esa-sceva/satcom-chunk-collection huggingface dataset:
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 4 DTN Routing Protocols in Disjointed Satellite Networks
### DTN Flooding-Based Techniques
In the encounter time, those messages with one hop away from their destination are delivered first while routing information will be given second priority. Additionally, all delivered messages are acknowledged by flooding acknowledgment messages, which are much smaller than the data itself, to all of the other nodes not only the source--a technique that serves to clear any other copies of the message from the buffers. MaxProp uses a hop list in the message that includes all of the encountered nodes to avoid sending the message to the same node twice, including the source node. When compared with the Dijkstra algorithm with predicated mobility, MaxProp showed better performance in achieving a lower average latency and higher delivery rates [58]. While some of these proposed techniques are favourable in satellite network scenarios while considering the periodic mobility of satellites, MaxProp still produces high overheads in a restricted resources network and path recalculation in the case of node failure is not considered. Similarly, a PRioritized EPidmic (PREP) was designed to mitigate the load on resources by assigning a scheduled priority to each message, but the transmission overheads are still excessive [60].
## 3 Materials and Methods
### Grape Inoculation and Incubation Conditions  
Grape berries were separated from bunches by cutting the stem at approximately 1 cm from each grape. The berries were sterilized according to the previously reported method [35]. Two microliters of the _A. carbonarius_ conidial suspension (\(1\times 10^{6}\) conidia/mL) were inoculated into each grape berry at a depth of approximately 2 mm below the skin using a sterile microsyringe. Six inoculated berries (50 g \(\pm\) 1 g) were placed initially in a Petri dish and then in a growth chamber under controlled temperature (25 \({}^{\circ}\)C) for seven days. Eight sampling points were considered, i.e., day 0, 1, 2, 3, 4, 5, 6, and 7, and the experiment was conducted in triplicate. Grapes that were inoculated with sterile distilled water without the addition of conidial suspension and subjected to the same experimental conditions were considered as the control (CK).
# A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid
## 2 System and Principle  
### System Configuration  
The schematic of the 2D micro-displacement measurement system based on the elliptical paraboloid is shown in Figure 1. A collimated laser beam generated from a signal-mode laser source (power 5 mW, wavelength \(\lambda\) = 650 nm, unpolarized light, laser diode) passes through the aperture and lens 1 into the polarizing beam splitter (PBS) with a reflection factor of 50%. The role of the aperture (diameter 300 \(\upmu\)m) is to control the diameter of the beam to eliminate the influence of non-measurable light and the function of lens 1 (focal length f\({}_{1}\) = 30 mm) is to converge the beam to obtain an excellent light spot image. After passing through the PBS, the laser beam, which has been converted into a linear polarized beam, travels through a quarter wave plate to the elliptical paraboloid. The function of the quarter wave plate is to convert the linear polarized beam into a circle-polarized beam. When the beam is reflected back to the quarter wave plate again, its polarized direction is rotated by 90\({}^{\circ}\) so that it can travel back to the PBS. Then, the surface of the elliptical paraboloid reflects the laser beam, whose function is similar to the plane mirror in the structure of an autocollimator. The elliptical paraboloid can realize the linear transformation of angle and displacement, so the displacement can be processed according to the principle of angle measurement. Finally, the reflected beam reaches lens 2 (focal length f\({}_{2}\) = 100 mm), acting as an objective lens and being focused by lens 2 on a CCD detector (pixels number \(2592\times 1944\), pixel size 2.2 \(\upmu\)m\(\times\)2.2 \(\upmu\)m) located on the focal plane of lens 2, which is linked to a personal computer. The displacement of the object can be calculated using the computer processing results for the displacement of the light spot. In fact, 2D displacement can be magnified so as to be measured using characteristics of the elliptical paraboloid. The magnification factor is determined by the parameter of the elliptical paraboloid and the focal length of lens 2.
## 4 Experimental Results and Discussion
### Security Test  
Watermark security is one of the important requirements of DIW schemes. Encrypted and decrypted watermark images obtained by using the proposed scheme are shown in Figure 14. Subjective analysis from Figure 14 indicates that encrypted images are very different from the original image, whereas the decrypted images are similar to the original image. To study the effectiveness of the proposed encryption and decryption scheme, Correlation Coefficient (CC) is used. CC is a commonly used statistical measure for assessing the degree of linear relation between two images. The mathematical relation for CC is shown in Equation (14):  
\[CC=\frac{\sum_{r=1}^{P}\sum_{c=1}^{Q}(W_{r,c}-\mu(W))(W_{r,c}^{1}-\mu(W^{1}))}{ \sqrt{\sum_{r=1}^{P}\sum_{c=1}^{Q}(W_{r,c}-\mu(W))}\times\sqrt{\sum_{r=1}^{P} \sum_{c=1}^{Q}(W_{r,c}^{1}-\mu(W^{1}))}} \tag{14}\]  
where W, \(W^{1}\) are the two images. \(\mu(W)\) and \(\mu(W^{1})\) are the mean values of W, \(W^{1}\) images. Two identical images have CC = 1, whereas two completely uncorrelated images have CC = 0. If the two images are completely anti-correlated, then CC = \(-\)1. The security of the proposed scheme is studied using CC in terms of Horizontal (row), Vertical (column), and Diagonal (Cross) directions. CC is examined for binary watermark images shown in Figure 14, and performance is tabulated in Table 6. CC between original-encrypted images and CC between original-decrypted images can be studied in Table 6. CC between original-encrypted images in horizontal, vertical and diagonal directions for all test images are close to zero, indicating that encrypted images are uncorrelated to the original image. From Table 6, it can be observed that CC of original-decrypted images in horizontal, vertical and diagonal are equal to 1, indicating that original and decrypted images are highly correlated and are completely the same. From this, it can be claimed that the proposed scheme generates a strong cipher image and successfully decrypts the original image. Furthermore, sensitivity of the random key has been evaluated by changing its bits. Even one bit of difference in the pseudo random key leads to unsuccessful decryption of the extracted watermark. To evaluate the random key sensitivity, CC is calculated between two encrypted images by changing one bit in random key and tabulated in Table 7. For all cases, CC in horizontal, vertical and diagonal directions is negative as in Table 7. It shows that, with one bit of change, random key forms are completely different than cipher images. From this discussion, it is evident that the random key is highly secured.
# The Chromosome Number and rDNA Loci Evolution in _Onobrychis_ (Fabaceae)
## 2 Results
### Molecular Phylogenetic Analysis of nrITS  
Molecular phylogenetic analysis of 30 accessions representing 21 diploid and eight polyploid _Onobrychis_ species was performed based on nrITS sequences. The total length of the analysed nrITS DNA regions differed among the analysed species and ranged from 491 to 593 bp. The final alignment was 626 bp long (including gaps), with 91 characters that were potentially parsimony informative. Maximum likelihood (ML) analyses of nrITS datasets assigned all analysed _Onobrychis_ species into two main clades (Figure 2). Clade II (BS99) consisted of diploid (_O. gaubae_, \(2n=2x=16\)) and polyploid species (_O. subacualis_, \(2n=4x=32\)) from subgenus _Sisyrosema_ section _Heliobrychis_ (Figure 2). Clade I comprised twenty diploids and seven polyploids from two subgenera _Sisyrosema_ and _Onobrychis_. Clade I was further divided into two subclades: (i) subclade Ia included species from subgenera _Onobrychis_ sections _Onobrychis_ and _Lophobrychis_. Among them were eight diploids (_O. megataphros_, _O. supina_, _O. alba subsp. laconica_, _O. huntilis_, _O. stenorha_, _O. gracilis_, _O. persica_ and _O. iberica_) and seven polyploids (_O. ibersteini_, _O. vicifolia_, _O. transcaucasia_, _O. arenaria_, _O. internis_, _O. cyri_, _O. altissima_) from section _Onobrychis_ and two diploids (_O. acput-galli_ and _O. crista-galli_) from section _Lophobrychis_; (ii) subclade Ib included nine diploids from subgenus _Sisyrosema_ section _Hymenobrychis_ (_O. sintenisii_, _O. vassilczanki_, _O. vaginalis_, _O. chorossanica_, _O. radita_, _O. michauxii_, _O. ptolemiaa_, _O. hpargyrea_) and one diploid from the section _Anthyllium_ (_O. grandis_; Figure 2). The polyploids in subclade Ia were included in two groups of closely related species. Interestingly, two analysed accessions of _O. vicifolia_ belonged to two different groups.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 2 Experiment  
### Participants and Equipment  
This study recruited 30 male graduate students from the School of Safety Science and Engineering at Xi'an University of Science and Technology, all with foundational knowledge in coal mine safety management. Participants, averaging 24.95 \(\pm\) 3.96 years old, had normal or corrected vision and hearing, clean scalps, and were right-handed. Due to the low accuracy rates (below 50%) of Subjects 7 and 14 in the hazard recognition task, their data were excluded, resulting in 28 effective datasets. This study received ethical approval from the Ethics Committee of Xi'an University of Science and Technology and adhered to the latest version of the Declaration of Helsinki. Written informed consent was obtained from each participant before any procedures were conducted.  
EEG signals were collected using a Neuroscan EEG system (Compumedics Neuroscan, Charlotte, NC, USA) with a 64-electrode cap placed according to the international 10-20 system (Figure 1). The reference electrode was placed at the Cz location, which is centrally located on the scalp to provide a reliable and stable signal reference. The ground electrode was positioned at the Fpz location to minimize external noise interference and ensure proper grounding of the EEG system. The recording frequency was 1000 Hz, and scalp resistance was kept below 10 k\(\Omega\). The participants acclimated to the equipment before the experiment and could stop at any time if uncomfortable.  
Figure 1: EEG equipment and electrode distribution.
## 2 Materials and Methods
### Microbial Communities' Characterisation
#### 2.5.2 Bioinformatics Analysis  
The methodology about sequence data processing used in this study is fully described in [PERSON] et al. [30] and [PERSON] et al. [31]. Raw reads were extracted from Illumina MiSeq(r) System in fastq format and quality-filtered with PRINSEQ version 0.20.4 to remove sequencing adapters, reads with less than 100 bases and trim bases with an average quality lower than Q25 in a window of 5 bases. The forward and reverse reads were merged by overlapping paired-end reads with Adapter Removal version 2.1.5 using default parameters. The filtered merged amplicons received from sequencing company (Genoinseq) in fastq format were converted into fasta format by Mothur software (mothur v.143.0; [32]) and processed by the automatic pipeline Silva Next Generation Sequencing (SILVAngs, [[https://ngs.arb-silva.de/silvangs/](https://ngs.arb-silva.de/silvangs/)]([https://ngs.arb-silva.de/silvangs/](https://ngs.arb-silva.de/silvangs/)) (accessed on 30 May 2020)) of the SILVA rRNA gene database project (SILVAngs 1.3). The taxonomic characterization of the prokaryotic community of the sediment samples was determined using SILVAngs pipeline default settings. Briefly, after quality control steps, unique sequences were clustered into operational taxonomic units (OTUs) at 98% of similarity; the classification was performed by a local nucleotide BLAST search (2.2.30+; [[https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi)]([https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi)) (accessed on 30 July 2020)) against the non-redundant version of the SILVA SSU Ref dataset (132 version) using blastn with standard setting [33]. Sequences without any BLAST hits or reads with weak BLAST hits remained unclassified and were labelled as \"no Relative\". Undesirable lineages, \"Chloroplast\", \"Mitochondria\" and \"Eukaryota\" were removed from the dataset.
# Statistical Process Control with Intelligence Based on the Deep Learning Model
## 3 Proposed Method  
This section describes the details of the proposed method. The input of the LSTM is the unprocessed data, such as quality data in the control chart and the frequency of each interval in the histogram, and the output is the category of the pattern. Feature extraction, and feature selection are all completed by LSTM through learning. Compared with unidirectional LSTM, Bi-LSTM can learn the front and back relation of sequence better.  
Figure 5 is a structural diagram of the proposed method. It has a bidirectional recursive layer as well as a multilayer structure. When the input of the model passes through a multilayer structure, the information transmitted by each layer will be represented in different dimension spaces. Therefore, data is gradually learned by increasing the number of layers of the network. The connection between input and output is improved to better describe the characteristics of the system [51]. In other words, bidirectional and multilayer recursive structure can raise the learning space and flexibility of the model [45]. Each small square in Figure 5 represents a LSTM cell.  
The network consists of multilayer Bi-LSTM and a Softmax classifier. The former is used to extract features from the raw data, and the latter is used to classify various patterns. The input of Softmax layer is the combination of the last forward LSTM output \(\mathbf{h}_{T}^{\rightarrow}\) and the last backward LSTM output \(\mathbf{h}_{T}^{\leftarrow}\), as shown in Equation (14). The initial learning rate was 0.05, and each LSTM cell has 10 neurons. The next section discusses and optimizes other structural and training parameters of multilayer Bi-LSTM, such as optimization algorithm, training batch size and network layer number.  
\[\mathbf{h}_{T}=\left[\mathbf{h}_{T}^{\rightarrow},\mathbf{h}_{T}^{\leftarrow}\right] \tag{14}\]  
Because of the end-to-end ability of deep learning method, the specific implementation process of the method proposed in this paper is as follows. Firstly, the Monte Carlo algorithm is used to simulate training set and test set of HPR and CCPR respectively. The one-hot encoding method is used to label the samples. Then, training sets are used to train two Bi-LSTMs, which are used for HPR and CCPR respectively. Finally, the performance of the optimized two Bi-LSTMs are verified by the test set and real production data.
# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 2 Preliminaries  
In this section, we introduce some fundamental concepts. Generally, the PPI network can be represented as an undirected unweighted, or weighted graph denoted by \(G=(V,E)\) where \(V\) are set of nodes denoting the proteins and \(E\) are set of edges corresponding to the interaction between pair of proteins. In our approach, we consider the PPI network to be an undirected edge-weighted graph given by \(G=(V,E,W)\), where \(W\) denotes the weight on the edge representing the confidence score in the range \((0,1]\) and function \(W:E\longrightarrow\Re^{+}\) quantifies the affinity of the interaction between each pair of nodes or proteins (_i.e._, edge mapping in \(E\)). For node \(v\), \(N(v)\) denotes the set of all neighboring nodes of \(v\). The nodes (proteins) of the PPI graph model can be classified into four major classes with respect to protein complexes (groups of two or more proteins that are physically linked together through non-covalent interactions) according to [21; 50; 51] (see also Figures 1 and 2). The first class is _core nodes_: a node is considered to be a core node in the complex if: it shows a high degree of physical interaction; it has a relatively high weighted degree of direct physical interaction among themselves within the complex and less interaction with nodes outside the complex; the set of core nodes unique in each complex. The second classification is _peripheral node_, a node is considered to be a peripheral node to a complex if: it has a close interaction with the complex core; it is stable and directly interacts with the complex core. The third classification is _overlapping nodes_; a protein is considered to be an overlapping protein to a complex if: it has a higher degree and acts as a betweenness node than the neighborhood nodes; it interacts closely to the complex core; it belongs to more than one complex. The remaining proteins are classified as _interspersed nodes_, which is probably just noise in the PPI network.  
Figure 1: A general structure of a PPI network comprised of two complexes, overlapping proteins, and peripheral and interspersed proteins.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 6 Conclusions and Future Work  
This document presents a covert communication method, using speech in speech hiding, which combines scrambling and steganography to provide high security in secret content privacy. It highlights the facts that the process is completely reversible and only depends on a key. Without knowledge of the key, users are not authorised, nor able to reveal the secret contents.  
In the proposed method, there is a close relation between the hiding capability and stego signal imperceptibility, given by the \(BH\) parameter. The higher the \(BH\), the lower the hiding capability of the method. In terms of the recovered signal in the transmitter with knowledge of a key, the method is at least 99.5% reversible. In other words, the recovered signal is perceptually identical to the original secret message. Additionally, the method guarantees that the host signal influence on the recovered signal quality is extremely low. That is, the quality of the recovered message does not depend on the used host signal.  
Regarding security, it was tested and verified that if an intruder knows the used steganography method and has access to the transmitted stego signal, but does not know the key, the extracted information from the stego signal has non-legible contents, and differs from the original secret message in a great way.  
As future work, the authors of this paper propose the following themes:  
* Measuring the resistance of the stego signal against signal manipulation attacks like MP3 (Moving Picture Experts Group Layer-3 Audio) compression, additive noise and filtering.  
Figure 9: Correlation coefficient trust ranges between the recovered message without having access to the key/password and the original signal, varying the host signal.  
* Propose alternative ways to obtain the super-Gaussian signal to be imitated by the secret message.
* Explore alternative ways of dynamic LSB substitution.  
**Author Contributions:** Conceptualization, D.M.B.; Formal analysis, D.R.; Investigation, D.R.; Methodology, D.M.B.; Software, D.M.B.; Validation, D.R.; Writing--original draft, D.M.B.; Writing--review & editing, D.R.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Mobility Models in FANETs
### Random Mobility Models
#### 2.1.3 Random Direction  
As a result of the increased possibility of moving to a new location near the simulated area's center, the Random Directions (RD) [41] model was designed to deal with concentration of nodes on the central area of the RWP mobility model. RD chooses a destination place on the simulation area's boundaries with every mobile node. Once it is on the boundaries, it stops and chooses a new random destination location again. The FANETs mobility pattern in RD is shown in Figure 6. Even in RD, we have the same unexpected motion feature, similar to that of RW and RWP. RD mobility model was implemented in FANETs in [42]; however, the RD model was shown to exhibit unrealistic movement characteristics similar to those of RWP.  
Figure 5: The trajectory of FANETs using RWP models.  
Figure 6: The trajectory of FANETs using RD models.
## 4 MSCT Topology
### MSCT Topology Control  
In this section, we explain how the MSCT topology based on the algorithm above is implemented in a real ZigBee sensor network. When the network is created, the only node to be connected is the coordinator. In order to add nodes, the network proceeds as follows:  
* When a node sends an association-request, the coordinator adds the node to the network (assigning the PAN id), but not to the topology (no address assigned yet). Thus, during a time \(\tau\), the coordinator does not create the topology, but only receives association-requests from nodes to store them in a table.
* All nodes added in the network calculate the weight of connections with their neighbors and send this information to the coordinator.
* After \(\tau\) time has passed, the coordinator constructs a map containing all nodes that joined the network, as well as their neighborhood tables. Thus, it creates the topology following Algorithm 1 steps.  
Figure 5: MST construction illustration: Part 1.  
Figure 6: MST construction illustration: Part 2.  
* After the construction of the topology, the coordinator defines a period of time \(\tau^{\prime}\) where the association-requests are received and stored in order to face any change of topology. This process allows the saving of energy by reducing the computational cost. This control process allows the reduction of the energy consumption in the network because of:
* The algorithms for the topology construction and control are executed at the coordinator node. The energy of the sensor nodes is saved for sensing, transmitting and routing tasks.
* The total cost of transmissions in the network is minimal.
* Topology maintenance is executed periodically, contrary to cluster-tree or mesh, where it is calculated after any change in topology.  
To better illustrate the topology control process, Algorithm 2 shows the operations performed by the coordinator. To simplify this algorithm, we assume that all association-requests are accepted by the coordinator.  
```
1:Coordinator joins the network
2:while t < \(\tau\)do
3: Coordinator receives requests
4:endwhile
5:Store nodes positions and links in Nodes Table
6:Use Algorithm 1 to construct MSCT
7:while t - \(\tau<\tau^{\prime}\)do
8: Coordinator receives requests and data
9:if Data Received then
10: Send data to user's computer
11:elseif Request Received then
12: Store request in Standby Table
13:elseif A node leaves the topology then
14: Remove node from Nodes Table
15: Exit While
16:endif
17: Update \(\tau^{\prime}\) value
18:endwhile
19:if Stanby Table contains nodes then
20: Join Standby Table to Nodes Table
21: Go to line 6
22:endif
23:if Node removed from topology then
24: Restart algorithm
25:endif
```  
**Algorithm 2** Network Operations at the Coordinator