esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Fade duration is the second most important link parameter required for designing a satellite link faded by rain because it gives the interval the rain attenuation time series \(A(t)\) exceeds an attenuation threshold \(S\) (dB), namely the link power margin compared to clear sky. Since no measurements are available for zenith paths of GeoSurf satellite constellations, we have simulated \(A(t)\) with the Synthetic Storm Technique at sites located in different climatic regions.  
We have studied the two stochastic processes of fade durations, A and B. Process B gives the statistics of outages, and process A gives the statistics of outage duration. We have found that fade duration \(D\) largely decreases as threshold increases, and it is exceeded with different probability at the diverse sites of Table 1.  
To show that the two processes are not independent, we have studied the relationship between their probabilities and defined a uniformity index \(U(S)\), which compares real cases--fade durations fragmented in many different intervals with changing threshold and site--with the limiting case of all fades lasting the same time. We have found that \(U(S)>\sim\)0.4 for all sites. As the threshold increases, \(U(S)\) increases; therefore, the two processes tend to be closer to the uniformity model at larger rain attenuation. \(U(S)\) approaches 1 at very large thresholds.  
These results should guide the designers of future GeoSurf satellite constellations to take care of the diverse communications services, differently affected by the two fade duration processes. Process B (occurrences) impacts, especially, on non-real-time services, such as data delivery, which can be more disturbed by the number of outages rather than by their duration. Process A (fraction of time) impacts, especially on real-time services such as television, video conference etc., which are more disturbed by the duration of the outage.  
Conceptualization, E.M. and C.R.; methodology; software, E.M. and C.R.; validation, E.M. and C.R.; investigation, E.M. and C.R.; data curation, E.M. and C.R.; writing--original draft preparation, E.M.; writing--review and editing, E.M. and C.R.; visualization, E.M. and C.R. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Uniformity index versus the threshold indicated in abscissa at the indicated sites. Spino d’Adda: continuous blue line; Gera Lario: continuous black line; Fucino: continuous red line; Madrid: continuous green line; Prague: continuous magenta line; Tampa: dashed red line; Norman: dashed magenta line; White Sands: dashed green line; Vancouver: dashed blue line.
## 2 Materials and Methods
### Estimation of Power Spectral Density  
Power spectral density (PSD) analysis is a widely used frequency-based method of EEG power distribution study [19]. In this work, PSD was calculated from EEG data during the processing of various visual stimuli. First, fast Fourier transform was computed and then squared to obtain the PSD estimate. The mean normalized PSD for every EEG channel in the frequency ranges mentioned above was determined by adding power values in a specific frequency range selected for further analysis. Lastly, the value of total powers in every range was divided by the total spectral power of the corresponding EEG channel.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 4 Case Study
### SSA Algorithm Optimization Search Results  
In this study, five parameters, namely training rate, number of training epochs (Epoch), number of neurons in the first layer (units1), number of neurons in the second layer (units2), and batch size, were used as hyperparameters for the optimization search. Combined with the model framework established in the previous paper, the SSA algorithm was initialized, while several experiments and optimization searches were conducted in the range of parameter values. After several observations and experiments, the five parameters were defined as  
Figure 4: Dam deformation monitoring point layout program.  
Figure 5: Reservoir water level and horizontal deformation process line.  
[0.001, 0.01], [50, 200], [1, 80, 200], and [20, 200], and the sparrow population size was set to 20, of which 10% were alerters and 20% were finders, with an alert threshold of 0.8 and 10 iterations. Due to the inconsistent data characteristics and data structure of each measurement point, and even the different intrinsic components of the data, the LSTM parameters are different in the optimization search. The results of the LSTM model parameter ranges obtained after multiple search for the four measurement points are shown in Table 1. Furthermore, considering the overall prediction effect of the model, the result of the search was averaged by running the search process several times. The analysis and comparison of the prediction effects of the averaged model found that it met the prediction requirements of most measurement points and could obtain better prediction results, which ensured the stability and reliability of the model. Therefore, the averaging of the search parameters with a comprehensive optimization strategy is able to obtain reliable search parameters. In this study, the model parameters obtained after averaging were applied in the later prediction analysis. The results of the averaging of the taken parameters are shown in Table 1.
# Emerging Applications of Machine Learning in 3D Printing
## 1 Introduction  
Previous studies and publications developed the concept that artificial intelligence (AI) is useful in many areas of research on 3D-printed products. This is particularly true for supporting 3D printing with machine learning (ML), i.e., a data-driven approach that extracts complex connections between input and output data and uses these connections for optimization and prediction. However, there is still a lack of large-scale reviews and comparative studies. This gap results, among others, from the diversity of 3D printing technologies, materials used for this purpose, ML solutions, and products manufactured using 3D printing technology [1]. The use of machine learning (ML) in 3D printing faces significant challenges related to data collection and analysis. Currently, there is a lack of larger systems that would effectively organize data and enable their comprehensive analysis, which is a serious limitation for the development of this technology [2]. Additionally, the data available in the 3D printing sector are often unstructured and incomplete, which makes it difficult to use them in ML algorithms. To improve the situation, it is necessary to conduct a detailed audit of data, both those already collected and those generated on an ongoing basis, in terms of their usefulness in ML systems [3]. In the future, significantdevelopment of ML applications in the design and production of 3D-printed products is expected [4]. Particular attention is paid to the potential of this technology in the areas of commercialization and fintech, including market analysis, costs, profitability of products, and their families. Nevertheless, there are significant barriers that may hinder the development of this field [5]. One of them is the limited depth of the investor market, which may make it difficult to finance new ventures. In the case of 3D-printed medical devices, regulatory requirements, including the need to implement the MDR [6] and ISO 13485 [7] standards, pose an additional challenge. Despite these difficulties, the potential of ML in 3D printing remains enormous, especially in the context of automated design and optimization of production processes. Success in this area, however, will require effective cooperation between the technological, industrial, and regulatory sectors.  
The observed gaps in emerging ML applications in 3D printing are presented in Table 1. These gaps highlight both gaps to be filled and opportunities for research and development at the intersection of ML and 3D printing.  
The aim of this article is to identify and disseminate more fully than before the role of ML in the design and production of personalized 3D-printed products, especially medical devices, in light of current research and economic practice as well as emerging research and industrial trends.
## 5 Conclusions  
In this paper, a depth-averaged two-dimensional model for the simulation of tidal current and non-uniform sediment transport in estuaries was developed based on a series of formulas proposed by [PERSON] [1; 2; 3; 4; 5]. We firstly evaluate and discuss several key parameters for non-uniform sediment transport, including the critical bed-shear stress for erosion and deposition, erosion coefficients, and the settling velocity. The critical bed-shear stress for erosion and deposition deduced from the incipient probability of non-uniform sediment are able to describe both cohesive and noncohesive non-uniform sediment transport. Erosion coefficients are derived from experimental data on the scour of drilling samples from the Giantang River estuary. The settling velocity formula is adequate for particles of any size and various passing flow states.  
Results from this model are compared with several datasets, including both an aggradation and an erosion experiment. Good performance in the tests indicates that the model can simulate the aggradation and erosion processes of non-uniform sediment. The model is also verified using measured data from Jiaojiang estuary, and the computed results agree well with the measured values. The model can reasonably replicate the turbidity maximum zone of Jiaojiang estuary. The proposed model is able to simulate the flow and non-uniform sediment transport in tidal estuaries.  
Conceptualization, W.N. and Z.S.; Methodology, C.G.; Validation, C.G.; Formal analysis, W.N.; Investigation, W.N.; Resources, Z.S.; Writing--Original draft, C.G.; Writing--Review & editing, W.N., Z.L. and R.Z.; Supervision, Z.S. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the doctoral program of Higher 300 Education Research Fund (Grant NO. 20120101110108) and the National Science and Technology Major Project 299 (Grant NO. 2009 ZX07424-001). This work was supported by the project of Zhejiang Provincial Key Research and Development Program (2023C03133).  
Not applicable.  
The authors declare no conflict of interest.  
The incipient probability of non-uniform sediment  
\(a_{k}\)The incipient probability of non-uniform sediment  
\(\rho\)density of water  
\(\rho_{s}\)density of sediment particles  
\(D_{k}\)average diameter for the k-th size fraction  
\(\xi_{k}\)coefficient of hiding and exposure effects  
\(\sigma_{g}\)geometric standard deviation of non-uniform sediment  
\(D_{m}\)mean diameter of bed material  
\(\rho^{\prime}_{s}\)dry density of sediment;  
\(\rho^{\prime}_{s*}\)stable dry density of sediment: \(\rho^{\prime}_{s*}\)= 1.6 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\)  
\(E(k)\)erosion rate for the k-th fraction of non-uniform sediment  
\(M\), \(m\)erosion coefficients and exponent  
\(\tau_{b}\)bed shear stress  
\(\tau_{ek}\)critical bed-shear stress for erosion  
\(D(k)\)deposition rate for the k-th fraction of non-uniform sediment  
\(\omega_{k}\)settling velocity  
\(S_{k}\)sediment concentration by weight: \(S_{k}=P_{k}S\)  
\(P_{k}\)percentage of non-uniform suspended sediment  
\(S\)total sediment concentration: \(S=\sum_{k=1}^{N}S_{k}\)
## 3 Our MDCIM Work
### Array of Sot-Mram  
The memory array we constructed based on SOT-MRAM is a part of the CIM macro. Compared to SRAM and STT-MRAM, the advantages of SOT-MRAM are primarily reflected in its higher write speeds and storage density. To evaluate their performance, we used CMOS 55 nm technology to separately synthesize three sets of 16 \(\times\) 13 bit memory arrays based on SRAM, STT-MRAM, and SOT-MRAM. We added peripheral read-write circuits to them and measured the relevant parameters. The models of STT-MRAM and SOT-MRAM referred to the designs in References [22; 23]. We evaluated the write speed of the model by measuring the write latency of circuits and assessed the storage density of the model by measuring the layout area of arrays.  
As shown in Figure 8, the memory density of SOT-MRAM arrays is not significantly different from that of STT-MRAM arrays, but both are much higher than SRAM arrays. Using SOT-MRAM arrays in CIM macros can reduce the area overhead of the chip. Meanwhile, SOT-MRAM resolves the slow writing issue of STT-MRAM. It can be observed from the figure that the write time of SOT-MRAM arrays has now surpassed that of SRAM arrays, which can further lower the latency in the calculation process.  
Figure 7: The dataflow of using multilevel adder tree to perform addition on partial products and addends.
# On the Benefits of Listen before Talk Scheme for NB-Fi Networks
## 1 Introduction
The contribution of this work is that we evaluate the performance of NB-Fi networks with different implementations of LBT and determine those that provide the best PLR, delay, throughput, and energy consumption. We consider two scenarios: a scenario with a small network, where most devices can sense transmissions of each other, and a scenario with a large network, where devices can be hidden from each other. Based on our studies, we develop recommendations on which type of LBT to use in each scenario. The rest of the paper is organized as follows. In Section 2, we review existing solutions related to NB-Fi and the LBT mode. In Section 3, we describe the most relevant features of NB-Fi. Section 4 introduces the CSMA algorithms used in the paper. Section 5 describes the considered scenario, and Section 6 states the problem. Section 7 presents and discusses the numerical results. Section 8 concludes the paper.
## 3 Flow Control Devices for Gust Load Alleviation
### Traditional Control Surfaces
#### 3.1.1 Trailing-Edge Flaps  
Trailing-edge flaps, known as plain flaps such as ailerons and elevators encompass the aft portion of the wing [44]. The trailing-edge flaps operate by rotating up or down to achieve a desire change in camber, and thus results in a desire change in lift [45]. Lift enhancement is realized by rotating the flap downward, which can be considered as an effective increase in the airfoil camber. When the flap is rotated upwards instead, a negative camber with mitigating lift is realized. On the commercial aircrafts, these trailing-edge flaps play a more important role as the high-lift device. It has been shown that the change in lift achieved by the flaps depends on the chordwise extent of the flap. Unsurprisingly, larger flap sizes have been shown to create larger changes in lift [46]. Therefore, to make a significant lift change, they tend to be relatively large sized and therefore heavy, taking up a large portion of the wing. As a high-lift device, the capability of the steady-state lift augmentation under a constant flap deflection angle is the key. However, as a gust alleviation device, apart from the capability of the lift reduction achieved by the flaps, the characteristics of these flaps under dynamic activations are also vitally importance.  
On a supercritical airfoil equipped with a spoiler and a flap for a two-dimensional flow, [PERSON] et al. [47] investigated the unsteady aerodynamic performance under subsonic flow. It was found that for the trailing-edge flap, the lift coefficient decreases significantly with the flap rotating frequency, therefore reducing the range in lift coefficient change to the steady state. [PERSON] et al. [48] also noted that the amplitude of lift change decreases with frequency. Hysteresis loops of the lift changes were present as a function of the flap deflection angle, indicating time lags in the dynamic responses. It was found that for the oscillating the aileron in a sinusoidal pattern, the range of lift coefficient change becomes smaller with angle of attack. For example, at a low angle of attack of \(\alpha=4.6^{\circ}\), the total lift change is 0.11, while this value is only 0.01 when the angle of attack is \(19^{\circ}\), for a fixed reduced frequency of \(k=0.082\).
## 3 A Taxonomy for Planar FSS
### Cross Cell
Multi-layer structures offer additional design possibilities, such as a two-layer FSS with conducting cross dipoles on one layer and resistive cross dipoles on another, functioning at 5.6 and 5.7 GHz with a size of 0.31 wavelengths [48]. An additional example is a multi-layer FSS with a metallic slot structure modified by square annular and Jerusalem slot designs, which operates in the 28 and 37 GHz bands, with a unit cell size of 0.56 wavelengths [49]. Figure 5 presents several of these cross-design unit cells, with conductive surfaces indicated in dark color.
## 2 Methodology
### Preprocessing  
#### 2.1.1 Data Representation  
When processing range data, most approaches are applied directly to the point cloud in 3D space. The hard task is to calculate the descriptive information in the irregularly distributed laser point cloud. To facilitate the processing, the point cloud is treated in its panoramic representation,\[\begin{bmatrix}x&y&z\end{bmatrix}^{\mathrm{T}}=\rho\big{[}\cos\!\varphi\!\cos\! \theta&\cos\!\varphi\!\sin\!\theta&\sin\!\varphi\big{]}^{\mathrm{T}} \tag{1}\]  
where \(\theta\) and \(\varphi\) are the latitudinal and longitudinal coordinates of the firing direction, and \(\rho\) the measured range. Because of the fixed angular spacing (defined by system specifications), a lossless grid structure can be set, thereby offering a compact image representation of the data (Figure 1(a)). Use of the image representation makes data manipulation and primitive extraction simpler to perform compared to directly processing the 3D point cloud. Thus, the other input channels are also transformed into it.
## 2 Overview of Spaceborne Metal Mesh Fabric  
When designing mesh reflectors for antennas, satisfying requirements such as mechanical, microwave electrical, foldability, wrinkle resistance, and light transmittance is essential [14]. Typical materials used in mesh reflectors include nickel-plated stainless steel, molybdenum, and tungsten, which offer high strengths and low thermal expansion coefficients [15; 16]. Additionally, the metal wires were gold plated to provide excellent corrosion resistance and high electrical conductivity. The mesh fabric for the satellite antennas was woven using methods similar to the traditional textile manufacturing techniques. Typical woven methods include weaving, warping, and non-woven techniques. Braided warp knitting is preferred for antenna meshes owing to its superior durability and elasticity. The patterns used were Tricot, Atlas, and SATIN [17]. The mesh fabric has a porous structure defined by the OPI, indicating its density. Additionally, OPI is linearly related to the mission frequency band. The amount of wire and knitting process used to produce the mesh depend on the wire material, pattern, and OPI, which determine the RF performance of the mesh. Knitting processes are complex and time consuming [7]. Therefore, pre-evaluating the characteristics of each mesh factor to ensure that the mesh fabric satisfies the antenna requirements while minimizing the manufacturing duration is necessary.  
As previously mentioned, mesh reflectors are designed to be thin and lightweight. In on-orbit thermal environments, the antenna is exposed to extremely low temperatures (below 100 K) during the eclipse period and excessive heat during the sunlight period. Moreover, temperature gradients can cause thermal deformation and reduce RF performance. To maintain surface accuracy in an on-orbit thermal environment, predicting thermal stability is essential. To predict the thermal behavior of an antenna, the optical properties of the mesh constituting the reflector must be investigated. The optical properties of the mesh have transmission characteristics that differ from those of solid antenna reflectors. The transmission characteristics change mainly with the OPI of the mesh, which is influenced by factors such as the mission frequency band, knitting pattern, number of twists, and initial tensile force that occur when the mesh is fastened to the rim structure. Consequently, it is essential to analyze the thermal behavior of the mesh antenna reflector by considering the changes in transmission characteristics depending on the factor of the mesh fabric.
## 3 Test Results and Analysis
### Analysis of Measured Curves
#### 3.3.4 Load-Deflection Curves of SRRAC Beams  
Figure 11 shows the measured load-deflection (_P-f_) curves of the specimens in mid-span. It can be seen from the graph that the specimens have experienced three stages: elastic stage, elastic-plastic stage, and failure stage. The initial loading stage is elastic, the elastic-plastic is after the cracking, the _P-f_ curve shows a turning point, and after the peak load point, it is the breaking stage.
## 3 Improvement via Clusterization
### Fading Noise in Satellite-Mediated Protocol  
From the form of Equation (3), \(\gamma_{AB_{1}}\) can be equivalently parameterized as originating from a stable link with fixed parameters, which can be rewritten as  
\[\mathrm{V}_{AB_{1}}=\left[\begin{array}{cc}\mu\mathrm{I}&\sqrt{\eta(\mu^{2}-1) }\mathrm{Z}\\ \sqrt{\eta(\mu^{2}-1)}\mathrm{Z}&[\eta(\mu-1)+2\bar{n}_{\mathrm{eff}}+1] \mathrm{I}\end{array}\right], \tag{5}\]  
with effective transmittance  
\[\eta=\langle\sqrt{\tau}\rangle^{2}, \tag{6}\]  
and effective noise  
\[\bar{n}_{\mathrm{eff}}=\frac{\langle\tau\rangle-\left\langle\sqrt{\tau} \right\rangle^{2}}{2}(\mu-1)+\bar{n}. \tag{7}\]  
We find that the effect of transmittance fluctuation is the increase in thermal noise by the additional term  
\[\bar{n}_{\mathrm{fad}}=\bar{n}_{\mathrm{eff}}-\bar{n}=\frac{\langle\tau \rangle-\left\langle\sqrt{\tau}\right\rangle^{2}}{2}(\mu-1). \tag{8}\]  
It is clear that the fading thermal noise \(\bar{n}_{\mathrm{fad}}\) is related to the statistics \(\mathrm{Var}\big{(}\sqrt{\tau}\big{)}=\langle\tau\rangle-\left\langle\sqrt{ \tau}\right\rangle^{2}\). Additionally, the fading noise will decrease if the signal variance \(\mu\) is decreased.  
Figure 3 shows the distribution of fading noise, \(\bar{n}_{\mathrm{fad}}\), versus the satellite altitude \(h\) and the zenith angle \(\theta\). The simulation parameters are shown in Table 1. We find that the fading noise in the uplink is generally more significant than that in the downlink. A joint analysis of Figures 2 and 3 shows that the asymptotic key rate drops rapidly in the area where \(\bar{n}_{\mathrm{fad}}\) is on the rise. Therefore, we consider improving the secret key rate by suppressing the fading noise in the following. Note that the uplink \(\bar{n}_{\mathrm{fad}}\) in Figure 3b decreases in the area with higher satellite altitude and a larger zenith angle, where the atmospheric effects are more severe. This can be attributed to the further decrease in transmittance (near zero), leading to a more'stable' channel.