esa-sceva/satcom-chunk-collection huggingface dataset:
# A Circularly Polarized mmWave Dielectric-Resonator-Antenna Array for Off-Body Communications
## 4 Conclusions  
This study presented a four-element circularly polarized DRA array for mmWave off-body applications. For the suggested antenna, cross-slot coupling, together  
Figure 21: Simulated and measured radiation patterns across the CP bandwidth of the DRA array.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{
\begin{tabular}{c} **Elements** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\(\mathbf{f_{c}}\) **(GHz)**} & \multirow{2}{*}{**Gain**} & \multicolumn{2}{c}{\(\mathbf{S_{11}}\) **BW**} & \multicolumn{2}{c}{**AR BW**} & \multirow{2}{*}{**Size (mm3)**} \\ \cline{1-1}  & & & & **(\%)** & & & \\ \hline
[5] & 4 & 30 & 12.7 dBic & 16.4 & 1.1 & - \\ \hline
[17] & 4 & 60 & 11.4 dBic & 26 & 15.9 & - \\ \hline
[18] & 4 & 60 & 10.5 dBi & 12 & LP & - \\ \hline
[19] & 8 & 32 & 12 dBi & 18.29 & LP & - \\ \hline
[20] & 4 & 30 & 9.5 dBic & 33.8 & 5 & \(20\times 20\times 1.52\) \\ \hline
[21] & 16 & 28.7 & 15.68 dBi & 9.81 & LP & \(46\times 46\times 1.5\) \\ \hline
[22] & 4 & 27.5 & 9.8 dBi & 31.6 & LP & \(47\times 8\times 1.084\) \\ \hline This work & 4 & 28 & 13.7 dBic & 29 & 13 & \(36.5\times 36.5\times 2.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the proposed antenna performance with those of previously published designs.  
with a sequential parallel feeding network, was utilized to achieve a wide impedance bandwidth of 29% while maintaining a high radiation efficiency, of 90%. The DRA offers a CP-bandwidth improvement of more than 160% compared to reported counterparts that operate at the same frequency range. In addition, exciting a higher-order resonance mode provided a notably higher gain over a frequency range of 26-31 GHz. The SAR was investigated and it was demonstrated that the proposed array meets the required safety limits when placed at a distance of 5 mm from the human-body tissue. The proposed antenna array was fabricated and measured with a gain of 13.7 dBic, which represents an enhancement of 5 dBi above that of a single DRA. The impact of human-body proximity was investigated and found to be negligible due to the presence of the ground plane. The results of simulations and measurements are in close agreement. The proposed antenna outperforms the mmWave DRA arrays reported previously in the literature in terms of gain and CP bandwidth and offers promising potential for mmWave off-body communications.  
T.S.A.: simulation, manufacturing and measurements, writing; R.S.: writing, S.K.K.: supervision and writing. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors would like to acknowledge the use of the National mmWave Measurement Facility and thank [PERSON] for his support with the measurements. Furthermore, the authors would like to thank [PERSON] for outlining the DRA positions on the feed network.  
The authors declare no conflict of interest.
# Suitability Evaluation of Urban Underground Space Development: A Case Study of Qingdao City
## 5 Results
### Distribution of Suitability Level Zoning
Their respective proportions of the district area exceed 30% and 25%. In Laoshan District, over 80% of the area is rated as moderately suitable for development, while in Licang District and Chengyang District, 75.76% and 86.45% of the areas, respectively, are deemed unsuitable for underground space development. Therefore, the focus of underground space development across various depth levels within the research area remains centered on specific areas within the southern and northern districts, as well as select areas within Laoshan and Licang districts. When formulating specific development and utilization plans for the main urban area of Qingdao, emphasis should be placed on these key development zones. \begin{table}
\begin{tabular}{c c c c} \hline
**Administrative Region** & **Suitability Level** & **Square Measure (km2)** & **Proportion (\%)** \\ \hline \multirow{4}{*}{Shinan District} & Very suitable & 14.22 & 26.85 \\  & More suitable & 7.77 & 14.67 \\  & Generally suitable & 20.74 & 39.15 \\  & Not suitable & 10.24 & 19.33 \\ \hline \multirow{4}{*}{Shibei District} & Very suitable & 12.29 & 17.33 \\  & More suitable & 7.92 & 11.17 \\  & Generally suitable & 38.02 & 53.62 \\  & Not suitable & 12.67 & 17.87 \\ \hline \multirow{4}{*}{Laoshan District} & Very suitable & 3.75 & 0.81 \\  & More suitable & 15.88 & 3.43 \\  & Generally suitable & 319.28 & 69.04 \\  & Not suitable & 123.49 & 26.70 \\ \hline \multirow{4}{*}{Licang District} & Very suitable & 0.19 & 0.13 \\  & More suitable & 7.63 & 5.04 \\ \cline{1-1}  & Generally suitable & 37.96 & 25.09 \\ \cline{1-1}  & Not suitable & 105.42 & 69.69 \\ \hline \multirow{4}{*}{Chengyang District} & Very suitable & 0 & 0 \\ \cline{1-1}  & More suitable & 1.91 & 0.85 \\ \cline{1-1}  & Generally suitable & 71.45 & 31.83 \\ \cline{1-1}  & Not suitable & 151.08 & 67.31 \\ \hline \end{tabular}
\end{table} 
Table 6: Distribution of suitability evaluation levels for shallow underground spaces in the main urban area.
## 3 Results and Discussion
### Adsorption Kinetics
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{5}{c}{**Kinetic Parameters for Adsorption with MB**} \\ \hline
**Kinetic Model** & **Parameters** & \multicolumn{3}{c}{**Adsorbents**} \\ \hline  & & **GO** & **GAC** & **NaY** \\ \hline  & q\({}_{\rm e}\) (mg/g) & 285.1 & 226.3 & 91.4 \\ \cline{2-5} Pseudo-first-order & q\({}_{\rm m}\) (mg/g) & 407.3 & 301.9 & 131.5 \\ \cline{2-5}  & K\({}_{\rm 1}\) (min\({}^{-1}\)) & 0.02 & 0.008 & 0.01 \\ \cline{2-5}  & R\({}^{2}\) & 0.730 & 0.967 & 0.578 \\ \hline  & q\({}_{\rm m}\) (mg/g) & 303.0 & 250 & 97.0 \\ \cline{2-5} Pseudo-second-order & K\({}_{\rm 2}\) (g/mg.min) & 0.0001 & \(3.07\times 10^{5}\) & 0.0002 \\ \cline{2-5}  & R\({}^{2}\) & 0.986 & 0.982 & 0.985 \\ \hline  & \(\beta\) (g/mg) & \(-12.04\) & 40.9 & 15.6 \\ \cline{2-5}  & \(\alpha\) (mg/g.min) & 54.4 & \(-61.3\) & \(-420\) \\ \cline{2-5}  & R\({}^{2}\) & 0.948 & 0.876 & 0.977 \\ \hline  & K\({}_{\rm 1d}\) (mg/g.h\({}^{1/2}\)) & 28.97 & 10.22 & 6.76 \\ \cline{2-5}  & C\({}_{\rm 1}\) (mg/g) & \(-8.4\) & \(-2.83\) & 3.28 \\ \cline{2-5}  & R\({}^{2}\) & 0.962 & 0.996 & 0.976 \\ \cline{2-5}  & K\({}_{\rm 2d}\) (mg/g.h\({}^{1/2}\)) & 7.7 & 6.90 & 3.69 \\ \cline{2-5}  & C\({}_{\rm 2}\) (mg/g) & 168.6 & 61.02 & 28.2 \\ \cline{2-5}  & R\({}^{2}\) & 0.936 & 0.943 & 0.944 \\ \cline{2-5}  & K\({}_{\rm 3d}\) (mg/g.h\({}^{1/2}\)) & \(-3.2\) & 1.91 & \(-1.01\) \\ \cline{2-5}  & C\({}_{\rm 3}\) (mg/g) & 332.2 & 106.6 & 106.6 \\ \cline{2-5}  & R\({}^{2}\) & 0.611 & 0.490 & 0.667 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of kinetic model parameters for removal of MB for the adsorbents studied. Figure 4: Pseudo-second-order kinetic model for removal of (**a**) MB and (**b**) RhB with NaY, GAC, and GO (5 mg of adsorbents added to 500 mL of dye solution with 4 mg/L concentration at 100 rpm at room temperature and no pH adjustment).
# Dynamic Interaction Analysis of Long-Span Bridges Under Stochastic Traffic and Wind Loads
## 2 Framework for Stochastic Traffic-Bridge Coupled Vibration Under Combined Wind Action
### Derivation of Wind Loads  
Wind load is a primary external force acting on bridges and is typically calculated based on wind speed, air density, and the bridge's windward area. Assuming that the wind speed is \(U(t)\), the air density (kg/m\({}^{3}\)) is \(\rho_{a}\), the wind resistance coefficient is \(C_{D}\), and the windward area of the bridge is \(A\), the wind load force can be expressed as  
\[F_{wind}(t)=\frac{1}{2}\rho_{a}AC_{D}U(t)^{2}e \tag{6}\]  
where \(e\) is a unit vector indicating the direction of action of the wind load. Wind loads usually act on the transverse degrees of freedom of a bridge, so this force has an important effect on the transverse vibration of the bridge. The wind speed \(U(t)\) can be modeled using a wind speed spectrum, and the fluctuating part of the wind speed is usually described by a Gaussian process:  
\[U(t)=U_{0}+\Delta U(t) \tag{7}\]  
where \(U_{0}\) is the average wind speed and \(\Delta U(t)\) is the fluctuating part of the wind speed. The pulsation characteristics of wind load, described by the wind speed spectrum, significantly influence the dynamic response of structures.  
Figure 3: The vehicle numerical dynamic model.
## 3 Results  
Figure 2 shows means and standard deviations of jump height of all jump types for both sexes. Males (SJ): 0.38 \(\pm\) 0.06; CMJ: 0.44 \(\pm\) 0.06; CMJA: 0.50 \(\pm\) 0.04; VSJ: 0.62 \(\pm\) 0.07 [m]) jumped higher than females (SJ: 0.24 \(\pm\) 0.05; CMJ: 0.26 \(\pm\) 0.04; CMJA: 0.31 \(\pm\) 0.05; VSJ: 0.36 \(\pm\) 0.08 [m]) across all jump types (\(F\)(1,1) = 1512.01, \(p\) < 0.001, pn2 = 0.98). Two-way ANOVA with repeated measures revealed differences between jump types (\(F\)(1,3) = 280.15, \(p\) < 0.001, pn2 = 0.91) and an interaction between jump type and sex (\(F\)(1,3) = 26.75, \(p\) < 0.001, pn2 = 0.49). Pairwise comparison revealed differences in jump height for all pairs of jump types (\(p<0.001\)). Figure 3 displays the distributions and correlations of jump height between general jumps and VSJ. In females, VSJ correlated with SJ (\(r\) = 0.88, \(p\) < 0.001), CMJ (\(r\) = 0.88, \(p\) < 0.001), and CMJA (\(r\) = 0.82, \(p\) < 0.001). In males, VSJ correlated with SJ (\(r\) = 0.80, \(p\) < 0.001), CMJ (\(r\) = 0.85, \(p\) < 0.001), and CMJA (\(r\) = 0.75, \(p\) < 0.001). Correlation results between VSJ height and kinematic and kinetic characteristics during SJ, CMJ, and CMJA in females and males are presented in Table 1.  
Figure 2: Mean and standard deviation of jump heights during SJ, CMJ, CMJA, and VSJ in females and males. Regression predictions of VSJ height based on general jump types were improved from the simple model (i.e., jump height of general jump types as the only predictor) in females for SJ from \(R^{2}\) = 0.76, \(p\) < 0.001 to \(R^{2}\) = 0.92, \(p\) < 0.05 and in males for SJ from \(R^{2}\) = 0.61, \(p\) < 0.01 to \(R^{2}\) = 0.71, \(p\) < 0.05 and for CMJA from \(R^{2}\) = 0.52, \(p\) < 0.01 to \(R^{2}\) = 78, \(p\) < 0.01. The equations of the improved regression models were as follows:  
\[ \begin{array}{l}\text{Female~{}VSJ height}=-0.09+1.48\text{-SJ height}+1.78\text{-}10^{-4}\text{-SJ maximal RFD FP2}-7.47\text{-}10^{-5}\text{-SJ maximal RFD FP1}\text{ and FP2},\\ \end{array}  \tag{1}\]
## 7 Future Challenges and Opportunities
The micromachined THz system platform also allows for the implementation of MEMS (micro-electromechanical systems) switches, allowing for reconfigurable antenna front ends. There is significant potential to realized silicon-micro-machined antenna systems with low-order (1 to 2 bit) MEMS-based phase control to support beamforming abilities of large-scale array antennas, with the benefits of low insertion loss, high compactness, convenience of control circuitry, and high-power packing abilities. The disadvantage associated with the limited number of degrees of freedom in beamforming can be overcomes by combining the above-mentioned array antenna topologies.
## 4 MADDPG-Based Integrated Space-Air-Sea Network System Cost Minimization Algorithm
### Partially Observable Markov Decision Process (POMDP) Strategy
#### 4.1.1 State Space and Observation Space  
At time \(t\), the intelligent agents \(i\) observe the network state. The overall state space mainly consists of the positions of the IoT devices and UAVs, as well as information related to the devices' tasks and transmission delays to the UAVs or LEO satellites, represented by \(s(t)=\left\{\mathbf{p}_{i}(t),\mathbf{q}_{j}(t),R_{ij}(t)\right\}\). Considering that the system has partial observability, each intelligent agent can only observe part of the information in the overall state space. The observation space for an intelligent IoT device at time \(t\) is defined as \(o_{i}(t)=\left\{\mathbf{p}_{i}(t),R_{ij}(t)\right\}\), which indicates that the edge server can obtain position information through the GPS. The observation space for the UAVs is defined as \(o_{i}(t)=\left\{\mathbf{q}_{j}(t)\right\}\), meaning that the edge servers can obtain the position information.
## 3 Simple Model of Bandwidth Consumption Calculation in 5G Fronthaul Interfaces
### Bandwidth Consumption in A-RoF Interfaces
On this basis, the total occupancy of the optical band in the wavelength domain, using the formulas (4) and (5), can be written as the optical wavelength spectrum (OWS) [23]  
\[\text{OWS}=\sum\limits_{i=1}^{S\cdot A}\frac{\left[\Delta f_{i}\middle/\Delta f _{\text{Bmin}}\right]\cdot\Delta f_{\text{Bmin}}\cdot c}{\left[f_{0}\pm\sum \limits_{j=1}^{i}\left(\left|\Delta f_{j}\middle/\Delta f_{\text{Bmin}}\right| \cdot\Delta f_{\text{Bmin}}\right)\right]^{2}} \tag{7}\]  
where \(S\)--a number of sectors per RRH, \(A\)--antenna modules array per sector; \(\Delta f_{i}\), \(\Delta f_{j}\)--radio frequency bandwidth of \(ij\)-th modulating signals; \(\Delta f_{\text{Bmin}}\)--minimal frequency bandwidth of optical channel, which according to the DWDM grid is 12,5 GHz [25] or smaller in the future EONs; \(f_{0}\)--the reference frequency for the optical carrier equal to 193,1 THz--according to [25] or other band carrier; \(c\)--a speed of light in free space equal to \(3\cdot 10^{8}\) mps. The Formula (7) shown above allows the frequency bands to be combined with the count-down (sign \"+\") or count-up (sign \"-\") of the optical channel number relative to the reference frequency \(f_{0}\).
## 3 Dataset for Industrial Bearings
### Vibration Dataset  
The vibration signals coming from any bearing are always affected by the noise coming from the surrounding parts of the machine, which can make the recognition of defects and faults difficult [5]. In fact, those paths are usually complex and have non-stationary  
Figure 1: Test rig for industrial medium-sized bearings [80].  
Figure 2: Interior of the ”self-contained box” [80]: (**a**) top view; (**b**) front view.  
behaviour and the extraction of the required information could be hard [49]. For those reasons, a preliminary analysis of the vibration signature of the test rig in regular operating conditions is essential before the beginning of any condition monitoring activity [80].  
In the experimental testing session carried out for the generation of the database of vibration data used in this work, four SKF spherical self-aligning bearings 22,240 CCK/W33 with a tapered bore and a 360 mm outer diameter were mounted inside the test rig. The vibration signals were acquired through the sensors and the DAQ system and were digitally sampled at \(f_{s}=20,480\) Hz with AC coupling, which was used to remove the offset DC component of the voltage that was used to energise the piezoelectric accelerometers from the acquisition. A sixth-order Butterworth filter with a \(10,200\) Hz cut-off frequency was initially applied to the acquired signals and some digital filters were then designed for the elimination of the mechanical and electromagnetic noise to improve the signal-to-noise ratio (SNR).  
Experimental data were obtained for three radial load cases over ten different rotational speeds and three defect conditions. The rotational speed refers to the three-phase motor synchronous velocity. Point defects were made by removing a circular portion of the material with a diameter of 2 mm and a depth of 0.5 mm from the raceways and only a single defect was present at a time in the bearing. The inner race (IR) and the outer race (OR) defects are shown in Figure 3, whereas Table 1 shows the operating conditions for the tests. Each recorded vibration path is 60 s long and the whole database consists of 90 vibration paths, labelled depending on the defect condition of the tested bearing.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
###### Abstract  
To address the issues of fault tolerance and retrieval efficiency in cloud storage space data, we propose an efficient cloud storage solution based on erasure codes. A cloud storage system model is designed to use erasure codes to distribute the encoded original data files across various nodes of the cloud storage system in a decentralized manner. The files are decoded by the receiver to complete data recovery and ensure high availability of the data files while optimizing redundant computing overhead during data storage, thereby improving the stability of encoding and decoding and reducing the bit error rate. Additionally, the Kademlia protocol is utilized to improve the retrieval efficiency of distributed disaster recovery storage data blocks. The proposed solution is tested on the Hadoop cloud storage platform, and the experimental results demonstrate that it not only maintains high availability but also enhances the efficiency of retrieving data files.  
cloud storage; redundancy computation; erasure code; Kademlia; Hadoop platform
# Study of the Dynamic Adaptive Calculation Method for River Water Environmental Capacity
###### Abstract  
Controlling the total amount of river pollutant discharge is an important means of water resource protection and management, and it is also a necessary condition for ensuring the normal functioning of water areas. The total amount of pollutant discharge is closely related to the water environmental capacity (WEC). Shifting from the traditional method of calculating WEC to dynamic analyses and calculations, concerning practical applications, in this paper, a dynamic adaptive calculation method is proposed for the river WEC that considers the changes in adaptive demand and hydrological conditions. In this method, the dynamic WEC is represented by intervals based on dynamic changes in different spatial and temporal scales, various calculation methods, hydrological conditions, and parameters. According to the calculation results for the WEC, a variable interval was formed. Taking the Shaanxi section of the main stream of the Wei River as the research object, with the support of an integrated platform, the dynamic adaptive calculation of the WEC in the Shaanxi section of the Wei River was realized, and a corresponding simulation system was constructed. The verification results show that (1) the dynamic calculation of WEC can be realized by freely combining different model methods and calculation conditions; (2) the WEC is described using a variable interval, which has strong applicability and operability; and (3) the simulation system can quickly adapt to the changing needs of practical applications and provide managers with visual and credible decision support. The research results provide a theoretical basis for river water environment pollution prevention and environmental management decision-making and help in the high-quality development of the river basin.  
water environmental capacity; multi-model; variable interval; dynamic simulation; Wei River Basin
# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 4 Experimental Setup
### Comparison Methods  
We used many baseline approaches from the area of link prediction to verify the efficiency of the proposed method. Translational distance, semantic matching, and multi-layer network approaches are examples of these methods. The following are the descriptions of these methods:  
* _TransE_: TransE [16] is one of the most common link prediction models for knowledge bases. To model multi-relational data, TransE transforms entities and relations into embedding spaces and regards relations as a translation from the head entity to the tail entity.
* _DistMult_: DistMult [21] is the most commonly used semantic matching model. By comparing latent semantics in the embedding space, DistMult estimates the possibility of a triplet.
* _ComplEx_: ComplEx [22] is an extension of DistMult. ComplEx embeds entities and symmetric or antisymmetric relations into the complex space instead of real-valued ones.
* _R-GCN_: R-GCN [12] is an extension of the graph convolutional network. For working with extremely multi-relational data in KBs, R-GCN proposes a generalization method for graph convolutional networks.
* _ConvE_: ConvE [13] is the typical convolutional neural network-based model. It is a relatively simple multi-layer convolutional framework for link prediction.
* _TorusE_: TorusE [20] embeds relations and entities on a compact lie group. It follows the TransE and regards relations as a translation. Because of its flexibility, it can handle large KBs.
* _CANet_: Convolutional adaptive network (CANet) is the method proposed in this paper. The weights of the convolutional kernels are set to be adaptive.
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## Appendix A  
Let \(G_{t}\) denote the channel gain of a typical link. Let \(I_{r_{s}}\) denote the interference from the outside of the sensing range \(r_{s}\), then \(I_{r_{s}}=\sum\limits_{X_{i}\in\Phi_{r_{s}}^{\infty}}G_{i}|X_{i}|^{-\alpha}P\), where \(G_{i}\) denotes the channel gain of each interfering link, \(\Phi_{r_{s}}^{\infty}\) is a set of interfering nodes outside of the sensing range \(r_{s}\), \(X_{i}\) is a point in \(\Phi_{r_{s}}^{\infty}\), and \(|X_{i}|\) is an euclidean distance between \(X_{i}\) and origin. The success probability \(p_{s}^{\text{non-IC}}\) can be expressed as follows,  
\[p_{s}^{\text{non-IC}}=\Pr\left[\frac{G_{t}r_{t}^{-\alpha}P}{I_{r_{s}}}\geq \beta\right]=\Pr\left[G_{t}\geq\frac{\beta r_{t}^{\alpha}}{P}I_{r_{s}}\right] =\mathbb{E}_{I_{r_{s}}}\left[\exp\left(-\frac{\mu\beta r_{t}^{\alpha}}{P}I_{r_ {s}}\right)\right]. \tag{1}\]  
Let \(s=\frac{\mu\beta r_{t}^{\alpha}}{P}\), then \(\mathbb{E}_{I_{r_{s}}}\left[\exp\left(-sI_{r_{s}}\right)\right]\) is expressed as follows,  
\[\mathbb{E}_{I_{r_{s}}}\left[\exp\left(-sI_{r_{s}}\right)\right] =\exp\left(-2\pi\left(\lambda_{t}+\lambda_{a}\right)\int_{r_{s}} ^{\infty}\left(1-\mathbb{E}_{\text{G}}\left[e^{-sGPv^{-\alpha}}\right]\right) vdv\right) \tag{2}\] \[=\exp\left(-2\pi\left(\lambda_{t}+\lambda_{a}\right)\int_{r_{s}} ^{\infty}\left(\frac{\beta}{\beta+\frac{\pi^{2}}{r_{t}^{2}}}\right)vdv\right)\] \[\therefore p_{s}^{\text{non-IC}} =\exp\left(-\pi\left(\lambda_{t}+\lambda_{a}\right)\sqrt{\beta}r_ {t}^{2}\arctan\left(\frac{\sqrt{\beta}r_{t}^{2}}{r_{s}^{2}}\right)\right).\]  
The value \(\mathbb{E}\left[\exp\left(-s_{a}I_{r_{s}}\right)\right]\) can be found in a similar way. Then,  
\[\mathbb{E}\left[\exp\left(-s_{a}I_{r_{s}}\right)\right]=\exp\left(-\pi\left( \lambda_{t}+\lambda_{a}\right)\sqrt{\beta}r_{a}^{2}\arctan\left(\frac{\sqrt{ \beta}r_{a}^{2}}{r_{s}^{2}}\right)\right). \tag{20}\]  
Finally, we get the result.