esa-sceva/satcom-chunk-collection huggingface dataset:
# Delamination Effect on the Buckling Behaviour of Carbon-Epoxy Composite Typical Aeronautical Panels
## 5 Conclusions  
In this manuscript, a sensitivity analysis was carried out on a composite delaminated aeronautical panel by varying different geometrical parameters: delamination radius, delamination depth, skin and stringer thickness and bay width. The main conclusions can be summarised as follows:  
1. The depth of the delamination influences the local buckling and damage propagation but does not affect the stiffness of the panel or the overall compressive behaviour.
2. The lower the radius of the initial delamination, the higher the propagation velocity, even if the triggering occurs later.
3. If the delamination initiation load is close to the overall buckling load of the panel, no damage propagation is observed.
4. By increasing the thickness of the panel and the stringers by 1.32 mm, corresponding to eight layers (quasi-isotropic, symmetrical and balanced layup sequence), the stiffness of the panel significantly changes, including damage propagation.
5. With the same geometric parameters, by reducing the width of the bay, the overall stiffness of the panel will not vary significantly, while the propagation of the damage will be completely different.  
The achieved results permit, in the frame of a damage tolerance design approach, one to preliminary assess the damage behaviour of the panels, according to predefined load conditions, and select the geometric parameters and stacking sequences according to the demands.  
Figure 16: Delaminated area vs. load and load vs. displacement charts. Comparison against CD4, CD8 and CD16 configurations.  
Figure 15: Delaminated area vs. load and load vs. displacement charts. Comparison against CD2, CD6 and CD15 configurations.  
**Author Contributions:** Conceptualisation, A.R. ([PERSON]); methodology, A.R. ([PERSON]) and A.R. ([PERSON]); formal analysis, R.C., A.R. ([PERSON]) and C.P.; writing--A.R. ([PERSON]), R.C. and C.P.; writing--review and editing. [PERSON]. ([PERSON]); supervision, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This material is based upon research supported by the Office of Naval Research under Award Number N62909-20-1-2042, and was supervised by [PERSON] (Office of Naval Research) and [PERSON] (Office of Naval Research Global--Tokyo).  
Not applicable.  
Data Availability Statement: Data available on request due to restrictions eg privacy or ethical.  
Authors would like to thank the National Research Council of Italy, and in particular [PERSON] from the Institute of Polymers, Composites and Biomaterials, for the support in the experimental test to characterize the composite material.  
The authors declare no conflict of interest.
## 4 System Design
### Optimized Routing Paths
```
1 Input hyper parameters of neural network, discount factor \(\gamma\)
2 Initialize experience replay memory \(RD\)  Initialize action-value function \(Q\) with random weights \(\theta\)  Initialize target action-value function \(\widetilde{Q}\) with weights \(\widetilde{\theta}=\theta\)  Initialize network route list \(P\)  Initialize complete network route 2d list \(LP\)
3 For1: \(i\) do (\((i=\ number\ of\ source\ nodes)\)
4 For episodes = 1:100 do
5 With the probability select random action \(a_{t}\), otherwise select \(a_{t}=argmax_{a}Q(s_{t},a;\ \theta)\)  Execution action \(a_{t}\), get reward \(r_{t}\) and next state \(s^{\prime}_{t}\) then update the network state
6 Store the experience \((s_{t},\ a_{t},r_{t},s^{\prime}_{t})\) to \(RD\)  Sample random mini batch perform \((s_{j},\ a_{j},r_{j},s^{\prime}_{j})\) from in \(RD\)
7 If episode terminates at step, \(j+1\) then  Set target \(y_{j}=\ r_{j}\)  Else  Set target \(y_{j}=\ r_{j}+\gamma max_{\widetilde{Q}}\Big{(}s^{\prime}_{j},\ a^{\prime};\ \widetilde{ \theta}\Big{)}\) EndIf  Perform a gradient descent step on learning rate and \(\Big{(}y_{j}-\ \widetilde{Q}\Big{(}s^{\prime},a^{\prime};\ \widetilde{ \theta}\Big{)}\Big{)}^{2}\) with \(\theta\)  Every \(C\) steps reset \(\widetilde{Q}=Q\) EndFor  Append the last route path to list \(P\)  List \(P\) append to list \(LP\)
9 Initialize all variable except \(LP\) (\(RD,\ Q,\ \widetilde{Q},\ P_{t},a_{t},\ r_{t},\ y_{t},\ \ldots\)) EndFor
10 Output: Complete network route path \(LP\) from all source node to destination node
```  
**Algorithm 1** DQN-based network routing in Erasure Coding network topology  
When a routing route from one source node to the destination node is completed, it is stored in a P-list as well as in a 2d LP list composed of dual lists (step 8). In addition, because another route from the source node to the destination node must be produced, the remaining variables are initialized except for the LP list that stores the final optimized route (step 9). At the end of every iteration, the LP list stores the entire routing path from each source node to the destination node, and thus in the erasure coding network topology, each node transfers the blocks required for decoding to the destination node according to the path (step 10).
## 2 Results
### Generation of AfsakA Deletion and Complementation Mutant Strains  
To study the role of _AfsakA_ gene in _A. flavus_, we constructed the knockout (\(\Delta\)_AfsakA_) and complementation mutant (\(\Delta\)_AfsakA::AfsakA_) strains by homologous recombination strategy (Figure 2A). To construct _AfsakA_ deletion strain, the ORF of _AfsakA_ gene was replaced by _pyrG_ gene. Whereas, in order to confirm the role of _AfsakA_ gene and to insure that _AfsakA_ was fully functional, we created _AfsakA_ complementation strain. Thus, we assembled complementation vector (pPTR-_AfsakA_) and transferred it into \(\Delta\)_AfsakA_ protoplast. The transformants were first proved by both PCR (Figure 2B) and the result showed that both AP and BP fragments were missing in the WT but present in \(\Delta\)_AfsakA_ and in \(\Delta\)_AfsakA::AfsakA_ (Figure 2B), indicating that _A. fumigatus pyrG_ had replaced ORF of _AfsakA_. The positive mutant strains were further confirmed by RT-PCR and the result in Figure 2C demonstrated that _AfsakA_ gene was present both in the WT and in \(\Delta\)_AfsakA::AfsakA_ strains but it was absent in \(\Delta\)_AfsakA_.  
Figure 1: Bioinformatics analysis of SakA protein from 13 different fungi (**A**) Phylogenetic analysis of SakA protein from different fungi such as _A. flavus_, _A. oryzae_, _A. arachidola_, _A. nomius_, _A. kawachii_, _A. niger_, _A. campetris_, _A. candidus_, _S. lignohabitans_ (_Sugiyamaella lignohabitans_), _A. clavatus_ and _A. fumigatus_. The phylogenetic tree was created from different SakA aligned protein sequences from all the above listed fungi. Neighbor joining and bootstrap methods with 1000 replications were used to generate this phylogenetic tree. (**B**) Conserved domain analysis of the fungi. The gray color stands for the non-conserved part, the red color corresponds to protein kinase domain and the scale symbolizes the protein length.  
Further results of qRT-PCR showed that _AfsakA_ could not be expressed in \(\Delta\)_AfsakA_ (Figure 2D). Hence, all these results revealed that \(\Delta\)_AfsakA_ strain and \(\Delta\)_AfsakA_::AfsakA_ strain were successfully constructed.
# Numerical Study on Scale Effect of Repetitive Plate-Loading Test
## 3 Numerical Analysis  
### Analysis Software and Modeling  
In this study, PLAXIS 3D, a universal finite-element analysis software program, was used for the numerical simulation of the repetitive plate-loading test. For the subgrade modeling, the Mohr-Coulomb elasto-plastic model, which can most accurately express the behavior of soil, was used. The elastic modulus (\(E\)) and Poisson's ratio (\(v\)) were used to express the elastic characteristics, whereas the internal friction angle (\(\varphi\)), cohesion (\(c\)), and dilatancy angle (\(\psi\)) were used as the input properties to express the plastic characteristics. This is a homogeneous and isotropic continuum model that excludes the effect of particle size on the soil material. Meanwhile, for the modeling of the loading plate, the plate element, which can simulate a thin two-dimensional structure with flexural rigidity, was used, and very high stiffness was set as the input property so that the displacement generated in the modeled loading plate itself by the applied load could not affect the analysis results.  
Regarding the soil models to numerically analyze the loading plate scale effect of the repetitive plate-loading test, we used a total of six models based on the loading plate diameter. Among them, Figure 2a shows the representative model, with a diameter of 762 mm. As shown in Figure 2b,c, all conditions, excluding the diameter of the loading plate and the surrounding mesh size, are the same in each model. The size of the numerical model was determined by considering the range of influence  
Figure 1: Study summary and flowchart.  
of the 762-mm loading plate. A range of influence of five times the diameter was considered for the x- and y-axis directions, and twice the diameter for the z-axis direction. Based on this, we selected a numerical model size of \(8\) m \(\times\) \(8\) m \(\times\) \(2\) m. PLAXIS 3D automatically generated meshes, and the size of the mesh varied depending on the size of the relative element size. A modulus of 1.0, which was the default value, was used for mesh generation, and the subgrade was set as a single layer with one property. Moreover, for the boundary conditions of the numerical analysis, the displacement was constrained in the horizontal direction at each side boundary (x and y directions) and in the vertical direction at the bottom boundary (z direction), and the upper surface represents the earth surface with a free-surface-boundary condition.
# Effects of a Dual-Task Paradigm and Gait Velocity on Dynamic Gait Stability during Stair Descent
###### Abstract  
Falls during stair negotiation have become one of the leading causes of accidental death. The effects of a concurrent cognitive or manual dual-task paradigm on dynamic gait stability remain uncertain. How much dynamic gait stability is influenced by gait velocity is also not clear. A total of 16 healthy young females descended a staircase under three different walking conditions: descend stairs only (single task), descend stairs while performing subtraction (cognitive dual-task), and descend stairs while carrying a glass of water (manual dual-task). An eight-camera Vicon motion analysis system and a Kistler force plate embedded into the third step of the staircase were used synchronously to collect kinematic and kinetic data. Gait velocity decreased and dynamic gait stability increased with both cognitive and manual dual-task conditions. The center of mass-center of pressure inclination angle increased with gait velocity but decreased with the manual dual-task condition compared to the single-task condition. Changes in gait velocity caused by the dual-task paradigm can partially explain the effects of dual-task dynamic gait stability. The influence of gait velocity should be considered in the assessment of dual-task effects.  
manual task; cognitive task; dynamic balance; posture control; stair negotiation Article
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 7 Simulations and Experiments  
A CubeSat-based NTN-mMTC system contains orbit topology design, network design, and DCS equipment design. A series of simulations and experiments are implemented to verify the design. The verification consists of two parts: constellation simulations and equipment simulations.
# CFD Simulation of Dry Pressure Drop in a Cross-Flow Rotating Packed Bed
## 4 Results and Discussion
### Cavity Zone A  
#### 4.2.1 Effect of Rotating Speed  
The rotating speed has significant effects on the pressure drop in cavity zone A, as shown in Figure 5. Figure 5a shows the pressure drop at the gas flow rate of 800 m\({}^{3}\cdot\)h\({}^{-1}\) decreases gently from 62.5 Pa to 38 Pa with the increase of rotating speed from 200 rpm to 1000 rpm, which is attributed to the gas reflux from the packing area to the cavity zone A. Figure 5b shows the effect of rotating speed on static pressure drop. Clearly, at a gas flow rate of 200 m\({}^{3}\cdot\)h\({}^{-1}\) and rotating speed below 400 rpm, the static pressure drop increases slowly from 2.5 Pa to 3 Pa and then more rapidly from 6 Pa to 20 Pa. A part of gas static energy is converted to internal energy and the rest is converted to dynamic pressure energy. At a gas flow rate of 800 m\({}^{3}\cdot\)h\({}^{-1}\), the static pressure drop decreases from \(-\)2.5 Pa to \(-\)6 Pa, which may be due to the packing 'block', low gas velocity and conversion of dynamic pressure energy to static pressure energy. In order to learn more about pressure drop, plane a\({}_{2}\) (Figure 1b) is added. The static pressure drop between a\({}_{2}\) and a\({}_{3}\) changes greatly because the cross section is expanded suddenly at the inlet and thus a part of the static pressure energy is converted to internal energy and kinetic energy. While the static pressure drop between a\({}_{1}\) and a\({}_{2}\) shows no significant change at the gas flow rate of 200 m\({}^{3}\cdot\)h\({}^{-1}\), it increases slightly at the gas flow rate of 800 m\({}^{3}\cdot\)h\({}^{-1}\). Figure 5c shows the effect of rotating speed on the dynamic pressure drop. It is seen that the dynamic pressure drop is kept at about 65 Pa at rotating speeds below 800 rpm but decreases rapidly from 64 Pa to 44 Pa when the rotating speed is above 800 rpm. The gas at the lower part of the packing zone is driven to the cavity zone A by centrifugal force, which leads to an increase of gas kinetic energy.
## 4 The Considered Nodes' Synchronization and Data Transmission Protocol
### Synchronization Protocol
In particular, node \(1\) sets \(R_{s}=0\) and, upon activating, transmits a SYNCH packet towards the second node using the overhearing and LPL functionalities described before. The SYNCH packet contains three control fields, namely, the elapsed time (\(ET\)), the number of packet to be forwarded (\(FP\)), and the _short_ reactivation time to forward the data packets (\(D_{short}\)). \(ET\) is a counter reporting the number of SYNCH packets transmitted in the chain and it is initialized to zero by node \(1\). Denoting by \(T_{p}\) the fixed duration of a packet (including overhearing), \(ET\) can be expressed in terms of number of slots of duration \(T_{p}\). Then, upon receiving the SYNCH packet from node \(1\), node \(2\) increases the value of \(ET\) by one, and starts transmitting the SYNCH packet towards node \(3\). \(ET\) is then increased by one at every unsuccessful packet transmission. This mechanism is repeated until the SYNCH packet is received by node \(S\). We denote by \(ET(s)\) the \(ET\) value seen by node \(s\). Note that the knowledge of \(ET\) allows each node to know the exact time the SYNCH operation has been terminated by node \(1\), and hence to re-synchronize its wake-up schedule with node \(1\). \(FP\) is a counter indicating the number of packets that each node will have to forward in the DATA period. Hence, in the transmission chain from node \(1\) to node \(S\), \(FP\) is increased by one by each node having a packet to transmit. Accordingly, the \(FP\) value in the SYNCH packet received by node \(s\), say it \(FP(s)\), contains the number of packets it will have to forward during the data transmission operation. As for the scheduled activation times \(R_{s}\), in the absence of synchronization errors the natural choice would be to set \(R_{s}=0\), for \(s=2\), and \(R_{s}=(s-2)(T_{p}-T_{OH})\), for \(s>2\), \(T_{OH}\) being the overhearing period. Indeed, in this case node \(2\) activates at time \(0\) and can receive the SYNCH packet from node \(1\), node \(3\) activates at time \(T_{p}-T_{OH}\) and receives the SYNCH packet from node \(2\), and so on down to node \(S\). Instead, in the presence of synchronization errors this is not the optimal choice, as it will be shown in the sequel. Figure 6 sketches the flow of the SYNCH packets for the first \(6\) nodes following node \(1\), underlining the increase in the FP and ET values with the progress of the synchronization. In the Figure the wake-up times \(r_{s}\) and the correspondent different nodes' modality, namely, idle listening, SYNCH and data packets reception (RX), SYNCH packet transmission (SYNCH TX) and overhearing are reported.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 4 Simulation
Correspondingly, the BER for the CB-Gardner is much less than \(10^{-4}\) when \(-28\),000 \(\leq PPM\leq 20\),000. The use of the CB-Gardner significantly enhanced the ability of the communication system against Doppler. Based on Figure 8, it can be seen that the BER of the system experiences a sharp increase when the \(PPM\) exceeds a certain value, irrespective of whether the Gardner or CB-Gardner algorithm is employed. The result indicates that the stability of the communication system undergoes an abrupt change instead of a smooth change as the \(PPM\) increases. Additionally, the results showed that the system can resist a greater Doppler when \(PPM<0\) in comparison with \(PPM>0\) for both Gardner and CB-Gardner algorithm. This could be attributed to the smaller jitter of the Doppler factor estimated by the NCO of the algorithm when \(PPM<0\), with the current loop filter parameters used in the simulation. We conducted further simulations to verify the performance of CB-Gardner on SNR. The \(PPM\) was set as \(-\)2000, and the SNR of the passband signal ranged from \(-\)10 dB to 50 dB with an interval of 5 dB. Figure 9 shows the BER variation curves for different SNRs when using the Gardner and CB-Gardner algorithms for BPSK- and QPSK-modulated signals. Figure 9 demonstrates that the BER using the Gardner algorithm for BPSK signals is less than \(10^{-6}\) when SNR \(>\) 10 dB. In contrast, when using CB-Gardner, the BER for BPSK signals is less than \(10^{-6}\) when SNR \(\geq\) 20 dB. However, for QPSK signals, the BER remains greater than \(10^{-2}\) when SNR \(\geq\) 50 dB. The simulation results indicate that CB-Gardner is  
Figure 8: BER for different \(PPM\) with Gardner and CB-Gardner algorithm, SNR = 25 dB. currently only suitable for BPSK signals, and the required SNR is less than 10 dB larger than that of the Gardner. This is because the error-detection points of the Gardner algorithm are located in the symbol envelope, whereas the error-detection point of the CB-Gardner algorithm are located within one carrier cycle. As a result, it can be inferred that the error detection in the CB-Gardner is more susceptible to noise than that of the Gardner. Although the error-detection equation (12) of the CB-Gardner can theoretically be used for QPSK, the BER is relatively high. Thus, the CB-Gardner is currently less applicable to QPSK signals.
# Advances in the Biosynthesis and Molecular Evolution of Steroidal Saponins in Plants
## 5 Distribution of Steroidal Saponins in Plants  
Steroidal saponins are synthesized and distributed in different plants. According to the current research, they are mainly distributed in monocots, such as the Dioscoreaceae (_Dioscorea_ species), Melanthiaceae (_Paris_ species), Smilacaceae (_Smilax_ species), Asparagaceae (_Asparagus_ species), Poaceae (_Avena sativa_), and Arecaceae (_Phoenix dactylifera_) [1; 3]. Steroidal saponins are also distributed in a few eudicots, such as the Solanaceae (_Solanum_ species) and Fabaceae (fenugreek) [1; 3].  
However, the composition of steroidal saponins in different plants varies greatly. For example, the main saponins that accumulate in _Dioscorea_ are the five-ring furostand saponins (parviloside, protodioscin, and protobioside) and the six-ring spirostand saponins (dioscin and gracillin). In addition to the accumulation of the above two steroidal saponins, four-ring pregnane saponins (such as parisyunnanoside J) and six-ring polyhydroxylatedsaponins (such as parisyunnanosides G/H/I and padelaoside B) accumulate in Paris plants [8; 70] (Figure 1, Supplementary Table S1). In addition, the organs or tissues in which steroidal saponins accumulate in different plants are also different; for example, _Dioscorea_ plants accumulate steroidal saponins in underground rhizomes (_D. zingigherensis_) or tubers (_D. composita_) and fenugreek accumulates them in its seeds [70; 71; 72; 73].
# A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion
## 2 Geometry Dictionary Construction and Fusion Scheme
### Dictionary Learning Analysis
It uses PCA classification to reduce the redundancy in dictionary and enhance the performance. There are many popular dictionary learning methods like KSVD. However, this paper cannot use other dictionary learning methods to substitute PCA to do dictionary learning. It compares PCA with KSVD to explain the benefits of using PCA in dictionary learning. For sparse representation-based image reconstruction and fusion techniques, it is difficult to select a good over-complete dictionary. To obtain an adaptive dictionary, [PERSON] [46] developed KSVD that learned an over-complete dictionary by using a set of training images or updates a dictionary adaptively by applying SVD operations. The image patches used by KSVD in dictionary learning were extracted either from globally trained dictionary (natural images) or adaptively trained dictionary (input images). Although KSVD shows better performance in dictionary learning than other methods, it takes high costs to do dictionary learning with a large number of training images iteratively. The high computational complexity constrains the learned dictionary size of KSVD in practical usage. [PERSON] [38] firstly applied clustering-based dictionary learning solution to image fusion. It clustered patches from different source images together based on local structural similarities. Only a few principal components of each cluster were analyzed and used to construct corresponding sub-dictionary. All learned sub-dictionaries were combined to form a compact and informative dictionary that can describe local structures of all source images effectively. The redundancies of dictionary (i.e., reducing the size of dictionary) were eliminated to reduce the computation loads of the proposed PCA-based dictionary learning solution. In various comparison experimentations, the proposed PCA-based dictionary learning solution (JPDL) had better performance than other traditional multi-scale transform-based and sparse representation-based methods, including KSVD. Comparing with KSVD, JPDL as a PCA-based method not only obtained a more compact dictionary, but also took less computational costs in dictionary learning process.