1.453. This is equivalent to about 72.6% of the voxel size. The RC occurs in the range of about 1.7% (0.01256) on average.  
When the voxel size was 1.55, the experimental result RMSE was measured with an average value of about 875. This result is equivalent to about 564.8 times the voxel size. This results is around 21.7% (1-564.8/721.8) less in comparison to the result when the voxel size is 2.0. The RMSE occurs in the range of about 6.0% (26.44) on average. The average value of the RC was about 0.706. This is equivalent to 45.5% of the voxel size. In comparison to the results achieved when the voxel size is 2.0, this is less by about 37.3% (1-45/72). The RC occurs in the range of about 0.4% (0.00145) on average.  
When the voxel size was 1.26, the experimental result RMSE was measured with an average value of about 830. This result is equivalent to about 659.0 times the voxel size. This result is higher by about 16.6% (1-564/659) when compared to the results when the voxel size is 1.55. The RMSE occurs in the range of about 6.0% (25.01) on average. The average value of the RC was about 0.564. This is equivalent to about 44% of the voxel size. This result is less by about 1.6% (1-44/45) when compared to the results when the voxel size is 1.55. The RC occurs in the range of about 0.3% (0.00099) on average.  
When the voxel size was 2.0, the average point-cloud to RMSE was about 721 times the voxel size, and the RC average was at its highest value, about 72% of the voxel size. When the voxel size was 1.55, the average of the RMSE from point-cloud was measured to be about 564 times the voxel size, and the RC average was measured to be about 45.5% of the voxel size. When the voxel size was 1.26, the average point-cloud to RMSE was measured at about 659 times the voxel size, and the RC average was measured at its lowest value, which was about 44%. Among them, voxel sizes of 1.26 and 1.55 were obtained with a small difference of about 16% in RMSE average and about 1% in RC average.
## 3 _HoloSec_: Proposed Conceptual Holochain Security Framework for IoT Distributed Networks
### Anomaly Detection and Response  
Anomaly detection is powered by lightweight ML models deployed either on edge devices or via a centralized analytics node. These models are trained on historical agent behavior to distinguish between normal and suspicious activity. As real-time logs are analyzed, patterns such as unusual traffic, irregular timing, or protocol violations trigger immediate alerts.  
Upon detecting anomalies, the system  
* Logs the incident immutably on the global DHT to ensure transparency and traceability.
* Adjusts the agent's TS in an adaptive manner, proportional to the severity of anomalous behavior.
* Initiates predefined security actions, including isolating affected agents, notifying stakeholders, and activating automated recovery workflows.
* Employs key rotation to revoke potentially compromised keys and replace them with new cryptographic pairs. This process not only re-establishes secure communication but also enforces access control by preventing further use of the invalidated credentials.
## 2 Network Description
### Satellite Cluster Network  
Similar to MWSNs [28; 29], this paper first supposes that there are \(N\) satellites in the SCNs. Then, the network can be described as \(G=(V_{[N,3]},E_{[N,N]})\) using the Graph Theory, where \(V_{[N,3]}\) denotes the vertex of nodes positions in an Earth-centered inertial reference frame (ECI) and \(E_{[N,N]}\) is the set of undirected ISLs among satellites. Let the network be composed of \(u\) clusters, and each cluster has \(b_{i}\) satellites and \(c_{i}\) links, i.e., \(N=\sum\limits_{i=1}^{u}b_{i}\). Figure 1 shows the structure of SCNs with four clusters and the cluster A and D run cooperatively to accomplish EO missions (satellites in cluster D are responsible for target detection and clusters A transmits the data of images to the ground station). Moreover, due to satellites in SCNs moves around each other varying the time, the network topology using snapshot can be represented as: \(G=\{G_{t_{0}},\cdots,G_{t},\cdots\}=\{G_{t_{n}}|t_{n}=t_{0}+n\eta\}\), where, \(t_{0}\) denotes the initial time of network topology, \(\eta\) is the time interval of every-two snapshots, and \(n\) denotes any positive integer. For an example, Figure 2 illustrates the topology snapshots of dynamic time varying network, where nodes a to f represent the satellites running with each other in a known range [30].  
**Definition 1**.: _(Satellite cluster networks, SCNs) If a number of satellites on orbit run around each other in a certain range and can establish ISLs with others, a area-network consisting of these satellites is a sub-SCNs, like cluster A in Figure 1, and several sub-SCNs  
**Remark 1**.: _In astrodynamics, as the satellites in SCNs fly around each other and also orbit the same central body earth, it signifies that they have the same orbit period and the uniform orbit altitude or semi-major axis [30]._  
The following content in this paper focuses on the dynamic topology control strategy in EO missions in SCNs and also makes the assumptions below: (1) during the process of inter-satellite communication, each satellite has the same maximum transmission range, and always keeps the maximum transmitting power if it is active; (2) antennas on satellites are in an ideal omni-directional pattern; (3) communication protocols on each satellite have ideal MAC layer, which means any two satellites can establish the communication link only if they are in the communication range; (4) each satellite using wireless links in SCNs shares with others its navigation information periodically [31], including position and velocity in ECI; (5) every satellite in SCNs runs in a circular orbit without control and ignores any perturbations from celestial bodies except earth.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 1 Introduction
### Importance of Chaotic Time Series Forecasting
#### 1.2.1 Chaotic Time Series Forecasting in Power and Energy  
Electricity demand and market price predictions have played a significant role in the electric power industry for over a century [15]. Moreover, due to the worldwide energy crisis and alarmingly rising air, water, and soil pollution levels, renewable energy has become increasingly popular for power generation in recent years. This popularity is because renewable energy is a pure and limitless energy source [11]. As a result, a rising number of nations are becoming involved, and investors are committing to developing renewable energy plants. However, the lack of consistent energy sources due to intermittent nature represents renewable energies' main problem. Thus, forecasting renewable generation is the key to integrating these intermittent energies into the electricity grid for several reasons [9; 16]. The main advantage of predicting the intermittent nature of renewable energy resources is that the number of backup systems can be reduced, thus, reducing the investments and need for electricity to meet the demand. Many forecasting approaches have been proposed using ANN, fuzzy, etc. These approaches are based entirely on time series analysis in which the chaotic time series data of renewable energy are one of the most challenging dynamics to be forecast.