esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Tests in Ideal Static Scenarios
#### 5.3.3 Scenario 2  
In this scenario, shown in Figure 9b, it is assumed that each node is located at the edge of the transmission range of the preceding node. For this reason, multi-hop communications are needed to disseminate traffic updates throughout the network, and this degrades the performance. In particular, the PMF of the retransmitted _DATA_ messages is presented in Figure 16. We note that, with respect to Scenario \(1\), the rebroadcasting probability is (obviously) higher for all SUs (\(d=z\)). The number of retransmissions is upper bounded by \(128\), which represents the number of chunks in which the archive is fragmented.  
In Figure 17, the PMF of the received _DATA_ messages is shown. It can be observed that, for each SU, there exists, also, the possibility to receive messages from other SUs, due to the feedback mechanism detailed in Section 4.2.2. For instance, some _DATA_ messages received by \(\mathrm{SU}_{3}\) are one-hop messages, because they come from \(\mathrm{SU}_{2}\) and are sent after the reception of a _REQUEST_ message. Nevertheless, we note that for all SUs, there is a dominant hop value centered at their distance (in terms of hops number) with respect to the PU.  
Figure 16: PMF of the number of rebroadcast _DATA_ messages in Scenario 2.  
Figure 15: Cumulative Distribution Function (CDF) of received _DATA_ messages before _REQUEST_ messages in Scenario 1.  
As can be seen from Figure 18, the number of missing chunks before the generation of the first _REQUEST_ message is larger than in the previous case, thus significantly penalizing Scenario 2 with respect to Scenario 1. In particular, it can be noted that: (i) each SU needs to use at least one _REQUEST_ message; and (ii) the number of missing chunks is, for each run, larger than 10. It can also be noted that \(\mathrm{SU}_{3}\) is particularly penalized, because the link losses generate cumulative effects.  
In Figure 19, the PMF of the archive completion time is shown. As expected, the reception time is longer than in Scenario 1. The difficulty of receiving all the chunks is evidenced by the larger number of _REQUEST_ rounds for missing chunks that contribute to increase the completion time.  
Figure 17: PMF of the hop index for received _DATA_ messages in Scenario 2.  
Figure 19: PMF of the archive reception time in Scenario 2.  
Figure 18: PMF of missing _DATA_ chunks, before first _REQUEST_ round in Scenario 2.  
Finally, in Figure 20, the PMF of the completion time before the first _REQUEST_ message is shown, which is around \(0.7\), \(0.65\) and \(0.55\), respectively, for \(\mathrm{SU}_{1}\), \(\mathrm{SU}_{2}\) and \(\mathrm{SU}_{3}\). The use of a single feedback _REQUEST_ message raises the completion rate to a value of about 0.9 for each node. Hence, on average, at least one or even more _REQUEST_ messages are required to complete the reception of the archive.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 4 Experiments and Evaluation  
This section will describe the experimental details. By comparing and analyzing the performance of different network models, we can evaluate the superiority of our design scheme. At the same time, through the comparison of power consumption of different hardware platforms, it is shown that our design scheme has significant advantages compared with GPU and CPU in the application scenario of spaceborne remote sensing images.
## 2 Materials and Methods
### Filtering Nonvoven  
Eight types of nonwoven composites were made using melt-blown technology. The matrix of the composites was Borealis HL 508] isotactic polypropylene (PP) supplied in the form of beads (NEXEO Solutions Poland Sp. zo.o., Poland), with the properties: Melting temperature (156-160 \({}^{\circ}\)C), melt flow index MFI (800 g/10 min), and density (50.3 g/cm\({}^{2}\)).  
In the melt-blown process, molten PP was air-blown to obtain elementary fibers of various thicknesses and lengths, with zeolite or silica materials introduced directly in the fiber stream. The resulting mixture of fibers and zeolite/silica material was electrostatically activated at 25 kV and settled on the collector to form a nonwoven zeolite/silica composite. The mean fiber diameter in the reference material was 968.78 nm.  
The produced nonwoven composites are characterized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variant** & **BET Surface Area, m\({}^{2}\)/g** & **BJH Pore Diameter, nm** & **BJH Average Pore Volume, cm\({}^{3}\)/g** \\ \hline Zeolite Na-A & 8.52 & 5.11 & 0.013 \\ Zeolite Na-A HDTMA & 3.31 & 4.75 & 0.008 \\ Zeolite Na-P1 & 38.06 & 12.07 & 0.160 \\ Zeolite Na-P1 HDTMA & 5.03 & 3.63 & 0.006 \\ MCM-41 & 953.30 & 3.10 & 0.850 \\ MCM-41 HDTMA & 264.31 & 2.40 & 0.230 \\ Molecular sieve SM 4  & 3.40 & 4.50 & 0.006 \\ Molecular sieve SM 5  & 325.60 & 2.21 & 0.130 \\ \hline \end{tabular}
\end{table}
Table 1: Textural parameters of the studied zeolite and silica materials.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variant** & **BET Surface Area, m\({}^{2}\)/g** & **Surface Density, g/m\({}^{2}\)** & **Composite Thickness, mm** \\ \hline PPQ + Na-A & 2.80 & 202.45 & 3.48 \\ PPQ + Na-A HDTMA & 2.32 & 214.01 & 2.48 \\ PPQ + Na-P1 & 13.38 & 340.57 & 3.70 \\ PPQ + Na-P1 HDTMA & 9.77 & 343.32 & 3.94 \\ PPQ + MCM-41 & 310.70 & 134.04 & 4.14 \\ PPQ + MCM-41 HDTMA & 6.27 & 299.94 & 4.49 \\ PPQ + SM 4 \(\AA\) & 1.38 & 163.36 & 3.47 \\ PPQ + SM & 111.73 & 131.72 & 4.25 \\ \hline \end{tabular}
\end{table}
Table 2: Basic textural parameters of the produced nonwoven composites.  
BET surface area is one of the basic measures of adsorptive properties, especially in relation to adsorbents. The performed study allowed observations to be made that of all the variants, the high BET surface area values are characterized by: PPQ + MCM-41 (310.70 g\({}^{2}\)/m) and PPQ + SM (111.73 m\({}^{2}\)/g).
# Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis
## 3 Dataset Description
### Buffering and Graphs Building Procedure  
As anticipated, many research works focused on single-frame analysis [1; 2; 10; 14; 20], mainly highlighting that a subset of attacks can be effectively identified on a per-frame basis. As instead, in this work, we perform a study of the anomaly detection problem in WiFi networks expressed in the graph domain.  
To this end, we define a preprocessing strategy that builds a new dataset, starting from a simple First-In-First-Out (FIFO) queue that collects \(B\) consecutive packets captured on the channel. We use this buffer as a \(B\)-length window that we slide over the time series of captured packets in order to build a new dataset made of sequences of packets. Meta-data are attached to each captured packet in the original dataset, assessing whether that packet belongs to legitimate traffic or, if it is an attack packet, what kind of attack it belongs to. Since each sequence could contain packets belonging to different classes, the new sequence-based dataset will be labeled in a non-exclusive manner, thus associating multiple labels to each sequence.  
At this point, we introduce the graph building procedure, which aims to extract the information brought by sequences built from the FIFO buffer and to build a corresponding graph for each sequence. Let us consider a sequence \(s_{i}\) of length \(B\). Formally, we define the mapping \(\Gamma:\mathcal{S}\to\mathcal{G}\), which associates the set of sequences \(\mathcal{S}\) with the set of graphs \(\mathcal{G}\). Each graph \(\mathcal{G}_{i}=(\mathcal{V}_{i},\mathcal{E}_{i})\) includes nodes corresponding to all MAC addresses found in frames that belong to \(s_{i}\), and edges defined by the source-destination MAC addresses pairs found in the frames of the sequence \(s_{i}\). Each edge, then, is weighted with a vector of elements described by the following equation:  
\[w^{(e)}(i,j)=\big{[}p_{\text{frame}}(i,j)\quad p_{\text{payload}}(i,j)\quad a _{\text{RX}}(i,j)\quad n_{\text{seq}}(i,j)\quad n_{\text{frag}}(i,j)\quad \Delta t(i,j)\big{]} \tag{5}\]  
where  
* \(i\) and \(j\) refer to MAC addresses found in frames belonging to \(s_{i}\);
* \(p_{\text{frame}}(i,j)\) and \(p_{\text{payload}}(i,j)\) are respectively the normalized mean length and payload size of frames sent on that link;
* \(a_{\text{RX}}(i,j)\) is the normalized mean of the received signal strength for that frame (taken from the field _radiotap.dbm_antsignal_ listed in Table 2);
* \(n_{\text{seq}}(i,j)\) and \(n_{\text{frag}}(i,j)\) are respectively the normalized mean sequence number and fragment number for data frames on that link;
* \(\Delta t(i,j)\) is the average time of arrival of frames on that link.  
The whole sequence-to-graphs transformation is sketched in Figure 1. Lastly, by using a multi-label iterative stratification procedure [26], we perform training (\(\mathcal{S}_{\text{tr}}\)), validation (\(\mathcal{S}_{\text{vd}}\)), and test (\(\mathcal{S}_{\text{ts}}\)) set splitting for the proposed experiments. Brief dataset statistics are resumed in Table 3.
## 5 Discussion  
This paper introduces a method that combines optimized Resonance Sparse Signal Decomposition (RSSD) with Refined Composite Multiscale Dispersion Entropy (RCMDE). Initially, the quality factor in RSSD is optimized via Atomic Search Optimization (ASO), and then the optimal quality factor is applied to the RSSD of reciprocating compressor fault signals. Noise interference in the original vibration signals can be effectively differentiated from the decomposed low-resonance components. The core parameters of RCMDE are optimized using a Genetic Algorithm (GA). Finally, RCMDE features with low-resonance components are extracted for use in pattern recognition. The experimental research demonstrates that the proposed method can effectively differentiate between spring faults, valve wear, and normal valve conditions in reciprocating compressors. The recognition accuracy of RCMDE with parameter optimization reaches 100% and the recognition result of RCMDE without parameter optimization is 87.037%. The recognition accuracy of MSE and MPE are 88.889% and 85.185%.  
One limitation of this study is that the operating environment of reciprocating compressors in practical applications is more complex; therefore, further exploration should be  
Figure 10: Fault identification results: (**a**) The recognition result of GA-optimized RCMDE; (**b**) The recognition result of RCMDE without GA optimization; (**c**) The recognition result of MSE; (**d**) The recognition result of MPE.  
conducted to extract the characteristics of reciprocating compressors in real-world engineering applications. The diagnostic method for a single fault of reciprocating compressors under fixed speed conditions was discussed in this study. Further research is needed on how to accurately and effectively identify fault modes when time-varying speed states, variable loads, and multiple types of faults occur simultaneously. In addition, the number of parameters which should be optimized renders a great impact on the efficiency of the proposed algorithm. Further research might focus on the selection of other parameters in the algorithm so as to interfere with the final result.
## 5 Results and Discussions
### Velocity Profile  
Equations (39)-(42) are solved with the proposed strategy of a numerical scheme, a Matlab solver, and an iterative method. Figure 1 shows the convergence of two different employed numerical schemes. The first-order implicit Euler method and proposed scheme are applied to solve the considered differential Equations (39)-(42). Figure 1 shows that the proposed scheme converges faster than an existing first-order scheme for finding the maximum of norms. The first-order Euler method requires fewer computations than the proposed scheme, which is one of the advantages of using the Euler method. Figure 2 deliberates the effect of the Casson parameter and an electric variable on the velocity profile. The velocity profile is declined and boosted by the rising Casson parameter and electric variable, respectively. Since an increment in the Casson parameter reduces the diffusion coefficient, this slows down the spreading procedure of molecules in the fluid flow, and, therefore, velocity tends to decay. The effect of the porosity parameter and inertia coefficient on the velocity profile is displayed in Figure 3. The velocity profile is declined by enhancing the porosity parameter and inertia coefficient. An increment in the porosity parameter produces resistance to the velocity of the flow, and, therefore, velocity declines. The growth in the inertia coefficient gives rise to the drag force, resulting in the decay of the velocity profile.  
Figure 2: Variation of Casson parameter and electric variable on velocity profile using \(N=25\), \(\lambda=0.1\), \(F_{r}=0.1\), and \(M=0.1\). (**a**) \(E_{1}=0.01\), (**b**) \(\beta=3\).
## 5 Performance Evaluation
### Lifetime of the Network  
Figure 6 elaborates the effect of depth level, the number of sinks, and the mobility of nodes on the energy consumption of the network. We also give a comparison of energy consumption in the form of the lifetime of RTC and the state-of-the-art approaches. From Figure 5(a,c), it is very much clear that varying the number of sinks and depth do not affect the lifetime of the network. The data forwarding hierarchy of the proposed scheme is very strong and involved number of sinks and depth levels cause no effect on the network lifetime. If we add more and more layers in our model, the data forwarding hierarchy remains the same and only DCPs of the MS will increase which does not affect the network lifetime. However, as the node mobility increases the network energy consumption also increases on a little scale due to the exchange of control packets as demonstrated in Figure 5(d).  
Figure 5: Performance analysis of proposed framework for packet delivery ratio in different scenarios.  
Figure 5(b) elaborates the comparison of energy consumption between RTC, H2-DAB, and AUV-PN algorithms. The given results reveal that RTC outperforms the counterpart schemes in term of energy consumption. In RTC, energy load on each FN is balanced in a distributed manner. Firstly, the FNs are forced to use the single-hop as well as the multi-hop transmission ranges to communicate with CHs depending on the situation. Secondly, RTC avoids the redundant transmissions and only selected FN can perform the sensing because nodes in the overlapping region always have the same data [19; 33; 35]. Thirdly, the RTC is sleep-awake aware to save the available energy of the network. The energy consumption of AUV-PN is more than RTC, as most of the network energy portion is utilized in creating the clusters. Then, in each cluster multiple head nodes are selected for data delivery, maintaining the routing table, and for correspondence with the AUV. The CH and the PN selection criteria is also very poor, which creates imbalance situation in the network. All these factors decrease the network lifetime of AUV-PN as compared to the RTC. According to depicted results, the energy consumption of H2-DAB is also higher as compared to the RTC, because it floods the control packets for finding the routes to the SS. Furthermore, it consumes a lot of energy in maintaining the routing table which leads to the shorter network lifetime.  
Figure 6: Performance evaluation of RTC based on total network energy consumption.
# A 3D Numerical Model to Estimate Lightning Types for PyroCb Thundercloud
###### Abstract  
Pyrocumulonimbus (pyroCb) thunderclouds, produced from extreme bushfires, can initiate frequent cloud-to-ground (CG) lightning strikes containing extended continuing currents. This, in turn, can ignite new spot fires and inflict massive harm on the environment and infrastructures. This study presents a 3D numerical thundercloud model for estimating the lightning of different types and its striking zone for the conceptual tripole thundercloud structure which is theorized to produce the lightning phenomenon in pyroCb storms. More emphasis is given to the lower positive charge layer, and the impacts of strong wind shear are also explored to thoroughly examine various electrical parameters including the longitudinal electric field, electric potential, and surface charge density. The simulation outcomes on pyroCb thunderclouds with a tripole structure confirm the presence of negative longitudinal electric field initiation at the cloud's lower region. This initiation is accompanied by enhancing the lower positive charge region, resulting in an overall positive electric potential increase. Consequently, negative surface density appears underneath the pyroCb thundercloud which has the potential to induce positive (+CG) lightning flashes. With wind shear extension of upper charge layers in pyroCb, the lightning initiation potential becomes negative to reduce the absolute field value and would generate negative (\(-\)CG) lightning flashes. A subsequent parametric study is carried out considering a positive correlation between aerosol concentration and charge density to investigate the sensitivity of pyroCb electrification under the influence of high aerosol conditions. The suggested model would establish the basis for identifying the potential area impacted by lightning and could also be expanded to analyze the dangerous conditions that may arise in wind energy farms or power substations in times of severe pyroCb events.  
cloud-to-ground lightning; lower positive charge structure; pyrocumulonimbus; surface charge density; tripole thundercloud Article
# The Iterative Exclusion of Compatible Samples Workflow for Multi-SNP Analysis in Complex Diseases
###### Abstract  
Complex diseases are affected by various factors, and single-nucleotide polymorphisms (SNPs) are the basis for their susceptibility by affecting protein structure and gene expression. Complex diseases often arise from the interactions of multiple SNPs and are investigated using epistasis detection algorithms. Nevertheless, the computational burden associated with the \"combination explosion\" hinders these algorithms' ability to detect these interactions. To perform multi-SNP analysis in complex diseases, the iterative exclusion of compatible samples (IECS) workflow is proposed in this work. In the IECS workflow, qualitative comparative analysis (QCA) is firstly employed as the calculation engine to calculate the solution; secondly, the pattern is extracted from the prime implants with the greatest raw coverage in the solution; then, the pattern is tested with the chi-square test in the source dataset; finally, all compatible samples are excluded from the current dataset. This process is repeated until the QCA calculation has no solution or reaches the iteration threshold. The workflow was applied to analyze simulated datasets and the Alzheimer's disease dataset, and its performance was compared with that of the BOOST and MDR algorithms. The findings illustrated that IECS exhibits greater power with less computation and can be applied to perform multi-SNP analysis in complex diseases.  
complex diseases; single-nucleotide polymorphisms; iterative exclusion of compatible samples workflow; qualitative comparative analysis; combination explosion +
Footnote †: journal:
# Effect of Impeller Trimming on the Energy Efficiency of the Counter-Rotating Pumping Stage
## 3 Results  
### Design Models  
The object of the study was a counter-rotating stage for a basic pump CPS-180/1900. The counter-rotating stage consists of an impeller \(I\) and blade disk \(I\) (Figure 2).  
A counter-rotating stage without trimming was also considered as a reference stage (Figure 2a), with which other results for four impellers, trimmed by 5, 10, 15, and 20 mm, were compared.  
The following different types of studies were realized. In the first case study, the outer diameter \(D_{4}\) of the blade disk was constant (Figure 2b). In the second one, the height \(h\) of the blade disk was constant (Figure 2c). The last case study was carried out for an impeller with trimming of 52 mm under the condition that the largest size \(D_{4}\) of the counter-rotating stage was equal to the size \(D_{2}\) of the reference impeller (Figure 2d).  
Geometric dimensions of the reference counter-rotating stage (without trimming) were as follows: the outer diameter of the impeller--\(D_{2}\) = 302 mm; the outer diameter of the blade disk--\(D_{4}\) = 410 mm; the gap between the working elements of the step--2 mm; the blade disk height--\(h\) = 52 mm.
# The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review
## 4 Effects of Axial Compressive Stress
### Detrimental Effects
In cases where the Bourdon effect due to internal pressure comes into account, specifically in pipe bends, the influence of axial compressive stress results in a significant change in the integrity of the pipe component [6]. As such, pipe bends are regarded as the weakest link in a piping system [68]. The significance of axial compressive stress on the failure pressure of a corroded pipe bend is high as it increases strain along the pipe wall [69]. A high concentration of stress and strain in the defect region drastically lowers the internal pressure required to cause the pipe bend to fail [70]. Despite this, only a limited amount of work has been conducted on the effects of axial compressive stress on the failure pressure of pipe bends. [PERSON] et al. [38] investigated the influence of a corrosion defect on the failure of a corroded pipe subjected to internal pressure and in-plane bending load, which incorporated axial compressive stress. This study was conducted using EEA, where the global deformation behavior of the pipe component was observed. It was found that axial compressive stress resulted in local buckling at the region of the defect, as illustrated in Figure 9 (red-colored region representing local buckling). It was emphasized that, generally, plastic instability is observed at locations of high deformations for the defects located at the extra-dos. However, in axial compressive stress, plastic instability was also observed in regions of lower deformations. For defects located at the intrados, it was observed that the severity of the significance of the axial compressive stress on the failure of the pipe was higher than that of the tensile stress acting on the pipe (caused by ovalization). Overall, it was concluded that the incorporation of axial compressive stress into the analysis resulted in an exponential reduction in the failure pressure of the pipe bend, and that the effects of axial compressive stress are more significant compared to the tensile stress acting on the pipe. Figure 8: Rupture failure of axially oriented corrosion defect [66].