esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
One of the issues is related to the effective calibration of the equivalent beam mechanical and dynamical parameters. For example, the equivalent 3D Timoshenko beam model calibration proposed in [8] was based on the availability of a detailed tall building FE model. A possible drawback of this approach is that the detailed numerical modeling of complex tall buildings can be a time-consuming activity. On the other hand, the approach first proposed by [PERSON] and [PERSON] in [11] is appealing, since the equivalent beam parameters are directly obtained by the stiffness features of each of the different horizontal load-resisting sub-structures. The drawback of this method is that the equivalent beam parameters are different for each considered mode shape. Dynamic analysis by modal superposition is therefore dependent on the load frequency content and preliminary estimation of the number of modes to be considered is required. Furthermore, direct time integration of the dynamic system equations is not possible. This paper proposes a novel two-step calibration procedure to merge the advantages of each of the two approaches described above and to overtake their drawbacks. First, the equivalent sandwich beam parameters of a tall building are evaluated with the approach proposed in [11] (e-SBM) for a chosen number of mode shapes, avoiding the time consuming detailed FE modeling. Second, the equivalent Timoshenko beam model (e-TBM) described in [8] is calibrated using a suitable optimization procedure aimed to target the e-SBM beam response in terms of modal natural frequencies and static displacements. The obtained e-TBM has the advantage of having a closed-form solution of all the natural frequencies and mode shapes that can be effectively used to estimate the response to dynamic random loads by modal superposition and/or direct time integration. The structure of the paper is as follows. First, the e-TBM and the e-SBM beam main features are briefly recalled and the optimization-based calibration strategy is described. Second, a case study is used to demonstrate the proposed novel approach effectiveness.
## 1 Introduction
[12] employed a space-filtering technique to quantify the inhomogeneity of the turbulence and the spectral energy flux. [PERSON] et al. [13] proposed an extended variational mode decomposition (VMD) method to discuss the multiscale characteristics of multi-component turbulence. [PERSON] et al. [14] studied the global and local properties of energy exchange and cascade in plasmas turbulence using the direct numerical simulations (DNS) model. It's worth noting that most of these aforementioned methods utilize the simulated turbulence data rather than the data observed in the actual deep-sea fields. Although some efforts have made to reveal the mechanism of turbulent energy transfer, there is no mechanistic framework that helps us understand how turbulent flows cascade and transfer. To quantitatively characterize the process of the turbulent dissipated energy among a wide range of turbulent scales and further examine the different physical processes occurring on these different time scales, an integrated spatio-temporal synchronous quantitative analysis method is innovatively proposed to exhibit the properties in energy transfer and the cascade process. Observed turbulence shear data using an autonomous VRP instrument are utilized to reveal the mechanism of kinetic energy cascade and the interactions of multiscale energy dissipation in an oceanic system. The rest of this article is arranged as follows. The actual field experiment of an autonomous VRP deployed on the mooring system is briefly described in Section 2. In Section 3, an integrated method combined with the VMD decomposition, the multiscalewavelet transform, and a local intermittency measure is proposed to capture the spatio-temporal intermittency features of energy transfer in turbulent flows. The preliminary results are presented in Section 4, including the time-frequency and wavenumber spectrum features, the temporal features of energy cascade and intermittency, and the spatial features of dissipation rates. Subsequently, some conclusions and future work are finally given in Section 5.
###### Abstract  
Existing deep learning-based spatiotemporal fusion (STF) methods for remote sensing imagery often focus exclusively on capturing temporal changes or enhancing spatial details while failing to fully leverage spectral information from coarse images. To address these limitations, we propose a Bidirectional Cross Spatiotemporal Fusion Network with Spectral Restoration (BCSR-STF). The network integrates temporal and spatial information using a Bidirectional Cross Fusion (BCF) module and restores spectral fidelity through a Global Spectral Restoration and Feature Enhancement (GSRFE) module, which combines Adaptive Instance Normalization and spatial attention mechanisms. Additionally, a Progressive Spatiotemporal Feature Fusion and Restoration (PSTFR) module employs multi-scale iterative optimization to enhance the interaction between high- and low-level features. Experiments on three datasets demonstrate the superiority of BCSR-STF, achieving significant improvements in capturing seasonal variations and handling abrupt land cover changes compared to state-of-the-art methods.  
s +
Footnote †: journal: Applied sciences  
article  
capitotemporal fusion; remote sensing; time direction and scale direction; spectral restoration
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 1 Introduction
* Compared to the single node cooperation mode which is widely used in the current cooperative MAC protocols, we consider the node cooperation mode with multiple optimal cooperative nodes. The number of cooperative nodes taking part in the node cooperation can be changed in this protocol according to the actual optimization results of the proposed LETPO algorithm, thereby taking full advantage of the multiple best cooperative nodes in the network to achieve better effect than the single node cooperation mode. * We propose a cooperative node selection mechanism to guarantee multiple cooperative nodes to access wireless channel orderly according to their channel gains, which can avoid contention collisions and reduce extra cooperation overhead. According to the contention results, it can also optimize the transmission powers of the cooperative nodes to balance the energy consumption of the local network, which further transfers the cooperative gain and energy utility improved by the LETPO algorithm from the PHY layer to the MAC layer. The remainder of the paper is organized as follows. In Section 2, we introduce the related work of current cooperative MAC protocols. In Section 3, the network model used in this paper is described. The proposed transmission power optimization algorithm is given in Section 4, and the PO-CMAC protocol is presented in Section 5, followed by performance evaluation in Section 6. Conclusions are summarized in Section 7.
## 4 Discussion
### Influence of the Thickness of the Back Wall on the Frequency Spectrum  
When reducing the thickness of the back wall of the pipe, the sound pressure level is preserved, when using both spruce and maple wood. The number of higher harmonic components within one wood species does not change. A similar result was obtained by [2], who also found that the wood species does not seem to have any significant effect on the sound quality of the organ pipe. However, the sound quality differs with the thickness of the pipe wall. The walls of the pipe can vibrate under the influence of internal acoustic air pressures, and thus can affect the quality of the tone. This will happen when the playback frequency or one of its harmonics approaches the vibration frequency of the pipe wall. [PERSON] and [PERSON] [6] demonstrated that the vibrating back wall reduce the fundamental frequency, which was also confirmed by our measurements in the case of back wall thicknesses of 2 mm, 1 mm and 0.5 mm for the species of wood investigated.  
The decrease in the fundamental frequency with the thinning of the back wall of the pipe can also be explained by the end correction. This means that the distance where the sound pressure of the pipe equalizes with the surrounding barometric pressure shifts outward from the pipe, increasing the wavelength of the oscillating air column and lowering the fundamental frequency. If the walls of the pipe are thick enough or made of wood with a higher density, the influence of the vibrations of the pipe walls on the sound is minimal; it can only be detected by acoustic measurements, not subjectively.  
The results of this study are in line with the results of similar research which was carried out on an experimental pipe with a different scale [13].
## 3 Results and Discussion
### Assessment of a Modelling Approach with a Quasi-3D Description of the Junction  
In order to explore the additional potential offered by the staggered mesh method regarding the approximate solution of the three-dimensional flow field inside the junction, such an approach (commonly referred to in the literature as quasi-3D) was finally considered. In Figure 14 the mesh used is shown together, for reference, with that used in the previous subsections. The four volumes at the endpoints of the part shown are then connected to a single volume, thus providing the connection with the one-dimensional computation at the ducts. The mesh chosen is relatively modest, in order to  
Figure 13: Comparison between the experimental results and the different models for the transmission coefficients of the Y-junction (frequency domain): (**a**) excitation at port 1, transmission through port 2; (**b**) excitation at port 1, transmission through port 3; (**c**) excitation at port 2, transmission through port 1; (**d**) excitation at port 2, transmission through port 3 (**e**) excitation at port 3, transmission through port 1; (**f**) excitation at port 3, transmission through port 2. Ports are denoted as in Figure 1.  
keep the computation time at reasonable values, but sufficient to show any potential advantages of this description.  
Additionally, in view of the previous results, only the MDT method will be used as a flow limiter, since overall it has appeared to be more robust and consistent, and only the case of the T-junction will be analyzed in the following, as no new qualitative issues have been identified in the Y-junction that were not present also in the T-junction.
## 4 Conclusions  
We successfully designed, constructed, and tested a sample environment (SE) for SANS measurements on liquid foams. The SE allows the control of the gas flow rate used for foam formation and offers the possibility to control the temperature. The complete foam cylinder is made from neutron-transparent quartz glass, which enables SANS measurements at any position along the foam height and the possibility to study different drainage stages in a single experiment. Additionally, a sample changer on the basis of a linear stage was constructed and completely integrated into the foreseen ESS control structure at the ESS test beamline V20 at the HZB. Finally, SANS test measurements were successfully performed at the KWS-1 beamline at the MLZ with a model foam already reported in literature.  
The presented SE is well-suited for studying liquid foams with SANS and should meet the special requirements of neutron sources with high brilliance such as the ESS, where an efficient use of the allocated beamtime becomes more important and the time required for sample changes should be reduced to an absolute minimum. This is accounted for by the sample changer for up to three foam cells and the possibility to access different states of the same foam in a single measurement cell. In return, a neutron source such as the ESS will allow new and/or more detailed SANS experiments using foams. The high neutron flux at the ESS will reduce the data acquisition time for a similar experiment as presented above at least by one order of magnitude to below 1 min. This will enable experiments with less stable foams or studies investigating structural changes in foams caused by external stimuli.  
Conceptualization, T.H., P.M.-B., and R.v.K.; planning and discussion of setup components, M.K., O.L., T.W., L.P.K., A.J.S., L.W., S.J., H.F., H.S., and A.H.; construction and test of setup components, M.K. and O.L.; software integration, M.K., O.L., T.B., and P.B.; SANS measurements, M.K., O.L., and H.F.; writing--original draft preparation, M.K.; writing--review and editing, O.L., T.W., L.P.K., A.J.S., L.W., S.J., H.F., T.B., P.B., H.S., A.H., T.H., P.M.-B., and R.v.K.; visualization, M.K.; supervision, O.L. and R.v.K.; project administration, T.H.; funding acquisition, T.H., P.M.-B., and R.v.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Federal Ministry for Education and Research (BMBF) within the project \"FlexiProb\" sample environment grant number 05K2016.  
Not applicable.  
Derived data supporting the findings of this study are available from the corresponding author upon reasonable request.  
We thank [PERSON] (ESS) for valuable discussions and [PERSON] (TU Darmstadt) for help with constructing the foam cell. Furthermore, We thank [PERSON] (TU Darmstadt) and [PERSON] (HZB) for wiring and testing the motion control rate. We would also like to acknowledge [PERSON] (ESS) and [PERSON] (ESS) for the help at the ESS test beamline.
# Analysis and Design of a Diplexing Power Divider for Ku-Band Satellite Applications
## 2 Geometry of the Proposed Diplexing Structure and Performance Analysis  
### Overall Structure  
The geometry of the proposed SIW diplexing network is depicted in Figure 1, encompassing all aspects of design, layers, and the advanced SMA port. It is composed of two stacked SIW channels from top to bottom, including two Ro 4003c PCB laminates (\(\varepsilon\)r = 3.55 and tan\(\delta\) = 0.0027). Both PCB laminates have the same height of 1.6 mm.  
There is a whole copper layer between the two PCB laminates, the thickness of which is 4.5 mm. Figure 1a shows the complete geometry of the proposed structure. In Figure 1b, the copper layer has been hidden for a better view of the internal connections in the proposed structure. The top view of PCB laminates and the copper layer is shown in Figure 1e.  
The proposed structure has three ports, which are numbered 1, 2, and 3. Ports 1 and 2 are made with two 50-ohm typical SMA connectors, as shown in Figure 1b with blue arrows. Port 3 is implemented using an SMA connector of 50 Ohms with an extended dielectric. The side view of the proposed structure is shown in Figure 1c.  
Figure 1: _Cont._  
The end pin of the SMA connectors 1 and 2 are soldered in the center of the SIW channel axis. Connector 3 has an extended dielectric, as shown in Figure 1d. The extended dielectric SMA port is the prepared interface that seamlessly penetrates the central copper layer without interference. The material of this dielectric is PTFE (_e_r = 2.1 and tan\(\delta\) = 0.0002). The PTFE dielectric is 4.5 mm long. It can be easily removed from the connector metallic pin for structure assembly. The cylindrical dielectric has an inside diameter of 4.1 mm, and an outside diameter of 1.27 mm. Figure 1d illustrates the extended dielectric and its connector together.  
The proposed diplexing network is made up of three stacked PCB layers, as can be seen in Figure 1. The primary component of the suggested architecture is the vertical transition. The transition starts at the top layer, and its end is soldered to the bottom copper layer. In this structure, the cut cylindrical PTFE (shown in Figure 1d) is placed inside a hole made in the copper layer. In the copper layer, there is a hole whose diameter is 4.1 mm (the same as the outer diameter of PTFE). The copper layer serves as a protective electromagnetic shield for the PTFE-covered conductor. The termination point of SMA connector pin 3 involves soldering it to the lower ground of the PCB's bottom layer, following its traversal through the first PCB layer, the copper layer, and eventually the lower PCB layer. This meticulous arrangement ensures optimal signal integrity and grounding in the design. In contrast to connectors 1 and 2, connector 3 is not located on the central axis of the SIW channel. The distance between SMA connector 3 and the SIW center axis is 3.2 mm.
## 3 Results and Discussions  
### Morphology and Structural Analysis  
SEM images of GO/FeSiAl composites with or without a coupling agent are displayed in Figure 3. GF-3 presents a blurred interface of GO and FeSiAl, suggesting a good compatibility, whereas GF-0 shows a clear interface, indicating poor compatibility. It can be concluded that the use of coupling agents enhances the compatibility of GO and FeSiAl particles, which is important for the generation of heterogeneous interfaces and a stronger interfacial polarization loss.  
Figure 4 shows the XRD spectra of GO, GO-1, FeSiAl, and GF-3. The diffraction peak at 2\(\theta\) = 11\({}^{\circ}\) derived from GO shifted to the lower region in GO-1, indicating that the interlayer space increased during the silylating process. It might be caused by long coupling agent molecules attaching to the GO sheet surface and exfoliating the GO sheets, causing the interlayer spacing to increase. In Figure 4 (c), there are three peaks associated with (220),  
Figure 2: Schematic illustration for the fabrication of GO/FeSiAl epoxy compounds and testing methods.  
(400), and (211) planes, consisting of the standard diffraction spectra of the body-centered cubic structure, FeSiAl. There is a fairly gentle peak within the range of \(2\theta=20^{\circ}\) to \(2\theta=35^{\circ}\) of GF-3 (Figure 4 (d)) compared to FeSiAl, which is the characteristic peak of GO, indicating that a small amount of GO was grafted onto FeSiAl.  
Figure 5 shows the FTIR spectra of the GF-0 and GF-3 samples. The absorption peak of GF-0 at 3450 cm\({}^{-1}\) is attributed to -OH from absorbing water and hydrogen bonds between the layered structure of GO. In addition, the absorption peak of GF-3 at around 3450 cm\({}^{-1}\) shifts to 3425 cm\({}^{-1}\) and is lower than that of GO, indicating that the force between GO layers is weakened and chemical bonding between GO and FeSiAl is generated instead. The relative intensity of the peaks at 1637 cm\({}^{-1}\) is derived from the C=O group, which represents functional groups on the GO surface and becomes wider in GF-3, resulting from several peaks overlapping each other. Those peaks are generated from the amide groups by the carboxyl group on the GO surface reacting with the amino group on the coupling agent. The peak at 1250 cm\({}^{-1}\) represents the stretching and bending vibration of C-N groups in the amide group or coupling agent. Moreover, the peaks around 1120 cm\({}^{-1}\) and 1250 cm\({}^{-1}\) are owing to the Si-O bond and the stretching and bending vibration of the C-N groups overlapping each other, which may be from the amide group, products of the coupling agent and epoxy groups, or the coupling agent itself. The peak at 658 cm\({}^{-1}\) is owing to the stretching vibration of N-H groups from amide or amino groups. Since FeSiAl is a metallic compound and shows little or no transmittance, these results suggest that the coupling agent was suc  
Figure 4: XRD spectra of (a) GO, (b) silylated GO, (c) FeSiAl, and (d) GF-3.  
Figure 3: SEM images of (**a**) GF-0 and (**b**) GF-3.
## 3 Experimental Setup
### Scenario 1: DRL-Based Scenario at the Signalized Intersection
#### 3.2.4 Parameters of the Training Process  
* Activation functions  
The activation function in a deep neural network plays an important role in the operation of the training process. It determines how the weighted sum is transformed into an output from input layers. In our model, we used a rectified linear unit (ReLU) to output the results.  
\[\text{f}\left(\text{x}\right)\ =\ \left\{\begin{array}{l}0\text{ for x }<\ 0\\ \text{x for x }\geq\ 0\end{array}\right. \tag{8}\]
* Optimization function  
We used adaptive moment estimation (Adam) for training optimization in DNN. This algorithm is used for the first-order gradient-based optimization of the stochastic objective function [53]. This method is easy to implement, has high computational efficiency, requires little memory, and is suitable for models with large amounts of data.  
* Experience replay  
Experience replay is a replay memory technique used in DNN to improve the performance of the agent. In this method, we stored randomly the information needed for training in groups of samples called batches. The experiences of the agent at each time step are stored in samples as  
\[\text{m}=\left\{\ \text{s}_{t},\ \text{a}_{t},\ \text{r}_{t+1},\ \text{s}_{t+1}\right\} \tag{9}\]  
where \(r_{t}\) is the reward when the agent performs the action in the state \(s_{t}\) and causes the environment to change to a new state, \(s_{t+1}\). This technique is used to solve the autocorrelation that renders the model unstable when training, since \(s_{t+1}\) is directly evolved from \(s_{t}\).  
This technique needs to specify the memory and batch size. The memory size is defined as the maximum number of samples that can be stored. In this model, we set the maximum memory size to 50,000 samples. The batch size indicates the number of samples taken from memory for each training iteration and is set to 100. If the memory is full, the oldest sample is removed, and the new sample is inserted.  
* Epsilon in Q-Learning Policy  
In this paper, we use the epsilon (\(\varepsilon\)) greedy policy to balance exploration and exploitation. Here, \(\varepsilon\) is the trade-off between exploration and exploitation, and it refers to the agent's probability of exploration when choosing an action. The equation for \(\varepsilon\) decay by episode is as follows:  
\[\varepsilon\ =\ 1\ -\ \frac{\text{Current\_episodes}}{\text{Total\_episodes}} \tag{10}\]  
All Q values are updated iteratively during the training of the agent. At the initial time, \(\varepsilon=1\), meaning that the agent completely explores the environment. The more an agent explores the environment, the more it understands the environment. \(\varepsilon\) gradually decays during training, and the agent will exploit more than explore. Based on the learned experience, the agent understands the environment more and will exploit more. In the last episode, \(\varepsilon=0\), and the agent fully exploits based on trained knowledge about the environment.