esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Piles in a Liquefied Slope Using a Modified Soil-Pile Interface Model
## 2 Numerical Simulation  
### Centrifuge Tests on Piles in a Liquefied Slope  
The applicability of numerical modeling was verified by comparing the results with the centrifuge tests conducted by [PERSON] et al. [33], which involved a series of centrifuge model tests that simulated a pile-supported wharf in a finite slope susceptible to liquefaction.  
Figure 1 shows the layout of the centrifuge test models. The prototype structure was a pile-supported wharf in Pohang New Port, Republic of Korea. The model deck, which was simulated by a mass block, was supported by 2 \(\times\) 2 group piles with a spacing of 5 m. The model ground and structures were constructed within a soil container covered with cork to minimize the boundary effect. The model ground was a finite slope of 17 m in length with varying heights according to the slope inclinations. A clearance space of 2.5 m was provided behind the toe and crest at both ends of the slope.  
The model ground was prepared using poorly graded silica sand, and the properties of the sand are summarized in Table 1[34]. The relative density and saturated unit weight of the model ground were 26% and 17.2 \(\text{kN}/\text{m}^{3}\), respectively. The ground was saturated using a viscous fluid, and the viscosity of the pore fluid was increased using hydroxypropyl methylcellulose (HPMC) solution while considering the scaling law for consolidation [35].  
The model deck and piles were constructed with aluminum, applying the similitude law for centrifuge experiments following Madabhushi [36]. The dimensions and engineering properties of the model piles and deck are summarized in Table 2. Detailed information on the experiment is given in [PERSON] et al. [33].  
\begin{table}
\begin{tabular}{c c} \hline
**Properties** & **Values** \\ \hline Specific gravity & 2.65 \\ Maximum dry unit weight (\(\text{kN}/\text{m}^{3}\)) & 16.1 \\ Minimum dry unit weight (\(\text{kN}/\text{m}^{3}\)) & 12.2 \\ Soil classification (USCS) & SP \\ \hline \end{tabular}
\end{table}
Table 1: Properties of silica sand ([PERSON] et al., 2016).  
Figure 1: Layout of the centrifuge test model of [PERSON] et al. (2023).  
Three test cases involved different combinations of slope inclinations and the amplitude of the input motions. Case 1 had a slope inclination of 27 degrees and an amplitude of 0.2 g, case 2 had 27 degrees and 0.1 g, and case 3 had 15 degrees and 0.2 g. The input motions were 1.5 Hz ramped and constant amplitude with maximum amplitudes of 0.2 g and 0.1 g, respectively, as shown in Figure 2.
# Human Action Recognition Based on Improved Two-Stream Convolution Network
## 4 Experiments
### Details of Experiments  
The main network structure is shown in Figure 3. Before the image is input to the neural network, a series of preprocessing needs to be taken to enhance the stability of the network. The preprocessing method used in our experiments is the transform method under the PyTorch framework. Firstly, the input images are randomly cropped and unified in the format, which is randomly cropped to the size of 224 \(\times\) 224. Then, the images are randomly flipped horizontally with a 50% probability. Then it is converted to tensor data type; Finally, standardized image data. The process of standardization is to subtract the mean value and then divide it by the standard deviation. After the preprocessing, the images will be decomposed into three channels: red, green, and blue, which will be input into the neural network in the form of a high-dimensional array.  
In terms of the loss function, This paper uses the cross-entropy loss function that can effectively avoid gradient dispersion [65], whose calculation formula is:  
\[L=-\frac{1}{N}\sum_{i}\sum_{c=1}^{M}y_{ic}log(p_{ic}) \tag{18}\]  
where \(M\) represents the number of action categories, here is 101; \(y_{ic}\) is a symbolic function, whose value is 0 or 1. If the value of sample \(i\) is equal to \(c\), take 1, otherwise, take 0; \(p_{ic}\) represents the prediction probability that sample \(i\) belongs to category \(c\).  
This paper employs the cross-entropy loss function, which is characterized by amplifying the loss when the model effect is poor, so as to speed up the learning speed. When the model effect is good, reduce the loss, slow down the learning speed, make the model converge quickly and improve the accuracy accurately.  
In terms of the optimization method, this paper adopts the random gradient descent algorithm (SGD), and the learning rate adopts the stagnant descent method to decline in segments. When the accuracy of the model verification set stops improving, the learning rate will be reduced to one-tenth of the original, and the training will continue. The initial learning rate of the spatial stream network is set to 0.0005. The initial learning rate of the motion stream network is set to 0.01.  
The hardware configuration of our experiments is: an Intel Xeon E5-2678 V3 CPU; Four GeForce RTX 2080 ti GPUs; 4 \(\times\) 16 GB, 64 GB memory in total. The experimental software is configured as Ubuntu 16.04.6 LTS operating system (Canonical Ltd.; Isle of Man, UK); python version 3.8 ([PERSON]; Holland); CUDA version 11.0. (NVIDIA; Santa Clara, CA, USA)
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 4 Performance Analysis  
When the number of antennas of a base station approaches infinity, the pre-coded A-ZF-LSFP and AO-LSFP can achieve excellent communication performance after channel inversion, and in the same network group, all users will reach the theoretically maximal network transmission rate.  
First, before the asymptotic analysis, the most suitable LSFP matrix is determined. According to the previous analysis, the channel inversion technology is applied to LSFP to obtain a suitable precoding matrix. The zero-forcing A-ZF-LSFP matrix for channel inversion processing is obtained as:  
\[W^{k}=\gamma_{A}\ast\beta\ast\left(B^{k}\right)^{-1}=\varphi_{A}\ast\left(B^{ k}\right)^{-1} \tag{37}\]  
where \(\gamma_{A}\) denotes the normalization factor that guaranteed the power limit and \(\varphi_{A}=\gamma_{A}\ast\beta\) denotes the normalization factor for the zero-forcing precoding A-ZF-LSFP.  
**Lemma 4**.: \[R_{D}^{kl}\geq log_{2}\Big{(}1+SINR_{D}^{kl}\Big{)}\] (38) \[SINR_{D}^{kl}=\frac{J_{0}^{kl}}{J_{1}^{kl}+J_{2}^{kl}+\frac{1}{M}}\] (39) \[J_{0}^{kl}=M\rho_{f}\rho_{\tau}\tau\big{|}\sum_{j=1}^{L}\beta_{j}^{kl}\alpha_{ j}^{kl}|\] (40) \[J_{1}^{kl}=\rho_{f}\rho_{\tau}\tau\sum_{v=1,v\
eq 1}^{L}|\sum_{j=1}^{L} \beta_{j}^{kl}\alpha_{j}^{kl}|\] (41) \[J_{2}^{kl}=\rho_{f}\sum_{j=1}^{L}\left[\sum_{n=1}^{K}\beta_{j}^{kl}\Bigg{(}1+ \sum_{s=1}^{L}\rho_{\tau}\tau\beta_{j}^{ns}\Bigg{)}\right]\left[\sum_{v=1}^{L }|\alpha_{j}^{km}|^{2}\right]\] (42)  
_Among them:_  
\[\alpha_{j}^{kl}=\frac{\sqrt{\rho_{\tau}\tau}\beta_{j}^{kl}\alpha_{j}^{kl}}{1+ \sum_{s=1}^{L}\rho_{\tau}\tau\beta_{j}^{ns}},\beta_{j}^{kl}\in B^{k},\omega_{j }^{kl}\in W^{k} \tag{43}\]  
_Therefore, when \(M\rightarrow\infty\), \(J_{1}^{kl}=0\), \(J_{1}^{kl}=M\rho_{f}\rho_{\tau}\tau\) we could get the idealized SINR of the A-ZF-LSFP:_  
\[\lim_{M\rightarrow\infty}SINR_{D}^{kl}=\frac{M\rho_{f}\rho_{\tau}\tau}{J_{2}^ {kl}+\frac{1}{M}}=\infty \tag{44}\]  
In the OFDM communication system, it means that noise and interference can be removed when the number of antennas tends to be infinite.  
In practical applications, the number of antennas in the LSAS is often tens or hundreds. In the following section, the channel transmission rate of the optimized precoding before and after channel inversion is compared in detail to prove that the optimized LSFP after channel inversion processing can achieve the same anti-contamination and anti-interference performance as equipped with a smaller number of base antennas than conventional LSFP.
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 2 Experimental Program
### The Assessment Technique  
#### 2.3.1 Mechanical Property Testing  
The mechanical properties were tested to assess the influence of incorporating CaO on the ability of _Bacillus subtilis_ to precipitate calcium carbonate. Mechanical property evaluation, in particular compressive and flexural strength tests, was conducted on all AAS specimens after 7 and 28 days through the universal testing machine of capacity 2000 KN. Mortar cubes of 40 mm have been tested under compression at a pacing rate of 240 kg/m\({}^{3}\) per minute according to the specifications of ASTM C109 [31] and ECP 203 [30]. Prisms of 40 \(\times\) 40 \(\times\) 160 mm were subjected to a three-point loading flexural test at the specified ages as per ASTM C348 [32] and ECP 203 [30]. Specimens were symmetrically placed between the two roller supports to have a center-to-center span length of 100 mm. The loading cell roller was placed in the midspan for applying the load at equal spaces from both supports. The rate of the applied load was about 50 N/s. The average of three separate specimens from each mixture and testing age were evaluated.
## 4 Discussion
### Limitations and Areas for Improvement  
Despite the advantages, the proposed system has some limitations and areas for further improvement.  
The computational complexity of the algorithms, especially the classification_parallel algorithms, can be significant due to the nested optimization loops and the need to compute MSEs for a large number of combinations of error types, sizes, and shifts. Further optimization of the code, perhaps through the use of more efficient optimization algorithms or methods to speed up MSE computation, may be beneficial.  
The quality of classification depends directly on the representativeness of the set of model \"error\" signals Error_signal. If the real data error types are not covered by the proposed set, the classification accuracy may decrease. Extending the Error_signal library by adding new, more realistic error models, as well as adapting the models to specific applications, is an important direction for future research.  
Sensitivity to parameters of algorithms, such as thresholds in find_peaks, wavelet decomposition parameters, and the number of Optuna iterations, can affect classification results. Further work is needed to investigate the robustness of the system to parameter changes and to develop recommendations for selecting optimal parameter values for different types of signals and tasks. In particular, the automatic adaptation of the parameters, possibly based on the characteristics of the input signal, may improve the practical applicability of the system.  
The use of MSE as the only quality metric may be a limitation. Depending on the classification task, other metrics such as accuracy, completeness, F1-measure, or other measures specific to the anomaly detection task may be more appropriate for evaluating classification quality. Investigating and comparing different metrics and their impact on classification performance is an interesting direction for future research.
# A Mechanism for Reliable Mobility Management for Internet of Things Using CoAP
## 2 Architecture and Procedure of Reliable Mobility Management  
In this section, reliable mobility management architecture is described. We also describe the detailed signaling procedure for reliable mobility management.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 4 Security Analysis and Performance Evaluation
### Security Analysis
#### 4.1.5 Data Privacy and Credibility  
In order to prevent the attack from a malicious vehicle attempting to gather the vehicle's private data, we consider combining blockchain smart contracts and the cryptography algorithm that guarantees the vehicle identity anonymity and communication security in VECNets. We propose the vehicle's message reputation to tackle fake messages that may cause traffic congestion or even accidents. The broadcasted messages obtain ratings from its neighboring vehicle assessments to ensure the message's credibility. Moreover, our proposed scheme enables vehicles with a good rating as well as vehicles that contribute to message assessment to obtain certain rewards. Thus, they are motivated to be honest to share their data in the vehicular network.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 5 Conclusions  
Laser ultrasonic bulk waves were used to detect the interface separation in Cu/Al bimetallic composites. The propagation process of laser ultrasonic bulk waves in Cu/Al bimetallic composites and the influence of composite interface on the propagation of bulk waves were analyzed by the finite element method. The interaction between ultrasonic bulk waves in different modes and delamination defects was analyzed, and the influence of PRD on ultrasonic bulk waves in different modes was studied. The results of finite element analysis showed that the reflected shear wave from the composite interface is very suitable for the detection of delamination defects in composite interfaces. The time-domain waveforms collected in the experiment have similar trends and characteristics with the waveforms simulated by finite element method. Therefore, the reflected shear wave from the composite interface can be used as a reliable feature to detect the delamination in Cu/Al bimetallic composites. The position and width of the delamination defects in the C-scan image are consistent with the T-shaped grooves machined by wire-electrode cutting in the specimen. Therefore, the detection method based on laser ultrasonic bulk waves can effectively detect the delamination defects in Cu/Al bimetallic composites, and its non-contact characteristics make it possible to be used for on-line inspection in the rolling process of laminated plates.  
[PERSON] conducted most of the simulation work, implemented the experiments, analyzed the data, and wrote the paper. [PERSON] supervised the project and discussed the results with [PERSON], [PERSON] prepared the experimental setup and specimen, and carried out the experiments. [PERSON] and [PERSON] reviewed and corrected the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant No. 51575040), the Natural Science Foundation of Beijing Municipality (grant No. 3182008), the Natural Science Foundation of Beijing Municipality (grant No. 3202019), and the Fundamental Research Funds for the Central Universities Project (grant No. FRF-TP-19-039 AZZ).  
The authors declare no conflict of interest.
# Indoor Microclimate Analysis of the San Panfilo Church in Tornimparte, Italy
## 3 Results
### Continuous Monitoring
#### 3.2.1. Apse Data Overview  
In the apse, the temperature is modulated by seasonality, with the highest values in August and the lowest in February. In addition, it is influenced by meteorological events and, inside the church, by the presence of people during the weekly Sunday service. The overlapping of seasonality (time scale of one year), meteorological events (time scale of one week) and people during the services (time scale of one hour) is recognizable in data collected in the apse near the walls and in the central side (Figure 8a). The air in the central part of the apse (crucifix top, green line in Figure 8a) shows more influence from people with respect to the air near the wall (orange line in Figure 8a). In fact, air temperature near the wall is influenced by the wall temperature itself, which varies slowly as a consequence of the walls' high thermal capacity.  
Figure 8: (**a**) Temperature at crucifix top (green line) and apse near wall right (orange line); (**b**) temperature on the case (blue line); (**c**) relative humidity at the crucifix top (green line) and on the case (blue line); (**d**) specific humidity at crucifix top.  
Figure 7: Apse wall temperature on 22 April 2022 at 14:30 UTC+1.  
The sensors over the glass showcase near the central window of the apse measure temperature values similar to those recorded near the crucifix with, in addition, a number of spikes from October to March (Figure 8b).  
Relative humidity can vary inside the church in the range of 30-80%. The sensors on the glass showcases recorded negative spikes of about 12% in conjunction with temperature peaks (Figure 8c).  
Specific humidity has been computed from temperature and relative humidity (Figure 8d). The time series show, as expected, high values in summer and low values in winter due to seasonality. In addition, variations due to meteorological events and increased values during the weekly services are present. In fact, respiration and transpiration produce moisture whose quantity depends on the environmental temperature and the physical activity of people [1]. In the specific humidity time series collected on the showcase, the spikes that appear in temperature and relative time series are not present.
## 3 Results
### Detection of Cardiac Structural Abnormalities  
To make a validation and test dataset of CHD for detection of cardiac structural abnormalities, we collected the ultrasound screening videos obtained from 14 CHD cases. We defined the abnormality score of each video through a calculation using the probability of the selected cardiac substructures for Heart and Vessels. The mean value of abnormality scores in CHD cases (Heart = 0.251, Vessels = 0.418) was significantly higher than normal cases (Heart = 0.087, Vessels = 0.083; \(p<0.001\)), as shown in Supplementary Figure S2. These results indicated that this abnormality score was suitable to use to distinguish morphological anomalies from a normal fetal heart and vessels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Test** & **Validation** \\ \hline Crux & 0.701 & 0.714 \\ Ventricular Septum & 0.708 & 0.571 \\ Right Atrium & 0.856 & 0.910 \\ Tricuspid Valve & 0.451 & 0.598 \\ Right Ventricle & 0.823 & 0.865 \\ Left Atrium & 0.900 & 0.831 \\ Mitral Valve & 0.289 & 0.635 \\ Left Ventricle & 0.830 & 0.833 \\ Pulmonary Artery & 0.677 & 0.767 \\ Ascending Aorta & 0.768 & 0.841 \\ Superior Vena Cava & 0.574 & 0.720 \\ Descending Aorta & 0.898 & 0.925 \\ Stomach & 0.969 & 0.951 \\ Spine & 0.974 & 0.932 \\ Umbilical Vein & 0.944 & 0.647 \\ Inferior Vena Cava & 0.472 & 0.276 \\ Pulmonary Vein & 0.416 & 0.091 \\ Ductus Arteriosus & 0.380 & 0.220 \\ mAP & 0.702 & 0.685 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average precisions (AP) of cardiac substructure detection and its mean value (mAP) were demonstrated.  
Furthermore, the ROC analyses were used to assess the performance of detecting cardiac structural abnormalities in Heart and Vessels, and Figure 5 shows our SONO compared to other conventional algorithms. The AUCs produced by SONO were 0.787 in Heart and 0.891 in Vessels. The AUCs produced by ConvAE-Iframe, ConvAE, AE + global feature, and AnoGAN in Heart/Vessels were 0.747/0.706, 0.517/0.542, 0.656/0.673, and 0.656/0.651, respectively (Table 2). Therefore, SONO demonstrated superior performance to any other conventional ones in this comparison analysis, and detected the abnormalities more accurately in Vessels than Heart.  
Figure 4: Barcode-like timeline in (**a**) a normal case, and (**b**) a tetralogy of Fallot (TOF) case. The vertical axis represented the 18 selected substructures, and the horizontal axis represented the examination timeline in a rightward direction. A probability \(\geq\)0.01 was set as well-detected and shown as a blue bar, and \(<\)0.01 as non-detected and a gray bar.
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 2 Materials and Methods  
### GNSS Devices  
In this work, the low-cost simple RTK2B V1 GNSS board (\(\sim\)200 EUR) was used, which contains the dual-frequency ZED-F9P GNSS chip from u-blox [8; 9]. The ZED-F9P can receive satellite signals in both frequencies (F1 (1575.42 MHz) and F2 (1227.60 MHz)) from all available satellite constellations [8]. In F1 frequency those devices can track L1 C/A, L1 OF, E1, and B1l signals while in F2 frequency, L2C, L2 OF, E5b, and B2l signals. The simple RTK2B V1 board was used in combination with the Survey calibrated low-cost antenna offered by Ardusimple (denoted as LC\(-\)1 below), which has a phase center error of 1 mm and known calibration parameters from NGS (Figure 1) [9]. Another simpleRTK2B V1 board was used in combination with a geodetic antenna (JAV_RINGANT_G3T) from the Javad manufacturer (San Jose, CA, USA) denoted as LC\(-\)2 below). We used a high-quality GNSS geodetic instrument as a reference, Leica GS18T receiver and LEISGS 18 antenna, denoted as GD\(-\)1 below (Wetzlar, Germany). The geodetic GNSS instrument was used for the comparison of GNSS data, as well as to determine the coordinates of all points (described below) that were used as reference values.
## 6 Streaming Machine-Learning Models
### Hyperparameter Tuning  
The regression and classification algorithms are sensitive to their configuration parameters, impacting not only the accuracy but also the execution time and the size of the model. Thus, during hyperparameter tuning, the objective is to identify the best set of configurations that works for all traces, increases the accuracy, and decreases the execution time and size of the model. We performed a grid search to find the best set of parameters for the Adaptive Random Forest (ARF) and the Hoeffding Tree (HT) over the selected features. Table 4 lists the three primary parameters that impact ARF performance, the tested parameter values, and the selected parameter values for each regression target. To define the search space, we leveraged the default values, literature, and domain knowledge [69]. We independently performed the grid search for each trace, finding (slightly) different sets of optimal hyperparameter settings per trace that minimize MAE. However, our objective is to find a single set of hyperparameter settings that performs well across all traces to ensure that the models will also work well with other traces. For this purpose, we first computed the minimum achievable MAE for each trace across all configurations. Next, we computed the difference of each MAE from the corresponding minimum MAE per trace. Finally, for each configuration, we computed the sum of the differences across all traces and ranked all configurations by this sum. The configuration with the smallest sum represents the configuration that predicted values closer to the original, with the smallest error across all traces. The HT has six major parameters that impact performance, as listed in Table 5. We use the \(F1\)_score_ metric (i.e., the harmonic mean of precision and recall) to evaluate the classifications because it provides accurate results for both balanced and imbalanced datasets. The employed hyperparameter tuning methodology is analogous to the one used for regression. We first computed the maximum achievable F1 score for each trace across all configurations. Next, we computed the difference of each F1 score from the corresponding maximum F1 score per trace. Finally, for each configuration, we computed the sum of the differences across all traces and selected the configuration with the smallest sum. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Tested Values** & \begin{tabular}{c} **Selected for Next** \\ **Offset** \\ \end{tabular} & \begin{tabular}{c} **Selected for Next** \\ **Hotness** \\ \end{tabular} \\ \hline \begin{tabular}{l} Ensemble Size (num trees) \\ Features Per Tree (\(m\)) \\ \end{tabular} & \begin{tabular}{c} 5, 10, 30, 40, 100 \\ 10, 20, 30, 60 \\ \end{tabular} & 30 & 40 \\ \
\begin{tabular}{l} Features Mode (\(M\)=\# features) \\ \end{tabular} &
\begin{tabular}{c} \(m\), \(M*(m/100)\), \\ \(\sqrt{M}+1\), \\ \(M-(\sqrt{M}+1)\) \\ \end{tabular} & \(M*(m/100)\) & \(M*(m/100)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tested and selected hyperparameter values for Adaptive Random Forest after hyperparameter tuning.