esa-sceva/satcom-chunk-collection huggingface dataset:
# Al-Foam Compression Tests in Parallel and Serial Concepts
## 6 Results  
### Single Compression Scenario  
In the first section, the foams were compressed separately, first the lighter one, then the heavier one. Both tests resulted in a typical stress-strain diagram. The distinction is made between the linear elastic, flat plastic, and densification zones. The lighter foam (SP-LD1) reaches the elastic-plastic transient at 1.31 MPa and 2.39% of the strain, while the heavier one (SP-HD2) at 2.046 MPa and 3.15% of the strain. The effect of the density could be well introduced among the plateau stress, since the SP-HD1 has a higher flat stress range resulting in 1.445 MPa of plateau stress during the 20-40% strain, compared to the SP-LD1 which was just 1.062 MPa in the same measurement period. Naturally, the density and the cell size also influence the densification range. Smaller-sized cells result in sharp steepness in the densification range relative to the lighter ones. The densification zone could be best described by the steepness of the characteristic. Figures 6 and 7 represent the stress-strain curves of the first test.  
In order for the results to be compared, they are collected in Table 2. The table data are presented in the following order: first maximum stress (\(\sigma_{\rm max}\)), and then plateau stress(\(\sigma_{\rm pl}\)) where both of them are given in MPa, and the end of plateau stress(\(\varepsilon_{\rm D}\)) as a percentage. As mentioned in \"The ideal absorber\" chapter, the absorbed energy is determined for the plateau range (E\({}_{\rm abs\text{-}plateau}\)) and the whole compression (E\({}_{\rm abs\text{-}total}\)) in \(\rm MJ/m^{3}\). These values are also available in the last two table columns. To verify the results of the compression test we employed a calculation method using the material properties of the samples and the Formulas (3) and (4) [23]:  
Figure 6: Stress–strain of single compressions up to 50% strain (own collaboration of authors).  
Figure 7: Stress–strain of single compressions up to 80% strain (own collaboration of authors).  
\[\sigma_{\rm plateau}\approx(0.25\ldots 0.35)\sigma_{\rm ys}\bigg{(}\frac{\rho}{ \rho_{\rm s}}\bigg{)}^{\rm m} \tag{3}\]  
The \(\rho/\rho\)s is the relative density, which is the ratio of the sample density and the base aluminum density. The formulas use the \(\sigma_{\rm ys}\) yield strength of the base material of the sample, while the \(\epsilon_{\rm D}\) is the densification strain. For the currently available metal foams, m lies between 1.5 and 2.0, and \(\alpha_{1}\) is between 1.4 and 2 [23].
## 2 System Model
### Rain Attenuation Time Series  
This subsection introduces the Dirac log-normal distribution model, which is proved to be able to accurately characterize rain attenuation [34]. The rain attenuation time series obey Dirac-lognormal distribution, which means the rain attenuation PDF (probability distribution function) \(P(A|A>0)\) is a lognormal distribution function with mean \(\mu\) and variance \(\delta\):  
\[P(A|A>0)=\frac{e^{-\frac{1}{2}(\frac{lnA-\mu}{\delta})}}{A\delta\sqrt{2\pi}} \tag{1}\]  
Consequently, the CCDF (complementary cumulative probability distribution function) of rain attenuation can be expressed as mixture Dirac-lognormal distribution:  
\[P(A\geq A_{0})=p_{0}\int_{A_{0}}^{+\infty}P(A|A>0)dA_{\mathrm{C}}=\frac{p_{0}} {2}\bigg{(}\frac{lnA_{0}-\mu}{\sqrt{2\delta}}\bigg{)} \tag{2}\]  
where \(p_{0}\) is the rainfall probability of communication link, and the mathematical statistical model of \(p_{0}\) can be found in ITU-R P.618 [35].  
By employing the method in [34], a stationary Gaussian process \(\mathrm{G(t)}\) with zero mean, one variance and correlation function \(c_{G}(\triangle t)\) should be generated as follows:  
(a) Generate \(N/2+1\) uncorrelated random complex numbers \((e_{k})_{k=0...N/2}\), whose real part and imaginary part are normally distributed with zero mean and one variance. When \(k=0\) or \(k=N/2\), set the imaginary part of \(e_{k}\) to 0;  
Figure 1: Beam hopping system.  
Figure 2: Beam hopping time plan.  
(b) Define \(e_{N-k}=e_{k}^{*}\), with \(k=\{0,\ldots,N/2\}\);  
(c) Define \(h_{k}=\left\{\begin{array}{ll}1&k=0,\ k=N/2\\ \frac{1}{2}&\text{else}\end{array}\right.;\)  
(d) Compute Fourier transform according to correlation function  
\(c_{G}\): \(\mathcal{F}\big{(}G_{j}\big{)}=\frac{1}{N}\sum_{j=0}^{N-1}G_{j}e^{-\frac{2\pi j }{N}}i\);  
(e) Define \(a_{k}=\sqrt{h_{k}\mathcal{F}(c_{G})}\times e_{k}\);  
(f) Then \(G(t)=\mathcal{F}^{-1}(a_{k})=\sum_{k=0}^{N-1}a_{k}e^{\frac{2\pi j}{N}kj}\).  
Finally, the desired rain attenuation time series \(A(t)\) is:  
\[\left\{\begin{array}{ll}A(t)=exp\left\{\delta\sqrt{2}erfc^{-1}\left[\frac{ \sigma fc\big{(}\frac{C(t)}{\sqrt{2}}\big{)}}{P_{0}}\right]+\mu\right\}&,G(t)>G _{0}\\ A(t)=0,\text{ \emph{otherwise}}\end{array}\right. \tag{3}\]  
where \(G_{0}=\sqrt{2}erfc^{-1}(2\times p_{0})\).  
Obviously, it is better to use measured rain attenuation data from a real satellite communication channel. However, the public data we can obtain are not detailed enough for beam hopping research. More importantly, the motivation of this paper is to provide a beam hopping algorithm under rainfall environment. Thus, a reasonable and accurate rain attenuation time series model is sufficient to validate the proposed beam hopping algorithm.
# A Faster Algorithm for Reducing the Computational Complexity of Convolutional Neural Networks
## 3 Proposed Algorithm
The Strassen algorithm shows less reduction of multiplication when the matrix size is small, but it incurs less additions. The Winograd algorithm shows a stable performance. Moreover, the number of additions slightly decreases as the matrix size increases. For small-sized matrices, the Strassen-Winograd algorithm shows a much better reduction in multiplication complexity than the Strassen algorithm. Although it incurs more additions, the number of extra additions is much less than the number of decreased multiplications. The Strassen-Winograd algorithm shows a similar performance to the Winograd algorithm. When the matrix size is small, the Winograd algorithm shows a slightly better performance, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Matrix Size** & \multicolumn{2}{c}{**Conventional**} & \multicolumn{2}{c}{**Strassen**} & \multicolumn{2}{c}{**Winograd**} & \multicolumn{2}{c}{**Strassen-Winograd**} \\ \hline N & Mul & Add & Mul & Add & Mul & Add & Mul & Add \\
2 & 294,912 & 278,528 & 250,048 & 303,104 & 131,072 & 344,176 & 114,688 & 401,520 \\
4 & 2,359,296 & 2,293,760 & 1,806,336 & 2,416,640 & 1,048,576 & 2,494,208 & 802,816 & 2,908,608 \\
8 & 1.89 \(\times 10^{7}\) & 1.86 \(\times 10^{7}\) & 1.26 \(\times 10^{7}\) & 1.81 \(\times 10^{7}\) & 8.39 \(\times 10^{6}\) & 1.65 \(\times 10^{7}\) & 5.62 \(\times 10^{6}\) & 215 \(\times 10^{7}\) \\
16 & 1.51 \(\times 10^{6}\) & 1.50 \(\times 10^{8}\) & 8.85 \(\times 10^{7}\) & 1.31 \(\times 10^{8}\) & 6.71 \(\times 10^{7}\) & 1.25 \(\times 10^{8}\) & 3.93 \(\times 10^{7}\) & 1.62 \(\times 10^{8}\) \\
32 & 1.21 \(\times 10^{9}\) & 1.20 \(\times 10^{9}\) & 6.20 \(\times 10^{8}\) & 9.39 \(\times 10^{8}\) & 5.37 \(\times 10^{8}\) & 9.69 \(\times 10^{8}\) & 2.75 \(\times 10^{8}\) & 1.23 \(\times 10^{9}\) \\
64 & 9.66 \(\times 10^{9}\) & 9.65 \(\times 10^{9}\) & 4.34 \(\times 10^{9}\) & 6.65 \(\times 10^{9}\) & 4.29 \(\times 10^{9}\) & 7.63 \(\times 10^{9}\) & 1.93 \(\times 10^{9}\) & 9.37 \(\times 10^{9}\) \\
128 & 7.73 \(\times 10^{10}\) & 7.72 \(\times 10^{10}\) & 3.04 \(\times 10^{10}\) & 4.68 \(\times 10^{10}\) & 3.44 \(\times 10^{10}\) & 6.06 \(\times 10^{10}\) & 1.35 \(\times 10^{10}\) & 7.19 \(\times 10^{10}\) \\
256 & 6.18 \(\times 10^{11}\) & 6.18 \(\times 10^{11}\) & 2.13 \(\times 10^{11}\) & 3.29 \(\times 10^{11}\) & 2.75 \(\times 10^{11}\) & 4.83 \(\times 10^{11}\) & 9.45 \(\times 10^{10}\) & 5.55 \(\times 10^{11}\) \\
512 & 4.95 \(\times 10^{12}\) & 4.95 \(\times 10^{12}\) & 1.49 \(\times 10^{12}\) & 2.31 \(\times 10^{12}\) & 2.20 \(\times 10^{12}\) & 3.86 \(\times 10^{12}\) & 6.61 \(\times 10^{11}\) & 4.29 \(\times 10^{12}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computational complexity of different algorithms. whereas the Strassen-Winograd algorithm and Strassen algorithm perform much better as the matrix size increases.
# A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network
## 4 Evaluation and Analysis
### Data Preparation  
The social network data source we adopted in this paper is Sina Weibo, which is the most popular and influential social network in China. It had 530 million active monthly users (MAU) in December of 2021. In order to realize our simulation, the original microblogs corpus dataset was collected from the Sina Weibo website using a crawler tool. We collected this dataset by starting from a seed set of active Sina Weibo users (we call these users _shop owners_) who have set foot both in social networks and in e-business. When these users market their commodities using microblogs (we call these microblogs promotional microblogs) in a social network, we trace the information in these microblogs and collect data both on the social network platform and the e-business platform. This process is shown in Figure 7.  
For the purpose of obtaining credible and complete user profiles, the collected data from the social network platform contains four main types of information, which are shown in Table 2 in detail.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Information Type** \\ \hline
1 & User’s whole microblogs since he/she became a weibo register. \\ \hline
2 & User’s basic registered information \\ \hline
3 & User’s tags which were set by user. \\ \hline
4 & User’s followees (The person that user follows.) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Four types of information collected from social network platform.  
The same collection method is used in commodity information collection process. The six types of commodity information are shown in Table 3.
# Sulfate Resistance in Cements Bearing Bottom Ash from Biomass-Fired Electric Power Plants
## 3 Results and Discussion
### Pore Structure  
The effect of 56 and 180 days sulfate soaking on the pore systems in the cement pastes analyzed is graphed in Figure 2.  
Figure 2 shows that total porosity was greater in the BA cements than in OPC due to the intrinsic impact of the new addition on pore structure. This effect has been reported in earlier studies on cement pastes containing other pozzolanic materials, such as slag, fly ash, or silica future [36; 37]. Total 56 days porosity (20% to 24%), for instance, was similar to the values observed by [PERSON] et al. [33] for matrices bearing 5% or 15% silico-manganese slag, or [PERSON] et al. [32] for cementitious systems with 20% masonry product sludge.  
Total porosity declined with exposure time in all of the cements, by 6.8% in OPC, 9.9% in OPC + 10 BA, and 10.7% in OPC + 20 BA. The steeper decline in the BA-bearing cements was associated with their pozzolanicity [19], and the precipitation of expansive compounds such as gypsum and ettringit in the pore system [38]. The combined effect of those two developments was lesser permeability and a delay in decay [37; 39], as attested to by the corrosion index values listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Blended Cement** & \multicolumn{4}{c}{**Exposure Time (Days)**} \\  & **14** & **56** & **90** & **180** \\ \hline OPC & 1.30 & 1.26 & 1.15 & 1.11 \\ OPC + 10 BA & 1.32 & 1.29 & 1.30 & 1.15 \\ OPC + 20 BA & 1.29 & 1.27 & 1.37 & 1.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Corrosion index at four exposure times.  
Further to Figure 2, between days 56 and 180, pore size declined with longer exposure time, by 13.0% in OPC, 13.5% in OPC + 10 BA, and 19.2% in OPC + 20 BA. This finding was related to the inside-pore precipitation of the new compounds and BA pozzolanicity, which improved the matrix pore structure by reducing the volume of macropores and raising the fractions of medium (0.01 \(\upmu\)m to 0.05 \(\upmu\)m) and small (0.002 \(\upmu\)m \(<\) 0 \(<<\) 0.01 \(\upmu\)m) capillary pores.
## 5 UAV Model  
### Speed and Power at Steady State  
The proposed framework can be configured to minimize distance travelled, mission time, or energy consumption. For the last two objectives, a model must be used to estimate the speed of the UAV and its energy consumption on each segment forming the path. Several papers define energy models for quadcopters based on the aerodynamic and electric properties of the vehicles such as [19; 20; 21; 22]. The disadvantage of using such model is that it does not consider the autopilot and the UAV as a single system. If the autopilot is not able to fly the trajectory exactly as described, the energy predicted is not correct. A method that is favored by the author of this present paper is to rely on experimental flights to model the UAV and autopilot as a single black box. The model used here is for the IRIS quadcopter equipped with the PX4 autopilot and was developed by [PERSON] and [PERSON] in [23]. To find the optimal speed at steady state during horizontal movement, the authors measure the power consumed at multiple speeds and calculate the energy per distance travelled in J/m. The lowest value is achieved at optimal speed. For horizontal movement, they find an optimal speed and power consumption of 12.5 m/s and 244.2 Watts. For vertical ascent and vertical descent, the authors find that the UAV/autopilot system travels at a speed of 2.08 m/s with a power consumption of 249.1 Watts when ascending and 1.28 m/s and 212.5 Watts when descending. In this work, optimal speed and power at steady state are used to set the edge weights when building the graph for the SSSP algorithm.
# Spatial Variability of Grape Berry Maturation Program at the Molecular Level
## 2 Materials and Methods
### Remote Sensing  
A multispectral image was acquired from a fixed-wing manned aircraft flying over the experimental site on 15 August 2017, within one hour of solar noon. The image, with a spatial resolution of 0.22 m/pixel, consisted of 5 spectral bands in the visible and near infra-red regions of the electromagnetic spectrum. The red (R) and the near infra-red (NIR) bands, centered at 670 and 800 nm, respectively, and with a full width at half maximum of 10 nm, were used to calculate the NDVI as follows:  
\[\mathrm{NDVI}=\frac{\left(\mathrm{NIR-R}\right)}{\left(\mathrm{NIR+R}\right)}\]  
At that time of the season, the vine canopy is the only active vegetation present, and the background is mainly dry cover crop. An NDVI value of 0.49 was set as a threshold to filter out mixed pixels or pixels representing the background using the _mclust_ function of the _mclust_ package in R (version 4.2.1) [29]. All the pixels with NDVI values below 0.49 were removed from the image. Then, the average NDVI value in each block was extracted.
# Analysis of Small-Loop Electromagnetic Signals to Detect Subsurface Anomaly Zones
## 3 Test Plan
### Exploration Method  
The CMD Mini Explorer used herein was able to survey depths of 0.52, 1.04, and 1.56 m using the equipment shown in Figure 3a. As a result, a tiny object was not able to be detected when it was situated between those distances. To overcome this weakness, the equipment was positioned at three different heights, including at the pavement surface, 0.26 m above the pavement surface, and 0.52 m above the pavement surface, to obtain data at depths of 0, 0.26, 0.52, 0.78, 1.04, 1.32, and 1.56 m below the pavement surface as shown in Figure 3b.  
Figure 2: Side views of the testbed sections. (**a**) Sections 1–4. (**b**) Sections 5–8.  
The EM properties of the subsurface were measured at 11 positions at 0.5m spacing in each section which is 5 m in length. It took about 5 s to measure each position. The equipment was set to a frequency range of 18 kHz to measure the three different depths concurrently. The EC was calculated by measuring the in-phase component and quadrature component of the secondary EM field. The calculated EC was stored and plotted by the equipment. Then, the ER was calculated by obtaining the reciprocal of the EC. The quantitatively expressed ER was then converted to a two-dimensional  
Figure 3: Increase in measurement depth by repeating the survey at different heights. (**a**) Original measured depths; (**b**) increased measurement depths by superposition.  
(2D) ER contour map using the inverse operation program of the CMD Data Transfer developed by GF Instruments Company to evaluate the distribution of the ER and detect the anomaly zones via visual inspection. In addition, the calculated ER was analyzed via a statistical method following the procedure shown in Figure 4 to detect the anomaly zones quantitatively.
###### Abstract  
Solder joints in thermally uncontrolled microelectronic assemblies have to be exposed to extreme temperature environments during deep space exploration. In this study, extreme temperature thermal shock test from \(-196\)\({}^{\circ}\)C to \(150\)\({}^{\circ}\)C was performed on quad flat package (QFP) assembled with Sn-37 Pb solder joints to investigate the evolution and growth behavior of interfacial intermetallic compounds (IMCs) and their effect on the pull strength and fracture behavior of Sn-37 Pb solder joints under extreme temperature environment. Both the scallop-type (Cu, Ni)\({}_{6}\)Sn\({}_{5}\)IMCs at the Cu lead side and the needle-type (Ni, Cu)\({}_{3}\)Sn\({}_{4}\)IMCs at the Ni-P layer side changed to plane-type IMCs during extreme temperature thermal shock. A thin layer of Cu\({}_{3}\)Sn IMCs was formed between the Cu lead and (Cu, Ni)\({}_{6}\)Sn\({}_{5}\)IMC layer after 150 cycles. The growth of the interfacial IMCs at the lead side and the Ni-P layer side was dominated by bulk diffusion and grain-boundary diffusion, respectively. The pull strength was reduced about 31.54% after 300 cycles. With increasing thermal shock cycles, the fracture mechanism changed from ductile fracture to mixed ductile-brittle fracture, which can be attributed to the thickening of the interfacial IMCs, and the stress concentration near the interface caused by interfacial IMC growth.  
intermetallic compound growth; Sn-Pb solder joints; pull strength; fracture behavior; extreme temperature; thermal shock +
Footnote †: journal: Applied Sciences
## 5 Experiments and Results
### Detailed Result Comparison of Three Studies
#### 5.1.3 Results on PV Module of Photowatt-PWP201 in SMM  
For the SDM on the PV module of Photowatt-PWP201, Table 5 shows the best identification results obtained by the DLMVO algorithm and its competitors. It can be seen from the table that the DLMVO, Rcr-JADE, IWOA, IJAYA, PGJAYA, and SATLBO can obtain the most negligible value of RMSE (2.4251 \(\times\) 10\({}^{-3}\)), while MVO, QMVO, and CLPSO did not yield the best RMSE results. Simultaneously, Figure 8 shows the comparison of I-V and P-V curves between simulated data by the DLMVO and the measured data. It can be seen clearly from Figure 8 that the identification data of the Photowat-PWP201 component agree well with almost all measured data, which
## 1 Introduction
### Passive Methods (Passive Coatings)
#### 1.1.2 Coatings Based on Interference  
Multilayer interference coatings can have the thickness close to \(\lambda\)/4. However these coatings are effective only in the narrowband frequency range of incident waves and a narrow band of arrival directions. Interference coatings are used to scatter incident waves in some direction, unexpected by receiver systems (or in an unexpected frequency range).
## 5 Obstacles and Challenges in Evolving V2X Communication
### Security Risks of 5G Ran
#### 5.2.3 Modulation and Coding Scheme (MCS) Saturation  
As a consequence of the adaptive properties of the communication link, the channel stability can be maintained in case of a poor channel, e.g., by applying a lower degree of modulation. However, if the channel is sufficiently strong, then the level of modulation can be higher, providing better channel capacity. This adaptive approach alters between discreet levels of modulation and coding schemes (MCSs). The set of a proper MCS is also essential for a secure MIMO communication, as usually [PERSON] has a higher signal-to-noise ratio (SNR) than [PERSON] (beam focusing), and a high enough MCS selection permits [PERSON] to receive but not [PERSON] with the lower SNR. Hence, a problem may arise when the channel properties are strong enough but the system already reached the highest MCS. Thus, [PERSON] can have a high enough SNR to receive even the packages with the highest MCS, as [PERSON] will not increase it further. In summary, the proper MCS selection can provide security against eavesdropping but also has limitations. MCS saturation can happen systematically, as a result of specific system features, and also artificially, e.g., by altering the channel measurement results (pilot contamination). Hence, to avoid MCS saturation flaws, the  
Figure 7: Change in secrecy capacity by the number of base station (BS) antennas in case of passive eavesdropping [111]. The secrecy capacity of [PERSON] remains constant as the MIMO system focuses a beam on [PERSON].  
communication system has to contain adequate power control and further security aspects preventing false channel measurements [107; 110].
## 4 Optimizing EE- and SE-Based Beam Training
### RBT-DRL L-Based Beam Training with EE and SE  
To reduce the error between the estimated and true channel parameters and optimize a beam training strategy that takes the current channel circumstances, the RBT-DRL-based beam training is essential because it maximizes EE and SE by reducing beam training overhead and dynamically adjusting to network conditions, improving system performance. Mm-wave communication has been proposed as a promising solution to meet the increasing demand for higher data rates in wireless communication systems. However, mm-wave communication faces significant challenges due to high path loss and blockage caused by obstacles in the transmission path. To lessen the effects of these difficulties, channel estimation is essential in mm-wave communication. To adaptively improve EE and SE while reducing computational and measurement overhead, the proposed beam training approach makes use of DNN. When compared to traditional methods, this creative approach guarantees that our technology achieves higher performance and adaptability. DNNs have been demonstrated to reduce the error between the estimated and true channel parameters, and the DNN is trained using a sizable dataset of input-output pairs. But to train and infer, DNN-based techniques need a lot of training data and processing power. Further study in this field is expected to increase the accuracy and efficiency of these methods. The DNN-based channel estimation shows promise as a workable solution for mm-wave communication systems. To optimize either EE or SE, a beam training strategy that takes the current channel circumstances into account and modifies the amount of activated RF chains in the mm-wave system may enhance overall performance and adjust to changing channel circumstances. The mm-wave system's efficiency and dependability in difficult conditions can be improved by adding RBT-DRL into beam training.