esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 4 Experimental Design  
To systematically evaluate the effectiveness of the proposed method, this study designed a series of comparative and ablation experiments. The comparative experiments aim to verify the performance differences between the algorithm presented in this paper and existing methods (including AugNet, SPQ, HiHPQ, CIBHash, and MeCoQ) on the SubCATHR-DET and CUB-200-2011 datasets; the ablation experiments were used to analyze the contribution of each core component (Omni-Dimensional Dynamic Convolution module, Bi-Level Routing Attention mechanism, and optimized loss function) in this algorithm to the model performance. All the experiments were conducted under the same hardware environment and software configuration to ensure the comparability and reliability of the results.
# Study of the Effects of Plasma Pretreatment on the Microstructure of Peanuts
## 3 Results and Discussion
### Impact of Plasma Treatment on the Appearance and Structure of Peanut Embryos
#### 3.2.2 Impact of Plasma Treatment Duration on the Appearance and Structure of Embryos  
The results are presented in Figure 5. Figure 5c shows the embryo treated with helium gas plasma for 30 s at 200 W. SEM images at 500\(\times\) magnification reveal an increased number of particles in the embryo. The embryo treated with helium gas plasma is \(\sim\) 100 W, 300 W, and 500 W, respectively. The right column shows SEM images at 2000\(\times\) magnification. (**b**) Corresponds to the control group peanuts. (**d,f,h,j**) Correspond to peanuts treated with cold plasma at 100 W, 200 W, 300 W, 400 W, and 500 W for 60 s, respectively.  
Figure 4: Appearance of peanut embryos under scanning electron microscope after treatment at different powers. Note: The left column shows SEM images at 500\(\times\) magnification. (**a**) Corresponds to the control group peanuts. (**c,e,g,i,k**) Correspond to peanuts treated with cold plasma at 100 W, 200 W, 300 W, 400 W, and 500 W for 60 s, respectively. The right column shows SEM images at 2000\(\times\) magnification. (**b**) Corresponds to the control group peanuts. (**d,f,h,j**) Correspond to peanuts treated with cold plasma at 100 W, 200 W, 300 W, 400 W, and 500 W for 60 s, respectively.  
of pores on the embryo surface compared to the control group (Figure 2a). Some lipid particles are also observed exuding. In Figure 5e, after 60 s of plasma treatment, SEM images at 500\(\times\) magnification show an increase in surface cracks. The peanut surface becomes more uneven, and cell boundaries gradually become clearer. Cells are sparsely arranged, and the overlapping wrinkles intensify. Lipid particles continue to exude and gradually aggregate, accompanied by a small number of protein particles and polymers. Figure 5g,i,k are SEM images at 500\(\times\) magnification, corresponding to treatment durations of 90 s, 120 s, and 150 s, respectively. As treatment time extends, surface etching becomes more pronounced, cracks increase, and lipid particles gradually migrate toward the cracks. Small protein particles and polymers also accumulate. The grooves and cell structures at the convex-concave intersections on the surface are increasingly covered by escaping lipid particles. These particles tend to accumulate in cell concavities and folded regions. [PERSON] et al. (2018) found that larger lipid bodies are formed by the fusion of smaller ones, and surface proteins envelop the lipid particles, resulting in continuous plasma exposure, which leads to the breakdown of starch granules, lipid bodies, protein particles, and macromolecular polymers and the exudation of lipid particles. Longer treatment duration results in greater decomposition of lipid bodies and protein particles, leading to increased lipid particle exudation. In summary, the surface etching phenomenon and oil exudation in peanut embryos are positively correlated with plasma treatment duration.  
Figure 5: _Cont._  
Figure 5: \(Cont.\)
## 4 Performance Validation
Additionally, the LSTM-IDS, RNN-IDS, PTDSAE-IDS, and DPCDBN-IDS models have demonstrated moderately closer values of TRT and TST. In line with this, the DBN-IDS model has shown a reasonable TRT of 61 s. At the same time, the AIMMFIDS model has demonstrated considerable TRT of 55 s. However, the OMLIDS-PBIoT technique has accomplished superior performance with minimal TRT of 35 s.  
At the same time, the AKNN-IDS, DT-IDS, AB-IDS, and RF-IDS, models have displayed the least performance with maximal values of TST. Following this, the LSTM-IDS, RNN-IDS, PTDSAE-IDS, and DPCDBN-IDS methodologies have illustrated moderately closer values of TST and TST. In compliance with this, the DBN-IDS method has shown reasonable TST of 34 s. Meanwhile, the AIMMFIDS model has demonstrated considerable TST of 25 s. However, the OMLIDS-PBIoT technique has accomplished superior performance with a minimum TST of 20 s. From the detailed outcomes and discussion, it is clear that the OMLIDS-PBIoT technique has resulted in more enhanced outcomes than other models. Figure 10: \(F1_{score}\) and \(accu_{y}\) analysis of OMLIDS-PBIoT technique with existing approaches.
## 1 Introduction
### Problem Definition  
Location-allocation is a long-established research problem. The overall objective of this problem is to position a certain number of facilities in optimal locations, so that they are able to serve specific demands or customers in an optimal manner. The location-allocation of emergency facilities, such as fire stations, is even more important among public facilities. Because these facilities are responsible for protecting the lives and property of people, these facilities must be in the optimal location to effectively meet demand in any region. According to National Fire Protection Association (NFPA) international standard, each fire station has a capacity of 50,000 people; that is, it has to serve up to 50,000 people [16; 17].  
If the capacity criterion for these facilities is not taken into account, too many demands may be allocated to the fire stations, which they will not be able to meet, threatening lives and property in the event of an incident. Many studies on location-allocation have not considered the capacity condition, such as [PERSON], 2014, [PERSON] et al., 2016, etc. This makes the problem space unrealistic because, in general, according to the NFPA, each station is considered for 50,000 people, and a fire station with its equipment and forces are unable to serve more than this population. So more than this population needs to create new stations, although the improper location of the stations will also disrupt their operation. Therefore, the capacity criterion is a very important one for the location-allocation model. At the same time, the fire service must be able to reach the scene of an incident in less than 5 min to be able to contain it. Hence, the purpose of the location-allocation model in this research is to minimize the arrival time of fire engines to demand sites. To this end, the present study uses the new unified VAOMP model, which can solve various location-allocation problems, to solve the specific problem of locating fire stations and with the aim of minimizing response time. Taking into account the importance of capacity in locating emergency facilities, this study also develops the VAOMP model with the added criterion of capacity.  
Location-allocation problems are NP-hard problems, so that metaheuristic methods need to be used for problem-solving to save valuable time. Thus, in this study, two algorithms are used: Tabu Search and Simulated Annealing algorithms. If there are not enough fire stations to serve the demand in an area, i.e., several demands remain without stations, then new candidate locations for stations need to be created. In this case, relocation-reallocation is performed to select the appropriate stations to serve all the demands. In a large city with a dense texture as a case study, it is necessary to check the status of its existing fire stations. If there are insufficient existing fire stations, it is necessary to create additional stations for optimal service. It is solving this problem for which location-allocation models are required.
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 3 Initiation Criterion for the Liquid Hydrazine Tank
### Minimum Power Density  
[PERSON] and [PERSON] studied the detonability of liquid hydrazine based on Walker-Wasley criterion [8], and defined the critical initiation energy \(E_{c}\) as minimum power density \(P_{dmin}\). If the shock wave pressure \(P\) produced by hypervelocity impact is equal to or greater than the C-J pressure \(P_{CJ}\) of liquid hydrazine, and the power density \(P_{d}\) exceeds minimum power density \(P_{dmin}\), a disastrous explosion will occur in the satellite tank. Otherwise, the satellite tank suffers structural damage under the effect of hydrodynamic ram, which is illustrated in Figure 1.  
For WSTF#HYP-2 test [6], SAIC test [7] and the designed TITANK test [8], calculations were carried out to determine the power density \(P_{d}\) in liquid hydrazine. Test conditions and results are summarized in Table 1. No explosion was observed in WSTF#HYP-2 test. Only some decomposition reactions were found in SAIC test. The simulation results of the designed TITANK test demonstrated that the shock wave in liquid hydrazine was sufficient to achieve an explosion. Moreover, the designed TITANK test replicates the actual tank existing on board Space Station, which is similar to the condition of satellite tank, so we are taking \(P_{dmin}=3.98\times 10^{5}\) kbar\({}^{2}\) us as the minimum power density of liquid hydrazine. It means the explosion will be triggered when the power density in liquid hydrazine exceeds \(3.98\times 10^{5}\) kbar\({}^{2}\) us.
## 3 Results and Discussion
### Combinations of TPZs with Conventional Antibiotics
To provide some evidence for the possible therapeutic application of TPZs in combination with other drugs, in this work we have conducted preliminary combined antibacterial tests for the inhibitory potencies of itrapazamine (**1**) along with its most potent derivative (**2**) in combination with ciprofloxacin (Cipro) and nitrofurantoin (NFT), which have been chosen as two of the most efficient and broadly utilized conventional antibiotics in clinical practice, particularly in the treatment of urinary tract infections ([52; 53; 54], and refs. therein). In addition, Cipro and NFT were shown to have potential as anticancer agents ([55; 56], and refs. therein). Along with representative bacterial strains, the inhibitory potencies of TPZs have been probed as sole agents and in combination with conventional antibiotics against the _S. enterica_ strain over-expressing the AcrAB-ToIC efflux pump system (one of the best examined resistance-modulation-division (RND) efflux systems), which extrudes a broad variety of structurally unrelated compounds, including different classes of antibiotics; the overproduction of the AcrAB-ToIC efflux pump system confers multiple resistance in Gram-negative bacterial strains, including _E. coli_ and _S. enterica_ serovar Typhimurium ([57; 58; 59], and refs. therein). In the current study, Cipro and NFT probed as single agents towards the _S. enterica_ strain over-expressing AcrAB-TolC exhibited half as much and four times less activity, respectively, compared to that as defined against _S. enterica_ SL 5676 strain (Table 1), while the activities of both the tested TPZs towards the _S. enterica_ strain over-expressing AcrAB-TolC were the same as those against _S. enterica_ SL 5676, suggesting that in contrast to the standard antibiotics, di-N-oxides do not seem to be substrates of the AcrAB-TolC efflux pump system. As described earlier, TPZ drug (**1**) along with the lead compound (**2**) in combination with conventional antibiotics have been quantified for their inhibitory potencies by varying concentrations of the compounds (by sequential two-fold dilution method) in the presence of the fixed sub-inhibitory concentrations of Cipro and NFT. The obtained results are reported in Table 3, along with Figure 2 representing the isobolograms plotted in terms of fractional inhibition concentration (FIC) values of the tested TPZs with Cipro and NFT used alone and in combinations. In Figure 2, the diagonal solid, dashed and dot-dashed lines represent the theoretical lines for additive (FICI \(\sim\) 1), partial synergistic (0.5 \(\leq\) FICI \(\leq\) 0.75) and total synergistic (FICI \(\leq\) 0.5) effects, respectively. As can be seen in Table 3 and Figure 2, the combined inhibitory potencies defined with respect to representative Gram-negative and Gram-positive strains fell in the range between weak partial synergistic (or nearly additive) effects (FICI of \(\sim\)0.75-0.77) and strong partial (or virtually complete) synergistic effects (FICI of \(\sim\)0.5-0.53).
###### Abstract
Based on the above design concept, the key contributions of the proposed method can be summarized as follows:  
* The schemes of the residual block, pixel shuffle, and CWAP layer are utilized in the proposed DAE for enhancing the feature extracting capability, and the results show that the proposed CPDAE, which uses fewer parameters, can achieve better denoising performance than state-of-the-art approaches. * The proposed CWAP layer between encoder and decoder not only avoids the ECG features disappearing through the deeper encoder layer, but also uses less memory than the shortcut connection. Furthermore, the key features are all averaged in the
* CWAP layer so that the number of channels is greatly reduced to one channel, and this also implies that it only takes \(1/C\) times the memory size in implementation. Figure 1: Noisy ECG signal mixed with different component ratios of BW, EM, and MA. (**a–d**) illustrate the noisy ECG with 3 dB SNR and clean ECG signal, BW, EM, and MA artifacts in the same segment. * The noisy ECG dataset obtained from NSTDB dataset is adopted to evaluate the denoising performance for various algorithms under the same conditions to ensure the same experimental environment can be completely rebuilt. * To test the generalizability of various DAE models, the other noisy ECG dataset with six noise-level inputs is generated by randomly mixing the first 30 min section of the ECG signal in NSRDB with the 30 min section of EM noise in NSTDB. The experimental results demonstrate that the proposed CPDAE has better noise suppression than other approaches. The remainder of this paper is organized as follows. Section 2 reviews the basic setup of the DAE. Section 3 describes the details of the proposed CPDAE methods. The experimental results and dataset are presented in Section 4. Finally, Section 5 concludes the paper.
# A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes
## 4 Conclusions  
Although a large number of methods have been published for countering UAV navigation system spoofing attacks, this topic is still relevant. To date, a number of successful attacks on the navigation system of UAVs have been demonstrated. Our new self-diagnosis method for analyzing and detecting system anomalies has several advantages that distinguish it from other methods.  
First of all, the method is universal and can be used for any UAV subsystems from which numerical readings can be taken. The main thing is to determine the correct type of probability distribution for the analyzed parameter. Secondly, the method is lightweight and energy efficient. The software implementation of the method has little impact on CPU load and the UAV's energy consumption. Thirdly, since the method allows analyzing any parameters, it does not matter what configuration the UAV has, and so the approach can work with whatever data is available. Fourthly, using our method, it is possible not only to detect anomalies but also to determine a change in the patterns of UAV behavior and a change in its states. If the values of entropy are not too high, and a single increase takes place, then this may indicate a change in flight mode. Correlation of the analyzed parameters can unambiguously reveal an attack and determine its type. Each attack affects a certain number of subsystems so the type of attack can be characterized by which parameters are affected. The data collected in the form of time series can be used in the future to train a neural network, which can be trained on these sets and help to decide about the presence of an attack.  
This method can be implemented and applied to detect attacks on the GPS system installed on a UAV to ensure its safe navigation. The method can be integrated into any type of UAV. In addition, the method can be used for the analysis of other sets of parameters and applied not only to UAVs, but to any cyber-physical system.  
Conceptualization, E.B. and A.B.; methodology, E.B., A.B. and A.N.; software, E.B. and M.G.; validation, E.B., A.B., A.N., C.F., J.G. and M.G.; formal analysis, E.B. and A.B.; investigation, E.B., A.B., A.N., C.F., J.G. and M.G.; resources, E.B., and A.N.; data curation, E.B., A.B., A.N. and J.G.; writing-original draft preparation, E.B., A.B., A.N. and C.F.; writing-review and editing, E.B., A.B., A.N. and C.F.; visualization, E.B., and J.G.; supervision, E.B., A.N. and C.F.; project administration, E.B. and J.G.; funding acquisition, A.B. and M.G. All authors have read and agreed to the published version of the manuscript.  
The work of Russian authors [PERSON], [PERSON], A.N. and Australian author [PERSON] was supported by the Southern Federal University. The work of Slovak author [PERSON] was supported by the Slovak Research and Development Agency under Contract APVV-18-0373. The work of Slovak author [PERSON] was supported by the Scientific Grant Agency (VEGA) under Contract 1/0584/20.  
Data sharing not applicable.  
The authors declare no conflict of interest.
## 2 Background Knowledge
### Conv-TasNet
#### 2.1.1 Encoder and Decoder  
The encoder part converts the input utterance to a series of frame-wise features, which are supposed to benefit the downstream mask-estimation network better than the original raw utterance. First, one splits the input utterance into \(K\) overlapping frames of length \(L\) in the time domain, for which each frame represented by a vector \(\mathbf{x}_{m}\in\mathbb{R}^{L\times 1}\), where \(k\) is the frame index, and \(K\) is the total number of frames. Then, we concatenate all \(\mathbf{x}_{k}\) to form a matrix \(X\in\mathbb{R}^{L\times K}\).  
The framed raw data matrix \(X\) is then transformed into a feature matrix \(W_{T}\in\mathbb{R}^{N\times K}\) by a 1-D convolution operation and is formulated as a matrix multiplication:  
\[W_{T}=H(UX), \tag{1}\]  
where \(U\in\mathbb{R}^{N\times L}\) consists of \(N\) vectors (termed encoder basis functions) each with dimension \(1\times L\), and \(H\) is an optional nonlinear function such as the rectified linear unit (ReLU) to make sure the output \(W_{T}\) is non-negative in an element-wise manner.  
In contrast, the decoder employs a 1-D transposed convolution operation to convert the enhanced feature matrix \(\tilde{W}_{T}\) (the output of the mask-estimation network) to the time-domain data matrix \(\tilde{X}\), and it can be reformulated as another matrix multiplication:  
\[\tilde{X}=V\tilde{W}_{T}, \tag{2}\]  
where \(\tilde{X}\in\mathbb{R}^{L\times K}\) is the reconstruction of \(X\), and the columns in \(V\in\mathbb{R}^{L\times N}\) are \(N\) vectors (termed decoder basis functions) each with dimension \(L\times 1\). The overlapping reconstructed frames in \(\tilde{X}\) are summed together to generate the final time-domain waveform.
## 6 Showcase Results
### Applications on a Highway  
In Figure 9, we can see the effect of the platooning application on the environmental parameters in the highway scenario. The most exciting part is fuel consumption, which does not always drop with the penetration increase as we would expect. This effect occurs for two reasons. The first is that the SUMO does not maintain the air drag of the vehicles depending on the preceding vehicle, such as decreasing the achievable advantages of the platooning application. The second reason is the limitations of the used initial model of the application. If a vehicle detects a platoon leader, it immediately accelerates. These accelerations increase fuel consumption. If the number of equipped vehicles reaches a threshold, then the platoons will decrease fuel consumption due to their effect on the smoothness of the traffic.  
Figure 9: Green gas emission and petrol consumption of highway traffic with platooning service use case.  
In Figure 10, we can see how the standard deviation of the speed develops in a highway scenario depending on the number of Road Works Warning equipped devices. The standard deviation of the speed is calculated in each second from the speed of the vehicles, and the average of these values was taken. As we can see, the standard deviation drastically decreases after a short and moderate increase. This means that the deployment of the relatively simple application version will likely increase the safety of the traffic and make it more fluent. If the application is not deployed, then the vehicles form a row behind the road works, resulting in significant speed differences among the vehicles. The slight increase, in the beginning, is due to the advantage of the equipped vehicles. In this case, the equipped vehicles are able to perform a lane change rapidly, and they can overtake the standing row. For this reason, the scenario can be considered unfair. We could also observe this from the user interface of the simulation; however, the diagrams helped us quantify the results.
## 4 Language Inclusion Checking of Timed Automata
### Timed Automata Language Inclusion Checking Algorithm with Concerned Events  
Algorithm 1 is the language inclusion checking algorithm of timed automata. There are two data structures in the algorithm, i.e., the _visiting_ stores for the nodes to be searched in the zone graph \(Zone(M\otimes N)\) and _visited_ stores for the nodes which have been searched. The initial element in _visiting_ is the initial node of \(Zone(M\otimes N)\), while _visited_ is an empty set. For the loop from line 3 to line 18, in each loop the current node is deleted from _visiting_ and then added into _visited_; if this node is the target one in the form of \((s_{m},\emptyset,\delta)\), then the algorithm returns \(false\) at line 7. Lines 9 to 17 check whether event \(e\) on the transition from the current node is an unconcerned event. If event \(e\) only belongs to the event set \(\Sigma_{m}\) but not \(\Sigma_{n}\), which means that \(e\) is an unconcerned event, then the nodes in \(succ_{1}(node,Zone(M\otimes N))\) are added into _visiting_. If event \(e\) belongs to both \(\Sigma_{m}\) and \(\Sigma_{n}\), then all the nodes in \(succ_{2}(node,Zone(M\otimes N))\) are added into _visiting_. Finally, if all the nodes have been visited without any node in the form of \((s_{m},\emptyset,\delta)\), then the algorithm returns \(true\) at line 19.  
```
1:tined automata \(M\) and \(N\)
2:verification result (\(true\) or \(false\))
3:let \(visiting:=Init\);
4:let \(visited:=\emptyset\);
5:while\(visiting\
eq\emptyset\)do
6: remove \(node:=(s_{m},X_{n},\delta)\) from _visiting_;
7: add \(node\) into _visited_;
8:if\(X_{n}=\emptyset\)then
9:return false;
10:endif
11:for all\((s_{m},e,g_{m},Y,s_{m}^{\prime})\)do
12:if\(e\in\Sigma_{m}\wedge e\
otin\Sigma_{m}\)then
13: add \(succ(node,Zone(M\otimes N))\) into _visiting_;
14:elseif\(e\in\Sigma_{m}\wedge e\in\Sigma_{n}\)then
15:for all\((s_{m}^{\prime},X_{n}^{\prime},\delta^{\prime})\in succ_{2}(node,Zone(M \otimes N))\)do
16: add \((s_{m}^{\prime},X_{n}^{\prime},\delta^{\prime})\) into _visiting_;
17:endfor
18:endif
19:endfor
20:endwhile
21:return true;
```  
**Algorithm 1** Timed Automata Language Inclusion Checking Algorithm with Concerned Events