esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Single-Crystal Quartz
However, there is considerable variability in the reported intensity of the phonon resonance at 3.855 THz, which varies from 5.8 cm\({}^{-1}\)[8] to 13.1 cm\({}^{-1}\)[11], whereas our value is 10.7 cm\({}^{-1}\). Some of the variation may be attributable to differences in the quality of the quartz crystals studied: crystals with low concentrations of defects and impurities exhibit stronger and narrower phonon resonances [31]. At frequencies above 4 THz, there are large inconsistencies in the published data, precluding comparison. Impurities may be responsible for some of the variations in the published data at both THz and MW frequencies (see below). In contrast, our data for extraordinary absorption disagrees with the majority of literature. It is, however, consistent with the work of [7]. Our e-ray data indicates that there is a weak and broad absorption band centered at 3.2 THz, previously unreported at room temperature. Notably, [9] observed a weak resonance at 3.9 THz at 10 K.  
A Z-cut quartz crystal (designated QI) with thickness 4.118 \(\pm\) 0.005 mm was measured in the open resonator at 36 GHz. The results were \(\epsilon^{\prime}\) = 4.435 \(\pm\) 0.007 and tan\(\delta\) = (37 \(\pm\) 6) \(\times\) 10\({}^{-6}\). The measured \(\epsilon^{\prime}\) is consistent with the results published by other workers [13; 29], although the tan\(\delta\) is higher than the value given in [13]. Our measurements confirm that Z-cut single-crystal quartz is an excellent reference material for THz and MW measurements. However, care must be taken in selecting samples of suitable thickness for THz TDS measurements [32]: between 2 and 5 mm is preferred.
## 2 Materials  
Materials used for this work are sheets and plates of polystyrene (PS), three aluminum alloys (Al-1100, Al-6061, and Al-7475), 410 stainless steel, soda-line glass, PMMA, polyvinyl chloride (PVC), high-density polyethylene (HDPE), and a cross-ply glass-fiber reinforced plastic (XP-GFRP): 14 sheet and plate samples in all. Their properties and sizes are tabulated in Table 1. The bulk-wave attenuation coefficients, \(\alpha_{\mathrm{L}}\) and \(\alpha_{\mathrm{T}}\), for longitudinal and transverse waves, were taken from [44; 45] when available and measured for PS, PMMA, Al-1100-H18, Al-6061-T6, and 410 stainless steel (410 SS). However, the attenuation of thinner metallic alloy and glass samples of less than 5-mm thickness could not be measured. For thin polymer samples (PS and PMMA), four sheets were glued together for attenuation tests. Attenuation measurements were also conducted for 6.3-mm-thick plate of PMMA, as the previous values were taken on PMMA plates of 18 to 47 mm thickness. For the Al-6061 plate, a 25.3-mm thickness sample of comparable T6 condition was used for attenuation tests. For the 410 SS sheet, a round bar of the equivalent material (G5b in Table 1) was used for attenuation tests. For the GFRP, selected values of velocity and attenuation were used in Table 1, and the complete set of these values are also given. The velocity values and density can be converted to orthotropic elastic stiffness coefficients [46]. The GFRP sheet used in this study was reinforced by a plain weave of fiber rovings. It had 0.38 fiber fraction, and the weave pitches were 1.3 mm and 0.8 mm along \(0^{\circ}\) and \(90^{\circ}\) (3- and 2-directions) [47]. Figure 1 shows a macrophotograph of this glass fiber fabric. More flexible warp fibers bundles are along the 3-direction, whereas rigid wef fiber rovings are oriented in the 2-direction. Differences in fiber densities produced 2% faster velocity in the 3-direction, which corresponded to the direction of Lamb-wave propagation. The wavelengths of Lamb waves examined in this work were always more than twice the weave pitches. At 500 kHz or below, the cross-weave structure can be considered as a stacking of uniform sheets. At frequencies above 1 to 1.5 MHz, all the bulk-wave attenuation coefficients increased sharply, reflecting the effects of millimeter-sized cross-weave patterns. In the thickness direction, this GFRP sheet had eight layers of cross-weaved fiber rovings, at 0.25-mm thickness per layer. Thus, this sheet can be treated as uniform below 500 kHz. Figure 1: Woven rovings of glass fiber cloth after burning the matrix resin. Lamb wave propagation was along 0\({}^{\circ}\) direction marked. Top scale has 0.5 mm spacing. \begin{table}
## 4 Results and Discussion
### Vibrational Analysis
#### 4.4.2. Phenyl Ring Moiety Modes (30)
The three planar C-H bending modes were computed to be 1325, 1133 and 1100 cm\({}^{-1}\) and were correlated to the observed frequencies 1325 (R, w), 1119 (IR, R) and 1097 (IR)/1095 (R) cm\({}^{-1}\). These modes were identified as the C-H planar bending modes corresponding to the benzene modes 3, 18a and 18b, respectively. The three non-planar C-H bending modes were assigned to the observed frequencies 943, 878 and 808 cm\({}^{-1}\) with the corresponding respective computed frequencies 942, 879 and 814 cm\({}^{-1}\). These three non-planar C-H bending modes could be identified as the modes corresponding to the respective modes 17b, 5 and 10a of the benzene molecule. The assignments of these planar and non-planar C-H bending modes are in agreement with the corresponding assignments in the 1,2,4-tri light substituted benzene derivative [23; 24]. The remaining nine modes of the phenyl ring moiety arise due to the bonds C\({}_{3}\)-C\({}_{14}\), C\({}_{1}\)-O\({}_{10}\) and C\({}_{6}\)-O\({}_{12}\) and correspond to \(\
u\)(C\({}_{3}\)-C\({}_{14}\)), \(\
u\)(C\({}_{1}\)-O\({}_{10}\)) and \(\
u\)(C\({}_{6}\)-O\({}_{12}\)); \(\beta\)(C\({}_{3}\)-C\({}_{14}\)), \(\beta\)(C\({}_{1}\)-O\({}_{10}\)) and \(\beta\)(C\({}_{6}\)-O\({}_{12}\)); \(\gamma\)(C\({}_{3}\)-C\({}_{14}\)), \(\gamma\)(C\({}_{1}\)-O\({}_{10}\)) and \(\gamma\)(C\({}_{6}\)-O\({}_{12}\)). The modes \(\
u\)(C\({}_{6}\)-O\({}_{12}\)), \(\
u\)(C\({}_{1}\)-O\({}_{10}\)) and \(\
u\)(C\({}_{3}\)-C\({}_{14}\)) were computed to have frequencies 1289, 1261 and 961 cm\({}^{-1}\) and correlated to the observed frequencies 1281, 1249 and 960 cm\({}^{-1}\), respectively. These modes correspond to the modes 7a, 13 and 7b of the benzene molecule. The modes \(\beta\)(C\({}_{3}\)-C\({}_{14}\)), \(\beta\)(C\({}_{1}\)-O\({}_{10}\)) and \(\beta\)(C\({}_{6}\)-O\({}_{12}\)) were found to have computed frequencies 210, 346 and 488 cm\({}^{-1}\) with the corresponding observed frequencies 192(R), 325 (R) and 527(IR)/531(R) cm\({}^{-1}\), respectively. These modes, respectively, correspond to the modes 15, 9a and 9b of the benzene molecule. The three non-planar bending modes \(\gamma\)(C\({}_{3}\)-C\({}_{14}\)), \(\gamma\)(C\({}_{1}\)-O\({}_{10}\)) and \(\gamma\)(C\({}_{6}\)-O\({}_{12}\)) were found to have computed frequencies 58, 248 and 375 cm\({}^{-1}\) and these are correlated to the observed Raman frequencies 64, 251 and 360 cm\({}^{-1}\), respectively. These modes correspond, respectively, to the 17a, 10a and 10b modes of the benzene molecule. The assignments of these modes were found to be in good agreement with those proposed by earlier works [20; 23; 24] excepting the mode \(\gamma\)(C\({}_{3}\)-C\({}_{14}\)), which was found to have a considerably lower frequency of 64 cm\({}^{-1}\) as compared to the lower limit of the proposed range 200-400 cm\({}^{-1}\).
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 2 Model and Method
### Model Description
#### 2.2.2 BiFPN Mechanism  
As the degree of feature loss increases with the number of network layers, the effective fusion of multi-scale features can enhance the correlation between the backbone and the neck. EAAnet introduces BiFPN [20] in the neck because it can solve the problem that traditional one-way FPN cannot make full use of different scale feature information, as shown in Figure 6. Compared with PANet [36] and ASHF [37], in Figure 6, BiFPN abandons the top and bottom convolution operations in the middle branch while maintaining the top-down and bottom-up paths. Moreover, it adds links as the blue dashed line shows to upsample the input in order to fuse features intensively and efficiently, which just increases the computational cost a little bit. And it also adds shortcuts as the red arrow indicates, which can fuse cross-layer features within the same scale. These improvements result in a few redundant information and loss.
# Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
## 2 Related Work
[19], trust is obtained using the Jacobian similarity between two users. State-of-the-art recommendation systems are heavily dependent on deep learning. In [20], [PERSON] et al. used a two-layered graphical model named restricted Boltzmann machines (RBM) as one of the earliest methods that use deep learning in CF. To avoid the high computation cost of the maximum likelihood method, the model uses constructive divergence (CD) as the objective function. Since then, many deep learning-based CF methods have emerged. For a detailed survey of state-of-the-art deep learning recommendation systems, interested readers are referred to [21]. In a very recent work, arguing the limited representation of collaborative signals in embeddings in deep learning models, [PERSON] et al. [22] proposed neural graph collaborative filtering (NGCF), in which the item embedding and user embedding are fed into two separated neural network layers. The inner product of the final outputs from the two networks are used to estimate a user's preference to the target item. The network learns the graph structure of the high-order connectivity between the items and the user. In the present work, this method is used as one of the baselines. Social Trust Ensemble (STE) is a model-based method proposed by [PERSON] et al. [23], in which the latent features of the target users' neighbors' ratings are used to model trust. This method combinesmatrix factorization and trust-based recommendation. Addressing the issue of trust propagation in STE, [PERSON] et al. [24] proposed social matrix factorization (SocialMF), which is a matrix factorization method that employs the trust link between users to increase prediction accuracy. Addressing the cold-start problem in MF methods, [PERSON] et al. [25] proposed a MF method that uses explicit trust and distrust. Similar to the STE and GraFC2T2 methods, this method uses explicit trust links. However, obtaining a trust link between users is difficult, and very few public data sets contain trust relationships. For this reason, they adapted the Pearson correlation coefficient (PCC) as a trust metric for their experimental data set. Among other issues, PCC-based trust metrics fail on binary data. Therefore, in this work, we propose a new trust inference mechanism for MF models that use binary data.
# Waveform Design for Space-Time Coded MIMO Systems with High Secrecy Protection +
###### Abstract  
In this paper, we present a new secrecy-enhancing scheme for multi-input-multi-output (MIMO) systems using a space-time coding scheme. We adopt a quasi-orthogonal space-time block coding (QO-STBC) scheme that was originally designed to improve the performance of the MIMO system, and propose an efficient waveform design that can enhance the secrecy, as well as improve the error rate performance. Channel- and signal-dependent artificial interference (AI) is added to the proposed waveform, so that only a legitimate receiver can successfully retrieve information. We investigate the secrecy capacity of the proposed scheme, and demonstrate that the proposed scheme provides highly enhanced secrecy performance, compared to the conventional schemes. The performance simulation results reveal that the transmitted information can be properly extracted only at the legitimate receiver.  
multi-input-multi-output (MIMO); space time block coding; physical layer security (PLS); secrecy capacity +
Footnote †: This paper is an extended version of our paper published in ICTC 2020.
## 4 Results and Analysis
### Influence of Connectivity Between Cracks and Solution Cavities on the Wellbore Stability  
Figures 26 and 27 illustrate the variations in stress and displacement distribution around the wellbore under different conditions of cracks and solution cavities connectivity. Figure 22 shows the following: (1) The disturbed stress area around the wellbore is 45.23% larger for the condition of connected cracks and solution cavities compared to the condition of disconnected cracks and solution cavities. (2) The stress around the wellbore is higher when the cracks and solution cavities are connected. For instance, when the cracks and solution cavities are disconnected, the stress at point A is measured as 222.25 MPa. However, when the cracks and solution cavities are connected, the stress at point A increases to 278.62 MPa, representing a 25.36% increase compared to the previous condition. As for Figure 27, it is observed that the connectivity of cracks and solution cavities has an insignificant impact on the displacement of various points around the wellbore. This may be because the connectivity of cracks and solution cavities does not significantly affect the integrity of the surrounding rock, resulting in minimal differences in rock deformation around the wellbore. However, when cracks extend through the solution cavities into the formation, deformation of the solution cavities might lead to stress release, which would increase the influence of cracks on the stress distribution around the wellbore. Consequently, stress disturbance for the condition of connected cracks and solution cavities would expand compared to the condition of disconnected cracks and solution cavities.  
The equivalent drilling fluid densities of collapse pressure (\(\rho_{\rm c}\)) under the conditions of disconnected and connected cracks and solution cavities are 1.202 g/cm\({}^{3}\) and 1.362 g/cm\({}^{3}\), respectively. It reveals that \(\rho_{\rm c}\) for the condition of connected cracks and solution cavities increases by 13.33% compared to that for the condition of disconnected cracks and solution cavities. This emphasizes the importance of carefully surveying the connectivity of cracks  
Figure 25: Curve between the equivalent drilling fluid densities of collapse pressure and solution cavities quantities.  
and solution cavities during drilling design. It also highlights the need to pay greater attention to carbonate formation with connected cracks and solution cavities during the drilling process in such formation.
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
## 2 Materials and Methods  
### System Description  
#### 2.1.1 System Design  
A schematic diagram of the intelligent control system is shown in Figure 1. A Zoomlion 4 YZL-8 BZ self-propelled maize combine harvester was equipped with sensors and an intelligent controller to optimize the settings automatically. The actuators of the harvester were converted from mechanical to hydraulic. The intelligent control system for maize picking includes walking control, steering control, manual control for traditional operation (up-and-down button control of the header, main clutch switch, bridge clutch switch, and unloading clutch switch), and automatic control. The system can be operated in manual or automatic control mode. The automatic control includes joint regulation of the rotational speed of the pulling rollers, operating speed, and header height.  
Two cylinders are used to adjust the header height; their motion is controlled by a proportional valve. The prototype is driven by the front wheel. The driving motor is connected with the main reducer. The variable pump controls the driving motor according to the current signal sent by the controller to realize walking control of the maize harvester. The header has two hydraulic motors; each motor drives four rows of pulling rollers. Two proportional valves control the hydraulic motors according to the electrical signals sent by the controller to control the rotational speed of the pulling rollers.
# Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials
###### Abstract  
The shear strength of rockfill materials (RFM) is an important engineering parameter in the design and audit of geotechnical structures. In this paper, the predictive reliability and feasibility of random forests and Cubist models were analyzed by estimating the shear strength from the relative density, particle size, distribution (gradation), material hardness, gradation and fineness modulus, and confining (normal) stress. For this purpose, case studies of 165 rockfill samples have been applied to generate training and testing datasets to construct and validate the models. Thirteen key material properties for rockfill characterization were selected to develop the proposed models. Validation and comparison of the models have been performed using the root mean square error (RMSE), coefficient of determination (R\({}^{2}\)), and mean estimation error (MAE) between the measured and estimated values. A sensitivity analysis was also conducted to ascertain the importance of various inputs in the prediction of the output. The results demonstrated that the Cubist model has the highest prediction performance with (RMSE = 0.0959, R\({}^{2}\) = 0.9697 and MAE = 0.0671), followed by the random forests model with (RMSE = 0.1133, R\({}^{2}\) = 0.9548 and MAE= 0.0665), the artificial neural network (ANN) model with (RMSE = 0.1320, R\({}^{2}\) = 0.9386 and MAE = 0.0841), and the conventional multiple linear regression technique with (RMSE = 0.1361, R\({}^{2}\) = 0.9345 and MAE = 0.0888). The results indicated that the Cubist and random forests models are able to generate better predictive results of the shear strength of RFM than ANN and conventional regression models. The Cubist model was considered to be more promising for interpreting the complex relationships between the influential properties of RFM and the shear strengths of RFM to some extent, which can be extremely helpful in estimating the shear strength of rockfill materials.  
shear strength; rockfill materials (RFM); random forests; Cubist; cross-validation 2019 9 1621 10.3390/app9081621 12 March 2019; Accepted: 16 April 2019; Published: 18 April 2019
## 3 Properties of an Antibiotic-Eluting Scaffold
### Mechanical and Structural Properties  
Given that bone scaffolds are implanted in areas of dead space, they should have mechanical properties consistent with that anatomical site to prevent the risk of fracture [27]. However, scaffolds must also exhibit sufficient porosity to allow cellular penetration, angiogenesis and the transport of oxygen, nutrients and waste products [35; 36]. In addition, a porous spatial arrangement enables drug release from deep inside the material. It has been shown that _in vivo_, that macroporous antibiotic-loaded calcium phosphates out-perform microporous variants due to a higher antibiotic release rate (up to 13\(\times\) more) [37]. This is not the case with PMMA which exhibits surface-level diffusion [38]. There is therefore a trade-off between mechanical strength and porosity.
# Enhanced Gain Difference Power Allocation for NOMA-Based Visible Light Communications
## 2 System Model
\(T_{s}\) denotes a constant of an optical filter gain, and \(g_{s}\left(\psi_{ji,k}\right)\) represents the gain of an optical concentrator for \(U_{k}\) with a refractive index \(n\), which is given as  
\[g_{s}\left(\psi_{ji,k}\right)= \begin{cases}\frac{n^{2}}{\sin^{2}(\psi_{C})}, &0\leq\psi_{ji,k}\leq\psi_{C}\\ 0,&\psi_{ji,k}>\psi_{C}\end{cases} . \tag{5}\]  
By substituting \(d_{ji,k}=\sqrt{r_{k}^{2}+L^{2}}\) in (4), the LOS channel gain can be expressed as  
\[h_{ji,k}=\frac{\Omega_{ji,k}(\bar{m}_{0}+1)L^{\bar{m}_{0}+1}}{(r_{k}^{2}+L)^{ \frac{\bar{m}_{0}+3}{2}}}, \tag{6}\]  
where \(\Omega_{ji,k}=\frac{A_{PD}T_{s,g}\left(\psi_{ji,k}\right)}{2\pi}\), \(L\) is the height of the light source. The channel gain primarily relies on the distance between the user and the LED, assuming a constant LED height and \(\Omega_{ji,k}\) values.
## 3 A Feasible Solution and the Mathematical Formulations
### Mathematical Formulations
}D_{ij}y_{i}y_{j}\] (16) \[tz_{2}=z_{1}\] (17) \[\sum_{j\in V}x_{ij}+y_{i}=1,\quad\forall i\in V,(i\
eq j)\] (18) \[\sum_{j\in V}y_{j}=K\] (19) \[x_{ij}<=y_{j},\quad\forall i,j\in V,(i\
eq j)\] (20) \[x_{ij}<=1-y_{i}y_{j},\quad\forall i,j\in V,(i\
eq j)\] (21) \[x\in\{0,1\}^{|V|^{2}},y\in\{0,1\}^{|V|},t,z_{1},z_{2}\in[0,\infty) \tag{22}\]  
Finally, to analyze the relevance of model \(M_{2}\), we weigh the latency of switch-controller connections versus the latencies of controller-controller links. For this purpose, the following variant is introduced, which modifies constraints (15) and (16) using parameter \(\alpha\in[0;1]\) to weigh the switch-controller communication latencies and \((1-\alpha)\) scales the latencies between controllers. **Observation 1**. : _Note that when \(\alpha=1\), constraint (24) eliminates the consideration of connections between controllers. Similarly, when \(\alpha=0\), we eliminate the switch-controller connections in constraint (24)._\[M_{3}:\min_{ \begin{subarray}{c}\{x,y,t,z_{1},z_{2}\}\\ \text{st}:\end{subarray} } t\] \[\text{st}: z_{1}=\alpha\sum_{ \begin{subarray}{c}i,j\in V\\ (i\
eq j)\end{subarray} }D_{ij}x_{ij} \tag{23}\] \[z_{2}=\alpha\sum_{ \begin{subarray}{c}i,j\in V\\ (i\
eq j)\end{subarray} }D_{ij}x_{ij}+(1-\alpha)\sum_{ \begin{subarray}{c}i,j \in V\\ (i<j)\end{subarray} }D_{ij}y_{i}y_{j}\] (24) \[tz_{2}=z_{1}\] \[\sum_{j\in V}x_{ij}+y_{i}=1,\quad\forall i\in V,(i\
eq j)\] \[\sum_{j\in V}y_{j}=K\] \[x_{ij}<=y_{j},\quad\forall i,j\in V,(i\
eq j)\] \[x_{ij}<=1-y_{i}y_{j},\quad\forall i,j\in V,(i\
eq j)\] \[x\in\{0,1\}^{|V|^{2}},y\in\{0,1\}^{|V|},t,z_{1},z_{2}\in[0,\infty)\]  
Notice that all the proposed models are NP-hard and, thus, are difficult to solve in polynomial time. In the next section, we present a meta-heuristic algorithm that uses model \((M_{2})\) to obtain approximate solutions in a short CPU time.
## 1 Introduction
On the other hand, the Sine fit method, which is the other method present in all commercial BTT systems, still works in fitting the data where split peaks are present, but it could have some shortcomings in the determination of the dynamic parameters of the system. Here, a method based on the fitting of the BTT sensors data by means of a 2 degrees of freedom (2 DOF) dynamic model, is presented. The aim is to obtain, with the best possible accuracy, not only the response curve versus frequency, but also the parameters of the system such as resonance frequencies and damping values. This is not so straightforward if there are two very close resonance peaks. The results obtained by the 2 DOF method are here compared with those coming from the classic Sine fitting method. In detail, the paper is organized as follows. In Section 2, the basic concepts of the BTT method are presented and the 2 DOF mathematical model is introduced together with the fitting procedure. The BTT sensor data are simulated numerically for a test case consisting of a mistuned bladed disk. In Section 3, the 2 DOF fitting method is compared to the Sine fitting method in different numerical test cases, proving the capability of the 2 DOF method to estimate the main parameters of the vibrating system.