esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 CKF Algorithm
### CKF Algorithm Based on a Third-Order Spherical-Radial Cubature Rule  
Here, we consider the Gaussian noise, and it is widely used in Bayesian filter theory. The key approximation is \(p(\mathbf{x}_{k}|\mathbf{D}_{k-1})\) and \(p(\mathbf{z}_{k}|\mathbf{D}_{k})\) are both Gaussian in the Bayesian filter, which deduces a Gaussian \(p(\mathbf{x}_{k}|\mathbf{D}_{k})\).  
Suppose an n-dimensional random vector \(\mathbf{x}_{k}\) obeys the Gaussian distribution \(N(\mathbf{x}_{k};\hat{\mathbf{x}}_{k},\mathbf{P}_{x})\), we can obtain:  
\[\hat{\mathbf{x}}_{k-1}\ =\ E[f(\mathbf{x}_{k-1},\  
Calculate the covariance matrix of state prediction:  
\[\begin{split}\mathbf{P}_{k|k-1}&=\ \frac{1}{2n}\sum_{j=1}^{2n}\mathbf{X} _{j,k-1|k-1}^{*}\mathbf{X}_{j,k-1|k-1}^{*\mathsf{T}}-\hat{\mathbf{x}}_{k|k-1}\hat{\mathbf{x }}_{k|k-1}^{\mathsf{T}}+\mathbf{Q}_{k-1}\end{split} \tag{16}\]  
**Measurement updating:**  
(1) Matrix factorization:  
\[\mathbf{P}_{k|k-1}\ =\ \mathbf{S}_{k|k-1}\mathbf{S}_{k|k-1}^{\mathsf{T}} \tag{17}\]  
(2) Calculate volume points:  
\[\mathbf{X}_{j,k|k-1}^{h}\ =\ \mathbf{S}_{k|k-1}\hat{\mathbf{x}}_{j}+\hat{\mathbf{x}}_{k|k-1} \tag{18}\]  
(3) Transform volume points based on the measurement equation:  
\[\mathbf{Z}_{j,k-1|k-1}^{*}\ =\ h(\mathbf{X}_{j,k|k-1}) \tag{19}\]  
(4) Forecast the measurement value:  
\[\hat{\mathbf{z}}_{k|k-1}\ =\ \frac{1}{2n}\sum_{j=1}^{2n}\mathbf{Z}_{j,k|k-1}^{*} \tag{20}\]  
(5) Calculate the prediction residual covariance matrix:  
\[\mathbf{P}_{zz,k|k-1}\ =\ \sum_{j=1}^{2n}w_{j}\mathbf{Z}_{j,k|k-1}^{*}\mathbf{Z}_{j,k|k-1}^{ *\mathsf{T}}-\hat{\mathbf{z}}_{k|k-1}\hat{\mathbf{z}}_{k|k-1}^{\mathsf{T}}+\mathbf{R}_{k} \tag{21}\]  
(6) Calculate the cross covariance matrix:  
\[\mathbf{P}_{xz,k|k-1}\ =\ \sum_{j=1}^{2n}w_{j}\mathbf{X}_{j,k|k-1}^{h}\mathbf{Z}_{j,k|k-1}^{ *\mathsf{T}}-\hat{\mathbf{x}}_{k|k-1}\hat{\mathbf{z}}_{k|k-1}^{\mathsf{T}} \tag{22}\]  
(7) Calculate the Kalman filter gain:  
\[\mathbf{W}_{k}\ =\ \mathbf{P}_{xz,k|k-1}\mathbf{P}_{zz,k|k-1}^{-1} \tag{23}\]  
(8) Update the state:  
\[\hat{\mathbf{x}}_{k|k}\ =\ \hat{\mathbf{x}}_{k|k-1}+\mathbf{W}_{k}(\mathbf{z}_{k}-\hat{\mathbf{z}}_{k|k -1}) \tag{24}\]  
(9) Update the state covariance matrix:  
\[\mathbf{P}_{k|k}\ =\ \mathbf{P}_{k|k-1}-\mathbf{W}_{k}\mathbf{P}_{zz,k|k-1}\mathbf{W}_{k}^{ \mathsf{T}} \tag{25}\]
## 4 Discussion
### Sensor Position and Balance in Relation to Activity Type
#### 4.2.3 Head  
During static tasks, the head, essential for spatial perception and motor coordination, played a crucial role in maintaining balance. The higher mean acceleration on the mediolateral axis (Y) indicated instability, often due to body oscillations or frequent postural adjustments. These results were confirmed by [PERSON] et al. (2023) [43] who found that measures such as RMS acceleration and frequency at the center of pressure particularly revealed balance impairment in older adults. On the contrary, increased acceleration on the vertical axis (Z) suggested better posture and more effective postural control. This result could be explained by the fact that higher vertical acceleration indicated a more stable postural alignment with the gravity.  
In dynamic tasks, increased vertical acceleration (Z) was associated with reduced postural stability, suggesting a greater solicitation of the vestibular system and visual-motor reflexes. Extended head movements could provoke sensory conflicts, affecting sensory integration and motor coordination that are essential for stabilizing the body. [PERSON] et al. (2018) [44] found that patients with bilateral vestibular hypo-function rely more on extra-ocular signals from eye movements to stabilize their posture, especially in tasks requiring visual tracking.  
All things considered, by providing a comparative assessment of these sensor locations, this study highlights the limitations of relying on a single IMU location to assess balance across varied activity types. This approach aligns with the need for task-specific balance metrics and demonstrates the value of integrating multiple sensor data points to create a comprehensive view of postural stability. Additionally, this study contributes to a more personalized approach to balance assessment, as different sensor placements may suit different individuals and balance challenges.
# Numerical Simulation of Oil Pipeline Leakage Diffusion in Dashagou Yellow River Crossing Section
## 4 Results and Discussion
### Surface Water Oil Spill Simulation Analysis
#### 4.5.2 Simulation and Analysis of Surface Water Oil Spill from Diesel Oil Leakage  
From the analysis of Figure 26, during the dry water period, the diesel oil spills on the river surface and reaches the Yellow River cross section in the range of Yingmantan Alternate Water Source after 35 min; it reaches Yintan Wetland Park after 70 min. Since the oil film began to leak and drift on the river surface, the thickness of the oil film remained at 0.02-0.05 mm for 100 min until the end of the simulation time of 200 min, and the thickness of the oil film did not change significantly and continued to move downstream, which indicated that the oil film migration and diffusion on the river surface had reached a certain degree of quasi-static equilibrium state. The oil film area with a thickness greater than 0.05 mm was maintained at 200,500-252,800 m\({}^{2}\) after 70 min from the beginning of the leakage, and the change in the oil film area did not show a continuous growth but a fluctuating trend, which may be due to the influence of the distribution of the flow velocity in the river. Due to the dry water period, the current speed is slower; the flow rate is smaller; the environmental capacity of the river itself is smaller; the turbulence of the oil cutting, dilution and other roles relative to other seasons are also weaker and the oil spill  
Figure 25: Effect diagram of oil film diffusion range of crude oil spill ((**a**, **b**, **c**, **d**, **e**, **f**, **g**, **h** and **i**) is 10 min, 30 min, 50 min, 70 min, 90 min, 110 min, 130 min, 150 min and 170 min, respectively).  
on the river surface of the rate of attenuation is also correspondingly slower so that the oil can be migrated along the river surface to the more distant areas without being completely diluted by the river attenuation.
# Aerodynamic Analyses of Airfoils Using Machine Learning as an Alternative to RANS Simulation
## 3 Proposed Methodology
### Performance Evaluation Metrics  
The following statistical measures were used as performance metrics to assess the performance of the supervised machine learning method that was used in this work to forecast the aerodynamic characteristics of the airfoils:  
1. Root mean squared error (RMSE), which represents the error rate using the square root of the mean squared error [33]. The lower the value of RMSE, the better the accuracy of the regression model [34]. It was calculated using the expression that is given in Equation (7): \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\lvert P_{i}-M_{i}\rvert^{2}}\] (7)
2. Pearson correlation coefficient (\(R\)), which is a key factor in regression analysis that signifies the correlation between the forecasted results and the real outputs. Its value ranges from \(-1\) to \(+1\). The accuracy of a regression model is considered to be the best when the absolute value of \(R\) is equal or close to 1 [35]. It was calculated using the relationship that is given in Equation (8): \[R=\sqrt{1-\frac{\sum(M_{i}-P_{i})^{2}}{\sum\bigl{(}M_{i}-\overline{M}_{l} \bigr{)}^{2}}}\] (8) where \(n\) denotes the number of data points in the testing subset, \(P_{i}\) and \(M_{i}\) are the predicted and measured values for the \(i\)th aerodynamic coefficient and \(\overline{M}_{l}\) is the mean of all measured values for the aerodynamic coefficients.  
Figure 4: Architecture of the BPNN model for the best performance case.  
Figure 3: Building blocks of the proposed BPNN algorithm.
###### Abstract  
Jurassic strata in the ST gas field of the northern West Siberia Basin have been regarded as a potential exploration target with undiscovered hydrocarbon resources. However limited research has been performed on the sequence stratigraphy of the Jurassic strata, as well as its sandstone distribution controlled by variable sea level change and sediment input. In this paper, four third-order sequences (SQ1, SQ2, SQ3, and SQ4) and nine fourth-order sequences for the Jurassic strata are interpreted based on seismic facies analysis and the lithology stacking patterns of seven wells. SQ1 is characterized by the special Bazhenov Formation which is featured by regionally distributed deep marine shales. SQ2 (J1) is composed of a coarsening upward sequence, the base of which is an uncontormable surface that can be recognizable in both seismic profiles and well logging data. SQ3 (J2-J8) is composed of a complete fining-upward and coarsening-upward sequence, showing a series of transgressive and regressive successions. A complete SQ4 has not been drilled through by all the seven wells, only showing a coarsening upward succession on its top (J9) which evolves into a fining upward succession at the base of SQ3. Combined with the seismic inversion result, which predicts sandstone distribution, a sequence evolution model was built for SQ3 showing a full unit of transgressive system tract and highstand system tract (TST-HST) which often occurs in shallow marine shelves. During sequence development, most reservoir sandstones are deposited in the shelf and tidal delta environment at the bottom of the TST and the top of HST, and mudstones are deposited as shelf mudstones, especially at maximum flooding surface. That is controlled by both accommodation and sediment input. Generally, under this sequence framework, the depositional architecture can be further analyzed with implications for source rock, reservoir sandstones, and sealing rock, which may guide future gas exploration and exploitation in this area.  
sequence stratigraphy; Jurassic strata; sea level change; West Siberia; seismic +
Footnote †: journal: Applied sciences
## 3 Results
### Partial Correlation Between NPP and Climatic Factors  
The partial correlation between temperature and NPP differed spatially from that between precipitation and NPP (Figure 4). A positive correlation between temperature and NPP was observed in 80.36% of the grasslands, out of which 20.68% reached a significant positive correlation. The high partial correlation coefficients were mainly concentrated in the central and northeastern TP, while the lower partial correlation coefficients were found on the southwestern margin. Precipitation was positively correlated with NPP in only 46.56% of the grasslands, and negatively correlated with NPP in the central, eastern, and northeastern parts of the TP.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **TP Grassland** \\ \hline NPP averages from 2001 to 2022 (gC/m\({}^{2}\)/yr) & 136.67 \\ average NPP\({}_{\text{slope}}\) (gC/m\({}^{2}\)/yr) & 1.01 \\ \hline proportion of slope \textless{} 0 (\%) & 7.66 \\ proportion of slope \textless{} = 0 (\%) & 0.43 \\ proportion of slope \textgreater{} 0 (\%) & 91.91 \\ \hline decreasing trend & 0.37 \\ no trend & 48.16 \\ increasing trend & 51.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics on NPP and its trends on the TP grassland.  
Figure 3: The mean value of NPP at 100 m intervals along with elevation (**a**) and the mean value of NPP slope at 100 m intervals along with elevation (**b**). Note: Each point represents the mean of NPP or NPP slope over the elevation gradient, and the shading represents the standard deviation of NPP or NPP slope over the current elevation gradient.  
We further counted the area proportions at various elevation gradients where NPP showed positive or negative correlations with both temperature and precipitation (Figure 5). A positive correlation between precipitation and NPP and a negative correlation between temperature and NPP dominated most of the area below 3400 m. Above 3400 m, a positive correlation between NPP and temperature dominated, and NPP was positively/negatively correlated with precipitation over a comparable area proportion.  
Figure 4: Partial correlations between NPP and climate factors: (**a**) partial correlation between NPP\({}_{\text{detrend}}\) and Temp\({}_{\text{detrend}}\); (**b**) partial correlation between NPP\({}_{\text{detrend}}\) and Pred\({}_{\text{detrend}}\).
# Innovative System for BIM/GIS Integration in the Context of Urban Sustainability
## 4 Results
### Infrawords  
Once the BIM modeling phase was completed, we moved on to the BIM/GIS integration methodologies to obtain optimal urban planning. A simple GIS environment of the study area, created for purposes like the research in question, was imported into Infrawords. Primarily, we used information layers related to topographic surveys of the area, 3D city models, roads, water bodies, and underground utilities such as sewage, potable water, and lighting (which we had created based on previous work). These types of data allow for the analysis of the building's surrounding context, including its solar exposure, orientation, and topography. When combined with BIM data (relating to the building's materials), it enables the prediction of the building's energy performance and the adaptation of the project to specific requirements to maximize overall energy efficiency. The integrated GIS data allows for the analysis of resource usage and, together with the BIM data, helps optimize material usage as well as provides a precise evaluation of the return on investment related to the installation of the chosen photovoltaic panels. However, having available more complete layers with a greater level of detail of the area under study through ArcGIS online, it was decided to use the latter for a more accurate visualization of the area and its characteristics, proceeding to the GIS modeling of the area, as shown in Figure 11.  
Figure 10: (**a**) Autodesk Revit: exterior 3D model; (**b**) Autodesk Revit: interior 3D model.  
Figure 9: (**a**) Autodesk Revit: for exterior, point cloud import phase; (**b**) Autodesk Revit: for interior, DWG file import phase.  
Figure 12 shows the building in question inserted within the urban context using the commercial software program Autodesk Infrawworks.  
Evident from the illustration, the choice to insert the photovoltaic panels on the facade was prudent: the building overlooks a parking lot and is not subject to shaded areas caused by other buildings or vegetation. Thanks to this visualization, the urban planner can make studied choices for better management of both the single building and the area in which it is inserted. Clearly, the limit remains the lack of information deriving from the loss of information from the importing process of the BIM model.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Balancing Data with SMOTE
#### 4.4.2 Application of SMOTE  
In this study, as shown in Figure 6, the dataset initially exhibited a significant class imbalance, with only 129 positive AAS cases compared to 148,578 negative cases. This imbalance posed a challenge for model training, as it could lead to biased predictions favoring the majority class. In the graph, the minority class is depicted as a small cluster of orange data points, while the majority class appears as a dense cluster of blue points.  
To address this, we applied the Synthetic Minority Oversampling Technique (SMOTE) using k = 5 nearest neighbors with the imbalanced-learn Python library. SMOTE generates synthetic samples for the minority class, helping to balance the class distribution. As illustrated in Figure 7, the minority class becomes better represented after SMOTE is applied, reducing bias in the model and improving overall prediction performance. This balanced dataset contributes to the model's ability to more accurately identify AAS cases and generalize effectively.  
Figure 6: Dataset before applying SMOTE.  
Figure 7: Dataset after applying SMOTE.
## 1 Introduction
In addition to this small-scale thermoelectric EHS (which produce only some watts of electrical energy), there are also large-scale thermoelectric EHSs producing a few hundred watts of electrical energy. They are mostly based on radioisotope thermoelectric generators and are applied in satellites, e.g., Apollo 14-17 (ca. 75 W), Galileo (287 W) or Voyager 1 and 2 (ca. 160 W), [16]. Here, a thermoelectric EHS at the exhaust pipe of a heating system is considered. In almost every German household, a heating system is installed; therefore, there is large potential for thermoelectric EHS, considering that 86.8% of heating energy used in Germany (2015) comes from fossil sources [17], and an estimated 71% of those heating systems (2013) have inadequate efficiencies [18]. That is why this contribution, in addition to the many other waste heat processes, deals with those of heating systems. The main idea is to generate electrical energy from the exhaust gas of the heating system. The solution should consist of commercially available components and be integrated as easily as possible into existing systems. In [19], a thermoelectric EHS at a heating system was also developed, but in contrast to this work, the system was directly integrated into a furnace, which would mean a major intervention in a real facility. The gained energy should be used as a primary goal for a variety of direct current (DC) home applications, e.g., to load mobile phones or accumulators. Moreover, as a secondary goal, it may supply the heating system, so it will be autarkical and operational during a blackout as well. According to [20], there was in Germany in 2015 an average power failure per customer (considering also force majeure) of 15.3 min; without force majeure of 11.9 min. This means that over the winter period, enough energy has to be produced by the thermoelectric EHS to overlap the time of blackouts. In the following, to investigate such an EHS, the mockup of a heating system is built up and a thermoelectric EHS is developed and optimized.
## 2 Ad hoc On-Demand Distance Vector Routing and Hotline Based Routing
### Route Discovery and Maintenance in AODV  
In reactive routing protocols, sending node is responsible for doing a route discovery process before it can send data to an undiscovered destination node. If the route path is already known the route discovery process is not required. The route discovery is an energy intensive operation as it depends on flooding of RREQ (Route - Request) packets. The sender, after sending the RREQ packets, waits for an expected RREP (Route - Reply) message which contains the hop by hop path towards the destination node. On receiving a RREQ packet, a reverse path by the node to the source node is set up using previous hop information in RREQ packet. This information is used for sending data backwards from the destination to the sender such as (acks, requests, _etc._). If the intermediate node has a valid route path available for the destination, it replies back to the sender using unicast on the reverse path with a RREP packet which contains the full path to the destination. If the intermediate node does not have the route path information about destination node, it rebroadcasts the RREQ packet by updating the previous hop information with its own address. Duplication of RREQ is controlled by sequence number, so each intermediate node only forwards the RREQ message once. When the first RREQ message arrives at the destination node, it replies back with a RREP message using the reverse path information in the received RREQ packet. RREP packet travels back on the same path from which it arrived. As the RREP packet travels towards the source node it establishes a forward path towards the destination node [1]. In case of route failure RERR packets are used. The link failure detection and recovery mechanisms at the mac level can be used to detect the path failure efficiently. RERR packet is sent to all the source nodes that were using the intermediate node for reaching their destinations. All such paths are purged for use and source nodes need to reinitiate the route discovery process in order to communicate with destination nodes. Timer based mechanisms at each node helps in cleaning up the stale routes.
# Copper Alloys Performance in High-Pressure and Low-Velocity Conditions Using a Custom Tribometer
There are several tribological experimental setups, including the pin-on-disc test, which is specifically concerned with the study of conformal contacts, and its applicability for the study of journal bearings is not impaired when considering situations of small radial clearances and large contact areas of the geometry of the concave shaft and convex bearing [21, 22, 23]. With the gradual technology evolution, commercial tribometers are available on the market, covering a significant range of tests standard to scientific and industrial interests. However, the normal force applied to this equipment does not usually exceed 200 N, mainly due to the mechanical restrictions of inserting suspended masses. Greater capacity equipment, such as the Plint TE67 manufactured by Phoenix Tribology Ltd, Kingsclere, is equipped with a pneumatic system capable of handling loads of up to 1000 N. Still, this feature is not commonly found among other suppliers [24]. Using self-made dedicated tribometers in tribological research is a relatively common activity, and a recent study, based on publications in the leading tribology journals during a period in 2022, reveals that 34% of researchers used some customized tribometers for their experiments, which are relevant numbers [22]. As the demand for innovative copper alloys and composites continues to rise in heavy machinery applications, it becomes crucial to consider the tribological and thermal effects present in high-load and low-speed conditions. Furthermore, broadening the range of alloys compatible with emerging manufacturing processes and technologies is imperative. The present study aims to disseminate the insights gained from developing, constructing, and validating a custom tribometer, specifically emphasizing these fundamental principles and discussing tribological results obtained from testing high-lead bronze and nickel aluminum bronze alloys.