esa-sceva/satcom-chunk-collection huggingface dataset:
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 5 Conclusions  
In this paper, a new 3 GPP-standard-compliant BSAE algorithm was designed, and a testing environment was created for different scenarios and configurations, focusing on BM procedures for idle users during IA. The system employs beam-sweeping techniques at both  
Figure 11: Performance BER comparison of the 3 different algorithms for FR2.  
Figure 10: Decoding percentage of DL-SCH for FR2.  
the gNB and UE ends to beamform each SSB within the burst through a spatial-scattering channel for subsequent UE signal processing over the multiple receive-end beams. Then, after we perform RSRP measurements for each of the transmit-receive beam pairs, the pair with the maximum RSRP value was chosen as the most suitable beam pair. At this stage, the UE performs cell -election procedures for the blind decoding of the DCI in the PDCCH. Finally, the UE decodes the PDSCH to successfully recover the transmitted information.  
From the results, it can be concluded that the design and employment of efficient algorithms that explore BM procedures for beam pair selection are of extreme importance and have significant impacts on the overall performance of the system. The results obtained for the designed BSAE algorithm showed a considerable performance improvement over the beam pair determination algorithm based on the RSRP maximization without the addition of much complexity. This emphasizes the benefits of designing algorithms that consider information about all the candidate beam pairs for effective, enhanced beam pair selection between the gNB and the UE. Additionally, when comparing the results between the proposed BSAE algorithm and the optimal case, similar performances were also noticeable, highlighting its potential for practical 5G mmWave mMIMO implementations following 3 GPP-compliant standards.  
Investigation, D.A. and R.M.; supervision, A.S., P.M. and R.M.; validation, A.S., P.M. and R.M.; writing--original draft, D.A.; writing--review and editing, A.S. and P.M. All authors have read and agreed to the published version of the manuscript.  
This work is funded in part by FCT/MCTES through national funds and when applicable co-funded by EU funds under the project UIDB/50008/2020-UIDP/50008/2020 and by Fundo Europeu de Desenvolvimento Regional (FEDER) through POCI-01-0247-FEDER-072224.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion on Enabling Technologies
### Bearings  
Bearings are of great design importance for the actual adaptation of the proposed radial turbine to provide support for the relative motion between the rotating parts. As the density of the fluid lowers from supercritical high density to subcritical conditions at the extreme pressure of the Allam cycle, a large axial force is generated on the turbine component; the axial thrust load issue is resolved using suitable thrust bearings [44]. Mechanical contact-type bearings such as rolling, sliding or flexing elements are not considered by researchers for sCO\({}_{2}\) applications. Non contacting, fluid-film hydrodynamic bearings are suggested by some [45; 46] for their prevalence in high load/high speed industrial applications. A conclusion was obtained that oil-film bearings are not suitable for supercritical CO\({}_{2}\) utilisation because of the solvent-like characteristics of the sCO\({}_{2}\) fluid [47]. Recommendations include a specific hybrid hydrostatically-assisted hydrodynamic gas-foil bearing design, developed by Mechanical Solutions Inc. [48], that provides both radial and thrust support in a single combination component. The maximum temperature cited is up to 650 \({}^{\circ}\)C with a material coating which limits its applicability to the design in hand if no cooling is provided. Shaft size and weight will also limit the suitability since the inner diameter of the bearing geometry is 2 inches (\(\approx\)51 mm) which is smaller than the expected shaft diameter of 120 mm (calculated for mechanical design). Another possibility is rare earth element magnetic bearings which could levitate for 100 MW shaft scales and are developed by Electron Energy Corporation [49]. In any case, because of the operating temperature and pressure ranges of the designed turbine, bearings, and other components like gears, need to be isolated from the turbine itself via shaft end seals (as discussed in the next heading). Turbomachinery design manufacturers can provide answers regarding bearing design for the configuration of the designed turbine based on shaft sizes, but it seems that commercially-ready solutions are not yet available.
## 2 Related Work  
### Telecommunication Functions on FPGAs  
Communication standards/protocols change with fast pace, and as they evolve, new requirements arise that need to be integrated in the network infrastructure. One of the key components of a network infrastructure is the baseband processing unit (BBU). The functionality of a BBU is to implement the low-level DSP function required for transmission at the physical layer (L1).  
The role of the BBU is often undertaken by an FPGA device, due to its adaptability to new functionalities and/or features. There are a lot of works in the literature that present OFDM-based telecommunication functions implemented on FPGAs. In [5], an FPGA-based OFDM transceiver that can support multiple IEEE 802.xx standards is presented. Dynamic partial reconfiguration is used in [6] to adapt the processing datapath of a non-continuous OFDM FPGA-based baseband architecture, while, based on the same principle in [7], an adaptable FFT core is given. Ref. [8] presents an SFO compensation method for OFDM. Finally, Ref. [9] presents the experimental results of transmitting/receiving an OFDM 5G NR signal using a real-time FPGA processing pipeline. All of these works consider a complete OFDM processing pipeline and/or target processing blocks that are more computationally demanding (e.g., FFT/IFFT, and SFO) while not considering any optimizations other than computation scheduling, parallel hardware architecture design and/or pipeline.
# A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
## 3 ML Approach for Multi-Class Classification
### Data Pre-Processing  
Data are an integral part of any ML classifier, and they are composed of input features as independent variables and labeled output as the dependant variable. The quality of data and the information derived from a dataset influence the learning capacity of the model. Therefore, pre-processing before feeding the data to the model is essential. Row data may contain null values, outliers, and non-categorical data. ML is taught to classify only using categorical data, and it can not process ordinal or numeric datatype. Library _\"Panda\"_ is well-known in such a situation for data manipulation and analysis. It fits 1 for the input observations that belong to the particular class, leaving the rest of the observation 0. Similar function is performed by _One-Hot-Encoding_ in _sklearn_ library. ML treats missing value as _NaN_ also known as _not-a-number_. There are several ways to deal with missing data, like omitting the complete row to which the missing value belongs, or replacing the missing value with mean, median, or most occurred observation. _SimpleImputer_ from _sklearn_ easily imputes the missing data with a constant value or using statistics like mean, median, or most common value. Often the dataset accommodates attributes with a mixture of scale. ML models anticipate or are more efficient if the data follow the same scale. Data standardization or normalization are commonly practiced methods for data re-scaling, availed in _sklearn_. Data standardization is the distribution of attribute between a mean of zero and a standard deviation of one (unit variance), whereas data normalization is scaling of attributes into the range 0 and 1.
## 4 Analytical Model: Signal-to-Crosstalk Ratio
### Cumulative Crosstalk Power Terms  
We first calculate the cumulative crosstalk power generated by MZIs. As seen before, for \(4\times 4\)-Benes: \(K_{MZI}(4,m)=3m+3m^{2}\). Benes is a recursive structure, thus, it is not difficult to determine \(K_{MZI}(N,m)\) for a higher scale. For \(8\times 8\)-Benes, we find \(5m+10m^{2}\). Then, we can derive a general formula for \(N\times N\)-Benes, with \(N=\ 2^{n}\) where \(\in\mathbb{N}^{*}\):  
\[K_{MZI}(N,m)=\ (2k-1)m+\Big{(}2k^{2}-3k+1\Big{)}m^{2} \tag{10}\]  
We note \(k=\ log_{2}(N)\). The results for the other three architectures are mentioned in [15; 17] and summarized in Table 3. We still have to calculate cumulative crosstalk power from waveguide crossings. In the Benes matrix, all waveguides are crossed by optical power, so all crossings are contributors to crosstalk. In the worst case:  
\[K_{X}(N,x)=2(N-k-1)x \tag{11}\]  
For dilated Benes (Figure 3), only one signal passes through each MZI, and therefore not all waveguide crossings are crossed by signals for a given network configuration. The cumulative crosstalk power induced by waveguide crossings in the worst case is:  
\[K_{X}(4,x)=4x \tag{12}\]\[K_{X}(8,x)=\frac{8}{2}x+K_{X}(4,x)+\frac{8}{2}x \tag{13}\]  
\[K_{X}(N,x)=N/2x+K_{X}(N/2,x)+N/2x \tag{14}\]  
By adding up:  
\[K_{X}(N,x)=2(N-2)x \tag{15}\]  
Similarly, for DLN (Figure 4):  
\[K_{X}(4,x)=\frac{4}{2}x+x \tag{16}\]  
\[K_{X}(8,x)=\frac{8}{2}x+K_{X}(4,x)+\frac{8}{4}x \tag{17}\]  
\[K_{X}(N,x)=N/2x+K_{X}(N/2,x)+N/4x \tag{18}\]  
We get:  
\[K_{X}(N,x)=3(N/2-1)x \tag{19}\]  
For \(N\times N\)-S&S (Figure 5), each \(1\times N\)-bloc is crossed by only one signal. Therefore, it is not difficult to find:  
\[K_{X}(N,x)=(N-1)x \tag{20}\]  
The following table summarizes the values corresponding to each topology. Note that an architecture's total crosstalk power could be slightly reduced by introducing intelligent algorithms [18].  
All the expressions needed are now computed for the four architectures. We will depict \(\hat{\mathcal{C}}R\) values as a function of \(N\) for the four architectures in order to compare them; this will be the subject of the next section.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 3 Proposed Approach
### Feature Extraction  
The proposed approach for feature extraction is different from previous machine learning approaches for detecting VoIP attacks, as designing features that accurately represent messages is necessary [12; 24]. Figure 2 illustrates the feature extraction process. It starts by tokenizing messages, padding them, and embedding them.  
In the tokenizing step, all the words in the dataset are used to create a dictionary. Each SIP message produces a sequence of tokens, where a token is an index to a specific word in the dictionary. In the second step, zeros are padded to the end of every sequence to make all the sequences equal in length. The length of the sequences is selected as 104.  
Word embedding [25] has shown remarkable performance in representing the semantic and syntactic characteristics of texts. It captures the resemblance of words (i.e., tokens) in a given dictionary. For the embedding step, a word embedding layer is employed as the first layer in the proposed model to map tokens from discrete to continuous embedded representation. Each token is mapped to an embedding vector of length 50. Therefore, each SIP message is converted to a 104\(\times\)50 feature map \(X\), before being fed to the encoder part of the proposed model.
## 6 Conclusions  
This study developed an RNN model for the seismic response simulation of a building structure with a semi-active control system. An 11-story building with a semi-active TMD and a 26-story building with a semi-active mid-story isolation system were used as example structures. A magnetorheological damper was used as the semi-active control devices for both example structures. Training and verification dataset were generated using historical and artificial earthquakes. A series of numerical simulations were performed to investigate the effect of hyperparameters on the prediction accuracy of the RNN model. It was found that the sequence length and the hidden layer size mainly influenced the accuracy of the RNN model, especially the hidden layer size, which turned out to be the most important  
Figure 16: Error variation versus epoch: (**a**) Loss variation in training; (**b**) RMSE variation in verification.  
hyperparameter. The other hyperparameters and functions did not considerably affect the prediction performance of the RNN model. In general, a longer sequence length increases the accuracy of the RNN model. However, too long a sequence length worsens the prediction performance of the RNN model, because it makes the RNN model confuse the correlations between different points in too lengthy time series data. A too large hidden layer size may result in overfitting; thus, attention needs to be paid to appropriately adjusting the hidden layer size. Compared to the FE model, the well trained RNN model very accurately predicted the seismic responses of the building with a semi-active control system. As the simulation time of the RNN model was extremely reduced, thousands of simulation runs of the RNN model could be conducted during only one simulation run of the FE model. The simulation results show that the size of the example structure has little effect on the accuracy of the RNN prediction model. Because the computational time of the RNN models is trivial compared to the FE model, the larger the size of the structure is, the more effective the RNN model is. The author believes that the RNN model developed in this study can be very useful for the numerical simulation and development of a building structure with a semi-active control system. Moreover, future studies are expected to apply the proposed RNN model to the optimal design of control algorithms for a semi-active control system.  
The author has read and agreed to the published version of the manuscript. This research was supported by a National Research Foundation of Korea (NRF) grant, funded by the Korea government (MEST), grant number NRF-2019R1A2C1002385. The author declares no conflict of interest.
## 3 Methodology
### Post-Processing
#### 3.4.4 Spatial Joining and Outlier Removal  
We utilized a 50-m spatial grid aligned with the 2020 PS points dataset and employed spatial joining in ArcMap (version 10.5, Esri, Redlands, CA, USA) to integrate data across the 2012-2020 span. This method ensured the comprehensive utilization of PS points within intersecting grid areas. Post-spatial joining, we addressed data quality by removing outliers using the interquartile range (IQR) method [43].  
We selected the IQR approach over simpler methods (such as percentile thresholding or standard deviation) because it is more robust to non-normal distributions, which are common in urban height data due to the mix of low-rise and high-rise buildings. The IQR represents the middle 50% of the data and is calculated as the difference between the upper (75 th percentile) and lower (25 th percentile) quartiles [43].:  
\[IQR=Q_{3}-Q_{1} \tag{4}\] \[Outliers=\{x:x<Q_{1}-1.5\times IQR\;or\;x>Q_{3}+1.5\times IQR\}\]  
Any data points that fell above \(Q_{3}+1.5 IQR\) or below \(Q_{1}-1.5 IQR\), where \(Q_{1}\) and \(Q_{3}\) are the 25 th and 75 th percentiles, respectively, were considered outliers and removed [44]. While standard deviation-based approaches assume normal distribution, our comparative analysis showed that the IQR method better preserved true height variations while effectively removing erroneous measurements. We also tested the standard deviation approach, but found that it retained too many noise points in our dataset.
# Calibration of a Low-Cost 8\(\times\)8 Active Phased Array Antenna
## 3 Active Phased Array Calibration  
The comparison of the S parameters between simulation and measurement is presented in this section. The passive unbalanced amplitude and phase of the passive antenna is presented. Finally, the performance of different methods of calibration is presented with the whole phased array, along with the comparison between simulation and measurement.
## 4 Discussion
### Optimization of the Relay UAV Transmit Power  
The relay UAV transmit power will affect the data transmission rate and energy consumption of the UAV. According to Equations (7) and (18), the transmission power is inversely proportional to the transmission rate, inversely proportional to the delay, and proportional to the energy consumption. Therefore, the system should have the best transmission power to achieve the lowest cost and the highest utility.  
Figure 11 shows the relationship between the UAV transmission power and the system utility function at different energy levels. As the UAV transmit power increases, the system utility function  
Figure 10: Effect of \(\varphi_{2}\) on the utility function of five scenarios.  
Figure 9: Effect of \(\varphi_{2}\) on offloading level.  
initially increases and then decreases, so the network must have the best UAV transmit power to maximize the utility function. The optimal transmission power is approximately 1W, but the specific value cannot be obtained (Figure 11). Optimizing the UAV transmission power and using resources efficiently are noteworthy directions for future research.
## 3 Results and Discussion
### Modelling of Pore Volume Variation in Bulk  
To establish a generalized semi-empirical model, pore volume \(\varepsilon\) was expressed as a function of bulk depth \(H_{b}\) analogous to the model proposed by [PERSON] et al. [69] and [PERSON] et al. [35] for bulk density, which is given as:  
\[1-\frac{\varepsilon_{0}-\varepsilon}{\varepsilon_{0}-\varepsilon_{min}}=aH_{b}+c \tag{13}\]  
where \(\varepsilon_{0}\) (-) refers to the default pore volume and \(\varepsilon_{min}\) (-) refers to the minimal pore volume observed at the highest bulk depth \(H_{b}\) of 3.4 m, while \(a\) and \(c\) are the empirical constants observed from fitting analysis. To determine the pore volume \(\varepsilon\), Equation (13) was rewritten as:  
\[\varepsilon=\varepsilon_{0}-(\varepsilon_{0}-\varepsilon_{min})\cdot(1-aH_{b} -c) \tag{14}\]  
Table 6 presents the equations and goodness of fit derived from the regression analysis for the different storage times \(t\). The constants \(a\) and \(c\) were embodied in the equations. A variation of constant \(a\) from \(-0.292\) to \(-0.156\) and \(c\) from \(-1.026\) to \(-0.557\) was observed accordingly, hence revealing a decreasing trend of constants \(a\) and \(c\) with the increase of storage time \(t\). The statistical indicators confirmed the capability of the employed model to predict closely the data with a high accuracy of \(R^{2}\geq 0.963\).  
In analogy with Equation (14), a model for describing pore volume \(\varepsilon\) as function of bulk height \(H_{b}\) and storage time \(t\) with an \(R^{2}\) of 0.972 could be established:  
\[\varepsilon=\varepsilon_{0}-(\varepsilon_{0}-\varepsilon_{min})\cdot(1+0.016H _{b}+0.002t-1.013) \tag{15}\]  
This allowed the inclusion of the influence of bulk depth \(H_{b}\) and storage time \(t\) in the Matthies and Petersen model (Equation (9)), hence yielding a generalized model:  
\[\Delta P=2.73\cdot\left(\frac{23.96\ \mu_{a}v_{a}}{d_{e}^{2}}+\frac{0.51\ \mu_{a}^{0.1}\mu_{a}^{0.9}v_{a}^{1.9}}{d_{e}^{1.1}}\right)\cdot\left(\frac{H_ {b}}{\left(\varepsilon_{0}-(\varepsilon_{0}-\varepsilon_{min})\cdot(1+0.016H _{b}+0.002t-1.013)\right)^{4}}\right) \tag{16}\]  
The generalized model was able to depict the airflow resistance of wheat with an \(R^{2}\) of 0.989, \(RMSE\) of 75.91 Pa and \(MAPE\) of 16.29%.
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 1 Introduction  
Mining subsidence, a common mining-induced hazard, can result in severe damage to buildings, infrastructure, and the environment [1]. Numerous mines have been abandoned without the implementation of appropriate mine reclamation measures, and subsidence events have frequently occurred at underground mine sites worldwide (Figure 1). Reliable subsidence predictions and mapping of future subsidence hazards in areas vulnerable to subsidence, based on continuous assessments and observations using accurate subsidence inventory data, represent a principal step toward effective mitigation of the risk of damage to property. Specifically, the presentation of engineering geological data in the form of an engineering geological map represents a useful tool for planners and developers in that the said maps indicate areas of potentially suitable and unsuitable land in relation to development. Numerous attempts have been made to assess mining subsidence using spatial information technology as it provides an effective approach to quantitative assessment at regional scales.  
A geographic information system (GIS) is a computer-based technology that enables the collection, management, analyzing, modeling, and presentation of geospatial data for a wide range of applications [2]. GIS can also be viewed as a computerized tool that provides a framework for solving geospatial problems [3]. Therefore, GIS is regarded as science or technology for spatial problem solving and can be used for scientific or engineering investigations, resource management, and development planning. In addition, GIS can be used to generate an engineering geology map. Because GIS can also be considered to be a form of a computer-coded digital mapping tool, it can be used to easily represent and combine factor maps to effectively derive the susceptibility, hazard, and risk indexes as well as perform effective modeling. Therefore, GIS plays a highly vital role in the entire process of mining hazard modeling and mapping production [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. In addition, GIS-based geohazard mapping basedon other concerned geospatial data sets can provide basic data for engineers and planners to help make spatial decisions.  
The scope of this paper was confined to reviewing only published literature concerning GIS-based mining-induced subsidence mapping research that included the following three keywords (or concepts): mine or mining, subsidence, and GIS. As such, the articles that were not concerned with mining areas such as karst subsidence (e.g., depression, collapse, and doline), and urban land subsidence induced solely by groundwater withdrawal (i.e., not underground mining activities) were not considered. Numerous studies have reported on the detection and monitoring of ground subsidence in mine areas (here, subsidence inventory mapping) using remote sensing observation technologies (e.g., interferometric synthetic aperture radar (InSAR) and unmanned aerial vehicles (UAVs)). However, these are beyond the scope of GIS technology and are therefore excluded from this study.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 3 Methodology
### Data Acquisition Unit  
A single data acquisition unit with 16 A/D channels was selected to record the necessary data throughout track tests. The data were sampled at 1000 Hz. The 1000 Hz sampling frequency was selected based on conducting a series of tests in the early stages of the study to assess the most beneficial sampling rate. The sampling frequency provides approximately 11 samples per tie, assuming an 8-inch-wide tie (\(\sim\)20 cm) and a forward speed of 40 mph (\(\sim\)65 km/h), representing the higher range of the intended speed for measurements.