esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Types of C-RAN  
Two approaches for splitting the base station functionalities between BBU and RRH within a C-RAN are presented by China Mobile in [4]. Based on these, C-RANs can be categorized into two types: fully centralized and partially centralized.
# Topology Optimization with Explicit Components Considering Stress Constraints
## 5 Numerical Solution Aspects
### T-Shaped Beam  
To further test the effectiveness of the algorithm, this paper selects a T-shaped beam with symmetrical load and boundary conditions as an example, as shown in Figure 11(a). The upper end of the T-shaped beam is fixed, and a downward force \(F=1N\) is applied at the center of both ends of the beam. Similar to the L-shaped beam, stress concentration will occur at the two right-angle corners. Because the boundary conditions and loads of the beam are symmetrical, it can be predicted that the optimized structure will also exhibit symmetry, thereby further testing the feasibility and convergence of the algorithm. Similarly, the finite-element analysis uses a four-node rectangular bilinear element with an element size of 0.025, totaling 5632 elements.  
Using the same initial values of the components as mentioned above ( _ini val_ = [0.33, 0.20, 0.08, 0.08, 0.04]), a total of 22 components are used, and the initial layout of the components is shown in Figure 11(b), with a total of 176 design variables. After approximately 100 iterations, it converges stably, and the final result of the optimization is shown in Figure 12(d), with a minimum compliance of 164.125. Using the same treatment method as mentioned above (a hybrid mesh dominated by quadrilaterals with an element size of 0.025, consisting of 2187 elements in Figure 12(b) and 2246 elements in Figure 12(e)), the optimization result is compared with the result obtained without adding stress constraints.  
Figure 11: Some key iterative steps in the optimization process of the L-shaped beam  
Figure 12: The design area and the initial layout of the components of the T-shaped beam.  
After adding the stress constraint, the angles at the right-angle corners on both sides of the T-shaped beam become gentler, and the stress concentration phenomenon is significantly improved. Additionally, the structure still maintains a high degree of symmetry. While maintaining the stiffness of the structure (the structural compliance only increases by 6.69%, from 164.125 to 175.108), the overall stress level is greatly reduced (the maximum von Mises stress is reduced by 57.56%, from 160.900 to 102.120). The iterative curves of the objective function and the constraint function for this case are shown in Figure 14.  
As shown in Figure 14, after the initial stage of component movement and deformation, both the objective function and the constraint function accurately converge. Compared with the iterative process of the L-shaped beam, the T-shaped beam has more complex working conditions, resulting in a relatively slower speed of iterative convergence. Some key iterative steps in the optimization process are shown in Figure 15.  
Figure 14: The iterative convergence curves of the objective function and the constraint function of the T-shaped beam.  
Figure 13: Comparison of the optimization results before and after adding stress constraints, including the finite-element mesh and stress distribution calculated from ABAQUS.
# Compatibility Analysis Between RedCap Non-Public Networks and 5G NR in TDD FR1 and FR2 Bands
###### Abstract  
RedCap technology is set to become a critical component in the growth of the Internet of Things (IoT), enabling sensors and wearable devices for medical, industrial, and commercial applications. However, because RedCap primarily operates in non-public networks and does not synchronize its time-division duplexing (TDD) mode with 5G NR networks, interference risks arise. This is particularly concerning as traditional 5G NR networks prioritize downlink communication, whereas RedCap is designed for uplink. This study investigates the potential interference between RedCap non-public networks and 5G NR in TDD FR1 and FR2 frequency bands using Monte Carlo simulation techniques. The results illustrate how RedCap deployments may impact 5G NR performance in urban and suburban environments. Key insights are provided to inform strategies for minimizing interference and ensuring coexistence between these technologies.  
RedCap; 5G NR networks; machine-type communication; internet of things +
Footnote †: journal: _Article_  
0000-0001-5697-2247][PERSON]  
1 Institute of Radio and Information Systems (IRIS), 1010 Vienna, Austria; [EMAIL_ADDRESS] (V.T.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (O.V)
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)  
#### 2.3.1. Deep Learning System (DLS) Using Contrast-Enhanced CT  
In 2018, [PERSON] et al. conducted a DLS for accurately staging liver fibrosis using contrast-enhanced CT images during the portal venous phase, leveraging a large dataset of CT images from 7461 patients with confirmed liver fibrosis. The system's effectiveness was validated on separate datasets comprising 891 patients, with detailed logistic regression analyses examining the impact of patient demographics and CT imaging techniques on diagnostic accuracy. The DLS's performance was compared to traditional methods such as radiologist evaluations, employing metrics like the area under the receiver operating characteristic curve (AUROC) and the Obuchowski index and achieving a staging accuracy of 79.4% and AUROC scores of 0.96, 0.97, and 0.95 for diagnosing significant fibrosis, advanced fibrosis, and cirrhosis, respectively [12].
## 7 Processing Architecture
### ZynqMP Hardware Utilization  
The CHP2 system is implemented and evaluated by the Xilinx Vivado EDA tool. Programmable resource consumption is within the XCZU9 EG specifications [86], and the implementation obeys the system timing constraints. About 63% of the LUT resources and 54% of the DSP blocks are utilized. The resource utilization profile is summarized in Table 3.
# Shearer-Positioning Method Based on Non-Holonomic Constraints
## 4 Conclusions  
Aiming at the divergence problem of the positioning error in the automatic face-0 positioning system in coal mines, this paper presented in-depth research around error sources, error-elimination algorithms, and positioning-algorithm experiments, and obtained the following conclusions:  
1. On the basis of the traditional INS/ODO combined-positioning method, according to the motion information in the working process of the shearer, this paper proposes a shearer-positioning method that combines the non-holonomic constraints and the output speed of the odometer to form a three-dimensional speed constraint on the shearer, which effectively suppresses the divergence of the INS mechanization result.
2. An experimental system was set up to verify the validity of the positioning method proposed in this paper. The experimental results show that, compared with a traditional INS/ODO combined-positioning scheme without non-holonomic constraints, the error of the positioning results in the forward direction was reduced by 66%, while in the lateral direction, it was reduced by 62% and in the vertical direction, it was reduced by 67% when the non-holonomic constraint was added.
3. Although the positioning method mentioned in this paper effectively improves the accuracy of shearer positioning, it was found that the positioning error of the shearer in the forward direction after the application of this method is still greater than the lateral and vertical errors, which are caused by the accumulation of errors such as the scale factor of the odometer. Further analysis and research are needed in future work.  
Conceptualization, D.S.; methodology, D.S.; software, D.S. and J.Y.; data curation, J.Y.; writing--original draft preparation, D.S. and J.Y.; writing--review and editing, D.S. and J.S.; supervision, J.S.; project administration, J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 4: Positioning error relative to reference point(**a**) Error on x-axis; (**b**) error on y-axis;(**c**) error on z-axis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Axis**} & \multicolumn{2}{c}{**Maximum Positioning Error/m**} \\ \cline{2-3}  & **INS/ODO** & **NHC** \\ \hline x & 0.88 & 0.30 \\ y & 0.08 & 0.03 \\ z & 0.12 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum positioning error.
## 2 Methodology
### Dataset
#### 2.1.4 Near-Infrared Radiance of Vegetation (NIRv)  
NIRv has been suggested as the effective substitution of satellite SIF through theoretical derivations and radiative transfer simulations [70; 71]. It is the product of the NDVI and near-infrared reflectance; it can be used as an approximation of the canopy-escaping probability of emitted SIF. NIRv can explain most of the variation in GPP on monthly and annual scales and has been successfully used to estimate global GPP [70; 72]. [PERSON] [72] and [PERSON] [73] also suggested that using satellite NIRv to estimate global GPP does not need additional information on environmental conditions. It can also be used to convert observed SIF recorded at a given angle to hemispherical SIF emissions, which are better correlated to GPP [71; 74]. In this study, we used NIRv at a spatial resolution of 0.05\({}^{\circ}\) and a monthly temporal resolution; it was calculated using surface reflectance data from MODIS data (MCD43C4) [47; 75]:  
\[\text{NIR}_{v}=\rho_{nir}\times\left(\frac{\rho_{nir}-\rho_{red}}{\rho_{nir} +\rho_{red}}-0.08\right) \tag{2}\]  
where \(\rho_{nir}\) and \(\rho_{red}\) are the reflectance acquired in the near-infrared (841-876 nm) and red (620-670 nm) portions of the electromagnetic spectrum, respectively [76]. The constant (0.08) was subtracted to reduce the impact of bare soil [77; 78].
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 4 Main Contribution
### CNN Module  
In this subsection, three classical CNN models are considered for the CSS, namely LeNet, AlexNet and VGG-16 [38; 39; 40].  
LeNet was proposed by [PERSON], the founder of the convolution neural network, in 1994 to solve the visual task of handwritten digit recognition [38]. As shown in Figure 3, LeNet contains 2 convolution layers, 2 pooling layers and 2 full connection layers.  
AlexNet carries forward the idea of LeNet and applies the basic principles of CNN to a very deep and wide network [39], as shown in Figure 4. The advantages of AlexNet can be described as five aspects. (1) ReLU is successfully used as the activation function of CNN, and its effect is proven to be better than that of sigmoid in the deeper network;  
Figure 3: The network architecture of LeNet [38].  
Figure 2: The dataset sample when SNR = \(-8\) dB.  
therefore, the gradient dispersion problem of sigmoid in the deeper network is solved successfully. (2) During training, Dropout is used to randomly ignore some neurons to avoid over-fitting of the model. (3) The overlapping maximum pooling is considered in CNN to avoid the blurring effect of average pooling. (4) The Local Response Normalization (LRN) layer is proposed to enhance the generalization ability of the model. (5) CUDA is used to accelerate the training of the CNN.  
Figure 5 provides the network architecture of VGG-16 [40]. VGG can be seen as a deeper version of AlexNet. The whole network is made up of a convolution layer and a full connection layer. Unlike AlexNet, VGG uses small convolution cores (3 \(\times\) 3).
# Mean Square Consensus of Nonlinear Multi-Agent Systems under Markovian Impulsive Attacks
## 5 Conclusions  
In this paper, a more general model for impulsive deception attacks where both gains and instants are stochastic was established, and the consensus problem of the nonlinear multi-agent system under stochastic impulsive attacks was considered. The sufficient conditions were derived with the help of some properties of the stochastic process, and some special cases with more or fewer assumptions were also considered, including the determined, linear, and stationary processes. It can be found that the convergence rate of the NMASs and the transition rates were crucial for stabilization of the considered systems. Moreover, we find that the consensus under impulsive attacks can be maintained if the frequency of attacks is small, i.e., the probability of the random variables cannot be too large. Finally, a numerical example with four cases was provided to verify the effectiveness of the obtained results. Further works will attempt to generalize the Markovian assumption to the semi-Markovian process and will take continuous disturbances into account, as the impulsive intervals with semi-Markov assumption can be used to satisfy any distributions instead of only the exponential distribution while the continuous disturbances are likely to exist in many cases.  
Conceptualization, H.L.; methodology, H.L. and Y.W.; software, H.L. and H.W.; validation, Y.W., X.Z., and P.G.; resources, X.Z.; data curation, P.G.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and Y.W.; supervision, H.W.; project administration, H.L.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by the Key-Area Research and Development Program of Guangdong Province (2019B010140002), and the National Natural Science Foundation of China (61803104 and 61673120).  
Not applicable.  
The authors declare no conflicts of interest.  
Figure 10: The error trajectories of agents with control but without an attack.
## 3 Results
### Morphology of Multi-Oriented Patterns  
The reason to rotate arbitrary patterns within the frame of calculation of the DStr, DSize, and PCom is that one version of a pattern orientation does not take into account the complicated configuration of lines that comprise layers. Charts DStr = f(angle of rotation)  
Figure 8: Morphological characteristics of arbitrary patterns. (**a**) Disorder of layer structure (DStr) and size (DSize) of sixteen arbitrary patterns. (**b.c**) Trajectory changes of DStr and DSize with pattern rotation. Credits: Butterfly-2—Wikipedia [(22)]. Plant cell—eLife [(23)]. Oblith—Shape R [(24)]. Drawing 1 and Drawing 2—Artist [PERSON] [(25)]. Insect wing—Wikipedia [(26)]. Bacteria—PLoS [(27)]. Fish skin—Wikipedia [(28)]. Leaf-1—Wikipedia [(29)]. Leaf-2—Wikipedia [(30)]. Butterfly-1—Wikipedia [(31)]. Bivalve shell—Wikipedia [(32)].  
and DSize = f(angle of rotation) (Figure 9a) illustrate that DStr and DSize substantially depend on pattern alignment, since the DStr varies from 0 to 0.16 and the DSize varies from 0.05 to 0.6.  
The morphological parameters of the layered pattern that makes up the human aorta also illustrate the high magnitude of its variability with respect to the pattern rotation; for instance, the DStr of the human aorta varies from 0.2 to 0.63 (Figure 9b). A geometric figure (Figure 9a) and the human aorta (Figure 9b) both exhibit the same general tendency in the variability of the DStr and DSize as the function of angle rotation. The variability is minimal for an angle of 0\({}^{\circ}\) and substantially increases for the angle of rotation between -80\({}^{\circ}\)-90\({}^{\circ}\). This is because the shape of all layers of the geometric figure (Figure 9a) and the shape of many layers of the human aorta looks like the shape of the sin/cos trigonometric functions. These layers are perpendicular or quasi-perpendicular to transects R\({}_{1}\),..., R\({}_{\rm j}\),..., R\({}_{\rm N}\) for the angle of a pattern rotation equal to zero. Thus, R\({}_{\rm j}\), j = 1, N only intersects with a layer once, resulting in a minimal number of anisotropic layers and minimal values for the DStr and DSize. Transect R\({}_{\rm j}\) intersects with layers at multiple points for rotation angles of -80\({}^{\circ}\)-90\({}^{\circ}\). In this case, the number of intersections of R\({}_{\rm j}\) with one layer will greatly increase, resulting in an increase in the number of anisotropic layers and the diversity of their thicknesses. This means that the values for DStr and DSize will considerably increase.  
Another reason to calculate the DStr and DSize for different pattern alignments is the possibility of detecting changes in the pattern's size and structure. Some experiments presented below in this section illustrate the sensitivity of the DStr and DSize to minor pattern changes.  
The geometric pattern (shown in Figure 9a), as well as the charts of DStr = f(angle of rotation) and DSize = f(angle of rotation) exhibit symmetry. This suggests the possibility of using the EM to reveal symmetry in binary patterns. However, the solution to this problem is outside the scope of the present work.
## 5 Data Analysis and Main Findings
### Testing the Main Hypotheses  
Three tests, \(t\)-value, \(p\)-value, and the standardised regression coefficient, were used to test the correlation between the constructs. The previous tests were conducted to identify weaknesses in the hypotheses (Hs) of this present study. The baseline values of H1 to H7 ranged from 0.888 to 0.644 and were statistically significant (0.001) while the coefficient of determination (R\({}^{2}\)) ranged from 11.598 to 8.722, which exceeded the recommended 1.96. As seen in Table 4, all the factors met the acceptance criteria for the modulus structure model, a requirement of SEM. The results of the entire construct indicated the positive effects of using BYOD programmes in different environments as well as mobile devices and smartphones. Furthermore, the correlation between the structures of the theoretical framework and the constructs was significant (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Relationship** & **PE** & **EE** & **SI** & **PT** & **FT-B** & **PT-P** & **EA** & **BI** & **Hypothesis** & **Estimate** & **\
\begin{tabular}{c} **\(t\)-Value** \\ **(R\({}^{2}\))** \\ \end{tabular} & **Path** & **
\begin{tabular}{c} **\(p\)-** \\ **Value** \\ \end{tabular} \\ \hline
**PE** & **0.796** & & & & & & & H1 & 0.739 & 8.722 & PE \(\rightarrow\) BI & **...** \\ \hline
**EE** & 0.739 & **0.823** & & & & & & H2 & 0.888 & 6.168 & EE \(\rightarrow\) BI & **...** \\ \hline
**SI** & 0.691 & 0.586 & **0.803** & & & & & H3 & 0.644 & 10.020 & SI \(\rightarrow\) BI & **...** \\ \hline
**PT** & 0.686 & 0.545 & 0.742 & & & & & H4 & 0.697 & 8.596 & PT \(\rightarrow\) BI & **...** \\
**PT** & **...** & **...** & **...** & & & & & & H4 & 0.697 & 8.596 & PT \(\rightarrow\) BI & **...** \\ \hline
**PT-B** & 0.825 & 0.673 & 0.754 & 0.898 & & & & H5 & 0.845 & 9.718 &
\begin{tabular}{c} PT-B \(\rightarrow\) \\ PT \\ \end{tabular} & **...** \\
**PT** & 0.644 & 0.569 & 0.710 & 0.823 & 0.731 & **0.870** & & & H6 & 0.698 & 11.598 &
\begin{tabular}{c} PT-B \(\rightarrow\) \\ PT \\ \end{tabular} & **...** \\
**EA** & 0.612 & 0.488 & 0.772 & 0.673 & 0.673 & 0.789 & **0.84** & H7 & 0.732 & 10.086 & EA \(\rightarrow\) BI & **...** \\
**BI** & **...** & **...** & **...** & **...** & & & & & & & & & \\ \hline
**\(p<\)**0.05, *** & 0.565 & & & & & & & & & & & & \\ \hline
**\(p<\)**0.05, *** & 0.506 & & & & & & & & & & & & & \\ \hline
**\(p<\)**0.05 & *** & 0.673\({}^{\text{\textdagger}}\) & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation matrix and discriminant validity of the measurement model, path coefficients, \(t\)-values, and \(p\)-values of the hypotheses.
## 1 Introduction  
In the field of surface science, research on the interactions between biomolecules, including their various functional groups (-NH\({}_{2}\), -COOH, -OH, and -SH), and substrates is focused on determining which structure is the most stable adsorption structure during initial coverage because these structures are important for practical applications, such as biosensors and bio-selective chips [1; 2; 3; 4; 5; 6; 7]. Notably, the adsorption structures of amino acids have yielded versatile applications because they include various functional groups, all containing a carboxyl group (-COOH) and an amino group (-NH\({}_{2}\)) [8; 9]. Previous studies of high-resolution photoemission spectroscopy (HRPES) and scanning tunneling microscopy (STM), as well as the density functional theory (DFT), have noted that the O-H dissociated-N dative bonded structure (with the carboxylate oxygen atoms and the nitrogen atom participating in the adsorption) and O-H dissociation bonded structure (with only the carboxylate oxygen atoms participating in the adsorption) are generally the stable adsorption features for amino acids on a Ge(100) surface, depending on the their adsorbed coverages [10; 11; 12; 13].  
To determine how adsorption structures depend on the substrate (semiconductor or metal), the adsorption structures of the amino acids leucine (Leu) and tyrosine (Tyr) on a Cu(110) surface at initial coverage were acquired. A previous STM study revealed the O-H dissociation bonded structure to be the most stable adsorption structure of most amino acids adsorbed on the Cu(110) surface [14]. As the carboxylate group has two equivalent oxygen atoms, they adsorb on the Cu(110) surface in two equivalent manners. For this specific purpose, a comparative investigation of the adsorption of Leu and Tyr on a Cu(110) surface at low coverage for clarifying the stable structure--less than 0.25 monolayer (ML)--was carried out using various surface analysis tools, such as reflection-absorption infrared spectroscopy (RAIRS), HRPES, and DFT calculations.
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 4 Experimental Results and Analysis
### Comparison with Existing Work
Therefore, the real-time air pressure monitoring scheme proposed in this paper utilizes a distributed Kafka stream processing engine to transmit real-time sensor data. The delay for transmitting one thousand messages is as low as 15.6 ms, and the throughput reaches up to 3000k msg/s. These results demonstrate that our scheme offers significant advantages in terms of transmission delay and throughput and exhibits excellent performance and potential application value in the field of real-time air pressure monitoring with large data volumes. This can provide a valuable reference and insights for related research and applications.