esa-sceva/satcom-chunk-collection huggingface dataset:
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 6 Conclusions  
A database of the natural periods and damping ratios of 58 RC buildings with heights ranging from 44.8 m to 305 m was constructed based on ambient vibration measurements. Design formulae of natural frequency and damping ratio for wind design were proposed using the database and compared to the empirical formulae provided in various design codes and research papers.  
The proposed equation for estimating the natural period was found to be close to the empirical formula of [PERSON] and [PERSON], which was derived using the measured data of Korean buildings shorter than 66 m. Moreover, the results obtained using all the empirical formulae for estimation of the natural period provided in previous research were very close to the measured values, except those obtained using the formulae provided in ASCE 7-10.  
In terms of estimation of the damping ratio, the results obtained using the empirical formula proposed by [PERSON] were the closest to the measured values only when the amplitude-dependent term was excluded. When the amplitude-dependent term was included in the calculation, the results obtained using the empirical formulae provided in ESDU 83009 yields were close to the results obtained using the proposed equation, while other empirical formulae overestimated the damping ratio considerably for buildings taller than 200 m.  
T.H. and H.K. conceived and designed the experiments; S.S. analyzed the data; H.K. wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Conclusions  
We characterized the attenuation of terahertz waves in foggy and rainy conditions at 0.1-1 THz frequencies using [PERSON] approximation theory and Mie theory. We found that at different temperatures, the influence of temperature on fog attenuation of terahertz waves is greater at high frequencies and smaller at low frequencies. Additionally, a decrease in visibility and an increase in frequency leadsto an exponential increase in fog attenuation, with visibility having a more significant influence than frequency. For advection fog, it is recommended to use [PERSON] approximation theory at 0.1-0.8 THz band and Mie theory at 0.8-1 THz band. On the other hand, for radiation fog, the preferable theory to use is [PERSON] approximation theory. Advection fog attenuation at 1 THz is 8488.33 dB, 1999.12 dB, 255.05 dB, and 26.44 dB, and radiation fog attenuation at 1 THz is 27.02 dB, 12.31 dB, 4.23 dB, and 1.46 dB, corresponding to visibility values of 30 m, 50 m, 100 m, and 200 m, respectively. In addition, the rain attenuation of terahertz waves exponentially increases with frequency and rainfall rate, with significant differences observed among diverse raindrop distributions. The rainfall rate and frequency have a greater impact than raindrop distribution on rain attenuation. For the Joss distribution, the attenuation values at 1 THz are 7757.33 dB, 532.83 dB, 16.59 dB, and 2.02 dB. The attenuation values at 1 THz for the M-P distribution are 4718.04 dB, 608.95 dB, 94.05 dB, and 16.93 dB. The attenuation values at 1 THz for the Weibull distribution are 4500.98 dB, 411.22 dB, 66.38 dB, and 19.32 dB. These four values correspond to rainfall rates of 2 mm/h, 5 mm/h, 15 mm/h, and 55 mm/h, respectively. In the future, actual measurements of terahertz wave attenuation will be conducted in foggy and rainy conditions, which will be beneficial for accurately estimating the path loss and link budget in outdoor environments.  
Conceptualization, X.L.([PERSON]) and Y.W.; methodology, X.L. ([PERSON]) and L.F.; investigation, X.L. ([PERSON]) and L.F.; resources, Y.W., Z.Y., G.W., X.L. ([PERSON]) and J.Z.; data curation, L.F.; visualization, X.L. ([PERSON]) and L.F.; writing--original draft preparation, L.F.; writing--review and editing, [PERSON] ([PERSON]) and L.F.; supervision, [PERSON] ([PERSON]) and Y.W.; project administration, funding acquisition, X.L. ([PERSON]), Y.W., Z.Y., G.W., X.L. ([PERSON]) and J.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Rain attenuation of THz waves under diverse rainfall rates and distributions.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under grants 62271095 and 62171071, the Natural Science Foundation of Chongqing under grant cstc2021 jcyj-msxmX0634, and the Natural Science Foundation Innovation and Development Joint Fund (Municipal Education Commission) project of Chongqing under grant 2022 NSCQ-LZX0107.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Probabilistic Seismic Hazard Assessment of the Southwestern Region of Saudi Arabia
## 2 Methodology
### Seismic Source Model
Moreover, to the west of the rift, three new seismic sources (designated as S02, S03, and S06) were proposed and revised, added to the sources already taken into consideration. Most of these sources are from the Ethiopian research conducted in [33], where seismic activity is mostly linked to the Afar Depression. Here, the diverging plate boundaries of the East African Rift, the Gulf of Aden and the Red Sea intersect to form a complex triple junction. Based on the work by [61], which identified and characterized seismic sources using regional geology, tectonics and seismicity data, is [PERSON]'s assessment for Ethiopia. In order to identify these extra seismic sources, these conclusions were further refined using fault plane solutions and additional seismic data. A doubly bounded exponential model (Equation (5)), as suggested by [62], was used to model the magnitude recurrence relationship in these sources. As such, the recurrence parameters were evaluated using this relation. Considering the different degrees of completeness in the catalog under analysis [42], the method described in [63] was applied. By taking into consideration varying completion durations at different magnitude levels, this method determines the coefficients for every identified seismic source. \[\mathrm{N}(\mathrm{M}_{\mathrm{max}}\geq\mathrm{M}\geq\mathrm{M}_{\mathrm{min}})= \mathrm{N}(\mathrm{M}_{\mathrm{min}})\frac{\exp[-\beta(\mathrm{M}-\mathrm{M}_{ \mathrm{min}})]-\exp[-\beta(\mathrm{M}_{\mathrm{max}}-\mathrm{M}_{\mathrm{min}} )]}{1-\exp[-\beta(\mathrm{M}_{\mathrm{max}}-\mathrm{M}_{\mathrm{min}})]} \tag{5}\]  
Here, _Mmin_ denotes a chosen reference lower magnitude that can impact engineered structures, _Mmax_ denotes the maximum expected earthquake that could be generated by the seismic source, and \(\beta\) is the coefficient \(b\) multiplied by _ln10_, \(b\) being the slope from the [64] model. The maximum expected magnitudes (Mmax) were then calculated using the techniques described in the publications [65; 66; 67]. This is due to varying earthquake completeness levels in the used earthquake catalog [42]; thus, the coefficients of the [62] model cannot be accurately determined through direct regression. Therefore, we applied the approach proposed by [63], which considers different completeness times for various magnitude levels. Software based on methodologies in [65; 66; 67] was utilized for determining maximum earthquake magnitudes. Table 1 provides the seismicity parameters, which include the highest potential magnitudes, \(b\)-values, and annual rates of events exceeding Mw 4.0 for all the seismic sources under consideration.
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 3 Visual SLAM and Visual Odometry Using Deep Learning: Survey
### Deep Learning-Based Module for Visual SLAM and Visual Odometry
#### 3.1.5 End-to-End for the Visual SLAM Algorithm  
As presented in Figures 1 and 2, the research on Visual SLAM and DL-based VOE is very diverse; the research can only be applied to one module of the Visual SLAM system-building framework. Currently, most research using the End-to-End DL approach mainly use for the VOE construction process, with the basic output being the camera's moving trajectory in the environment. [PERSON] et al. (2019) proposed a CNN for extracting and training temporal features on videos using the Slow Fusion Network and Early Fusion Network, with dimensions of (\(390\times 130\times 10\times 3\)) and (\(390\times 130\times 2\times 3\)) to estimate ego-motion. The Slow Fusion Network has input data of 10 consecutive frames of a video and uses up to five conv layers. The Early Fusion Network uses the input of two consecutive frames of a video, and all convolution layers are 2D convolution. [PERSON] et al. (2007) proposed an end-to-end framework, called DeepVO, for VO estimation: The process of extracting the conventional feature-based monocular VO is based on a CNN, while the process of learning the CNN features extracted from motion information and estimating poses of two consecutive monocular RGB images is using deep RCNN. [PERSON] et al. (2010) proposed a model called Sun-BCNN (Sun Bayesian CNN for VOE, in which a Bayesian CNN is used to detect the direction of the sun from a RGB image using global orientation information as a mean and covariance. The final VOE is built upon a sliding window bundle adjuster. [PERSON] et al. (2002) proposed an unsupervised DL, called UnDeepVO, for VOE. The input data to train the model are stereo image pairs, and features are extracted from both spatial and temporal geometric constraints, but the model can perform VOE, 6-DoF poses, and depth estimation with monocular images. To estimate pose, UnDeepVO uses features extracted from a VGG CNN; to estimate depth, UnDeepVO uses an encoder-decoder architecture to generate dense depth maps. [PERSON] et al. (2013) proposed a Depth-VO-Feat framework to estimate image depth using a CNN and other CNN-based VOEs from stereo sequences. The Depth-VO-Feat framework can estimate single-view depths and two-view odometry which can reduce scaling ambiguity issues.
## 3 Modification of the _RMR_ and \(Q\) Systems Since Their Establishment
### Modification in Terms of Characterization
#### 3.1.2 \(Q\) System  
Since the introduction, the \(Q\) system has not been tremendously updated like the \(RMR\) system in terms of characterization. The foremost modifications in characterization were suggested in relations of \(\sigma_{c}\) and \(SRF\)[68; 69; 70]. In the rock-mass properties, the role of \(\sigma_{c}\) is significant. Thus, a normalization factor is applied to Equation (1) for a modified \(Q_{c}\):  
\[Q_{c}=\left(\frac{RQD}{J_{n}}\right)\times\left(\frac{J_{r}}{J_{a}}\right) \times\left(\frac{J_{w}}{SRF}\right)\times\left(\frac{\sigma_{c}}{100}\right). \tag{9}\]  
The initial maximum value of \(SRF\) for the condition of competent rock with a rock-stress problem was 20 [9]. The first relationship for \(SRF\) characterization was based on the principal field-stress ratio (\(k\)), cover depth of the tunnel (\(H\)) and \(\sigma_{c}\)[71]. The key changes were made for \(SRF\) characterization in hard massive rock in a high-stress environment and the rating for \(SRF\) increased from 20 to 400 [68]. These changes were based on the relation between \(RQD/J_{n}\) and \(SRF\) in hard rock under stress and no real stress problem was experienced for low values of \(RQD/J_{n}\). The ratio \(RQD/J_{n}\) is the relative block size that is appropriate for differentiating massive, rock-burst-prone rock and the rock-burst-prone rock has \(RQD/J_{n}\) ratio from 25 to 200 whereas typical jointed rock mass has an \(RQD/J_{n}\) of 10 [24]. Tunneling in jointed rock under high stress is likely to be less hazardous than in massive rock because the rock-bursting phenomenon is less severe [72]. For Australian mining, where the high stresses are either due to mining depth or the advancing mining front acting on jointed rock, a relation was proposed by [PERSON] for a low \(SRF\) (\(SRF\) = \(f\) (strength stress ratio)) value [73], which was confirmed by [PERSON] [74]. The continuous rating of \(SRF\) based on tunneling data under a highly stressed jointed rock-mass environment was modified such that \(SRF\) is a function of relative block size (\(RQD/J_{n}\)), intact rock strength (\(\sigma_{c}\)) and strength-to-stress ratio (\(\sigma_{c}\)/\(\sigma_{1}\)) [70].
###### Abstract  
The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests.  
congested cells; ultra-dense networks; mobility management; handover; B5G networks Article
# Design and Experiment of a Moving Magnet Actuator Based Jetting Dispenser
## 2 Configuration of the MMA Dispenser  
The configuration of the MMA dispenser including the MMA part and dispensing parts is shown in Figure 3. The MMA includes an annular-shaped permanent magnet (11) made of strong Neodymium ND-48H placed between two iron-ring poles (10). The magnetic field of the permanent magnet is axially oriented. Two identically serial coils (12) are wrapped around the bobbin (16). The bobbin is made of aluminum alloy AL6061 because AL6061 has high thermal conductivity and obtains low effective inductance from the electric coils. The ferromagnetic shell (13) and iron-ring poles (10) are made of carbon steel 1020C, both of which have high permeability and magnetic saturation density 1.6 T to avoid losing the magnetic field [24]. The magnets and iron-ring poles are mounted on a non-ferromagnetic core (7) made of AL6061, and this core is connected to the ball-needle (5) with a connector nut. The stroke-adjusting nut (15) with a thread pitch of 1 mm adjusts the ball-needle displacement from 0 to 2.5 mm. For the dispensing parts, the fluid (17) in the syringe (1) is pressurized with air (18) to form a steady continuous flow. The fluid passes through the fluid connector (2) to the chamber (4) inside the fluid body (6). When the voltage pulse (14) is applied, a current is available. A magnetic field is generated and strongly concentrated at the coil center. This electromagnetic field interacts with the permanent magnetic field to produce the Lorentz force. The Lorentz force drives the core moving upward. The return spring (8) pushes the needle downward in the dispensing stroke. The force direction depends on the direction of the current direction. As a result, the ball-needle, which is made of stainless steel, moves upward or downward to open or close the nozzle orifice (3).
## 2 Background: Data and Autoregressive Models
### AutoRegressive Integrated Moving Average (ARIMA) Models
#### 2.4.1 ARIMA Parameter Determination  
The optimal selection of the ARIMA p, d, and q parameters is crucial to the success of the forecasting procedure. The combination of the p and q parameters is based on the examination of the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots for the specific dataset, while the d parameter is chosen in order to stationarize the time series.  
In most of the scientific literature presented in the rest of this work, it is often needed to identify the best-performing ARIMA model among a number of different ARIMA (p,d,q) models for a specific forecasting application. In these cases, the selection criteria include the Akaike (AIC) and Bayesian (BIC) information criteria. The AIC measures the quality of a forecasting model, keeping a balance between overfitting and model complexity. In the case of BIC, the same rule applies, but with a higher penalty for complex models. In both criteria, lower values signify a better model.  
There have been many attempts to automate the selection of the ARIMA model parameters over the years, using different tests in order to determine the optimal values for both seasonal and non-seasonal models. The auto.arima function in R [15] constitutes a popular heuristic method for parameter selection in an ARIMA application, and it is based on a simple step-wise algorithm:  
1. Start with a small number of basic ARIMA models and select the one with the minimum AIC value.  
2. Consider up to a number of variations of the selected model and calculate the AIC value for each one. Whenever a model with a lower AIC is found, it replaces the reference model, and the procedure is repeated. The algorithm finishes when we cannot find a model close to the reference model with a lower AIC value.  
A valid ARIMA model is always returned by the above algorithm due to the fact that the model space is finite and that at least one of the starting models will be accepted as a solution [16].  
\begin{table}
\begin{tabular}{c c} \hline
**Forecasting Equation** & **ARIMA(p,d,q)** \\ \hline first-order autoregressive model & ARIMA(1,0,0) \\ random walk & ARIMA(0,1,0) \\ differenced first-order autoregressive model & ARIMA(1,1,0) \\ simple exponential smoothing & ARIMA(0,1,1) \\ simple exponential smoothing with growth & ARIMA(0,1,1) \\ linear exponential smoothing & ARIMA(0,2,1) \\ linear exponential smoothing & ARIMA(0,2,2) \\ damped-trend linear exponential smoothing & ARIMA(1,1,2) \\ \hline \end{tabular}
\end{table}
Table 1: Nonseasonal ARIMA models for time series forecasting. Random-walk and random-trend, autoregressive and exponential smoothing models constitute specific cases of the general ARIMA models. The additional presence of a constant in the models, accounts for any underlying trend or mean in the data, in order to improve their predictions and overall performance in the forecasting tasks.
## 5 Integrating 5G and Tactile Internet to Industrial Case Studies
### Integrating 5G and Tactile Internet to Educational Teaching Factory Case Studies  
Over the last few years, communication latency has been a significant barrier for many applications deployed in various network domains. Until now, the Internet of Things (IoT) allowed the interconnection of low-power, limited-transition sensors, and latency-tolerant smart devices. Despite the advancement of communication protocols and standards, the latency problem persists, lowering the quality of services (QoS) and quality of experience (QoE) in many applications. As a result, the tactile internet's high availability, security, and ultra-fast reaction times will add a new dimension to human-machine interaction by enabling haptic and tactile sensations. Furthermore, the tactile internet can thus be used as a backbone for delay mitigation in collaboration with 5G networks, particularly for ultra-reliable low latency applications such as smart healthcare, virtual and augmented reality, and smart education and e-Learning. To address these challenges, the authors in [67] identified the challenges and opportunities highlighted by the integration of the tactile internet and emerging 5G systems in modern education. A framework for the fusion of a virtual, simulated machine shop to its physical counterpart is presented in Figure 18 within the scope of a teaching factory [68], highlighting the key aspects for successful human-machine interface (HMI) and real-time communication of the physical and digital machine shop.
## 2 Literature Review and Cost Estimation Methods
### Study of the Construction of the BP (Back-Propagation) Neural Network Model  
The widespread use of the BP neural network as an application model is based on a neural network propagation network with multilayer feedforward. It is characterized by the fact that it manages to propagate errors in the direction of return. The BP neural network has three layers, and these include the input layer, the hidden layer, and an output layer as shown in Figure 2. Perhaps the most important perspective in this field comes from the area of gray system theory, which is used in applied determinations and optimizations of estimates within BP neural networks. We can say that it collects costs in the field of construction engineering and then forms data sets and training samples in order to establish the most reliable cost estimation models. Subsequently, the analysis is exemplified by gray system prediction fences, comparing aspects related to accuracy and prediction testing [32].  
According to the illustration above, within the three-layer perceptron, the input vector is defined as follows:  
\[Xi=(Xi_{1},Xi_{2},\dots Xi_{1n})^{T} \tag{1}\]  
Thus, the variable of the hidden layer is noted as follows:  
\[Yi=(Yi_{1},Yi_{2},\dots Yi_{1m})^{T} \tag{2}\]  
Therefore, the variable of the output layer is noted as follows:  
\[Oi=(Oi_{1},Oi_{2},\dots Oi_{l})^{T} \tag{3}\]  
We can say that, in a last process, the waiting vector of the output layer is noted as follows:  
\[Di=(Di_{1},Di_{2},\dots Di_{l})^{T} \tag{4}\]  
In order to take into account all aspects and characteristics in the cost estimation process, the weight matrix must be defined. This responds to the aspects regarding the weight between the training sample from the input layer to the hidden layer, this being represented by \(Vj\).  
\[Vj=(Vi_{1},Vi_{2},\dots Vj_{m})^{T} \tag{5}\]  
where \(Vj\) can represent the vector that identifies the weight of the column corresponding to the neuron with the property \(j\) in the hidden layer, the matrix weight between the sample in the hidden layer and the output layer is represented by the characteristic \(W\),  
\[Wk=(Wk_{1},Wk_{2},\dots Wk_{l})^{T} \tag{6}\]  
Thus, the column vector \(Wk\) can represent the weight vector that accurately indicates the neuron \(k\) in the output layer.  
Figure 2: Illustration of the back-propagation neural network model in estimation process.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 2 System Characterization  
This study explores the design of receiver architectures for LIS systems. In such systems, the number of parallel data streams is determined by the count of \(T\) transmitting antennas, while the diversity order is characterized by the number of \(R\) receiving antennas (uplink is considered). The analysis assumes that the Single-Carrier Frequency Domain Equalization (SC-FDE) signals employ Quadrature Phase-Shift Keying (QPSK) modulation [20].