esa-sceva/satcom-chunk-collection huggingface dataset:
# Swarm Intelligence Algorithms for Feature Selection: A Review
## 5 Swarm Intelligence for Feature Selection
### Agent Representation  
The continuous and binary versions of the SI algorithms have been used for FS. The main difference in SI approaches is in their representation of the search agent. Generally, in the binary versions of the algorithms, an agent is formed as a binary string, where the selected feature is represented with number 1 and the not selected feature with 0. In the continuous approaches, the search agent is represented with real values, and the selection of features is based on a threshold \(\theta,0<\theta<1\). The feature is selected only if its value is greater than the value assigned as a threshold.  
Based on that, there are mainly three different encoding mechanisms. One of the most used is the binary encoding [30; 34], followed by continuous encoding [29], and the hybrid combination of the two [31].  
A particular case is the ACO algorithm, in which the features are represented as graph nodes. When an ant moves and visits the node in the graph, the node becomes part of the feature subset. Therefore, each feature subset is represented by an ant.
# Perfect Roman Domination: Aspects of Enumeration and Parameterization +
## 4 Parameterized Complexity
### Dual Parameterization  
Different from Perfect Code, it makes sense to consider the dual parameterization \((|V|-k)\), as it is even NP-hard to find out if the graph has any perfect code (see [24]). So, the problem is para-NP-hard for the dual parameterization. This is not the case for Perfect Roman Domination and Unique Response Roman Domination since the constant \(1\) function is always a perfect Roman dominating function/unique response Roman dominating function. Therefore, these problems are trivially solvable if \(n-k=0\). We can even see this parameter as a _distance from triviality_ parameter in the sense of [43].  
**Theorem 21**: \((n-k)\)-Perfect Roman Domination, \((n-k)\)-Unique Response Roman Domination\(\in\)_FPT_._  
Let \(G=(V,E)\) be a graph and \(f\in\{0,1,2\}^{V}\). Then,  
\[n-\omega(f) =\sum_{v\in V}(1-f(v))\] \[=\left(\sum_{v\in V_{0}(f)}(1-0)\right)+\left(\sum_{v\in V_{1}(f )}(1-1)\right)+\left(\sum_{v\in V_{2}(f)}(1-2)\right)\] \[=|V_{0}(f)|-|V_{2}(f)|.\]  
We will use this equality to bound the diameter and degree by \(n-k\).  
Assume there is a vertex \(v\) with \(\deg(v)\geq n-k+1\). Then, \(f_{v}\coloneqq\chi_{V}+\chi_{\{v\}}-\chi_{N(v)}\) is clearly a unique response Roman dominating function, and therefore a perfect Roman dominating function with  
\[n-\omega(f_{v})=|V_{0}(f_{v})|-|V_{2}(f_{v})|=\deg(v)-1\geq n-k.\]  
Hence, \(\omega(f_{v})\leq k\), and we observe a yes-instance and can hence use the consideration of a vertex of maximum degree as a reduction rule.  
Assume there is a shortest path \(p=v_{1},\ldots,v_{3\cdot(n-k)}\) of length \(3\cdot(n-k)\). Define \(f_{p}\coloneqq\chi_{V}+\sum_{i=0}^{n-k-1}(\chi_{\{v_{3i+2}\}}-\chi_{N(v_{3i+2} )})\). Clearly, each vertex in \(V_{0}(f)\) has at least one neighbor in \(V_{2}(f)\). Assume there exists a vertex \(u\in V_{0}(f)\) with \(v_{3i+2},v_{3j+2}\in V_{2}(f)\cap N(u)\) with \(i\
eq j\) (without loss of generality., \(i<j\)). This would imply that \(v_{1},\ldots,v_{3i+2},u,v_{3j+2},\ldots,v_{3\cdot(n-k)}\) is a shorter path. Hence, \(f_{p}\) is a perfect Roman dominating function. Analogously, \(V_{2}(f_{p})\) is an independent set. Therefore, \(f_{p}\) is even a unique response Roman dominating function. Further,  
\[n-\omega\big{(}f_{p}\big{)}=|V_{0}(f_{p})|-|V_{2}(f_{p})|\geq 2\cdot(n-k)-n-k \geq n-k.\]  
So, \(\omega(f)\leq k\), and we observe a yes-instance and can hence use the consideration of the graph diameter as a reduction rule.  
As the maximum degree and diameter are bounded in \(n-k\), the number of vertices is bounded by the _Moore bound_, giving \(\mathcal{O}\Big{(}(n-k)^{3\cdot(n-k)}\Big{)}\) as the kernel size; see [44].
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 3 A 2-Port MIMO System Using Two Flipped Parallel 1 \(\times\) 2 Arrays Antenna
### Measurement Results Discussion of Two 1 \(\times\) 2 Arrays MIMO
#### 3.3.2 MIMO Performance Results of Two \(1\times 2\) Arrays MIMO
The comparison between the simulation results is shown in Figure 17e. \[\text{MEG}_{\text{i}}=\left(1-\sum_{\text{j}=1}^{\text{M}}\left|\text{S}_{ \text{j}}\right|^{2}\right). \tag{12}\]  
It has been observed that TARC in Figure 17d exhibits higher variability between simulation and measurement compared to other parameters. This is because TARC is highly sensitive to phase changes. In contrast, ECC, MEG, DG, and CCL are mathematically derived from stable quantities of S-parameters. These parameters utilize averaging techniques or statistical models that do not require precise phase control or active excitation. Despite this variability, both the simulated and measured TARC values remain below \(-10\) dB, particularly at the desired frequencies, indicating an effective design.
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
## 3 Results and Discussion
### Characterization and Propertiesof BOC/AC Composites
#### 3.2.3 Photocatalytic Properties
The photo-excited electrons (e\({}^{-}\)) on the BOC and AC could reduce the O\({}_{2}\) adsorbed on the surface to active species of superoxide anions (\(\bullet\)O\({}^{2-}\)). Consequently, the \(\bullet\)O\({}^{2-}\) active oxidizing species can oxidize toluene into intermediate products and finally into CO\({}_{2}\) and H\({}_{2}\)O [39]. Meanwhile, the photo-generated holes (h\({}^{+}\)) left on the BOC oxidize H\({}_{2}\)O on their surface into another highly active species of hydroxyl radicals (\(\bullet\)OH). Finally, the produced \(\bullet\)O\({}^{2-}\) and \(\bullet\)OH, with strong oxidizing ability, can efficiently degrade the intermediates into CO\({}_{2}\) and H\({}_{2}\)O, which are not harmful. Figure 8: The photocatalytic performance of the BOC/AC samples for toluene removal in air (**a**), theCO\({}_{2}\) productivity of the samples (**b**), the property of BOC/AC_100 and AC for toluene removal (**c**), CO\({}_{2}\) productivity of BOC/AC_100 and AC (**d**), the stability of BOC/AC_100 for three cycles (**e**), and the toluene removal efficiency of the different amounts of BOC/AC_100 (**f**).
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 2 Materials and Methods
### Methods
#### 2.2.1 Calibration  
The COSMO SkyMed SAR calibration was carried out using the calibration module ([[https://step.esa.int/main/wp-content/help/versions/10.0.0/snap-toolboxes/eu.esa](https://step.esa.int/main/wp-content/help/versions/10.0.0/snap-toolboxes/eu.esa)]([https://step.esa.int/main/wp-content/help/versions/10.0.0/snap-toolboxes/eu.esa](https://step.esa.int/main/wp-content/help/versions/10.0.0/snap-toolboxes/eu.esa)). microwavetbx.sar.op.calibration.ui/operators/CalibrationOp.html, accessed on 15 November 2024) implemented in the SNAP software (SNAP--ESA Sentinel Application Platform v8.0, [[http://step.esa.int](http://step.esa.int)]([http://step.esa.int](http://step.esa.int)), accessed on 15 November 2024). This module is designed to convert the raw digital numbers (DNs) of SAR data into backscatter intensity. Calibration is important because it helps make the data more accurate, meaningful, and consistent across different times and locations. Specifically, the calibration module converts the DNs of the Level 1B images into backscatter coefficients, which represent the normalised radar cross-section of the surface. The backscatter coefficients could be sigma0, beta0, and gamma0. Sigma0 represents the radar reflectivity per unit area on the ground, normalised by the cosine of the incidence angle; beta0 represents the radar brightness, which is the backscatter per unit area in the radar slant range plane; and gamma0 is similar to sigma0 but normalised by the cosine of the local incidence angle, which accounts for variations in terrain slope. Moreover, the calibration module corrects for instrumental and environmental factors: the calibration corrects for the effects of the instrument's characteristics and environmental conditions to provide a more consistent and standardised measurement. This includes correcting for differences in the radar's look angle, range distance, and power variations.
# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 2 Related Work
### Vector-Based Systems  
[PERSON], [PERSON], and [PERSON] [18] proposed a probabilistic vector-based approach to route characterization. The authors first grouped AIS data into traffic groups. The authors state that despite the Douglas Peucker (DP) algorithm's primary usage for trajectory compression, by reducing the number of points that characterize the line segment according to a defined threshold, it can also be used for detecting relevant turning points. The DP algorithm is used to find the point the farthest away from the initial and final points of a segment, and if this point is farther away than a defined threshold, the trajectory is split in that point and the same method is applied to both trajectories. Otherwise, if no point is found that is bigger than the threshold, the trajectory can be simplified to just be represented by the initial and final point, as a line. The found turning points, which connect two straight lines, simplified by the DP algorithm, are then clustered by using DBSCAN. Finally, turning sections of the route, ship speed distribution, and traffic density are analysed. The anomaly detection is performed by analysing the motion of the ships, described by using multiple distributions. Deviations from the inferred Gaussian distributions are considered to be anomalous. [PERSON] et al. [19] present an anomaly-detection method based on MapReduce and unsupervised learning methods. Vessel trips are defined as groups of consecutive messages from the same vessel which begin and end at a seaport. Messages have attributed an ID according to the current port they are located in (or none, if they are navigating in the open sea). The final data set includes connections between origin, destination, and vessel type. The authors then apply an adaptation of the K-means clustering algorithm, which results in k clusters, calculated from all vessels departing from one port to another, for each of the ship types. Vessel positions that fall out of the found polygons are considered to be unusual.
# Effects of Nozzle Exit Angle on the Pressure Characteristics of SRWJs Used for Deep-Hole Drilling
## 3 Experimental Setup and Procedures  
### Facilities and Procedures  
Since the intensity of the pressure oscillation is an important indicator used to evaluate the performance of SRWJs, experiments were carried out to obtain the axial pressure oscillation peaks  
Figure 1: Schematic diagram of an organ-pipe nozzle and the generation of a self-resonating waterjet (SRWJ).  
and amplitudes of the jet. Figure 2 is a schematic diagram illustrating the experimental setup for acquiring the time-resolved axial pressures of the SRWJ. It should be noted that a series of experiments on SRWJs have previously been conducted using the same equipment and facilities, and the same pressure acquiring method. Figure 2 is thus the same as in our previous studies [20; 23; 25; 26; 33].  
The experiment was performed using a multifunctional waterjet test bench that has been applied in the previous investigations [15; 17; 18; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 33]. The test bench had a horizontal moving table with X directional motion and a loading platform that had \(Y\) and \(Z\) directional motions. The tested organ-pipe nozzle was mounted at the end of a hollow-rod fixed on the horizontal moving table, and a small tank filled with water was positioned on the loading platform. A target plate, which had a pressure tap of 1 mm diameter at its center, was fixed on the wall of the tank. The pressure tap was communicated with a dynamic pressure transducer (Model: XPM 10), which was connected with a data logger (Model: MX840B) used to collect the axial pressures of the SRWJ. The maximum and minimum pressures could be directly displayed on the laptop connected to the data logger. Furthermore, the pressure oscillation amplitude was defined as the difference of the two pressures, which is:  
\[P_{a}=P_{max}-P_{min} \tag{6}\]  
where \(P_{a}\) is the pressure oscillation amplitude, \(P_{max}\) is the pressure oscillation peak, and \(P_{min}\) is the minimum pressure.  
The standoff distance, \(S\), was defined as the distance from the nozzle exit to the surface of the target plate, and it was varied from \(S=10\) mm to \(S=100\) mm in increments of 10 mm during each group of tests. Before each measurement, the pressure tap on the target plate was made coaxial with the nozzle. The maximum working pressure and flow rate of the plunger pump were 60 MPa and 120 L/min, respectively. In addition, two bladder accumulators were used in the water line to reduce the disturbances generated by the pump, so as to improve the accuracy of the experimental results. More specifically, one accumulator was located near the pump and the other was close to the nozzle. Another pressure transducer (Model: DMK 331P) was used to monitor the inlet pressure, \(P_{i}\), and inlet pressures of \(P_{i}=10\) MPa and 20 MPa were applied in the experiment. The duration time was 3 s for each measurement. The main parameters of the two pressure transducers are shown in Tables 1 and 2, respectively.  
Figure 2: Experimental setup of the axial pressure measurement [20; 23; 25; 26; 33].
# Production Optimization in a Grain Facility through Mixed-Integer Linear Programming +
## 6 Experimental Evaluation
### Evaluation Methodology  
The proposed formulation was implemented using the AMPL modeling program language and solved with IBM CPLEX Solver ver. 20.1.0.0. The model was executed on an Intel Core i9-9900K CPU @ 3.60 GHz, 16 processors with 64 Gb. of RAM. The operating system was CentOS Linux release 7.7.1908.  
All executions were run with a preset amount of time, chosen as one of the stopping criteria in the experiments. The other criterion for stopping the execution is the gap between the best integer solution found by the solver and the best value of the objective function computed so far (CPX_PARAM_EPGAP parameter), set to \(1\times 10^{-5}\).  
The performance of the proposed method is evaluated using two metrics: an execution time threshold and the gap of the solution found with respect to a certain lower bound computed  
\begin{table}
\begin{tabular}{c c c} \hline \hline _Parameter_ & _Variable_ & _Value_ \\ \hline number of production lines & \(L\) & 2 \\ processing capacity line 1 & \(\pi_{1}\) & 6.175 tons/h \\ processing capacity line 2 & \(\pi_{2}\) & 6.650 tons/h \\ cleaning time line 1 & \(L_{1}\) & 6 h \\ cleaning time line 2 & \(L_{2}\) & 8 h \\ total storage capacity & \(\hat{C}\) & 2700 tons \\ \hline number of inoculated products & \(|V_{l}|\) & 7 \\ shell life of inoculated products & \(\tau\) & 2 weeks \\ number of treated products & \(|V_{T}|\) & 10 \\ \hline operators shift (Monday to Friday) & \(H_{f}\) & 7.5 h \\ operators shift (Saturday) & & 3.75 h \\ number of shifts & & 1 to 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for the considered case study.  
by the solver. The gap is related to the execution time; an inversely proportional relationship is expected, as increasing the total execution time of the model allows computing more accurate results. For the considered case study, several executions of the proposed model are performed considering as stopping criterion 1, 5, 30, 60, 120, 180, and 240 min, respectively.
# Improving Landslide Recognition on UAV Data through Transfer Learning
## 4 Experimental Results and Discussion
### Model Training and Optimization  
The experiment is based on the TensorFlow. Developed and maintained by Google Brain, TensorFlow is a symbolic mathematical system based on dataflow programming. It is widely used in the programming implementation of various machine learning algorithms [44]. The essence of training a deep learning model is to make good use of the loss function, which is used to evaluate the difference between the predicted value of the hypothetical function and the real value. The fitting effect refers to the size of the loss value. The better the fitting effect, the smaller the loss. The step of training the model is gradient descent which is to minimize the loss value. The size of the loss value can be used to measure the fitting effect. The loss curve will drop at the beginning during the training process. Then it will begin to converge, and gradually stabilize after a period of time. The loss curves of the SSD model and the transfer learning model can be seen in Figure 9.  
Figure 8: LabelImg operation interface.  
Figure 9: The loss curves of the SSD model (**left**) and the transfer learning model (**right**).  
The SSD model takes 25 epochs to converge, while the transfer learning model tends to smooth after five epochs to reach equilibrium. The transfer learning model requires less time to complete the model training and is more efficient.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 1 Introduction
### Goal, Subject, and Scope of This Study  
The overarching goal of this research is to develop and validate an IT-supported maintenance solution that addresses the identified challenges in military aviation maintenance. The focus is on the M-346 BIELIK aircraft operated by the Polish Air Force. The study explores how IT technologies can enhance maintenance efficiency, optimize task scheduling, and improve reliability analysis. Specifically, the research aims to identify characteristics of maintenance task durations, develop a mobile application for real-time data collection and analysis, and evaluate the potential of this solution in reducing organizational inefficiencies.  
The research scope includes a detailed examination of existing maintenance practices, the development of a mobile application tailored to the needs of the M-346 BIELIK, and the integration of statistical methods to analyze task durations. By addressing these objectives, this study aims to provide practical insights and tools that enhance aviation maintenance processes. Additionally, the research aligns with global sustainability goals by promoting resource-efficient practices and minimizing environmental impacts. Through the innovative use of IT systems and advanced statistical techniques, this study aspires to set a new standard for maintenance operations in military aviation, providing a framework that could be adapted to other sectors of aviation as well.
## 1 Introduction  
For applications like outsourced storage, a client's data is stored on an untrusted server [1; 2]. Even if the data is fully encrypted, sensitive information might still be leaked to the server or adversaries that can observe the entire communication between the client and the server [3]. Oblivious RAM (ORAM) is a protection guaranteeing that the server and such adversaries cannot distinguish an access pattern from another. ORAM has been widely studied and its performance has been improved both in theory [4; 5; 6; 7; 8] and in practice [9; 10]. In addition to the outsourced storage, it has also been applied in other various applications, such as secure processor [11; 12; 13] and secure multi-party computation [14; 15; 16; 17; 18].
## 3 Results and Discussion
### Tests with Two Modules with 24-bit Sigma-Delta Converters and Two Chassis

Table 3: Phase errors for Sigma-Delta converters. Figure 6: Histograms for phase measurements with Sigma-Delta modules. In total, 95% of the measurements were below 20 ns and 100% were below 25 ns. This analysis serves as a basis for test type assurance. The results corroborate the manufacturer's specifications concerning the phase error between channels. The TSN tests show two limits at 95% depending on the cable length. For 2 m, \(\pm\)57 ns was obtained and for 10 m, \(\pm\)85 ns. In both cases, 100% of the measurements were below 125 ns. Figure 8 shows the non-parametric probability density (PD) for the four-time series, which allows us to estimate the degree of symmetry of the hypothetical distribution curve and also allows us to rule out behaviours with a bimodal distribution, but it can be seen that the series obtained did not have a behaviour with a normal distribution. In both cases, the median had been included to observe the degree of symmetry. If the distribution is normal, the median should be at the centre of the curve and should coincide with the mode. In all cases, the distribution is not Gaussian, but for the Sigma-Delta converters capturing with two chassis and connected with a 2 m Ethernet cable, a very asymmetric distribution was obtained and in some cases with null values. This fact may demonstrate a certain level of error in the data capture process but similar results were obtained in consecutive tests.
# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 3 Module Design
### Training Pipeline
#### 3.2.3 Calculation of Leap Rate Matrices  
The discretized dataset is the basis for the calculation of the matrices that model the leap rates between adjacent nodes for different vessels. A leap is defined as an oriented vector \((initial,final)\), where \(initial\) corresponds to the ID of the rectangle position of the vessel at time \(t\), and \(final\) corresponds to the ID of the rectangle to which the vessel has navigated. The model explained in the present section learns the rate of a leap associated with each of these oriented vectors, for each vessel. In an instance where the vessel has never performed a navigation between two nodes, the rate will be equal to zero.  
Bayesian conditional probabilities are often used as the basis for the calculation of grid-based probabilities on the maritime subspace, as seen in the work by [PERSON] et al. [24] and [PERSON] et al. [25], which is the basis for the suggested approach.  
The vectors are generated by iterating the discretized trip list. For each element in a trip, a vector with a tail corresponding to the message grid node ID, and a tip corresponding to the following message's grid node ID is generated. If the tail and tip are the same node, meaning no navigation between nodes has been observed, no vector is generated. For each vessel \(v_{i}\), being \(ais_{k}^{v_{i}^{v_{i}}}\) the k-th AIS message present in a certain trip j of vessel \(v_{i}\):  
\[ais_{k}^{v_{i}^{v_{i}}}\mapsto(ais_{k}^{v_{i}^{v_{i}^{v_{i}}}} \times_{NodeID},ais_{k+1 NodeID}^{v_{i}})\\ \forall\quad 1\leq j\leq J\quad\&\quad 1\leq k\leq K\quad \&\quad ais_{k}^{v_{i}^{v_{i}}}\times_{NodeID}\
eq ais_{k+1 NodeID}^{v_{i}^{v_ {i}}}.\]  
The generated vectors are now grouped by the grid node ID, tail of the vectors, counting the total number of leaps from each grid node ID and the frequency of each unique vector. The leap rate for a certain vector \((a,b)\) is the frequency of the navigation \((a,b)\) in the data, divided by the total number of leaps from node \(a\). Both the frequency and the total number of leaps are stored for online learning, explained in Section 3.3.  
With the computed values, we calculate the count of navigations for each node. We generate a three-by-three matrix for each grid node, where the centre of the matrix, representing the navigations between the grid node and itself, is equal to the total number of navigations performed by the vessel from the node to any adjacent node. Each of the other elements represents the total number of navigations with the adjacent nodes.  
The result of this calculation, important for inference, is a data cube of registered leaps, as depicted in Figure 10, where for each vessel \(v\), a different grid layout is associated with it, mapping latitude \(i_{(\lambda)}\) and longitude \(i_{(\varphi)}\) coordinates to grid node IDs, with associated matrices of navigations.