esa-sceva/satcom-chunk-collection huggingface dataset:
# Storage and Processing of Big Data for Geomagnetic Support of Directional Drilling
## 1 Introduction
The results of numerical experiments on the modeling of well-trajectory parameters distinctively illustrate the influence of natural geomagnetic disturbances (e.g., geomagnetic storms) on the DD procedures. The solution to this problem requires new statistical algorithms for predicting the magnetosphere dynamics [20] and the construction of magnetic observatories in the proximity of oil- and gas-extraction sites [6]. A qualitative increase in interest in the correction method using observatory data occurred with the transition to high-latitude and offshore production. There was a transition from academic research [21; 22], which modeled the potential for meeting direction accuracy requirements using real-time processing, to implementation in commercial production [6; 13]. Compensation for geomagnetic rapid variations in the real-time mode using high-precision magnetometric equipment is currently an indispensable element of the DD routine, especially in the Arctic region. Along with operational geomagnetic data, the control of DD parameters requires the implementation of modern data management technologies and mathematical methods of real-time data analysis [17]. To date, there are many examples of the successful application of IIFR by oilfield service companies [15; 23; 24]. In this paper, we present the results of the practical application of modern data management techniques based on the Big Data approach with respect to the needs of DD operation and support of the IIFR.
## 3 Results and Discussion
### Mixing Process Tracking
#### 3.1.3 Model Validation
The results of all the binary (\(\Circle\) and \(\square\)) and ternary (\(\otimes\) and \(\boxtimes\)) mixtures are represented for PFK5 and HFOSE, respectively. It has been possible to predict the concentration profiles of a mixing process of gas mixtures made of PFK5 to be better than the ones made of HFO3E, using air as the carrier gas. The low absorptivity of HFO3E in the UV spectrum has led to higher residual values in the lines that have the highest and lowest concentration of both binary and ternary mixtures. This indicates that UV-Vis could not be the best technique to monitor the concentration changes of this component. Through this work, the physical characterization of the diffusion process and mixing stability of binary and ternary gas mixtures made of PFK5 and HFO3E was started. All this information is postulated as a methodological and diffusional modeling basis for future studies that take into account other disturbances, such as temperature, humidity, and electrical discharge, among others. Nevertheless, attention also needs to be paid to the chemical stability of the mixtures using analytical methods in future studies. Conceptualization, J.I.L. ; methodology, A.E. and L.C. ; software, E.A., A.E. and B.P.-A. ; validation, A.E., M.L.A. and R.M.A. ; formal analysis, A.E. ; investigation, A.E. and L.C. ; resources, J.I. ([PERSON]) and J.I. ([PERSON]); data curation, A.E. ; writing--original draft preparation, A.E. and L.C. ; writing--review and editing, J.I.L., M.L.A. and R.M.A. ; visualization, J.I.L. ; supervision, J.I.L. ; project administration, J.I.L., R.M.A. and J.I. ([PERSON]); funding acquisition, J.I.L., R.M.A. and J.I. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the Basque Government, grant numbers KK-2017/00090 and KK-2019/00017. Not applicable. The authors declare no conflict of interest.
## 7 Limitation and Future Work
Lastly, while our study focused on analyzing activity patterns in a large-scale digital random community, it is important to acknowledge ethical considerations surrounding data use and user classification. Although the data we collected consisted only of publicly visibleposts and comments that do not include personally identifying information, the act of clustering users based on their behavior can raise broader concerns if applied to user-facing systems. In our case, the clustering was used solely for analytical purposes and not for real-time user monitoring or interface personalization. The design implications we propose are intended to support different styles of engagement at the community level, without disclosing or operationalizing users' cluster identities. Nevertheless, future research should further explore the ethical implications of algorithmic categorization, especially in contexts where such classifications may become visible to users or used to drive platform decisions. Ensuring transparency, informed consent, and safeguards against algorithmic bias will be essential for the responsible application of user modeling techniques. Despite the aforementioned limitations, this study makes an important contribution to the HCI community by highlighting how different forms of engagement (posting versus commenting) can reveal unique user behaviors and linguistic styles in a large-scale random setting. By refining this methodology and addressing the outlined limitations, researchers and practitioners can apply our approach to a broader range of platforms, user populations, and content domains--ultimately advancing our understanding of community engagement and user behavior in digital environments.
## 4 Simulation and Results
### Comparison with Other Methods  
In this section, an index is set to evaluate different collision avoidance algorithms. In most cases, relevant methods can efficiently avoid the potential collision in UAV trajectories [23; 27]. Generally, run duration depends not only on the efficiency of the algorithms, but also on the size of the map and velocity in the simulated scenarios. As a consequence, the index should remove the effects of the artificial settings and focus on the computation complexity of the methods. The index evaluating run duration of collision avoidance systems is shown in Equation 13. Therein, \(T_{i}\) denotes the run duration of the \(i^{th}\) collision avoidance algorithm; \(D_{i}\) represents the total distance of UAVs in the simulated scenario of the \(i^{th}\) method, while \(v_{i}\) describes the average velocity of UAVs in the scenario of the \(i^{th}\) system.  
\[Rd_{i}=\frac{T_{i}}{D_{i}/v_{i}} \tag{13}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Number of UAVs** & **Run Duration in 10 Iterations (s)** \\ \hline
7 & 35.72 \\
8 & 62.08 \\
9 & 72.44 \\
10 & 84.51 \\
11 & 98.46 \\
12 & 90.79 \\
13 & 123.62 \\
14 & 148.21 \\
15 & 223.41 \\
16 & 265.83 \\
17 & 293.84 \\
18 & 295.52 \\
19 & 304.33 \\
20 & 387.68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Run duration of cases with 7 to 20 UAVs in the first 10 iterations.  
The results of the evaluation index are shown in Table 5. The comprised methods are the algorithms which are currently widely used and effective. \"ROA1\" is the result of one iteration with six UAVs, which can generate collision-free trajectories, though the total distance is longer. \"ROA5000\" shows the result of 5000 iterations. The result generates the collision-free waypoints with the shortest total distance. The evaluating indices of proportional navigation method and rapidly-exploring random tree method are between those of \"ROA1\" and \"ROA5000\". Therefore, it is validated that our proposed algorithm can generate trajectories for multiple UAVs without any collisions, and can obtain optimized waypoints by rolling process.
## 2 Theoretical Background
### Solving Nonlinear Dynamic Systems  
The OPTCON algorithm allows us to find approximately optimal control solutions for nonlinear stochastic systems. In the solution process, the nonlinear dynamic system (2) should be solved in a large number of iterations. For this reason the choice of an appropriate solver is important to guarantee obtaining reliable solutions taking the computational costs into account. In the OPTCON algorithm, different solution algorithms are included attaching different importance to the trade-off between reliability and computational speed. At the moment the following methods can be used: Levenberg-Marquardt [24], trust region [25], Newton-Raphson [26], or Gauss-Seidel [27].
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 3 Analysis of OpenCV Object Tracking Algorithms
### Object Detection Algorithm  
For resource-constrained smart devices, object detection algorithms in OpenCV are an attractive solution. Although recent deep-neural-network-based object detection algorithms, such as YOLO [31], R-CNN [32], and SSD [33], show better performance in terms of accuracy, smart devices are not easily affording their high resource demands; for example, YOLO requires several gigabytes of GPU memory [34; 35]. Therefore, in OpenCV, we target the two object detection algorithms, Haar [36] and local binary patterns (LBP) [37; 38], whose resource demand is affordable in smart devices with pre-trained models.
## 2 Construction of Finite Element and Neural Network Models
### Finite Element Modeling Principles  
When calculating the magnetic field, the model is regarded as a steady-state field and the displacement current is ignored. At this time, the Maxwell equations are satisfied:  
\[\
abla\times\mathbf{H}=\mathbf{J} \tag{1}\]  
\[\
abla\cdot\mathbf{B}=0 \tag{2}\]  
In the formula, \(\mathbf{H}\) is the magnetic field strength (A/m), \(\mathbf{J}\) is the current density (A/m\({}^{2}\)), and \(\mathbf{B}\) is the magnetic induction intensity, also known as the magnetic flux density (T).  
There are constitutive equations in the medium:  
\[\mathbf{B}=\mu_{0}\mu_{r}\mathbf{H} \tag{3}\]  
where \(\mu_{0}\) and \(\mu_{r}\) are the vacuum permeability and relative permeability, respectively.  
The vector magnetic potential \(\mathbf{A}\) is introduced so that \(\mathbf{A}\) satisfies:  
\[\mathbf{B}=\
abla\times\mathbf{A} \tag{4}\]  
The boundary conditions in the air domain are expressed as:  
\[\mathbf{n}\times\mathbf{A}=0 \tag{5}\]where \(\mathbf{n}\) is the outer normal unit vector of the conductor surface in the eddy current region.  
In addition, the current density also satisfies the equation:  
\[\mathbf{J_{e}}=\frac{NV_{coll}}{SR_{coil}}\mathbf{e}_{coil} \tag{6}\]  
where \(\mathbf{J_{e}}\) is the source current density; \(N\) is the number of turns of the winding coil; \(V_{coil}\) is the voltage excitation applied on the winding; \(R_{coil}\) is the winding resistance; \(\mathbf{e}_{coil}\) is the direction vector of the current density at each point of the current-carrying coil; and \(S\) is the winding coil cross-sectional area.  
The simultaneous equation to obtain the relationship between the vector magnetic potential \(\mathbf{A}\) and the source current density \(\mathbf{J_{e}}\) is:  
\[\
abla\times\left({{\mu_{0}}^{-1}{\mu_{r}}^{-1}\
abla\times\mathbf{A}}\right)=\bm {J_{e}} \tag{7}\]  
Since the inductor parameters are known, the core vector magnetic potential \(\mathbf{A}\) and the magnetic flux density \(\mathbf{B}\) can be solved together with Equation (3) to complete the calculation of the magnetic field.
## 4 Experimental Validation
### Eq3. Effects of Parameter Sharding  
As the number of workers in a distributed cluster increases, the communication overhead required in distributed training inevitably increases as well. With the increase of the communication overhead, the training throughput per unit time of each worker decreases, which may adversely affect the training performance of the entire distributed training. To efficiently process the communication overhead, as explained in Section 3.3, we apply the parameter sharding technique to SHAT. In this experiment, we evaluate  
Figure 4: Comparison of convergence rate with respect to training epochs for ResNet-50 on CIFAR-10; (a) ASP (\(\alpha=1\)); (b) SHAT (\(\alpha^{i}=1-\frac{h^{i}}{\log n}\)).  
Figure 5: Convergence rate of SHAT under three different heterogeneous clusters; (a) ResNet-50; (b) VGG-16.  
the scalability of SHAT with the increasing number of workers and the effectiveness of parameter sharding techniques. We compare the following three versions of SHAT. (1) **No-sharding**: SHAT without parameter sharding, where a single PS processes all parameters in the training model; (2) **Layerwise**: SHAT with the layerwise parameter sharding, where four PSs are in charge of the same number of layers (but different numbers of parameters). (3) **Memorywise**: SHAT with the memorywise parameter sharding, where four PSs are in charge of the same number of parameters. We measure the training throughput of each method with numbers of workers, in the ResNet-50 and VGG-16 training on CIFAR-10. Figure 6 shows the results, where the \(x\)-axis represents the number of workers and the \(y\)-axis represents the speedup results.  
Clearly, for both models, the memorywise parameter sharding improves the training throughput of SHAT the most. This indicates that the memory-wise parameter sharding successfully splits large layers and distributes sharded parameters to PSs evenly, which helps to mitigate the PS bottleneck problem. In the case of the ResNet-50 training, the layerwise sharding improves the training throughput of SHAT well. This is because the numbers of parameters are similar to each other across layers in ResNet-50. On the other hand, in the case of the VGG-16 training, the layerwise sharding rarely improves the training performance since a few layers have more than 90% of all parameters which leads to the problem of PS being a bottleneck.
# Structural Design and Optimization of Herringbone Grooved Journal Bearings Considering Turbulent
## 2 Numerical Model and Digital Simulation
### Performance Calculation of HGJBs  
The dimensionless horizontal and vertical load capacities of the bearings are:  
\[\overline{F}_{x}=\int_{0}^{2\pi}\left(\int_{-1}^{1}Pd\lambda\right)\sin\varphi d\varphi\]  
\[\overline{F}_{y}=\int_{0}^{2\pi}\left(\int_{-1}^{1}Pd\lambda\right)\cos\varphi d\varphi \tag{21}\]  
The actual load capacity of the bearing is:  
\[F=\frac{\omega r^{3}\mu}{c^{2}}\sqrt{\overline{F}_{x}^{2}+\overline{F}_{y}^{2}} \tag{22}\]  
Figure 6: Schematic diagram of the boundary of the herringbone grooved journal bearing.  
The bearing deflection angle is:  
\[\theta_{i}=\theta_{i-1}-\arctan\left|\overline{F}_{x}/\overline{F}_{y}\right| \tag{23}\]  
The side leakage is:  
\[Q_{L}=-4\omega l\left[\left(\left.\frac{r^{2}}{l^{2}}\frac{H^{3}}{k_{z}}\frac{ \partial p}{\partial\lambda}\right|_{\lambda=1}\right)-\left(\left.\frac{r^{2 }}{l^{2}}\frac{H^{3}}{k_{z}}\frac{\partial p}{\partial\lambda}\right|_{\lambda= -1}\right)\right]d\varphi \tag{24}\]  
The bearing friction resistance is:  
\[F_{t}=\frac{\omega r^{2}\mu l}{2c}\iint\left(\frac{1}{H}\tau_{c}-\frac{12H}{k_ {x}}\frac{\partial p}{\partial\varphi}\right)d\varphi d\lambda \tag{25}\]  
In the formula: \(\tau_{c}\) is the Coot shear stress.  
The bearing friction power is:  
\[N_{t}=F_{t}\cdot U \tag{26}\]  
The average temperature rise of the bearing is:  
\[\Delta T=\frac{F_{t}U}{Q_{L}\rho c_{v}} \tag{27}\]  
When the axis only makes small movements near the static equilibrium position, eight linearized stiffness and damping coefficients can be used to express the dynamic performance of the oil film:  
\[\left\{\begin{array}{l}K_{e}\\ K_{\theta e}\end{array}\right\}=-\int_{-1}^{1}\int_{\varphi_{1}}^{\varphi_{2}} P_{\xi}\left\{\begin{array}{l}\cos\varphi\\ \sin\varphi\end{array}\right\}d\varphi d\lambda \tag{28}\]  
\[\left\{\begin{array}{l}K_{e\theta}\\ K_{\theta\theta}\end{array}\right\}=-\int_{-1}^{1}\int_{\varphi_{1}}^{\varphi_{ 2}}P_{\theta}\left\{\begin{array}{l}\cos\varphi\\ \sin\varphi\end{array}\right\}d\varphi d\lambda \tag{29}\]  
\[\left\{\begin{array}{l}D_{e}\\ D_{\theta e}\end{array}\right\}=-\int_{-1}^{1}\int_{\varphi_{1}}^{\varphi_{ 2}}P_{\xi^{\prime}}\left\{\begin{array}{l}\cos\varphi\\ \sin\varphi\end{array}\right\}d\varphi d\lambda \tag{30}\]  
\[\left\{\begin{array}{l}D_{e\theta}\\ D_{\theta\theta}\end{array}\right\}=-\int_{-1}^{1}\int_{\varphi_{1}}^{\varphi_{ 2}}P_{\theta^{\prime}}\left\{\begin{array}{l}\cos\varphi\\ \sin\varphi\end{array}\right\}d\varphi d\lambda \tag{31}\]  
Therefore, in the specific calculation, first of all, we must divide the grid, given the basic parameters of the bearing, initialize the oil film pressure, and obtain the convergent oil film pressure by iterating the oil film pressure and correcting the deflection angle. Then calculate the static and dynamic performance of the bearing. The whole calculation process is carried out in the Matlab software. The specific program flow chart is shown in Figure 7.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 5 Covert Channel Design and Implementation
### Transmission through Covert Channel  
Algorithm 2 illustrates the procedure of preparing frames from the information, and finally modulates these frames into the physical medium by generating Ethernet packet patterns on the cable. Line 1 of the algorithm checks whether there is a valid string to be transferred. It will split the string into chunks of maximum payload size and, in each iteration, only one chunk will be considered. Line 3 conducts the frame preparation from the supplied payload. From Line 5 onward, bits are modulated to the covert channel. Line 6 defines the time limit given to transfer the current bit, and it should transfer the bit in that given time limit. In Manchester encoding, a rising edge denotes binary 1. Then, the algorithm sleeps for half of the _bitTrxTime_. In another half of the _bitTrxTime_, it sends the packets through the Ethernet cable, which mimics a rising edge. The opposite happens for binary 0. First, it sends a packet for half a _bitTrxTime_ and sleeps for the other half. In an opposite pattern to before, this mimics a falling edge.  
```
0: string, bitTrxTime, payloadSize
1:while\(string\_length>0\)do
2:\(payload\gets getNextChunck(payloadSize,string)\)
3:\(frame\gets prepareFrame(payload)\)
4:\(bitTrxEndTime\gets getCurrentTime()\)
5:for all bit \(\gets frame\)do
6:\(bitTrxEndTime\gets bitTrxEndTime+bitTxTime\)
7:\(halfBitTxEndTime\gets bitTrxEndTime+(bitTxTime/2)\)
8:if\(bit==1\)then
9:\(sleep(bitTrxTime/2)\)
10:while\(getCurrentTime()<bitTrxEndTime\)do
11:\(sendPacketPattern()\)
12:endwhile
13:endif
14:if\(bit==0\)then
15:while\(getCurrentTime()<halfBitTxEndTime\)do
16:\(sendPacketPattern()\)
17:endwhile
18:\(sleep(bitTrxTime/2)\)
19:endif
20:endfor
21:endwhile
```  
**Algorithm 2** EM signal modulation algorithm
###### Abstract  
During the last decade, Wireless sensor networks (WSNs) have attracted interest due to the excellent monitoring capabilities offered. However, WSNs present shortcomings, such as energy cost and reliability, which hinder real-world applications. As a solution, Relay Node (RN) deployment strategies could help to improve WSNs. This fact is known as the Relay Node Placement Problem (RNPP), which is an NP-hard optimization problem. This paper proposes to address two Multi-Objective (MO) formulations of the RNPP. The first one optimizes average energy cost and average sensitivity area. The second one optimizes the two previous objectives and network reliability. The authors propose to solve the two problems through a wide range of MO metaheuristics from the three main groups in the field: evolutionary algorithms, swarm intelligence algorithms, and trajectory algorithms. These algorithms are the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Multi-Objective Artificial Bee Colony (MO-ABC), Multi-Objective Firefly Algorithm (MO-FA), Multi-Objective Gravitational Search Algorithm (MO-GSA), and Multi-Objective Variable Neighbourhood Search Algorithm (MO-VNS). The results obtained are statistically analysed to determine if there is a robust metaheuristic to be recommended for solving the RNPP independently of the number of objectives.  
deployment; energy cost; metaheuristic; multi-objective; relay node; reliability; sensitivity; wireless sensor network +
Footnote †: journal: Sensors  
1
###### Abstract  
Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on NC and CS (Compressed Data Gathering--CDG) have been proposed. However, these schemes assume that the sparsity of the network sensed data is constant and the value of the sparsity is known before starting each data gathering epoch, thus they ignore the variation of the data observed by the WSNs which are deployed in practical circumstances. In this paper, we present a complete design of the feedback CDG scheme where the sink node adaptively queries those interested nodes to acquire an appropriate number of measurements. The adaptive measurement-formation procedure and its termination rules are proposed and analyzed in detail. Moreover, in order to minimize the number of overall transmissions in the formation procedure of each measurement, we have developed a NP-complete model (Maximum Leaf Nodes Minimum Steiner Nodes--MLMS) and realized a scalable greedy algorithm to solve the problem. Experimental results show that the proposed measurement-formation method outperforms previous schemes, and experiments on both datasets from ocean temperature and practical network deployment also prove the effectiveness of our proposed feedback CDG scheme.  
data gathering; sensor networks; compressed sensing; network coding +
Footnote †: journal: Sensors
# COALA: A Protocol for the Avoidance and Alleviation of Congestion in Wireless Sensor Networks
## 3 Proposed Protocol Description
### Initialization Phase  
The initialization phase of COALA aims to perform all the initial calculations of both the level \(L\) and the neighbor table of all networks nodes. These calculations are necessary for the inception of the congestion avoidance algorithm and this is why it is executed only once.  
Specifically, this phase is initiated as soon as the sink transmits a \"hello\" message, which includes the sink ID number along with its level \(L\), which is equal to 0. Next, every node that receives this  
Figure 1: A typical example of a WSN topology in: (**a**) Initial arrangement of interconnected network nodes; (**b**) Level-based taxonomy of network nodes.  
message, sends back to the sink a corresponding acknowledgement message that includes its ID number. As soon as the sink receives such an acknowledgement message from a node, it sends another message back to this node, which confirms that this node is a neighboring node of the sink and that it is a level 1 node. Every level 1 node initiates its neighbor table, which includes the details of the sink and transmits a \"hello\" message, which includes its ID number, its level \(L\), which is equal to 1, its position, its current energy, and its current buffer occupancy. Every node that receives this message, sends back to the transmitting node a corresponding acknowledgement message that includes the current values of its neighbor table parameters, which are explained later on. As soon as the level 1 node receives such an acknowledgement message from a node, it sends another message back to this node that includes its updated neighbor table along with a confirmation that this node is its neighboring node. If the new neighboring node has not already acquired a level number, then it is recognized as a next (i.e., 2) level node.  
This procedure carries on until every individual node in the network not only has been assigned a corresponding level number, as illustrated in Figure 0(b), but also has become aware of its neighbor table. In this way, each node constructs an overall view of the network topology and becomes aware of all the available routing paths towards the sink, avoiding the formation of routing loops.