esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow-Induced Noise Characteristics Analysis of a Pipeline Structure in a Cabin Rigid Corner
## 2 Flow Excitation Equation for Piping System  
### Pipeline Fluctuation Pressure  
Discussion of the internal flow functions of the pipeline is necessary to obtain the turbulent pulsating pressure equation of the pipeline structure system. In the calculation of turbulent pulse pressure, the transient turbulent pulse motion decomposes into two parts according to the large eddy simulation method (LES): the Navier-Stokes equation for the resolvable scale (which can be solved directly to obtain the large-scale motion), and the subgrid scale model (which uses the filtering method to simulate the impact of small-scale motion on large-scale motion).  
The LES method is implemented for parametric filtering of the motion in the flow field and the parametric analysis and the filtering process of the flow field is denoted by the equation:  
\[\widetilde{f}(x_{i},t)=\int\limits_{D}G(x_{i},x_{i}^{\prime},\overset{\rightarrow }{\Delta})f(x_{i}^{\prime},t)\text{d}x_{i}^{\prime}, \tag{1}\]  
where \(f=\widetilde{f}+f^{\prime\prime}\), \(\widetilde{f}\) is the resolvable large-scale quantity; \(f^{\prime\prime}\) is the intractable fluctuation quantity, and \(f\) is the subgrid fraction. \(D\) is the computational domain of the flow field,and \(G\) is the filter function. \(x_{i}^{\prime}\) and \(x_{i}\) are the pre-and post-filtering vectors, respectively. According to the box filter theory, the filter function can be written as follows:  
\[G(x_{i},x_{i}^{\prime},\overset{\rightarrow}{\Delta})=\left\{\begin{array}{l} \frac{1}{\Delta x_{1}\Delta x_{2}\Delta x_{3}},\ |x_{t}^{\prime}-x_{i}|\leq\frac{\Delta x_{i}}{2}\ i=1,2,3\\ 0,\ |x_{t}^{\prime}-x_{i}|\leq\frac{\Delta x_{i}}{2}\ i=1\ \text{or}\,2,\,3 \end{array}\right. \tag{2}\]  
Applying the box filter to the Navier-Stokes equation (N-S equation), and the filtered N-S equation is obtained:  
\[\left\{\begin{array}{l}\frac{\partial\widetilde{u}_{i}}{\partial x_{i}}=0\\ \frac{\partial\widetilde{u}_{i}}{\partial t}+\frac{\partial\widetilde{u}_{i} \widetilde{u}_{j}}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial\widetilde{p} }{\partial x_{i}}+\
u\frac{\partial^{2}\widetilde{u}_{i}}{\partial x_{j} \partial x_{j}}+\frac{\partial^{\tau LES}}{\partial x_{j}}\\ \frac{\partial^{\tau LES}}{\tau_{ij}^{LES}}=-\overline{u}_{i}^{\prime\prime} \,u_{j}^{\prime}\end{array}\right. \tag{3}\]  
where \(-\overline{u}_{i}^{\prime\prime}\,u_{j}^{\prime\prime}\) is the subgrid stress parameter, which can be expressed as \(-\overline{u}_{i}^{\prime\prime}\,u_{j}^{\prime\prime}=v_{SGS}\left(\frac{ \partial\widetilde{u}_{i}}{\partial x_{j}}+\frac{\partial\widetilde{u}_{j}}{ \partial x_{j}}\right)\), and \(v_{SGS}\) is the vortex viscosity coefficient in the Smagorinsky vortex viscosity model [2;4].
Therefore, to compensate the PAPR reduction performance degradation, the proposed scheme introduces phase rotation factors before the injection of dither signals. These phase rotation factors are helpful to reduce PAPR. Such hybrid system can select one signal with the minimum PAPR for transmission from a number of candidates with different PAPR thanks to the introduction of phase rotation factors. Moreover, in order to detect the index of the phase rotation factor selected by the transmitted signal, in this paper, a detection method is also proposed based on energy detection. It is shown by extensive simulation results that the proposed PAPR reduction method can obtain better error performance and energy efficiency than that of the previous dither signal-based works. Furthermore, by utilizing the phase rotation factor, the proposed PAPR reduction scheme can also achieve a superior PAPR reduction performance than previous schemes. Conclusively, the contributions of this study can be summarized and highlighted as follows:* This paper proposes a novel PAPR reduction method based on dither signals. The proposed method injects optimized constant amplitude signals on partial inactive sub-carriers. Comparisons show that utilizing partial idle sub-carriers can achieve better error performance than that of the conventional dither signal-based schemes. * In order to solve the PAPR reduction performance degradation caused by the insufficient use of idle sub-carriers, the proposed PAPR reduction scheme introduces phase rotation operations. Simulation results show that compared with the conventional works, the proposed hybrid scheme can achieve better performance in terms of both PAPR and BER. * This paper proposes an energy-based detection method in order to recover the transmitted signal. The proposed detection method is able to correctly detect the index of the transmitted phased rotation factor. Simulation results show that the decoding performance, which utilizes the proposed detection method is very close to the case where the side information is correctly received. Therefore, without sending side information, the spectral efficiency of OFDM-IM is higher than that of the previous works. The remaining part of this paper is organized as follows: Section 2 provides the preliminary knowledge, including OFDM-IM, PAPR, and the conventional PAPR reduction with dither signals as a background introduction that is required for the following part of this paper. The proposed hybrid PAPR reduction scheme and the corresponding analysis are given in Section 3. Simulation results are provided in Section 4, and the conclusion part is given in Section 5.
## 2 Literature Review
### High-Order Community Detection in Aviation  
A high-order connection usually refers to a sub-network, which is a graph within a larger graph, whose vertex set is a subset of the vertex set, and edge set is a subset of the edge set. A complete subgraph in the network is also called a clique. It is a common network topology class and one of the basic concepts in the mathematical area of graph theory. It requires every pair of distinct nodes to be connected by a unique edge in a simple undirected graph or a pair of unique edges in each direction [28]. For instance, a three-node clique denotes a triangle, while a four-node clique represents a quadrilateral with two diagonal lines.  
Typically, the clique structures in the airline network represent a group of highly connected destinations with overall better connectivity. In general, airline hubs or bases obtain the highest connectivity within the country and to the hubs of other corresponding airlines [29]. Consequently, cliques exist more commonly among hub airports. In return, the cliques in the airline network also imply the market potential and strategic position of those distinct destinations. In this sense, proper measurements for those cliques could help an airline to facilitate its core market and adjust its strategy when necessary.  
Subsequently, clique-based methods have become popular for airlines to quantify the contributions of their highly connected destinations. For instance, [PERSON] et al. have displayed the characteristics of three-node cliques in a multi-layer airline network [29]. They highlighted that the merging of different airline networks generates a large density of triangles, which represent the three-node cliques in the graph. They not only noticed the opportunity brought by the self-connections between major airlines and low-cost carriers, but also believed that one airline can hardly provide round trips for all the flights in those triangles. In fact, the ever-growing codeshare partnerships allow an airline to market its partners' products and services, and maximize the profits on more routes, without investing any additional capacity. Since the interline tickets are commonly bonded with codeshare agreements, it is not appropriate to merge airline networks randomly. Instead, the impacts of the aggregating codeshare networks need to be further examined.  
Furthermore, the existing literature generally did not consider the patterns of the high-order connections, such as communities formed by cliques. Although [PERSON] et al. tried to tackle this problem by proposing a higher-order multi-layer community detection method [25], their approach is motif-driven rather than clique-driven. Therefore, quantitatively characterizing the clique communities becomes essential to shed light on the high-order structures in the network.
# Insights into Membrane Damage by \(\alpha\)-Helical and \(\beta\)-Sheet Peptides
## 2 Materials and Methods  
### Design of \(\alpha\)-Helical Peptides  
This work was inspired by two 8-residue peptides made from the permutation of 4 phenylalanine (F), 2 lysines (K), and 2 glutamic acids (E). These peptides, labeled M01 (Ac-FKFFEFKFE-NH\({}_{2}\)) [20,29] and M02 (Ac-FFKKFFEE-NH\({}_{2}\)) [28], segregate nonpolar and charged side chains to diametrically opposite sides when folded in a \(\beta\)-sheet and \(\alpha\)-helix, respectively. Consequently, they exhibit large hydrophobic moments [47] (0.83 and 0.7, respectively) when folded in these secondary structures. Recently, we have shown that both peptides adsorb on the surface of a 7:3 POPC-POPG lipid membrane [20,28]. Moreover, M01 peptides spontaneously self-assemble into large \(\beta\)-sheets that become inserted into the dry core of the lipid bilayer forming a barrel-like structure with water in its interior. In contrast, M02 peptides remain dispersed on the membrane surface, without inserting themselves into it. This difference in behavior may be rationalized by the length of the hydrophobic face of the \(\beta\)-strand and the \(\alpha\)-helix, which is 2 nm and 1 nm, respectively--Figure 1A,B. As a result, the hydrophobic face of the \(\beta\)-strand (but not of the \(\alpha\)-helix) can span more than one leaflet of the lipid bilayer.  
Here, we also design two peptides that have twice the number of amino acids (16 residues) as M02 and can segregate charged and nonpolar residues to opposite directions when folded in a \(\alpha\)-helix. Accordingly, the distance spanned by nonpolar residues  
Figure 1: Secondary structures and characteristic lengths of peptides (**A**) M01, (**B**) M02, (**C**) M03, and (**D**) M04. Lysine (K, blue) and glutamic acid (E, red) represent positive and negative charges, respectively. In the helical wheels of M02 and M04, a clear boundary separates non-polar phenylalanine (F, grey) from charged residues, unlike in M03. The end-to-end distances of the peptides are shown. (**E**) Molecular structure of the lipid bilayer with its average thickness of 3.64 nm.  
along the main axis of the \(\alpha\)-helix is twice that of the M02 peptides, and similar to that in M01 peptides (\(\sim\)2 nm)--see Figure 1. The M03 peptide is composed of two consecutive M02 sequences (Ac-(FFKKFFEE)\({}_{2}\)-NH\({}_{2}\)). This sequence pattern does not optically allow a sharp partition of polar and nonpolar residues at the water-lipid interface (Figure 1C), and results in a hydrophobic moment of only 0.53. The M04 peptide (Ac-FFKKFFEEKKFFEE-NH\({}_{2}\)) is a correction to this deficiency, whereby one phenylalanine from the middle of the M03 sequence is shifted to the C-terminus. This gives the M04 sequence a hydrophobic moment of 0.87, where charged and nonpolar residues are sharply partitioned to opposite faces of the \(\alpha\)-helix.
# Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
###### Abstract  
Previous studies have attempted to find autonomic differences of the cardiac system between the congestive heart failure (CHF) disease and healthy groups using a variety of algorithms of pattern recognition. By comparing previous literature, we have found that there are two shortcomings: (1) Previous studies have focused on improving the accuracy of models, but the number of features used has mostly exceeded 10, leading to poor generalization performance; (2) Previous works rarely distinguish the severity levels of CHF disease. In order to make up for these two shortcomings, we proposed two models: model A was used for distinguishing CHF patients from the normal people; model B was used for diagnosing the four severity levels of CHF disease. Based on long-term heart rate variability (HRV) (40000 intervals-8h) signals, we extracted linear and non-linear features from the inter-beat-interval (IBI) series. After that, the sequence forward selection algorithm (SFS) reduced the feature dimension. Finally, models with the best performance were selected through the leave-one-subject-out validation. For a total of 113 samples of the dataset, we applied the support vector machine classifier and five HRV features for CHF discrimination and obtained an accuracy of 97.35%. For a total of 41 samples of the dataset, we applied k-nearest-neighbor (\(K=1\)) classifier and four HRV features for diagnosing four severity levels of CHF disease and got an accuracy of 87.80%. The contribution in this work was to use the fewer features to optimize our models by the leave-one-subject-out validation. The relatively good generalization performance of our models indicated their value in clinical application.  
CHF; pattern recognition; HRV; SFS; leave-one-subject-out validation 2019 9 2544 10.3390/appr9122544
## 4 Results
### SEM Analysis  
The evaluation of the structural model exhibited a good fit, indicating that the hypothesised relationships among the PEU, PU, and BI aligned with the data (\(\chi^{2}/\mathrm{df}\) = 1.067; CFI = 0.992; TLI = 0.989; RMSEA = 0.028; SRMR = 0.056). The model explained a large proportion of variance, as measured by the squared multiple correlations (R\({}^{2}\)), in both PU (71.8%) and BI (55.4%), suggesting moderate-to-high predictive accuracy for the constructs under investigation [96].  
PU emerged as a key determinant of BI, exerting a positive and statistically significant impact (\(\beta\) = 0.443, \(p<0.001\)), thus confirming H1. The direct effect of PEU on BI was weak and insignificant (\(\beta\) = 0.064, \(p\) = 0.610), consistent with the expectation stated in H2. However, PEU demonstrated a statistically significant positive direct effect on PU (\(\beta\) = 0.535, \(p<0.001\)), thus supporting H3. The mediation pathway from PEU to BI via PU and the total effect of PEU on BI were positive and statistically significant (\(\beta\) = 0.375, \(p<0.001\) and \(\beta\) = 0.443, \(p<0.001\), respectively). The results support H4, revealing that the influence of PEU on BI was entirely channelled through PU (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Latent Construct** & **Items** & **SFL** & \(\alpha\) & **AVE** & **CR** & **Model Fit Indices and HTMT** \\ \hline \multirow{4}{*}{Perceived Ease of Use (PEU)} & PEU 1 & 0.619 & & & & \\  & PEU 2 & 0.649 & & & & \(\chi^{2}(51)=54.326\), \(p=0.349\) \\  & PEU 3 & 0.838 & 0.790 & 0.504 & 0.793 & \(\chi^{2}/\mathrm{df}\) = 1.065 \\  & PEU 4 & 0.683 & & & & CFI = 0.992 \\ \hline \multirow{4}{*}{Perceived Usefulness (PU)} & PU 1 & 0.531 & & & & & TLI = 0.990 \\  & PU 2 & 0.830 & & & & & RMSE = 0.028 \\  & PU 3 & 0.879 & 0.855 & 0.552 & 0.872 & SRMR = 0.052 \\  & PU 4 & 0.709 & & & & HTMT \\  & PU 5 & 0.821 & & & & PEU-PU = 0.474 \\ \hline \multirow{2}{*}{Behavioural Intention (BI)} & BI 1 & 0.705 & & & & PEU-BI = 0.435 \\  & BI 2 & 0.757 & 0.759 & 0.521 & 0.762 & PLB-BI = 0.742 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confirmatory Factor Analysis and model fit.
## 2 Materials and Methods
### Field Evaluation  
The field experiments were conducted under commercial growing conditions in 2013 and 2014. We evaluated the 'field-high' dispensers, described above, from Alpha Scents as a repellent for _D. citri_. Both experiments were conducted in a sweet orange grove in Winter Garden, FL (40 km north from Lake Alfred where the release tests of the dispensers were conducted). The experiment was deployed in a 12 \(\times\) 30 tree block of 4-year-old Valencia sweet orange reset trees. Five blocks of 12 (4 m tall \(\times\) 3 m across) trees were treated with fir oil (\(n=5\)) or negative controls (empty dispensers) (\(n=5\)). Treatments were assigned randomly to each block. Four 'field-high' fir oil dispensers or blank controls were hung within the canopy of each treated tree in 2013. These blocks were arranged as two vertical rows of trees with two row buffers (16 m) separating blocks. No insecticides were applied to the entire grove during the experiment. The grove was surrounded by resets of the same age and variety on the north and east side, while fallow fields were to the south and west. _D. citri_ were counted by tapping citrus branches using a 40 cm section of plastic pipe three times to dislodge insects onto a white laminated 21.6 \(\times\) 28 cm paper surface for counting. Two tap counts were taken per tree from each plot. Tap counts of adult _D. citri_ were conducted the day before the fir oil treatment, and weekly thereafter.  
The second field experiment was conducted in 2014 identically to that described above; however, the number of fir oil dispensers applied per tree was doubled in an effort to improve treatment efficacy. In this case, eight 'field-high' release devices were attached per tree and this was compared against a blank negative control (8 empty vials per tree). The location of the experiment, experimental design, and sampling protocol were identical to that described for 2013.  
Weather data were obtained from the Florida Automated Weather Network (FAWN) stations located approximately 1 km from the experimental sites in Lake Alfred and Winter Garden, respectively.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 4 ITGA Design
### Crossover and Mutation Operations  
The process of selecting two populations as parent populations using the TS method and performing crossover operations with crossover probability \(\text{r}_{\text{c}}\) is as follows: Firstly, two parent chromosomes are selected from each of the two parent populations. Secondly, two target points are randomly chosen, and the sequences between these two target points in the two parent chromosomes are swapped. Through a mapping approach, duplicate chromosomal genes are avoided, ensuring that the newly generated offspring chromosomes also adhere to the encoding rules. Finally, the two generated offspring chromosomes are added to the offspring population. This crossover operation is repeated for the parent populations until the offspring population is fully generated. The crossover operation process is illustrated in Figure 7.  
In addition, due to the potential for excessively long search times required for unrestricted exchange sequence lengths, it is stipulated that the length of the exchange sequence must be less than 10% of the chromosome length.  
After obtaining the offspring population through crossover operations, mutation operations are performed on the offspring population according to the mutation probability. In this paper, the swap operator is used for chromosome mutation. The operation steps are as follows: two gene positions, P1 and P2, are randomly selected on the chosen chromosome,  
Figure 7: Crossover operation. Different colors indicate chromosomal position changes.  
and the genes corresponding to these positions are swapped to obtain a new chromosome. The operation process is shown in Figure 8.
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
###### Abstract  
In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches.  
deep auto-encoders; parallel structure; structural damage identification +
Footnote †: journal:
## 6 System Development
### Client-Side Implementation  
A Single-Page Client Application has the well-defined purpose of handling UI updates, managing the client-side state of the application, and handling the long-lived connections.  
It is worth mentioning the implemented local-state organization paradigm. By analyzing the flow of data in the application, it is easy to grasp the unidirectional nature of the user interaction, e.g., a user edits a resource, the event gets broadcasted and, in the end, the results are received and applied to the local state. This type of predictable local application state is easy to maintain test and handle. For this work, Pinia, an open-source library was used to handle the state changes [68].  
Pinia works on the so-called Redux model, where a piece of data is updated using actions [69]. Actions are small atomic events that trigger the creation of a new state starting from the previous one and altering the data based on the desired business rules.  
Based on this model, the client-side state was defined in multiple low-level stores (entities of data): auth, documents, notifications, and document session stores. The most complex piece of the client-side state is the document session storage because it handles actions emitted by the current user and the peers joined in the communication session. Generally, it should act as a domain layer, combining multiple data sources and object types into a single model required by the user interface.  
Due to the strategy of having backup instances available and the stateless capability of the server-side system, a connection can be re-established without a long waiting time. The stateless nature of the system enables a \"zero\" latency in terms of message processing time because the instance that picks up the new connection does not need to load any in-memory state. To further increase availability, a message queue can be added to store the un-delivered events, and a processing logic that will re-send the messages when a connection is available. This step was not included in this work due to the fast enough recovery time but certainly represents a valuable addition in the area of redundancy and disaster recovery.  
The client-side application created for this work acts mainly as a presentation layer for the server-side exposed interfaces, but it also has the task of handling the in-application user state as notifications, user sessions, document collaboration sessions, disaster-recovery, and CRDT state.