esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Computational Implementation
### Numerical Meshing  
To ensure the reliability of the results, a mesh sensitivity analysis was first carried out for laminar and early transitional regimes, where the flow remains relatively stable and grid convergence can be assessed with good confidence. From this preliminary study, a practical refinement strategy for higher Rayleigh and Reynolds numbers, where the simulations become significantly more demanding in terms of computational cost is derived.  
For the same order of magnitude (e.g., from \(1\times 10^{a}\) to \(5\times 10^{a}\)), a constant mesh resolution could be maintained without a noticeable loss of accuracy is founded. However, when transitioning to a higher order of magnitude (e.g., from \(1\times 10^{a}\) to \(1\times 10^{b}\)), the mesh was further refined to ensure numerical precision and capture the intensified flow structures.  
This approach was particularly necessary given the large size of the computational domain, the high values of Rayleigh and Reynolds numbers, and the use of periodic boundary conditions that increase the representativeness of the modeled configuration. These factors substantially elevated computational requirements.  
Despite the inability to perform a full Grid Convergence Index (GCI) analysis for all configurations, the adopted strategy guarantees a balance between computational feasibility and numerical accuracy. The selected mesh resolutions provided stable, physically consistent, and quantitatively reliable results throughout the range of parameters studied. Table 1 summarizes the mesh resolutions employed for the various configurations investigated in this work.
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
###### Abstract  
Due to the complex topological relationship, poor data locality, and data racing problems in unstructured CFD computing, how to parallelize the finite volume method algorithms in shared memory to efficiently explore the hardware capabilities of many-core GPUs has become a significant challenge. Based on a production-level unstructured CFD software, three shared memory parallel programming strategies, atomic operation, colouring, and reduction were designed and implemented by deeply analysing its computing behaviour and memory access mode. Several data locality optimization methods--grid reordering, loop fusion, and multi-level memory access--were proposed. Aimed at the sequential attribute of LU-SGS solution, two methods based on cell colouring and hyperplane were implemented. All the parallel methods and optimization techniques implemented were comprehensively analysed and evaluated by the three-dimensional grid of the M6 wing and CHN-T1 aeroplane. The results show that using the Cuthill-McKee grid renumbering and loop fusion optimization techniques can improve memory access performance by 10%. The proposed reduction strategy, combined with multi-level memory access optimization, has a significant acceleration effect, speeding up the hot spot subroutine with data races three times. Compared with the serial CPU version, the overall speed-up of the GPU codes can reach 127. Compared with the parallel CPU version, the overall speed-up of the GPU codes can achieve more than thirty times the result in the same Message Passing Interface (MPI) ranks.  
unstructured-grid; CFD; shared memory parallelization; GPU; data racing +
Footnote †: journal: Applied sciences
# Software Development Process Considerations in GNSS-Denied Navigation Project for Drones
###### Abstract  
This article will discuss the software development process utilised in the GNSS-denied navigation project targeted for drones. The process was implemented in an environment where software developers were oriented to developing a source code only and who showed great reluctance to follow any formal process. The mentioned process was a lightweight AGILE-based process that, by assumption, minimised developers' engagement in activities other than those related to the source code development. The process described in this paper was designed to support and reflect product quality characteristics like functional completeness and correctness, time behaviour, resource utilisation, analysability, modifiability and testability in an \"implicit\" way. It allowed the developers to achieve those characteristics in an \"invisible\" manner. This allowed us to achieve acceptable product quality without needing to engage experienced architects. The described process needs improvements and extensions to satisfy certification needs. However, for the prototyping or research phases, it may be a perfect solution for facilitating future product modifications and saving costs. The process improves product quality by underlying the value of transparency, performing specific actions and incorporating specific attributes related to team tasks. This research also focused on finding a metric related to process acceptance in a given environment, resulting in a three-degree scale showing whether the process needed improvements or was accepted by the environment. Such a metric is a new finding and the metric implies that there are some factors that stand behind any process, which determine whether it will be friendly to the environment (people with their habits, work style, personal traits, etc.) or not. However, investigating factors that determine the process acceptance and that are connected to the environment traits were beyond the scope of this research and will be considered in the future.  
software engineering; systems engineering; quality; development processes; drone-dedicated software development +
Footnote †: journal: Applied sciences  
10.3390/app142210347
## 2 Tunnel Roof Rock System Model  
### Basic Theories of Cusp Catastrophe Model  
The failure of rock formations located between a tunnel and a cave under external loads can be understood as a phenomenon characterized by the dissipation of stored elastic energy [16]. The critical state of this process is associated with a pivotal value of the tunnel vault's displacement. Once the deformation of the vault surpasses this critical displacement, the rock mass tends to become unstable, potentially leading to collapse. The thickness of the rock layer separating the tunnel from the karst cave is identified as the critical safe thickness for the karst tunnel roof. Consequently, modern nonlinear catastrophe theory can be effectively employed to evaluate the safe distance between the tunnel and cave, facilitating the prediction of potential instability.  
The fundamental concept of this approach involves predicting the system's response to varying influential variables by formulating diverse differential equations within the system. Presently, the cusp catastrophe model stands as the most prevalently utilized framework for addressing engineering problems in karst regions. This model provides a standard form for the potential function, offering invaluable insights into system behavior and stability [23].  
\[V(x)=\frac{1}{4}x^{4}+\frac{1}{2}ux^{2}+vx \tag{1}\]  
where \(x\) represents the state variable; \(u\) and \(v\) denote the control variables  
In the context of catastrophe theory, the potential function of the cusp catastrophe model is derived  
\[\Delta=4u^{3}+27v^{2}=0 \tag{2}\]  
By integrating Equation (2), it becomes feasible to transition through the bifurcation set when '\(u\)' is less than or equal to zero. The essential criterion for the failure of the rock system corresponds to Equation (3).  
\[u\leq 0 \tag{3}\]  
When the control variables \(u\) and \(v\) satisfy both Equations (2) and (3), the entire system is in a state of malfunction. By applying the aforementioned theory, it can be concluded that obtaining the total potential energy of the system is sufficient to estimate the safe state of the tunnel. Figure 1 illustrates the fundamental model of cusp catastrophe theory.
## 3 Experiments and Results
### Performance Analysis
#### 3.2.3 Test Scenario-3  
Figure 5 presents the computed testing accuracies of ensemble and binary DT algorithms for \(p=1\) with length of the shapelets in the horizontal axis. In this case, \(i_{max}\) is selected as either 15 or 60. It can be noticed that different \(i_{max}\) does not yield significantly different results. Therefore, a question may arise over the selection of \(i_{max}\) value. For this selection process, analysis of accuracy obtained for ensemble algorithm for different values of \(p\), \(i_{max}\) and \(m\) is carried out here. Ensemble classifier is chosen because it provides better results than other methods.  
Figure 6 presents classification accuracies of ensemble computed for different values of \(p\) and \(i_{max}=15\) & 60 with lengths of shapelets \(m=80\), 90, 100, 110, 120 & 130, respectively. It can be noticed that for \(p<15\)  
Figure 4: Accuracy vs the beginning point \(p\) for \(i_{max}=15\) for different classifiers considering different values of \(m\) (legends).  
Figure 3: Accuracy vs length of shapelets for \(i_{max}=15\) and different values of \(p\) for different classifiers.  
the accuracy curves obtained for \(i_{max}=15\) & 60 in each case are pretty similar. Therefore, it is suggested that \(i_{max}\) should be slightly greater than or equal to 15. For the further performance evaluations in this article, \(i_{max}\) is selected as 15.
## 2 Materials and Methods
### Interventions  
#### 2.3.1 Neuro-World Cognitive Training Program  
The _Neuro-World_ cognitive training program, developed by Woori Soft Co., Ltd. (Daegu, South Korea), is a service to provide suitable education considering the individual characteristics of elderly people with dementia, or children and adolescents with intellectual disabilities, communication deficiencies, or psychological problems. The program is a cognitive training software developed by grafting advanced ICT technologies such as AI, big data, and 3D game technology using cognitive rehabilitation science.  
_Neuro-World_ is a game training program that uses digital media such as Endeavor Rx, which can be easily used without restrictions on space such as home or school. It is considered more practical and effective than conventional cognitive training programs because the participating children show more interest and voluntarily participate due to the element of games. Specifically, this program uses an AI module that customizes the difficulty level as per the users' ability and intensively proceed their inadequate sections, based training data regarding initial performance. It reduces the training duration; moreover, the cognitive abilities of children with intellectual disabilities can be judged more objectively. On the other hand, most existing digital-based cognitive rehabilitation programs are in the form of quizzes; hence, their range of changes in methods of progress can be limited; additionally, being single-form training methods, such programs are less self-active and lack in its adaptability to changes. However, _Neuro-World_ can be effective for children with intellectual disabilities, as various forms of learning are possible because training tools are created by learning forms through AI systems.  
In this study, the cognition training program was conducted in the form of an online provision method using a tablet PC and a smart device; the latter was a Samsung Galaxy Tab S7 (SM-T976M. 2020). The _Neuro-World_ cognitive training program consists of four major cognitive areas--memory, attention concentration, visual perception, and execution--and six sub-items: visual perception, target memory, selective and continual attention, search and observation, and daily task planning. The composition and contents of the program are shown in Table 2.
## 4 Research and Analysis
### Kalman Filter  
The first pre-processing method is the Kalman filter. The Kalman filter is often used to reduce the fluctuation of data [39; 40].  
Kalman filter consists of two procedures. The first procedure is called prediction. The prediction procedure calculates the predicted action estimate and predicted estimate covariance. This procedure is represented in Equation (1).  
\[\hat{x}_{k}^{-}=A\hat{x}_{k-1} \tag{1}\] \[P_{k}^{-}=AP_{k-1}A^{T}+Q\]  
Figure 5: RSSI data with various pre-processing types. (**a**) Raw RSSI data. (**b**) RSSI data with KF. (**c**) RSSI data with DAE. (**d**) RSSI data with KFAE. (**e**) RSSI data with KF + DAE. (**f**) RSSI data with KFAE + DAE. (**g**) RSSI data with DAE + KF. (**h**) RSSI data with DAE + KFAE.  
where \(\hat{x}_{k}^{-}\), \(P_{k}^{-}\), \(A\), \(Q\) are the estimated value at time \(k\), estimate error covariance, the action transition matrix, and noise covariance matrix, respectively.  
The second procedure is called an update. The update procedure is divided into calculating Kalman gain, \(\hat{x}_{k}\), and error covariance. These parts are shown in Equations (2)-(4).  
We calculate the Kalman gain (\(K_{k}\)),  
\[K_{k}=P_{K}^{-}\hat{H}(HP_{k}^{-}\hat{H}+R)^{-1} \tag{2}\]  
We calculate the predicted value (\(\hat{x}_{k}\)),  
\[\hat{x_{k}}=\hat{x_{k}}^{-}+K_{k}(z_{k}-H\hat{x_{k}}^{-}) \tag{3}\]  
We calculate the predicted error covariance (\(P_{k}\)),  
\[P_{k}=P_{k}^{-}-K_{k}HP_{k}^{-} \tag{4}\]  
where \(K_{k}\), \(P_{k}\), \(x_{k}\), \(z_{k}\), \(H\), and \(R\) are Kalman gain, predicted error covariance, predicted value, measured value, observation matrix, and noise covariance matrix, respectively.  
The result of the Kalman filter is shown in Figure 5b. RSSI data in Figure 5b are obtained by applying the Kalman filter to the data in Figure 5a.
## 3 Simulation Results and Analysis
### Three Directional Stress Fields of the Shaft  
With the MSD of 0.5 m at the horsehead roadway's roof, the derived three-dimensional stress field of the shaft is depicted in Figure 10. In the Z-plane, the distribution of stress in the Z-direction exhibits minor variations; however, there are pronounced disparities in this stress component at different shaft elevations (see Figure 10a). At the model's apex, Z-direction stress registers approximately 20 MPa, markedly increasing to about 160 MPa at the interface of the shaft and the horsehead roadway, where it is oriented perpendicularly to the roadway's axis. Lateral pressure coefficients predominantly influence the X-direction stress, which fluctuates within the Z-plane between 0 and 60 MPa, displaying relatively uniform distribution across elevations (see Figure 10b). The stress in the Y-direction not only exhibits significant variability within the Z-plane but also intensifies in proximity to the horsehead roadway, peaking at the shaft-roadway nexus with an estimated value of around 140 MPa (see Figure 10c).  
With the MSD of the horsehead roadway's roof escalating to 1.0 m, Figure 11 delineates the resultant three-dimensional stress field of the shaft. While the variability in Z-direction stress within distinct Z-planes remains minor, notable disparities emerge at the interface of the shaft and the roadway's roof. This contrast is particularly significant at different shaft elevations (see Figure 11a). For example, the stress in the Z-direction at the model's summit is approximately 20 MPa, yet it surges to 180 MPa at the shaft-roadway junction. Influenced by the lateral pressure coefficient, the stress in the X-direction predominantly varies within the Z-plane, exhibiting a range from 0 to 70 MPa (see Figure 11b). The stress in the Y-direction not only increases proximal to the horsehead roadway but also peaks at the shaft-roadway connection, where the maximum stress escalates to 180 MPa (see Figure 11c).  
Upon the maximum subsidence displacement of the horsehead roadway's roof escalating to 1.5 m, the resulting three-dimensional stress field of the shaft is depicted in Figure 12. The comparative distribution pattern of the stress remains relatively stable, yet the stress magnitudes exhibit substantial elevation. Specifically, the Z-direction stress at the model's apex approximates 20 MPa, in stark contrast to the stress at the shaft-roadway junction, which intensifies to approximately 200 MPa (see Figure 12a). Within the Z-plane, the X-direction stress undergoes minimal fluctuations, with values spanning from 0 to 70 MPa (see Figure 12b). Meanwhile, the stress in the Y-direction witnesses its maximum magnitude rising to an estimated 200 MPa (see Figure 12c).  
Figure 11: Three directional stress fields of the shaft (MPa) (\(d_{\max}\) = 1.0 m).  
Figure 10: Three directional stress fields of the shaft (MPa) (\(d_{\max}\) = 0.5 m).
## 2 Materials and Methods
### Data Collection Campaigns  
#### 2.2.1 Thermal-RGB Imagery  
Thermal images of the experiment plots were acquired using a commercial imager (Krykard TCA 1950, Atandra Energy Private Limited, Chennai, India), positioned 1 m away from the plant and angled 45 degrees from the horizontal. Through a similar configuration, RGB images were acquired using a 18 MP camera (Canon EOS 3000D, Tokyo, Japan). A total of 3200 images were captured (thermal: 1600 and RGB: 1600) from 25, 40, 60, and 75 days of sowing (DAS) in two seasons. These images were acquired on clear days between 11 a.m. and 1 p.m. Representative RGB and thermal images of stressed (@ 25% ETc, sprinkler irrigation) and non-stressed (@ 100% ETc, flood irrigation) okra plants are shown in Figure 2.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 3 Parameter Optimization
### Magnetometer-Based EKF Configuration  
The magnetometer-based EKF was run using the configuration shown in Table 2, where the best value for the measurement noise matrix \(\mathbf{R}_{B}\) was determined in the following way.  
In this work, the magnetometer measurements were simulated using the CHAOS-7 model described in Section 4.3.2 while the EKF computed the \(\mathbf{Z}\) matrix using the IGRF magnetic field model (Section 4.3.1). The difference between the filter's and measurements' geomagnetic model was taken into account in the matrix \(\mathbf{R}_{B}\) that was used as a weight matrix. Since complete knowledge of the noise process would require a perfect match between the two models, \(\mathbf{R}_{B}\) was tuned by performing different runs with the EKF in order to find the best value to minimize the EKF errors. Figure 2 shows the results of different runs with different values in the model mismatch parameter \(\alpha\) such as that \(\mathbf{R}_{B}=\alpha^{2}\cdot\mathbf{I}_{3\times 3}\) in terms of the state mean and maximum Root Sum Squared Error (RSSE).  
Similarly to the generation of the received GPS data, the magnetometer measurement vector \(\mathbf{\varrho}_{k}^{B}=[B_{x},B_{y},B_{z}]\) was simulated by adding a zero-mean Gaussian noise, \(\mathbf{B}_{noise}\), with the standard deviation \(\sigma_{B}=20\) nT to the magnetic field computed in the actual position of the spacecraft:  
\[\mathbf{\varrho}_{k}^{B}=\mathbf{B}(\mathbf{x}_{true})+\mathbf{B}_{noise}. \tag{20}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Measurement noise, \(\sigma_{B}\) & 20 nT \\ Initial position error, \(\Delta x\) & 5 km = 3\(\sigma_{x}\) \\ Initial velocity error, \(\Delta v\) & 0.5 km/s = 3\(\sigma_{v}\) \\ Process noise, \(u_{v}\), \(u_{a}\) & 0 km/s, 10\({}^{-5}\) km/s\({}^{2}\) \\ Model mismatch parameter, \(\alpha\) & 1000 nT \\ Weight matrix, \(\mathbf{R}_{B}\) & \(\mathrm{diag}\{\mathbf{a}^{2},\mathbf{a}^{2},\mathbf{a}^{2}\}\) \\ Covariance matrix, \(\mathbf{P}\) & \(\mathrm{diag}\{\mathbf{\sigma}_{x}^{2},\sigma_{x}^{2},\sigma_{x}^{2},\sigma_{v}^{ 2},\sigma_{v}^{2},\sigma_{v}^{2}\}\) \\ Process noise matrix, \(\mathbf{Q}\) & \(\mathrm{diag}\{w_{v}^{2},w_{v}^{2},w_{v}^{2},w_{a}^{2},w_{a}^{2},w_{a}^{2}\}\) \\ Measurement sampling time, \(T_{s}^{B}\) & 10 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Magnetometer-based Extended Kalman Filter configuration.  
Figure 2: The tuning process of the measurement noise matrix \(\mathbf{R}_{B}=\alpha^{2}\cdot\mathbf{I}_{3\times 3}\). The position (**left**) and velocity (**right**) mean (blue) and maximum (red) RSSE of different EKF runs are shown against the corresponding value of \(\mathbf{R}_{B}\). The best configuration is obtained for \(\alpha=1000\) nT. Each EKF run has a duration of 100,000 s, corresponding to 10,000 iterations.  
The value of \(\sigma_{B}\) was chosen according to the typical accuracy of onboard magnetometers [10; 19; 20; 21].
## 2 Mitochondria and Kidney Diseases  
Mitochondria are the primary suppliers of cellular energy, producing large amounts of ATP through the electron transport chain (ETC) located in the inner mitochondrial membrane. Mitochondria have their own genetic material encoded by mitochondrial DNA (mtDNA), which consists of 37 genes: 22 encode transfer RNAs, two encode ribosomal RNA, and the remaining ones encode polypeptides that are part of the OXPHOS enzyme complexes I-V (7 units of complex I, cytochrome b of complex III, three subunits of complex IV, and two subunits of complex V) [12; 13]. Mitochondria contain channels and carrier proteins that facilitate the accumulation of xenobiotics in the matrix and/or the inner mitochondrial membrane through selective trapping mechanisms [14; 15].  
The kidneys are the second most energy-demanding organs in the body, with the second highest mitochondrial content and oxygen consumption after the heart [13]. Mitochondria, as a dynamic network of organelles, produces ATP to supply the energy required for basal cellular functions in the kidneys. They modulate metabolic reactions, generate reactive oxygen species (ROS), maintain intracellular calcium homeostasis, thermogenesis, and regulate proliferation and intrinsic apoptotic pathways. Evidence shows that both acute and chronic kidney injuries can lead to mitochondrial respiratory chain-derived oxidative stress, ultrastructural defects, abnormal activation of the mitochondrial pathway of apoptosis, unstable mtDNA, and defective clearance of damaged mitochondria. Mitochondrial dysfunction increases the risk of developing tubulointerstitial diseases, cystic kidney disease, and nephrotic syndromes. A deeper understanding of the cellular and molecular mechanisms governing mitochondrial dysfunction in kidney pathology could facilitate the development of improved therapeutic strategies [16].  
In the past decade, both clinical and experimental studies have suggested a causal link between acute kidney injury (AKI) and chronic kidney disease (CKD), a process termed AKI-to-CKD transition. This transition is often due to incomplete or maladaptive repair after AKI. AKI has been identified as a significant risk factor for the onset and progression of CKD, with symptoms including hypoxia, endothelial dysfunction, nephron loss, alterations in renal cell functions, cell cycle arrest, inflammation, mitochondrial fragmentation, and epigenetic modifications [17]. Mitochondria are particularly sensitive to environmental changes that can cause mitochondrial dysfunction, leading to decreased ATP generation, increased ROS levels, and the induction of apoptosis (Figure 1). These factors collectively contribute to the development and progression of AKI, CKD, and the AKI-to-CKD transition. CKD is one of the fastest-growing causes of death worldwide and is projected to become the fifth leading global cause of death by 2040 [18].