esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The main objective of this paper was to develop a distributed navigation algorithm for a swarm of UAVs to estimate the position of a target in the operational scenario. The aim of the algorithm is to provide situational awareness to the aircraft formation and eliminate the need for a central unit, thereby making it scalable and computationally efficient.  
The proposed algorithm combined the advantages of the hybrid consensus on measurements and consensus on information (HCMCI) technique with the internodal transformation theory (ITT). This combination allowed for the distributed estimation of the swarm overall state and the detection of the target position. The algorithm was designed to be adaptable to various swarm architectures and environments, enabling the swarm to self-organize and adjust its configuration over time.  
Numerical analyses were conducted to evaluate the algorithm effectiveness. A sensitivity analysis was performed to assess its performance by varying the main operational parameters and communication link schemes.  
Results highlight that performance is not significantly improved by increasing the number of consensus steps which, on the other hand, increases the time required to execute the consensus algorithm. Furthermore, as expected, the graph topology can improve algorithm convergence by optimizing communication.  
Regarding reliability, it is observed that communication delays can decrease the convergence capability, as intuitively expected. However, in realistic scenarios with small delays (approximately 0.1 s), it does not adversely affect performance.  
Furthermore, some simulation tests were also conducted to evaluate the algorithm's ability to resume estimation after communication interruptions and to estimate the position of a fixed target. A comparison with a centralized version of the EKF was included to evaluate the performance trade-offs of the decentralized technique.  
The proposed navigation algorithm successfully achieved the objectives outlined in this paper, demonstrating its capability to estimate the target position and maintain overall situational awareness.  
Future research in this field should explore the possibility of detecting and isolating multiple targets, which would represent another important factor in enhancing the effectiveness of such algorithms in emergency scenarios.  
Conceptualization, E.D., M.M. and I.N.; methodology, M.M., I.N. and S.R.B.; software, S.R.B.; validation, E.D. and S.R.B.; formal analysis, I.N. and S.R.B.; investigation, S.R.B.; resources, M.M.; data curation, S.R.B.; writing--original draft preparation, S.R.B., E.D. and I.N.; writing--review and editing, E.D., I.N. and M.M.; visualization, S.R.B.; supervision, E.D. and M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 5 Data Fusion Processing Method of Target Coordinates on Multi-Acoustic Basic Arrays
\begin{array}{l}f(x,y,z|x_{A},y_{A},z_{A})=f(x|x_{A})\times f(y|y_{A}) \times f(z|z_{A})\left(\frac{\pi}{2}-\theta\right)\\ f(x,y,z|x_{B},y_{B},z_{B})=f(x|x_{B})\times f(y|y_{B})\times f(z|z_{B})\left( \frac{\pi}{2}-\theta\right)\\ f(x,y,z|x_{\text{C}},y_{\text{C}},z_{\text{C}})=f(x|x_{\text{C}})\times f(y| y_{\text{C}})\times f(z|z_{\text{C}})\left(\frac{\pi}{2}-\theta\right)\end{array} \right. \tag{14}\]  
where \(\theta\) is pitch angle of the target position in the system. It can be found by Formula (15). \[\theta=\arctan(-\frac{2H\sqrt{\left(\tau_{01}-\tau_{03}\right)^{2}+\left(\tau _{02}-\tau_{04}\right)^{2}}}{D(\tau_{01}+\tau_{02}+\tau_{03}+\tau_{04})}) \tag{15}\]  
According to Formula (13), Formula (14) is expanded to Formula (16). \[\left\{ \begin{array}{l}f(x,y,z|x_{A},y_{A},z_{A})=\frac{1}{2\pi\sqrt{2\pi} \sigma_{x_{A}}\sigma_{y_{A}}\sigma_{z_{A}}}\exp\left\{-\frac{(x-x_{A})^{2}}{2 \sigma_{x_{A}}{}^{2}}-\frac{(y-y_{A})^{2}}{2\sigma_{y_{A}}{}^{2}}-\frac{(z-z_{ A})^{2}}{2\sigma_{z_{A}}{}^{2}}\right\}\\ f(x,y,z|x_{B},y_{B},z_{B})=\frac{1}{2\pi\sqrt{2\pi}\sigma_{x_{B}}\sigma_{y_{B}} \sigma_{z_{B}}}\exp\left\{-\frac{(x-x_{B})^{2}}{2\sigma_{x_{B}}{}^{2}}-\frac {(y-y_{B})^{2}}{2\sigma_{y_{B}}{}^{2}}-\frac{(z-z_{B})^{2}}{2\sigma_{z_{B}}{} ^{2}}\right\}\\ f(x,y,z|x_{C},y_{\text{C}},z_{\text{C}})=\frac{1}{2\pi\sqrt{2\pi}\sigma_{x_{ \text{C}}}\sigma_{y_{\text{C}}}\sigma_{z_{\text{C}}}}\exp\left\{-\frac{(x-x_{ \text{C}})^{2}}{2\sigma_{x_{\text{C}}}{}^{2}}-\frac{(y-y_{\text{C}})^{2}}{2 \sigma_{y_{\text{C}}}{}^{2}}-\frac{(z-z_{\text{C}})^{2}}{2\sigma_{z_{\text{C} }}{}^{2}}\right\}\end{array} \right. \tag{16}\]  
The traditional probability of test values in basic arrays A, B, and C can be found by Formula (17). \[ \begin{array}{l}f(x,y,z|x_{A},y_{A},z_{A};x_{B},y_{B},z_{B};x_{C},y_{C},z_{ C})\\ =f(x,y,z|x_{A},y_{A},z_{A})\times f(x,y,z|x_{B},y_{B},z_{B})\times f(x,y,z|x_{C},y_{C},z_{C})\end{array}  \tag{17}\]  
By resolving the spatial coordinates linked to the peak value in Formula (17), we can derive the actual target position \(P(x,y,z)\) through the data fusion of target positions across distributed multi-acoustic basic arrays. The fusion calculation results are expressed by Formula (18). \[[x,y,z]=\underset{x,y,z}{\operatorname{argmax}}f(x,y,z|x_{A},y_{A},z_{A};x_{ B},y_{B},z_{B};x_{C},y_{C},z_{\text{C}}) \tag{18}\]
## 3 Estimation of Motion Parameters Based on the CCAE Method  
### \"Stretching\" Idea of the Proposed CCAE Method  
In radar systems, when a target is tracked, the range profile of the target varies from pulse to pulse because of the rotational and the translational motions of the target. In this paper, the rotation of the target is not taken into consideration. However, within the interval of two adjacent transmitted pulses, the change in motion parameters is generally small. Therefore, the range profiles of two adjacent echo signals are highly correlated [29]. Consequently, the energy of two adjacent echoes can be accumulated. Thus, the motion parameters may be estimated by the correlation result of adjacent echoes.  
The idea of the proposed method is shown in Figure 2, where the UR signal is utilized for the convenience of illustration. It is already known that the stretching operation can transform a wideband echo signal into a narrow-band signal. In the CCAE method, considering one of the two adjacent echo signals (e.g., the echo at slow time \(t_{m}\)) as the \"local reference\" signal, the other one (e.g., the echo at slow time \(t_{m+1}\)) is \"stretched\" into a single tone signal. The frequency of the \"stretched\" signal contains the information of the distance differences between the slow time \(t_{m}\) and \(t_{m+1}\). The motion parameters can be estimated according to the distance differences. In the next subsection, the CCAE method is derived mathematically to prove the idea.
## 3 Signal Analysis
### Bispectrum Analysis
#### 3.2.1 AR Model and Third-Order Cumulants
For \(\omega=\omega_{1}=\omega_{2}\) and according to Equation (15), the calculation of \(1\
icefrac{{1}}{{2}}\)-dimensional slices of the bispectrum is given by  
\[S_{3_{u}}\left(\omega,\omega\right)=\lambda H^{\;2}\left(\omega\right)H^{\;*} \left(2\omega\right) \tag{17}\]  
The \(1\
icefrac{{1}}{{2}}\)-dimensional slices usually achieve high resolution ability and have quick calculation speed [47]. They can also reflect the nonlinearity, the quadratic phase coupling information and restrain Gaussian noise of a random process. They can be used to analyze arc fault features quickly.
# A Survey on Malleability Solutions for High-Performance Distributed Computing
## 2 Background
The first two types use static allocation because resources are maintained during the whole execution, whereas the latter two use dynamic allocation since allocated resources may vary on execution time, and therefore, include in their codes the corresponding resize commands. Rigid and evolving job sizes are determined always by the user, while moldable and malleable are decided by the system. In addition, rigid and moldable jobs cannot be resized during their execution. In this way, malleable jobs are the most flexible since they can be adapted to the cluster workload, being capable of reallocating their resources at any time, increasing the system throughput. In the practice, malleable jobs incorporate synchronization points where reconfigurations can be performed. Moreover, these types of jobs are provided with hints, such as the optimal range of processes, provided by the user to narrow down resizing options. Malleable jobs are the main goal of study of this work. In order to be able to execute these jobs in a cluster, it will be necessary to have three major components:  
1. A **parallel runtime** able to rearrange processes and redistribute data. 2. An **RMS** that supports dynamic reallocation of resources. 3. A **mechanism to coordinate** the previous two subsystems. Additionally, there are some other aspects to be considered when malleable jobs are executed on an HPC system:  
* The increase in the resources does not always assure more throughput; therefore, the RMS has to be prepared to analyze the job performance. * The incorporation of some fairness in shrink/expand could promote the use of malleability among the users. Thus, the scheduling strategy should maximize the system efficiency including as much fairness as can be delivered. * Usually, the expansion is faster than shrinking, because in the first the nodes are idle whereas in the second they are executing a task and it has to arrive to a checkpoint. * Some feedback application can be used by the scheduler to make decisions, although the corresponding overhead has to be considered. Hence, the balance on the overhead and the improvement of the throughput cannot be ignored and has to be analyzed.
### Research Methodology  
This article is based on a systematic literature review conducted between 2020 and 2024, encompassing peer-reviewed journal articles, conference proceedings, and white papers in the fields of AI, the IoT, and acoustic sensing. The research process involved utilising scientific databases, including IEEE Xplore, ScienceDirect, SpringerLink, and MDPI. Keywords included combinations of \"acoustic sensing,\" \"machine learning,\" \"IoT,\" \"edge computing, \"5G, \"MEMS microphones,\" and \"sound-based diagnostics.\"  
The articles were selected based on their relevance to the thematic scope, novelty of the approach, and clarity of methodological description. Each publication was analysed in terms of the following aspects: type of acoustic signal, sensor technology, data preprocessing and feature extraction methods, AI/ML models applied, and practical application domain. The findings were categorised and synthesised to identify common patterns, gaps, and promising future directions. Additionally, real-world case studies were reviewed to assess the feasibility and impact of the technologies in operational environments.
# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 4 Results and Discussion
### Ablation Experiments  
In order to ascertain the efficacy of this paper's model for the enhancement of dermoscopic images, hair removal, and the utilisation of the focal loss function, ablation experiments were designed to determine whether these three methods should be employed. The experimental design is as follows:  
Experiment 1: Using hair removal and focal loss without data enhancement;  
Experiment 2: Using data enhancement and focal loss without hair removal;  
Experiment 3: Using hair removal and data enhancement without focal loss;  
Experiment 4: Using all elements.  
Table 5 presents the overall mean accuracy, precision, recall, and F1-score for the four experiments. As can be seen from the table, all three methods contribute to the overall performance, with the metrics decreasing after removal. The greatest impact on the overall model is that of focal loss. The use of focal loss compared to not using it resulted in a 4.6% improvement in accuracy, a 6.1% improvement in F1-score, and a 5.3% improvement in precision. Among the aforementioned factors, data enhancement had the greatest impact on recall, with an improvement of 8.8%. The results of the ablation experiments demonstrate that the combination of data enhancement, hair removal, and the use of the focal loss function can effectively improve the overall classification performance of the model.
# Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline
**Heavy Metal** & **Adsorbent** & **Model** & **Reference** \\ \hline Copper & Biochar & ANN & [PERSON] et al. (2021) [14] \\ \hline Arsenic & A hybrid material & ANN & [PERSON] et al. (2015) [15] \\ \hline Arsenic & Botryococcus braunii algal biomass & ANN & [PERSON] et al. (2016) [16], [PERSON] et al. (2017) [17] \\ \hline Arsenic & Cupric oxide nanoparticles & ANN and genetic algorithm & [PERSON] et al. (2017) [18] \\ \hline Lead, cadmium, nickel, arsenic, copper, zinc & Biochar & ANN and random forest & [PERSON] et al. (2019) [19] \\ \hline  & Superheated steam-activated granular & & \\  & carbon, ragi husk powder, & & \\  & antep pistachio, pistacia vera, red mud, & & \\  & synthesized functional polydopamine & & \\  & nanocomposite, eucalyptus leaves, & & \\  & spirulina maxima, spirulina indica, & & \\  & spirulinaiensis, reduced graphene & & \\  & oxide-supported nanoscale zero-valent & & \\  & iron composites, & & \\  & cupric oxide nanoparticles, and & & \\  & cerium hydroxylamine hydrochloride & & \\ \hline  & Hybrid material, Botryococcusbraunii & & \\  & algal biomass, microalgae C. & & \\  & pyenoidosa, cupric oxide & & \\  & nanoparticles, Oyster shell, Palm bark & & \\  & biomass, and Bacillus cereus & & \\  & biomass & & \\ \hline  & & & \\ \hline Copper & Attapulgite clay & ANN, support vector & \\  & & machine, and random forest & \\ \hline Lead, copper, nickel & Date seed-derived biochar & ANN & [PERSON] et al. (2021) [23] \\ \hline Lead, cadmium, nickel, arsenic, copper, zinc & Biochar & ANN & [PERSON] et al. (2021) [24] \\ \hline Chromium & Clay & ANFIS & [PERSON] et al. (2020) [25] \\ \hline Copper & Saw dust & ANFIS & [PERSON] et al. (2018) [26] \\ \hline Copper & Biochar & ANFIS & [PERSON] et al. (2020) [27] \\ \hline \end{tabular}
\end{table} 
Table 1: Examples of machine learning algorithms used to model adsorption processes. capability, and rapid learning capacity. It is a sub-category of ANN that integrates the principles of neural networks and fuzzy logic to acquire the advantages of both in a single computational platform. Compared to other MLAs, ANFIS has not been used to model the adsorption of _As_. Although it was applied earlier for modeling the adsorption of other heavy metals, such as copper and chromium [25; 26; 27], those models are based on a single dataset with a relatively high prediction error. Therefore, a generalized model, using different datasets with different adsorbents, needs to be developed. In this work, a highly efficient ANFIS model was developed to predict the adsorption removal efficiency of arsenate (_As_(III)) from aqueous solutions. A parametric investigation was also performed to identify the contribution of the input parameters in predicting the output parameter (_As_ removal %).
# Study on Experimental and Constitutive Model of Granite Gneiss under Hydro-Mechanical Properties
## 3 Results and Discussion
### Impact of Seepage Pressure on Deformation and Strength Parameters  
In light of test results under the hydro-mechanical properties, we can see that the seepage pressure has a major affection on granite gneiss strength and deformation parameters under the hydro-mechanical properties. Basing the identical confining pressure, rising seepage pressure, rock peak strength, elastic modulus, and deformation modulus gradually appear to be a prominent decreasing trend. This is the confining pressure unloading effect. In accordance with the principle of effective stress, the larger the seepage pressure, the lower the actual confining pressure. Furthermore, the elastic modulus decrease rate takes on a declining trend with the seepage pressure rising, while the deformation modulus decrease rate appears an increasing inclination along with the seepage pressure increase. Observed this rock mechanical behavior characteristic evolution under compression in similar research [27; 28].  
In the same confining pressure, the yield strength changing its trend in pace with the seepage pressure increase is not obvious. This is because the development and closure of microcracks are very limited and can be ignored in linear elastic deformation as the rock specimen is mainly subjected to pressure from the rock skeleton, and the infiltration of permeable water would also be limited at this stage. The seepage water mainly couples seepage and stress through the flow of microcracks in the rock specimen. Compared with the confining pressure influence, the pore pressure affection on yield strength is very limited.  
The experimental rock specimen was processed from the rock specimen drilled during exploration, which is often disturbed by large machinery and external forces in the early stage. The initial internal damage is unknown, and there is a certain possibility of dispersion. And the axial peak strain is closely bound up with the mechanical parameters, that is, the sample peak strength and elastic modulus. A weakening effect on the pore pressure to peak axial strain appears to have poor regularity, which is different from the trend of gradually decreasing parameters, that is, the peak strength as pore pressure rises. The yield axial strain appears to have a gradual growth trend with the pore pressure increasing.
## 3 ALS-KF Based Visual Object Tracking Algorithm
### Noise Covariance Estimation Using ALS
**Theorem 1** (**Computation complexity)**: _The computational complexity of the proposed AKF-ALS algorithm in (1) is \(O\left(N^{2}n_{x}^{4}n_{z}^{4}\right)\), where \(n_{x}\) and \(n_{z}\) are the dimensions of the state and measurement, respectively._  
In (10), the computation of \(\bar{F}_{n_{x}\times n_{z}}\) matrix involves \(O(n_{x}^{2})\) for matrix addition, \(O(n_{x}^{3})\) for matrix multiplication and \(O(n_{x}^{3})\) for solving the Lyapunov equation in (7). Computing \(\bar{F}\otimes\bar{F}\) involves \(O(n_{x}^{3})\) for fast Kronecker product computation, then the inverse matrix \(\left(I_{n_{x}^{2}}-\bar{F}\otimes\bar{F}\right)_{n_{x}^{2}\times n_{z}^{2}}\) involves \(O(n_{x}^{6})\) using the fastest matrix inversion method. \(\mathcal{O}_{Nn_{x}\times n_{z}}\) and the fourth term \(\mathbb{I}_{Nn_{x}\times n_{z}}\) involve concatenating matrices, with a complexity of \(O(Nn_{x}^{2}n_{z})\) and \(O(Nn_{x}^{2}n_{z})\), respectively. \(\mathcal{A}_{Nn_{x}^{2}\times(n_{x}^{2}+n_{z}^{2})}\) has a complexity of approximately \(O(max(n_{x}^{6},Nn_{x}^{4}n_{z}^{2}))\). Then, the ALS estimate \(\hat{\theta}\) involves \(O(N^{2}n_{z}^{4}n_{x}^{4})\) for computing \(\mathcal{A}_{i}^{\mathbb{T}}\mathcal{A}_{i}\) and \(O(n_{x}^{6})\) for the matrix inversion \((\mathcal{A}_{i}^{\mathbb{T}}\mathcal{A}_{i})^{-1}\). Given the computation complexity of KF is \(O((max(n_{x}^{3},n_{z}^{3}))\), the proposed AKF-ALS algorithm has the computation complexity of \(O(\max(n_{x}^{6},N^{2}n_{x}^{4}n_{z}^{4},(\tau-j)Nn_{z}^{2}))\). Since \(n_{x}\) and \(n_{z}\) are near and \((\tau-j)N\) is smaller than \(n_{x}^{4}n_{z}^{2}\) in most cases, the computation complexity is streamlined into \(O\big{(}N^{2}n_{x}^{4}n_{z}^{4}\big{)}\). As indicated by the above theorem, incorporating the ALS into the KF does not produce a significant rise in the computation complexity when \(n_{x}\) and \(n_{z}\) are small.
# A Novel Real-Time Match-Moving Method with HoloLens
## 3 Proposed System
### Pre-Visualization System  
The pre-visualization in the proposed system is used to preview the virtual-real image synthesis while recording the video. It uses both the HoloLens and RGB camera to display the virtual objects in a real scene. For this purpose, we used Unity Game Engine, which synchronizes the scene information from both devices using the spatial understanding of HoloLens in real time. The 3D position information used to pre-visualize the scene is transferred to the PC via Transmission Control Protocol/Internet Protocol(TCP/IP) protocol wirelessly, which provide the 3D position information to virtual camera at 30 fps. Consequently, it helps to prevent the errors by pre-visualizing the image synthesis result and reduces the time required for video composition in a later process.  
Currently, there are some systems that can be used for live streaming through HoloLens and an RGB camera. These systems include Spectator View and mixed reality view compositing (MRVC) [25] and can display virtual object (images) in a real scene in real time. However, these systems face  
Figure 5: Max script generated from HoloLens and 3 ds Max program. Synchronization of unit and coordinator system is required for using HoloLens data to 3 ds Max. (**a**) Max script; (**b**) 3 ds Max.  
difficulties in live streaming when high polygon 3D models are uploaded from the server. In order to solve these problems, we previously proposed and developed Holo-Studio (Seoul, Korea) [22]. This system was built to use in a broadcasting virtual studio that was capable of loading high-polygon 3D models at higher frame rates and with lower network speeds. The main difference between MS SV and Holo-Studio is the exchange of object transformation information between HoloLens and the server. This information is exchanged in bidirectional and unidirectional modes in the former and latter case, respectively, as shown in Figure 6.  
In this work, we propose a pre-visualization system similar to that of our previous system with some melioration. For pre-visualization only, in fact, we do not need to share and exchange the objects' transformation information with other HoloLens devices or the server. We only require the position information of the HoloLens mounted on the RGB camera. This position information is sufficient to display the virtual objects in a real scene. This melioration has two advantages. Firstly, we do not need to share the objects and their transformation information with other devices which were causing the delay in pre-visualization due higher number of polygons--for instance, in MS SV. Therefore, eliminating the process of object's transformation information exchanged with other devices would make it possible to use a virtual object of any number of polygons. Secondly, as the position information from the HoloLens is acquired in the form of XYZ coordinates, we can set the frame rate to be the same as that of the RGB camera. This prevents the issue of frame delay.