esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model  
This section introduces the demand response management for the SG network model. This network model comprises two entities: a SG device and an UC, as shown in Figure 1. A SG device collects electricity data and provides efficient power management services. An UC manages monitoring data, including electricity consumption, load forecasting, demand response, real-time pricing, etc. The UC collects these data and estimates a total electricity capacity of a SG device in the power grid. However, as SG devices are deployed within the SG fields, the recording and transmission of power consumption data may cause serious privacy issues. A SG device usually sends sensitive power consumption reports via communication channel in the SG environments. A malicious adversary can intercept such reports to invade the privacy of users. For instance, it is easy to notice that inhabitants are at home or not by checking the power usage. In addition, privacy-sensitive data, such as usage of appliances, can be released to adversaries [26; 27]. Consequently, privacy of users could be violated and sensitive data of users could be used for criminal purposes. Therefore, privacy-preserving authentication protocol in the SG environments should be supported.  
Figure 2 introduces the authentication process of the proposed scheme in the SG environments to provide user privacy, including daily routines and electricity consumption habits. The proposed scheme comprises three parties: trust authority (TA), SG device, and UC. The SG device and the UC first register their identities to TA, and then TA issues credential information for the SG device and the UC. After that, the SG device and the UC perform mutual authentication. After authentication, the SG device and the UC use the session key to exchange power consumption reports and feedbacks, and so on. Consequently, they can communicate safely through the secure channel established by the session key. The meaning of the communication session involves identifying devices in the network and authorizing what each device should carry out in the network. The maintenance of communication session in the proposed scheme may change monthly or yearly, depending on security requirements.  
Figure 1: Network model for smart grid environments.  
Figure 2: Authentication process of the proposed scheme in smart grid environments.
## 5 Simulation Experiments
### Result of Leak Point Localization Experiment  
In this part, we use signal mode signals extracted from the original leak signals as the processing objects. According to the process of leak point localization algorithm in Section 4.2, five sensor nodes send their signal preprocessing results to the sink.  
Then the sink compares the average amplitudes of single mode signals and divides the signals into two groups \(S_{1}\) and \(S_{2}\). \(S_{1}=\{\)1,2,3\(\}\) and \(S_{2}=\{\)4,5\(\}\), where signals in group \(S_{1}\) come from the sensor nodes on the left of the leak point, while signals in group \(S_{2}\) are from the sensor nodes on the right of the leak point. According to the Equation (18), \(N_{P}=6\). The six pairs of signals are (1,4), (1,5), (2,4), (2,5), (3,4) and (3,5), respectively. The cross-correlation results of above signal pairs are shown in Figure 13.  
It can be seen from the Figure 13 that the cross-correlation function of each pair of signals has obvious maximum value. According to the cross-correlation TDOA principle, the position and error of leak point calculated by each pair of signal is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline BPA & \(A_{1}\) & \(A_{2}\) & \(A_{3}\) & \(\Theta\) \\ \hline \(m_{1,2}\) & 0.2742 & 0.4040 & 0.2742 & 0.0476 \\ \(m_{1,2,3}\) & 0.2667 & 0.4469 & 0.2667 & 0.0197 \\ \(m_{1,2,3,4}\) & 0.2601 & 0.4296 & 0.3012 & 0.0091 \\ \(m_{1,2,3,4,5}\) & 0.2467 & 0.4638 & 0.2851 & 0.0044 \\ \hline \end{tabular}
\end{table}
Table 3: Result of decision making.  
Figure 13: Correlation result of each pair of signals.  
In light of the node distance and the value of \(N_{P}\), we obtain \(\mu_{1}=0.11\), \(\mu_{2}=0.09\), \(\mu_{3}=0.17\), \(\mu_{4}=0.11\), \(\mu_{5}=0.34\) and \(\mu_{6}=0.17\). After weight average combination, the final coordinate of the leak point is:  
\[P_{leak}=\sum_{j=1}^{N_{e}}\mu_{1}\cdot P_{j}=11.79 \tag{24}\]  
Accordingly, the absolute error is 0.21 m and the relative error is 1.75%. It is thus clear that weighted average localization method can effectively enhance position accuracy.
## 3 System Architecture and Assumptions
### Attack Model  
UASNs are susceptible to a variety of malicious threat attacks. The capabilities of the malicious nodes discussed in this paper align with the Dolev-Yao model [24] and can be modeled as the following attack models (malicious nodes that only perform eavesdropping attacks are not considered in this paper, as the attacker performs eavesdropping attacks without active attack behavior and cannot be easily monitored) [25]:  
* DDoS attack: Distributed denial of service attack. During operation, the malicious node continuously sends meaningless data traffic to neighboring nodes, preventing them from properly functioning. Malicious node b keeps sending virtual packets to neighboring nodes, as shown in Figure 2a.
* Selective forwarding attack: The malicious node selectively forward packets that pass through it, dropping some of them. As shown in Figure 2b, malicious node b drops packets received from neighboring node c.
* Data pollution attack: The malicious node can carry out data pollution attacks by means such as tampering with data packets, injecting false information, or corrupting data. As shown in Figure 2c, malicious node c transmits altered fake packets to node b, which transmits the packets to cluster head a as it should, and cluster head a receives the altered fake packets from node b.
* On-off attack: The attackers continuously toggle between normal and malicious behaviors to obfuscate and evade detection. The malicious node takes measures to conceal its malicious activities to avoid detection by other nodes within the network.
# Lung Field Segmentation in Chest X-rays:
## 2 Materials and Methods
### Minimizing the Irrelevant Lung Area  
The solution so far roughly demarcates the actual lung field. However, in many cases the lung contour intensity range was not directly separable from that of other anatomical structures in the image, as the last row of Figure 2 demonstrates. It was experimentally proven that in most cases the separation was possible as a postprocessing step. In the example in the last row of Figure 2, which is repeated in Figure 3a, there is a clear indication of the separation point between the two contours. We are particularly interested in the points that violate the convexity property of the initial contour, that is, the points where the shape varies with angles greater than 180 degrees. Specifically, first the convex hull of the contour is determined, which is the minimum fitting convex boundary around it. Afterwards, as Figure 3b demonstrates, the convexity defects are isolated. Each of those is represented by the indices of three points in the original contour: the points where the defect starts and ends, and the point that is located farthest away from the two aforementioned points. The distance metric \(\mathbf{d}\) implemented in OpenCV [27] is calculated as follows: in the 2D image space, given the start and end points \(\overset{\rightarrow}{\mathbf{s}}\) and \(\overset{\rightarrow}{\mathbf{e}}\) and their integer indices \(\mathbf{s},\mathbf{e}\) in the contour, the distance of a contour point \(\overset{\rightarrow}{\mathbf{p}}\) with index \(\mathbf{p}\in[\mathbf{s},\mathbf{e}]\) is:  
\[\mathbf{d}=\left|det(\overset{\rightarrow}{d_{0}},\overset{\rightarrow}{d_{p}} )\right|\text{-}C \tag{1}\]  
with \(\overset{\rightarrow}{\mathbf{d_{0}}}=\overset{\rightarrow}{\mathbf{e}}-\overset{ \rightarrow}{\mathbf{s}},\overset{\rightarrow}{\mathbf{d_{p}}}=\overset{ \rightarrow}{\mathbf{p}}-\overset{\rightarrow}{\mathbf{s}}\) and \(\mathbf{C}=\left\{ \begin{array}{l}\mathbf{0},\text{ \emph{if }}\overset{\rightarrow}{d_{0}}=\overset{ \rightarrow}{0}\\ \frac{1}{|\overset{\rightarrow}{d_{0}}|},\text{ \emph{otherwise}}\end{array} \right.\), a scaling factor. Figure 2: Issues after initial segmentation. When the various contours are well separated, the distribution of rib cage points within them can hint at the valid lung contour. For the last row, the algorithm has undesirably identified a region below the arm, connected to the actual lung region. The separation of undesirable areas succeeds, as described in the next section. From now on, we avoid the vector representation of the 2D points, for simplicity in the notation of the mathematical formulas. Useful prior knowledge for the modification of the contour is the density of the points that probably belong to the rib cage, as presented in Figure 3b. Having these constraints in mind, minor convexity defects, referring to the ones where all three points are very close together, can be fixed by simply filling up their area. A more interesting analysis should be made for the points that very loosely (including their neighborhood) connect two otherwise distinct contours.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 4 System Model  
### Low-Orbit Constellation Network  
The low Earth orbit (LEO) satellite network consists of LEO satellites, satellite terminals, and gateway stations, as shown in Figure 1. The network comprises satellites, terminals, and gateway stations. The network element equipment includes on-board routing and switching payload equipment, mobile satellite terminals, broadband satellite terminals, and protocol networks at the gateway station. The LEO constellation primarily consists of multiple communication-enabled satellites, interconnected through intra-constellation and inter-constellation links, strategically deployed across different orbital planes to achieve seamless global coverage. Additionally, satellite payloads with routing and switching capabilities are deployed onboard the LEO satellites to facilitate the routing, switching, and forwarding of data within and between satellites. Satellite terminals, categorized as mobile and broadband terminals, enable user data access, protocol conversion, and satellite-to-ground user routing. Gateway stations serve as the core hubs and management control centers of the LEO satellite constellation, responsible for network formation control, information exchange, and distribution.
# Associating Images with Sentences Using Recurrent Canonical Correlation Analysis
## 4 Experimental Results
### Analysis of the Number of Time Steps  
For a sequential sentence, the number of time steps in conventional LSTM-RNN is naturally the length of the sentence. While for a static image, we have to manually set the number of time steps \(T\). Ideally, \(T\) should roughly equal to the number of salient objects appeared in the image, so that the model can separately attend to these objects within \(T\) steps to collect all information. In the following, we gradually increase \(T\) from 1 to 9, and testify the impact of different numbers of time steps on the performance of RCCA-ac and RCCA-nc. From Table 4 we can observe that both RCCA-ac and RCCA-nc achieve their best performance when the number of time steps is 3. It indicates that they can get all the important information in an image by iteratively visiting the image three times. Intuitively, most images usually contain at most three objects, which is in consistent with the optimal number of time steps. Note that when \(T\) becomes larger than 3, the performance of RCCA-ac and RCCA-nc slightly drops, which results from the fact that an overly complex network architecture could lead to overfitting. When comparing RCCA-ac with RCCA-nc, we find that the usage of context information can greatly improve the performance in all cases, regardless of the number of time steps. Especially for image annotation, with the aid of context information, RCCA-ac seems to exploit more time steps to attend to more contents of the image so that it can produce more accurate results. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Image Annotation**} & \multicolumn{3}{c}{**Image Retrieval**} & \multirow{2}{*}{**Sum**} \\ \cline{2-2} \cline{4-9}  & **R@1** & **R@5** & **R@10** & **Med \(r\)** & **R@1** & **R@5** & **R@10** & **Med \(r\)** & \\ \hline
**RCCA-ac** & & & & & & & & & \\ \(T=1\) & 42.5 & 74.9 & 86.0 & 2 & 32.1 & 67.7 & 81.4 & 3 & 384.6 \\ \(T=3\) & **44.9** & **79.6** & **87.7** & 2 & **35.8** & **71.2** & **83.3** & 2 & **402.5** \\ \(T=5\) & 44.0 & 78.0 & 86.8 & 2 & 35.4 & 71.0 & 83.1 & 2 & 398.3 \\ \(T=7\) & 44.1 & 76.9 & 86.7 & 2 & 35.5 & 71.1 & 83.2 & 2 & 397.5 \\ \(T=9\) & 43.2 & 76.5 & 87.1 & 2 & 34.7 & 71.0 & 83.1 & 2 & 395.6 \\ \hline
**RCCA-nc** & & & & & & & & & \\ \(T=1\) & **38.5** & 70.1 & 80.9 & 2 & 28.0 & 63.2 & 76.1 & 3 & 356.8 \\ \(T=3\) & 37.4 & **70.3** & **81.5** & 2 & **29.7** & **65.3** & **79.8** & 3 & **364.0** \\ \(T=5\) & 34.9 & 67.1 & 78.9 & 3 & 28.8 & 64.5 & 77.5 & 3 & 351.7 \\ \(T=7\) & 36.2 & 65.6 & 77.9 & 3 & 28.9 & 64.0 & 77.2 & 3 & 350.9 \\ \(T=9\) & 32.6 & 64.7 & 76.6 & 3 & 27.1 & 62.6 & 75.7 & 3 & 339.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different numbers of time steps on the Microsoft COCO dataset. \(T\): number of time steps in contextual attention-based LSTM-RNN.
## 4 Conclusions  
Pervious concrete modified by crumb rubber with different rubber particle sizes and incorporation levels were investigated in the lab. Permeability, compressive strength, flexural strength, flexural strain, and freeze-thaw cycle tests were conducted. The influences of rubber content and particle size on pervious concrete properties were analyzed. Based on the test results, the following conclusions can be drawn.  
* The permeability of rubber-modified pervious concrete is lower than that of control pervious concrete. The permeability decreases slightly with the increasing rubber content. Rubber particle size has little effect on permeability.
* The addition of rubber has a negative impact on the compressive strength of pervious concrete. Compressive strength decreases with the increase of rubber incorporation level. Although the addition of rubber reduces the compressive strength of the pervious concrete, it can still meet the requirements of non-structural use. Fine crumb rubber is more advantageous than coarse crumb rubber in modifying pervious concrete without sacrificing excessively compressive strength.
* The influence of rubber content on flexural strength is the same as on compressive strength. There is an obvious linear relationship between the compressive strength and flexural strength of rubber-modified pervious concrete. The addition of rubber enhances the flexural strain of pervious concrete. Flexural strain increases with the increasing rubber incorporation level. Fine crumb rubber significantly improves ductility without severely compromising the compressive strength, contrary to coarse crumb rubber.
* Freeze-thaw resistance of pervious concrete can be improved by the addition of crumb rubber. Rubber-modified pervious concrete has a better freeze-thaw resistance than that of control pervious concrete when subjected to the same freeze-thaw cycles. A high rubber incorporation level generates a high freeze-thaw resistance. Fine crumb rubber is superior to coarse crumb rubber in improving the freeze-thaw resistance of pervious concrete.  
Conceptualization: [PERSON]; investigation: [PERSON] and Y.G.; writing (original draf): [PERSON], [PERSON], and [PERSON]; writing (review and editing): [PERSON].  
This research was funded by the Science Technology Development Program of Jilin Province (grant number 3 DC18E022417) and the Transportation Science and Technology Program of Jilin Province (grant number 2018-1-9).  
Figure 11: Compressive strength loss ratio of control pervious concrete (CPC) and RMPC under freeze–thaw cycles.  
**Acknowledgments:** The authors would like to appreciate anonymous reviewers for their constructive suggestions and comments to improve the quality of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Simulation and Analysis  
The formation of SG is closely related to some imaging parameters, and studies have shown that SG is very sensitive to SVA [9; 10]. In this study, the ASTER data are used as the basic reference. By  
Figure 2: Error transfer simulation model.  
adjusting some key parameters, different sensor parameters and different environmental parameters were simulated. The influence of SVA in the range (-90\({}^{\circ}\), 90\({}^{\circ}\)) and in steps of 0.5\({}^{\circ}\) on the SG radiation transmission was analyzed, and effect of SVA combinations on inversion results of SSR was investigated by simulating the distribution of the relative error (\(\Delta\sigma_{0}^{2}\)) under different SVA combinations. According to the simulation results under most conditions, in the distribution of error transfer, only SVA in the range [-50\({}^{\circ}\), 50\({}^{\circ}\)] were displayed. In actual remote-sensing images, in order to ensure the accuracy of SSR estimation, the SG radiation should be at least not less than the sum of Rayleigh scattering and aerosol scattering. According to [PERSON] et al. [11], the normalized SG radiance threshold is 0.004. When the normalized SG radiance exceeds this threshold, it can be used for SSR estimation. In order to analyze the applicability of SVA combinations under different conditions, we separately counted the number of SVA combinations \(n_{10}\) and \(n_{20}\) under the conditions of \(\Delta\sigma_{0}^{2}<10\%\) and \(\Delta\sigma_{0}^{2}<20\%\). The total number \(n\) of SVA combinations in the simulation range was: \((180\times 2-1)\times(180\times 2-1)=128,881\). Then, the applicable probabilities of SVA combinations, named as \(\rho_{10}\) and \(\rho_{20}\), were obtained by calculating the ratios between \(n_{10}\) and \(n\), and \(n_{20}\) and \(n\). With higher probability, the SSR estimation model is more effective, and thus, model parameters should be adjusted to increase this possibility. The general settings of the parameters used in the simulation are shown in Table 1. In order to explore the influence of imaging geometric parameters and different environments on SSR inversion results, we mainly simulated changes in four key parameters: pointing angle (\(P\)), sea surface wind speed (\(W\)), sun azimuth angle (\(\phi_{0}\)), and sun zenith angle (\(\theta_{0}\)). Then, we analyzed the optimal SVA combinations under different parameter changes.
## 3 Results
### Series SRP127667  
In order to further test the generality of the proposed method, we applied it on RNAseq count table data, which follow different distributions than gene expression and methylation data. The application of rank statistics on RNAseq count data is novel within the Bioinformatics field. Nevertheless, in the field of Astrophysics RPs have been successfully used in pipelines performing occultation [57] and gravitational wave [58] event verification. These \"discovery enumeration\" phenomena are Poisson point processes, similar to the discovery-based formation of RNAseq count tables from RNA reads. Given that the requirements of RP hold for the count tables except for the independence of variance (which has been shown to affect statistical threshold selection [59], which we do not perform), we extend the verification testing of our non-parametric methodology to RNAseq data, aiming to assess the specificity of the method, despite the biological and computational problems generated by RNAseq data as opposed to transcriptomics. Additional analysis of our data using EdgeR verified the relevance of entropic ranks results. We compared the gene counts obtained from RNA sequencing of cardiac myocytes from 10 adult patients with terminal heart failure to three control samples from the BioProject study SRP127667.  
EnrichR was used to map the differentially expressed genes to Panther 2016 [60], Jensen DISEASES and Reactome. Panther 2016 highlighted as the first term by combined ranking the Wnt signaling pathway, which has been implicated in cardiovascular syndromes [61]. Jensen DISEASES terms ranked first by combined score were \"hypertension\", \"coronary artery disease\" and \"cerebrovascular disease\". Reactome highlighted as the second term by combined ranking the Ca2+ pathway. Using BioInfoMiner to map the list onto Gene Ontology and Human Phenotype ontology showed terms related to thrombosis abnormalities, tyrosine phosphorylation of Stat3 protein, and the regulation of body fluid levels through the urinary system--the last of which is a known regulator mechanism of blood pressure (Figure 4). Lastly, the highly connected genes identified through BioInfoMiner are specifically associated with pharmaceuticals prescribed for cardiovascular conditions.  
Figure 4: SRP127667: differentially expressed genes mapped onto Gene Ontology using BioInfoMiner.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 4 Channel Access
### Frame Structure  
The frame structure employed for this swarm of AUVs is depicted in Figure 7. The information contained at each field is explained hereafter.  
* **Synchro**: This field contains the synchronization signal, that is the first OFDM symbol. This initial signal is useful as well for the detection.
* **@Dest**: This is the identification of the message recipient (destinatary). Notice that each AUV is assigned with a distinct identification number. There is also an identification number for the case the message is designated for all users.
* **@Src**: This field carries the source identification, i.e., the AUV that is currently transmitting information.
* **Control**: It is a control field, which can be used for many purposes. As an example, it might contain the congestion status of the local queue [19].
* **Frame Number**: This field contains the frame number.
* **Hour**: It contains the transmission time: hour, minutes, seconds, and milliseconds.
* **Information Data**: This field carriers the information data. For example, it might contain the information about the AUV position.
* **FCS**: This is a space reserved for a sequence that is added for detecting possible frame error transmission.