esa-sceva/satcom-chunk-collection huggingface dataset:
# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 2 Related Work
However, these approaches struggle to provide effective initialization and guided updates when applied to large-scale datasets. One research study addressed this issue introducing an importance-guided Particle Swarm Optimization based on MLP (IGPSO) to enhance feature selection for high-dimensional datasets [20]. The approach leverages a neural network-generated importance vector to guide both population initialization and evolution, ensuring that optimization focuses on the most relevant features. A two-stage training process refines this importance vector by first identifying useful features from positive samples and then filtering out irrelevant ones using negative samples. To further improve performance, IGPSO replaces traditional PSO acceleration factors and inertia weight with importance-guided updating, where more critical features receive stronger influence while less important ones are de-emphasized. This strategy balances exploration and exploitation, leading to efficient feature selection with fewer features and improved classification accuracy on large-scale datasets. However, EC-based feature selection methods are generally effective for small-scale problems with feature dimensions ranging from tens to hundreds [21,22]. When dealing with large-scale datasets containing thousands of dimensions, these methods become computationally expensive and struggle to achieve satisfactory performance. Additionally, the effectiveness of swarm-intelligence-based algorithms is highly dependent on population initialization [23]. Thus, our approach enables the exploration of large datasets with high-dimensional feature spaces by introducing a new feature selection method based on PSO. By incorporating a guided particle scheme with three filter-based methods, the proposed algorithm effectively tackles critical challenges in high-dimensional data analysis, such as premature convergence to suboptimal solutions, which often hinder the performance of traditional PSO-based techniques. This advancement is particularly important for complex, high-dimensional datasets, where the large number of features typically poses significant challenges. The algorithm's ability to efficiently navigate large search spaces and identify the most relevant features makes it a robust and promising algorithm for enhancing the accuracy and efficiency of data analysis across diverse applications.
## 5 Numerical Study
### Deterministic Analysis and Validation
#### 5.1.2 Validation for the Misalignment Effect  
The above results are calculated while disregarding the misalignment effect. To clarify the impact of the misalignment effect, the parameters of the HDB provided by [PERSON] and [PERSON] [20] are adopted for calculation. The parameter details are displayed in Table 4.  
Accordingly, the maximum pressures are calculated under various misaligned angles and shown in Table 5; they demonstrate that misalignment has a significant effect on maximum pressure. The results are compared with those presented by [PERSON] and [PERSON] [20]. The comparison shows good agreement.
## 2 Related Work
### Existing Defense Mechanisms against Jamming
During this time, normal traffic routed to the jamming area may become congested or dropped. Moreover, the best single route to a destination generated by general routing protocols could easily become congested again. This may be worse, as traffic destined to a destination such as a sink could be greater or the jammed region could be bigger. Our simulation results will show these effects in Section 4. On the other hand, researches on control channel jamming [25, 26] have progressed, because jamming the control channel in wireless networks reduces the required power for performing a DoS attack by several orders of magnitude, and control channel jamming is particularly devastating for wireless _ad-hoc_ networks due to their cooperative nature. [PERSON] _et al._[25] implemented multiple control channels over specified frequency bands and time slots, so that any subscriber can listen to them. [PERSON] _et al._[26] proposed a randomized distributed scheme that allows nodes to establish a new \begin{table}
\begin{tabular}{p{85.4 pt}|p{85.4 pt}|p{85.4 pt}} \hline \hline
**Type** & **Layer** & **Characteristics** \\ \hline Spectrum Spreading [18, 19, 20] & Physical Layer & It is too energy-consuming to be widely deployed in resource-constrained sensors. \\ Channel Hopping [21, 22, 23] & Link Layer & The jammer can also change the jamming channel continuously and then it enlarges the channel switching overhead on nodes on the entire network. \\ Retreat [24] & Network Layer & It is limit to mobile environment. \\ JAM [8] & Network Layer & It simply focused on a mapping service for the jamming area, thus the best single route detouring the jamming zone can easily become congested again. \\ DMP & Network Layer & As a general approach irrelative with specific protocols on physical and link layer, it can enhance the robustness against jamming on existing routing protocols. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of DMP with relative evasion approaches. control channel using frequency hopping in multi-channel ad-hoc networks. The protection of control channel utilizing multiple channels is important, but it is out of scope in this paper. Thus, in this paper, we focus on a general evasion approach on network layer, independent of the specific characteristics on physical and link layer. Our scheme, DMP, provides immediate multiple paths for detouring normal traffic destined for the jamming area, and we will show that the immediate routes provide a higher PDS and lower delay than AODV or JAM with a route by various parameters. Table 1 is a summary of the most relevant related work.
# Power Efficient Random Access for Massive NB-IoT Connectivity
## 4 Average Power Consumption in Existing NB-IoT RAP
### If the Device Starts RAP in CE1  
Let us consider \(P_{E_{1}}[n]\) as the power consumed over \(n\) reattempts if the device starts RAP in CE1.  
1. For \(n\leq L_{1}\): If the device starts RAP in CE1 and succeeds in \(n\) attempts (such that \(n\leq L_{1}\)) while power ramping is applied, then the power consumed (\(PE_{1}[n\leq L_{1}]\)) by this device can be expressed as \[P_{E_{1}}[n\leq L_{1}]=\sum_{s=1}^{n}R_{1}(P_{1}+(s-1)\Delta P_{1})\] (22) If the device starts RAP in CE1, then the probability that it succeeds in the first attempt can be expressed as \((\mathbb{P}_{r}(S_{11})\{n=1\}=1-\mathbb{P}_{r}(C))\). However, on failure, first it reattempts in CE1 itself for \(L_{1}\) attempts and subsequently moves to CE2 if the failure persists. The probability that the device that starts RAP in CE1 and succeeds in CE1 itself in \(n=2,3,\ldots L_{1}\) reattempts can be expressed as in Equation (23). \[\mathbb{P}_{r}(S_{11})\{1<n\leq L_{1}\}=\mathbb{P}_{r}(C)(1-\left((1-\mathbb{ P}_{r}(C))q_{1}\right))^{(n-2)}\left((1-\mathbb{P}_{r}(C))q_{1}\right)\] (23) The probability that a device that starts RAP in CE1 but does not succeeds for designated \(L_{1}\) reattempts in CE1 can be expressed as \[\mathbb{P}_{r}(F_{11})\{n=L_{1}\}=\mathbb{P}_{r}(C)\left(1-(1-\mathbb{P}_{r}(C )q_{1})\right)^{(L_{1}-1)}\] (24) Subsequently, the device that does not succeed in \(L_{1}\) reattempts in CE1 moves to CE2.
2. For \(n>L_{1}\): The device that started RAP in CE1, that succeeded in \(n\) (such that \(n>L_{1}\)) attempts, and then that was power consumed by this device can be expressed as \[P_{E_{1}}[n>L1]=R_{1}P_{1}L_{1}+R_{1}\Delta P_{1}\frac{(L_{1}-1)(L_{1})}{2}+ \sum_{t=1}^{n-L_{1}}R_{2}(P_{2}+(t-1)\Delta P_{2})\] (25)The probability that the device succeeds in CE2 after having started in CE1 in \(n=L_{1}+t\) attempts can be expressed as  
\[\mathbb{P}_{r}(S_{12})\{n>L_{1}\}=\mathbb{P}_{F_{11}}\mathbb{P}_{r}(C)^{(n-L_{1}- 1)}\bigg{(}1-\mathbb{P}_{r}(C)\bigg{)} \tag{26}\]  
The average power spent by a device in the existing system after having started in CE1, unconditioned to \(n\), can be expressed as  
\[P_{E_{1 avg}}=\sum_{n=1}^{L_{1}}P_{E_{1}}[n\leq L_{1}]\mathbb{P}_{r}(S_{11})\{n \}+\sum_{n=L+1+1}^{L_{1}+t}P_{E_{1}}[n>L_{1}]\mathbb{P}_{r}(S_{12})\{n\} \tag{27}\]
## 2 MOFs
### Most Useful Types of MOF for Wastewater  
As mentioned above, MOFs consist of an inorganic part, usually transition metals (Cr, Mn, Co, Ni, Fe, Cu, Zn,... ), as they have a high charge density, and an organic part [18]. Hence, when the ligands and the coordination environment remain the same, the metal ions can form stronger coordination bonds and, consequently, such a MOF will be more stable. This behaviour is due to Pearson's hard/soft acid/base (HSAB) principle [18].  
Metal ions and ligands produce different morphologies, depending on their combination. Among the great variety that currently exists (Figure 1), the MIL (Lavoisier Institute Material), the ZIF (Zeolite Imidazolate Framework), the UiO (Universitetet i Oslo) and the HKUST (Hong Kong University of Science and Technology) stand out.  
These MOFs have been widely used in wastewater treatment in recent years, as shown in Figure 2. Figure 2 also includes the PBA (Prussian Blue Analogues) [20] and PCN (Porous Coordination Network) [21; 22] because, although the number of current publications is very small, these materials are promising, since several studies have demonstrated that PCN and PBA can be viable alternatives to other MOFs when it comes to removing pollutants from water [23; 24; 25; 26].  
In Figure 2, the results of the literature search for the range 2018 to 2022 are represented and the keywords used were \"metal-organic framework\", \"wastewater\" and the abbreviation of the given MOFs (MIL, ZIF,... ). Clearly, these profiles showed a considerable increase in the number of papers, which demonstrates the progress and feasibility of MOFs in water treatment, mainly when MIL, ZIF and UiO MOFs are used. As for the results for the year 2022, since the year has not yet ended, they could not be fully evaluated. The only conclusion that can be reported is that, even before the end of the year, the number of publications was very similar to 2020, so, with this evolution, it is intuited that the numbers will be higher than in 2021.  
Figure 1: MOF structures (reproduced from CSD MOF Collection [19] under license CC BY-NC-SA 4.0).
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 1 Introduction
Secondly, the insulator image is binarized and the contour is extracted. Thirdly, Zernike moments are calculated according to the obtained contours of the insulator pieces, which are described in detail. Finally, the damage judgment of catenary insulators is completed by calculating the similarity value and similarity standard deviation between insulator pieces. This method combines the advantage of the Mask R-CNN algorithm in small target identification, which is less affected by the complex background of the image, and the advantage of the Zernike moment algorithm in feature detail extraction, which has low requirements for the number of samples and strong ability to describe the contour details, so as to achieve the effect of high accuracy of catenary insulator damage detection.
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
## 3 Technical Realizations  
### SAW Devices  
LiNbO\({}_{3}\) piezoelectric crystal features a larger electromechanical coupling coefficient (\(K^{2}\)), which benefits the reduction in insertion loss of SAW device. Therefore, 128\({}^{\circ}\) YX-LiNbO\({}_{3}\) piezoelectric crystal with large \(K^{2}\) (5.5%) was chosen as the piezoelectric substrate of the sensing chip with a delay-line pattern, and FeGa thin film was sputtered to the SAW propagation path between the two photolithographically defined 300 nm Al transducers separated by a path length of \(\sim\)2 mm. Single-phase unidirectional transducers (SPUDTs)  
Figure 3: (**a**) The magnetostriction curve characteristic of FeGa; (**b**) the predicted sensitivity of the 150 MHz SAW-based current sensor.  
confining the acoustic energy predominantly in one direction on the piezoelectric substrate surface were used to form the transducers to reduce the insertion loss [1]. The operation frequency was designed to 150 MHz, and the corresponding wavelength \(\lambda\) was 25.84 \(\upmu\)m. The electrode widths in SPUDTs were 3.23 \(\upmu\)m (\(\lambda/8\)) and 6.46 \(\upmu\)m (\(\lambda/8\)). The lengths of the input transducer and output transducer were designed to be 130\(\lambda\) and 40\(\lambda\), respectively. In addition, a comb structure was designed for the transducers to eliminate other unwanted vibration frequencies to achieve a single oscillation mode [25]. After the Al electrodes preparation, a 30 nm SiO\({}_{2}\) thin film was covered on the transducers by PECVD to protect the electrodes in FeGa deposition.
## 4 Results and Discussion
### Comparison between the Conventional and Improved Methods  
The conventional and improved methods are compared for large bubbles in Figure 5. Here, it can be seen that a smaller value of \(B\) corresponds to a larger bubble size (Figure 5a). Thus, when \(B=1\times 10^{-7}\), the bubble shapes obtained using the two different methods are almost identical. However, when \(B=1\times 10^{-14}\), there is a slight difference between the two results, and when \(B=1\times 10^{-22}\), the two methods show a significant discrepancy in the obtained bubble shapes. The cause of inaccuracy in the conventional method is demonstrated in Figure 5b, where the dimensionless cap radius \(R_{0}\) is seen to increase abnormally as the parameter \(B\) decreases in the conventional method.  
The conventional and improved methods are compared for small bubbles in Figure 6. Here, when \(B=1000\), the bubble shapes obtained using both methods are almost identical (Figure 6a). However, when \(B=2000\) and \(3000\), the conventional method raises the bubble in a non-physical manner because the bisection method does not converge and gives a negative value of \(H\). As shown in Figure 6b, when \(B\) is small, the dimensionless absolute height \(|H|\) is the same in both methods; however, as \(B\) becomes larger, \(|H|\) fluctuates significantly in the conventional method. These results clearly demonstrate that the improved method should be used to calculate large or small bubbles robustly.
## 4 Process Mining Functionality
### The Process Mining Approach: \(\rho\)-Algorithm
* _STEP-3: Rediscovering Structured Process Patterns_: The final step (STEP-3) of the \(\rho\)-Algorithm is to discover all the primitive process patterns of a structured information control net process model from the mined process pattern graph discovered from all the groups of temporally ordered adjacent-activity pairs. The eventual goal of the \(\rho\)-Algorithm is accomplished through this step. Note that the structured information control net process model must be satisfied with the proper nesting and the matched pairing properties in forming gateway activities in each primitive process graph pattern. Based upon the \(\rho\)-Algorithm, the authors' research group has successfully developed a process mining system that is able to rediscover a structured information control net process model coping with all the four types of process patterns from a dataset of process enactment event logs. By using the process mining system, this paper carries out a series of experimental analyses by deploying the implemented system onto a dataset provided by the BPI challenges of 4 TU.Centre for Research Data [17] and formatted in the IEEE XES standardized format [30]. Summarily, the following are the characteristics of the \(\rho\)-Algorithm and the process mining framework:  
* First, the process mining framework is able not only to rediscover the process patterns but also to discover the enactment proportions [27] of the process patterns from a dataset of the IEEE XES-formatted enactment event logs of a corresponding process model. * Second, the process mining framework is theoretically supported by the information control nets modeling methodology [16] of process models. * Third, the essential algorithm of the process mining approach is named the \(\rho\)-Algorithm (rho-Algorithm) that is able to rediscover a structured information control net model with the enactment occurrences of the activities associated with an underlying process model. * Fourth, the \(\rho\)-Algorithm was firstly developed in the process management and mining literature as a process mining algorithm that discovered a structured process model theoretically supported by the structured information control net modeling methodology. * Fifth, the \(\rho\)-Algorithm is able to rediscover all the primitive process patterns, such as linear (sequential), conjunctive (parallel-AND), disjunctive (exclusive-OR), and repetitive (iterative-LOOP) process patterns, and discover the enactment occurrences and proportions of each branch of the process patterns. Figure 5: The \(\rho\)-Algorithm with three steps of graphical transformations.
## 2 Survey of Commercial Small Platforms
### Passive Optical  
This type of technology has shown its feasibility for small missions [59; 60]. For example, for an optical instrument in the visible part of the spectrum, with a ground resolution better than 10 m, and an aperture of 10 cm (CubeSat size), the altitude of the satellite should be less than 500 km.  
The data provided by passive optical instruments, from the ultraviolet to the far-infrared wavelengths can be used for weather forecast, vegetation, atmosphere, ocean and land studies. The main limitation of optical sensors is that data cannot be acquired in night-time (visible and near infrared parts of the spectrum) or cloudy conditions, and cloudy weather is very frequent in Polar Regions.  
In this manuscript, the classification of optical sensors as radiometer imager and atmospheric sounders, and its subclassification between multispectral and hyperspectral is studied. Radiometer imagers measure the intensity of electromagnetic radiation in the visible or infrared bands, and sounders measure the vertical distribution of atmospheric parameters such as pressure, temperature, and humidity. Multispectral instrument refers to a maximum number of tens of bands, and hyperspectral radiometers consist of hundreds of narrow and continuously distributed bands (10-20 nm).
## 4 Clutter Suppression Method  
The process of eliminating clutter is divided into two distinct steps: first, discriminating the location of the clutter, and second, removing the clutter.
## 4 Experimental Validation and Analysis
### Prediction Results  
In order to investigate the impact of the method proposed in this paper on different datasets, experiments were conducted using four datasets: FD001, FD002, FD003, and FD004. Since FD001 and FD003 only contain the life degradation information of 100 engines under a single working condition, the prediction difficulty is relatively low. In contrast, FD002 and FD004 contain historical degradation data of 248 engines under multiple working conditions, which increase the prediction difficulty of the model. Therefore, the prediction results for the FD001 and FD003 datasets are better than those for the FD002 and FD004 datasets.
# An Indirect Identification Method for Train Basic Resistance Parameters
## 4 Case Analysis
### Case Studies  
#### 4.2.1 Effectiveness Test without Disturbance  
Firstly, to verify the effectiveness of this method under no disturbance, we created four sets of simulation data with different TBRPs. The actual basic resistance parameter values are shown in Table 3. This method was used to identify the TBRPs with random initial parameters. The acceptable deviation \(\epsilon_{1}\) was adjusted to \(10^{-5}\). The four identification results are recorded in Table 3. In the case of no data disturbance, the TBRPs obtained by this method were basically the same as the actual values. In these four identification processes, the minimum number of iterations was 4 and the maximum was 7. The mean square errors in each case were very small and met the acceptable deviation \(\epsilon_{1}\), which proves the effectiveness of the parameters and this method. The same conclusion can be drawn from Figure 3, where the fitting curve calculated by the identified parameters is consistent with the actual curve.  
\begin{table}
\begin{tabular}{c c} \hline \(N\) & 100 \\ \(\theta_{\max}\) & [3, 0.2, 0.01] \\ \(\theta_{\min}\) & [0.4, 0, 0] \\ \(c_{1}\) & 0.1 \\ \(c_{2}\) & 0.9 \\ \(\epsilon_{1}\) & 0.05 \\ \(\Delta\theta_{T}\) & [\(10^{-5}\), \(10^{-6}\), \(10^{-7}\)] \\ \(\Delta\ \theta\) & [0.1, 0.002, 0.0001] \\ \hline \end{tabular}
\end{table}
Table 2: Parameters setting of the [PERSON]’s method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Case**} & \multicolumn{3}{c}{**Actual Parameters**} & \multicolumn{3}{c}{**Identified Parameters**} & \multirow{2}{*}{**Iterations**} & \multirow{2}{*}{**Mean**Square**} \\ \cline{2-2} \cline{5-6}  & & & & & & & **Error** \\ \hline
1 & 0.79 & 0.0229 & 0.001493 & 0.7901 & 0.0229 & 0.001493 & 5 & \(2.8\times 10^{-9}\) \\
2 & 0.2955 & 0.0242 & 0.00159 & 0.2956 & 0.0242 & 0.00159 & 4 & \(5.6\times 10^{-8}\) \\
3 & 1.0501 & 0.0253 & 0.00140 & 1.0525 & 0.0252 & 0.00140 & 6 & \(1.7\times 10^{-6}\) \\
4 & 0.3488 & 0.0596 & 0.00112 & 0.3485 & 0.0596 & 0.00112 & 7 & \(1.9\times 10^{-7}\) \\ \hline \end{tabular}
\end{table}
Table 3: Parameter identification results for four cases.
# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 2 Related Work
### Panoptic Segmentation Methods  
Early research often combined separate semantic and instance segmentation networks. Methodologies have evolved into three main streams:  
Top-Down Methods: These typically extend existing object detectors. An initial detection stage identifies bounding boxes for 'thing' instances, followed by mask prediction within each box. UPSNet [6], for example, integrated a semantic segmentation head into Mask R-CNN [5]. While intuitive, these methods can suffer from conflicts between the separate prediction heads and challenges in resolving overlapping instances.  
Bottom-Up Methods: These methods first perform pixel-level grouping or embedding, followed by clustering or voting mechanisms to form instances. Panoptic-DeepLab [7] predicts semantic maps, instance centers, and pixel offsets to group pixels into instances. While potentially faster, bottom-up methods often struggle with accurately delineating boundaries in dense scenes, leading to instance adhesion.  
Unified Framework Methods: More recent approaches aim for end-to-end prediction, often leveraging Transformer architectures. MaskFormer [9] reframed segmentation as a mask classification problem using learnable queries. Mask2 Former built upon this with masked attention, achieving State-of-the-Art results by treating both semantic and instance segmentation uniformly via queries. K-Query [10] further explored query-based methods. While powerful, even these unified methods can face challenges, such as the Mask2 Former's noted lower accuracy on small objects like traffic lights [9], motivating our work on targeted improvements. Our proposed PSM-FFEG builds on the unified approach but introduces specific modules for dynamic fusion and edge refinement to address these remaining gaps.