esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Engineering Application of the Proposed Method  
### Project Description of Qingdao Jiaozhou Bay Subsea Tunnel  
The Jiaozhou bay tunnel project is the second subsea tunnel project in mainland China, which is an important channel connecting the main city and the auxiliary city of Qingdao in the Shandong Province of China (Figure 9). Comparing to the Jiaozhou bay bridge, it has the characteristics of all-weather traffic and without prejudice to shipping. The average head of water level in Jiaozhou bay (i.e., location of the tunnel) is 3.8 m, while the maximum water depth is 42 m. The tunnel length  
Figure 8: Multi-objective optimization program of the ground reinforcement parameters of subsea tunnels.  
is 6170 m, of which 3950 m is buried within the sea channel. The land area which the tunnel passes through generally consists of granite, the sea area is mainly erupting volcanic rock, and most of the bedrock is exposed.  
The tunnel passes through six groups of 18 fault fracture zones with widths ranging from meters to dozens of meters (Figure 10), which are mostly crushed rock, cataclastic rock and mylonite. The tunnel consists of three holes, including two main tunnels and a service tunnel (Figure 11). The excavation height, width, and cross-sectional area of the main tunnels in order are 12 m, 16.03 m, and 150 m\({}^{2}\). Each of these main constituents is equivalent to a circular tunnel with a radius of 6.9 m based on the area equivalence principle [25].  
The remainder of the paper focuses on tunneling through f\({}_{3-2}\), of which the seawater depth is 42 m. The rock cover \(H_{0}\) is 30 m approximately equal to that of f\({}_{3-1}\), then the far field range \(R_{0}\) was calculated as 78.9 m. Since \(R_{0}\) is greater than 10 \(r_{0}\), the water pressure applied on the boundary can be regarded as a constant value [26; 27; 28]. Moreover, due to the infinity of seawater recharge and the steady flow considered in this paper, the seawater level can be treated as a constant. According to  
Figure 11: Cross-sectional layout of the Jiaozhou bay tunnel.  
Figure 9: General location of the Jiaozhou bay tunnel project.  
the geological exploration results, the average bulk density of the overlying rock is \(\gamma\) = 20 kN/m\({}^{3}\) and the sea water volume-weight is \(\gamma_{w}\) = 10 kN/m\({}^{3}\). The other mechanical parameters are provided in Table 4. The reinforced parameters in Table 4 were obtained by employing the actual values pertinent to f\({}_{3-1}\)[29].
# Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
## 3 Experimental Setup
### Sensor Data Acquisition  
The physical and simulated TurtleBot 3 Waffle Pi mobile robot comes with two types of sensors: a 2D LiDAR LDS-01 sensor and an 8 Mp V2 Raspberry Pi Camera [37]. The 2D LiDAR sensor is a 360 degrees laser distance sensor that has been widely used to perceive the environment. This type of sensor evaluates the surroundings in the form of a point cloud by measuring the intensity and the flight time of the laser reflected from the object after emitting it from the sensor [38]. The development of microcomputers and single board computers has helped to develop low-cost solutions for the domain of the mobile robots [39]. The Raspberry Pi is an example of this, and it allows an online environment for viewing through the Pi Camera.  
The acquisition sensor data that enters the LSTM neural network comes from the LiDAR LDS-01, which has an operating range from 12 cm to 350 cm, a sampling rate of 1.8 kHz and an angular resolution of 1\({}^{\circ}\)[37].  
The authors [PERSON] and [PERSON] [40] state that it is necessary to apply an appropriate dimensional reduction method to a dataset and extract the most meaningful principal features of the data that cover a reasonable percentage of the explained variance of the dataset, in order to avoid a slow convergence and learning process of a neural network due to the large number of features of the training patterns. For this reason, only values obtained every two degrees within a laser scan sample from 0 to 180\({}^{\circ}\) at the front of the robot were considered. A ROS node named \"get_values\" was initialized to subscribe to the laser scan topic of the TurtleBot3 (mobile robot) and obtain the raw LiDAR scan data. A function was defined to achieve an appropriate dimensionality data reduction. For every time instance (scan sample), a list with 90 features describing the distances from the reference frame (LiDAR) to an obstacle (static or dynamic) was obtained.
## 4 Experiments and Findings
### Experimental Findings on StanWiFi Dataset  
Table 5 presents the experimental results of the CNN-GRU-AttNet model applied to HAR on the StanWiFi dataset. The table clearly demonstrates that the proposed model achieves impressive performance with an average accuracy of 98.66%, precision of 98.43%, recall of 97.88%, and F1-score of 98.14%. These results show that the CNN-GRU-AttNet model performs well in recognizing human activities. Furthermore, compared to five other baseline DL models, the CNN-GRU-AttNet model achieves the highest recognition accuracy.  
The confusion matrix of the CNN-GRU-AttNet model on the StanWiFi dataset is illustrated in Figure 10. The results demonstrate that the model has high recognition accuracy for lie down, walk, and stand up activities, achieving over 96% accuracy. In contrast, fall and run activities show slightly lower performance, around 94%. Additionally, sit down activities have a recognition accuracy of 92% or higher. However, there needs to be more clarification between lie down and sit down activities, and this may be due to the similarity in signal patterns between the two activities. Figure 11 illustrates the accuracy and loss metrics of the CNN-GRU-AttNet model applied to the StanWiFi dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recognition Performance (Mean \(\pm\) Std)**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline CNN & 89.08\% (\(\pm\)4.61\%) & 87.52\% (\(\pm\)5.47\%) & 89.49\% (\(\pm\)3.48\%) & 87.55\% (\(\pm\)4.74\%) \\ LSTM & 93.95\% (\(\pm\)2.15\%) & 91.32\% (\(\pm\)2.66\%) & 94.80\% (\(\pm\)1.64\%) & 92.75\% (\(\pm\)2.25\%) \\ BiLSTM & 94.73\% (\(\pm\)1.73\%) & 92.25\% (\(\pm\)2.15\%) & 94.74\% (\(\pm\)1.18\%) & 93.18\% (\(\pm\)1.77\%) \\ GRU & 94.84\% (\(\pm\)2.52\%) & 92.68\% (\(\pm\)3.44\%) & 94.84\% (\(\pm\)2.32\%) & 93.35\% (\(\pm\)3.12\%) \\ BiGRU & 95.73\% (\(\pm\)2.64\%) & 94.70\% (\(\pm\)2.39\%) & 95.02\% (\(\pm\)3.78\%) & 94.62\% (\(\pm\)3.38\%) \\ CNN-GRU-AttNet & 98.66\% (\(\pm\)0.26\%) & 98.43\% (\(\pm\)0.29\%) & 97.88\% (\(\pm\)0.59\%) & 98.14\% (\(\pm\)0.42\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance results of both the proposed CNN-GRU-AttNet model and the five baseline models on the StanWiFi dataset.  
Figure 9: The accuracy and loss metrics of the CNN-GRU-AttNet model on the CSI-HAR dataset: (**a**) train and validation accuracy curves; (**b**) train and validation loss curves.
## 2 Materials and Methods
### Percentage Protein Solubility  
The percentage protein solubility of HPI samples was determined according to the method described by [PERSON] et al. [8] with some modifications. Briefly, 50 mg of HPI powder was dissolved in 100 mM phosphate buffer at a dry mass content of 10 mg/mL at a pH of 3, 4, 5, 6, 7, 8, 9, 10, and 11. The total protein content was determined using an HPI solution (10 mg/mL) prepared with 0.1 M NaOH solution (pH = 13.0). The samples were subjected to orbital shaking at room temperature overnight, followed by centrifugation at 4383\(\times\)\(g\) for 10 min. The same BCA assay (Section 2.4) was applied to determine the protein concentration from the supernatant samples. Protein solubility was calculated using Equation (6)  
\[\text{PS (\%)}= \frac{\text{C}_{n}}{\text{C}_{\text{t}}}\times 100 \tag{6}\]  
where C\({}_{n}\) represents the solubilised HPI at pH value \(n\) (\(n\) = 3.0-11.0), and C\({}_{\text{t}}\) is the total protein content at pH 13 (assuming that all proteins were solubilised at pH 13).
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 4 Interference-Aware RRM Strategy
### User Grouping  
In the JSDM-based RRM framework, user grouping plays a decisive role in the efficiency of the interference cancellation [35; 36]. Aiming at eliminating both the intra-system CCI among satellite users as well as inter-system CCI between satellite and terrestrial users to protect primary terrestrial communications, we divide satellite users and terrestrial users together into several groups. Specifically, in order to suppress the inter-group interference, the pre-beamforming matrix \(\mathbf{V}_{g}\) for the \(g\)-th group shall be carefully designed based on all group centers \(\mathbf{R}_{1},\cdots,\)and \(\mathbf{R}_{G}\). Moreover, since only the users within the group can be scheduled for each group, user grouping also has significant impacts on subsequent user scheduling.  
When partitioning users into groups, the following qualitative principles need to be obeyed. Firstly, users in the same group have channel covariance eigenspaces spanning approximately a given common subspace, which characterizes the group. Secondly, the subspaces of groups are mutually orthogonal, or at least have empty intersections. According to these two principles, users can then be grouped based on a certain similarity metric. However, it is usually assumed that each group managed by a single gateway comprises geographically close users [37]. In reality, users do not come naturally partitioned in groups with similar spatial correlations. Thus, user grouping based on geographical locations may degrade the system performance dramatically.  
To efficiently exploit the superiority of the proposed JSDM framework, an elaborated user grouping scheme is worth being studied. With the rise of artificial intelligence, machine-learning-based clustering algorithms are attracting increasing attention from both academia and industry. In this paper, without the loss of generality, we take the K-means method [38], a heuristic algorithm in clustering, as an example to illustrate the advantage of user grouping schemes based on channel correlations. In the K-means clustering algorithm, chordal distance is used as the similarity measure which is defined as  
\[d_{c}\left(\mathbf{U}_{k},\mathbf{B}_{g}\right)=\left\|\mathbf{U}_{k}\mathbf{U} _{k}^{H}-\mathbf{B}_{g}\mathbf{B}_{
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 4 Results
### Kalman Filter Algorithm Implementation
#### 4.2.1 Prediction  
The first step of the algorithm is prediction, also known as time update. The prior estimate and covariance matrix of a prior estimate error in the current period is obtained according to the optimal estimation and covariance matrix of the estimated error of system state at the last moment, expressed by the following equations:  
\[\left\{\begin{array}{l}\hat{x}_{ikk-1}=F_{k}\hat{x}_{k-1 ik-1}+B_{k}u_{k}\\ P_{ikk-1}=F_{k}P_{k-1k-1}F_{k}^{T}+Q_{k}\end{array}\right. \tag{5}\]  
where \(F_{k}\), \(B_{k}\) and \(Q_{k}\) are the state transition matrix, the control input matrix, and the covariance matrix of the process noise, respectively.  
Since the a priori estimate \(\hat{x}_{ikk-1}\) is not the optimal estimate at time \(k\), it is necessary to correct \(\hat{x}_{ikk-1}\) in combination with the observation of the sensors.
## 8 Results and Discussion
### Fuzzy C-Means Results  
The same methodology that is used with K-means is applied with the Fuzzy C-means method. Figure 14 shows the performance of Fuzzy C-means clustering, where Figure 14a shows the CDF of distance error for MSSIDs as compared to a single SSID at q = 10 (number of clusters), and the value of membership, which is issued by C-means, is (m = 1.8). It is clear that the performance of MSSIDs outperforms the behavior of a single SSID. Figure 14b shows the average distance error of MSSIDs and a single SSID for various values of q. From Figure 14b, we can see that the mean of error is increased slightly with the increasing number of clusters. The number of APs is also 34 APs for both experiments.
## 4 Conclusions  
In this paper, the effectiveness of using rubber bumpers in order to reduce the negative effects of earthquake-induced bounding between base-isolated buildings was investigated. The analysis was conducted for different gap sizes between buildings as well as for various values for the thickness, number and stiffness of the rubber bumpers.  
The results of the study show that the peak impact force decreases with increasing thickness, stiffness, and number of bumpers. Moreover, the peak impact forces decrease with increasing gap size.  
The results of the investigation clearly indicate that the use of additional rubber bumpers can be considered an effective method for improving the behavior of closely spaced base-isolated buildings. It can be successfully applied in practice to reduce the negative effects of earthquake-induced bounding between structures. Therefore, the method can be recommended to designers of bounding-prone base-isolated buildings exposed to ground motions.  
Conceptualization, S.M.K., H.N. and A.S.; methodology, S.M.K., H.N., A.S., A.M., N.L. and R.J.; software, S.M.K. and A.S.; validation, S.M.K., H.N., A.S., A.M., N.L. and R.J.; formal analysis, S.M.K. and A.S.; investigation, S.M.K. and A.S.; writing--original draft preparation, S.M.K., H.N., A.S. and A.M.; writing--review and editing, N.L. and R.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 14: Peak values of impact force for different rubber bumper stiffnesses and gap sizes under different seismic excitations: (**a**) Parkfield; (**b**) Kobe; (**c**) San Fernando; (**d**) Tabas.
# Numerical Simulation of Tunnel Face Support Pressure in Layered Soft Ground
## 1 Introduction
It can be seen from the literature that the current research mainly considers the stability analysis of an excavation face for ground with homogeneous soil, and the applications to layered soft ground are relatively few. The influence of soil on the support force of the excavation face under different stratification conditions needs to be further studied. Therefore, this paper mainly carried out two aspects of research: The first is to study the change law of the support pressure of the excavation face with different layered soft grounds. The second is to develop a robust and accurate equation to estimate the limiting face support pressure in layered soft ground based on numerical modeling. The results provide a reliable reference for evaluating face stability and safety control during tunnel excavation. In this paper, Section 1 offers an introduction, which includes a brief literature review. Section 2 presents our numerical modeling of the TBM face support pressure, which introduces the established model, model parameter selection, and related research methods. Section 3 gives the numerical simulation results and discussion, which studies the change rule of the support pressure of the excavation face under different layered foundation conditions and the calculation formula of the tunnel face support pressure in layered soft ground. Section 4, finally, outlines the conclusion, which summarizes the related research results.
# A Unified PUF and Crypto Core Exploiting the Metastability in Latches
## 3 Physical Unclonable Function  
### Physical Phenomena  
Figure 5 illustrates the typical physical phenomena that generate static entropy for a PUF application. Commonly, static entropy is obtained by the process variation of the implementation, which causes alterations in the frequency response in ring oscillators (ROs) [36; 37] and the time of frequency collapse in multi-modal RO [38; 39]. However, external circuits are necessary for collecting the static entropy. On the other hand, the dynamic entropy caused by metastability can affect the typical response of the latch, generating a random number [20; 21; 22]. Nevertheless, the process variation can generate enough static entropy in latches.  
Figure 4: Block diagram of one-time key generation.  
Figure 3: ChaCha20-Poly1305 AEAD procedure.  
Figure 6 shows the response scenarios in a metastable latch. First, the latch presents the same probability of obtaining a one and zero in the response when the latch is balanced. The balanced latches are used for a TRNG application. However, the process variations impose problems when balancing the latch, which require a calibration step [7; 20; 21]. On the other hand, the unbalanced latches produce a stable and unique response. Therefore, the stable and unique response in the unbalanced latches can be used for PUF applications, exploiting the process variations in the latch. In addition, the latches are used as a memory, independent of the balance. Therefore, the unbalanced and balanced latches can be combined with digital implementations, unifying the logic functions with a PUF or a TRNG application.
## 2 Point Cloud Data Registration Method
### Verification of the Methodology
#### 2.2.1 Coarse Registration  
Figure 2 shows the feature point extraction effect. Figure 2a shows the initial point clouds at two stations, and each station includes 35,947 sampling points; Figure 2b-d show the extracted SIFT feature points, SURF feature points, and Harris feature points respectively, where the red points are the initial point cloud and the green points are the extracted feature points.  
According to Figure 2, the quantity, accuracy rate, and elapsed time of each kind of feature point were calculated respectively, as shown in Table 1.  
As can be seen from Table 1, since the Stanford Bunny is a kind of standard model data and contains no noise [43], both the reference point cloud and point cloud to be registered can find strict points with the same name, therefore, the registration accuracies of the three algorithms were relatively high. Comparably, the extraction accuracy rate of the SIFT feature points reached 100% and took the least time, and the overall performance was better than the SURF feature points and Harris feature points. Therefore, it is reasonable and feasible to perform point cloud coarse registration based on SIFT feature points.
## 2 Description of the Method
### Development of the Warning System  
The proposed method entails the analysis of MT-InSAR timeseries to reveal analogies among reference behaviors that are assumed to be precursors of critical events, at different times and geographical positions. To this end, the capability of self-organizing maps to cluster the distribution of patterns on the basis of their similitude is exploited. In this method, the patterns are MT-InSAR displacement timeseries of preset duration. The historical data of the examined area are mapped by the self-organized map (SOM), and the clusters containing patterns that precede critical events are labeled as warning clusters. During the monitoring phase, the current timeseries acquired in the whole area are mapped by the trained SOM, and those falling within the warning clusters are taken to be further enquired by means of in situ inspections. It is to note in fact that the method assumes that similar critical events have similarly featured precursors. Although being reasonable and generally confirmed by the literature [43; 44], this assumption is of course an empirical one, which may also lead to false warnings.  
The criticalness of an event could be assessed on the basis of a feature of the timeseries (velocity, range, maximum/minimum value, etc.) or by referring to a reference record (an event occurred in the past). The assessment based on a feature of the timeseries (feature-based assessment) can be applied automatically to an available database of sequences, without the need for reference records. The assessment based on a reference record (record-based assessment) carries out the monitoring of the area referring to a past event, requiring a preliminary analysis of the historical data by an expert.  
Regardless of which criterion is adopted for stating the criticalness of an event, the observation time adopted in the analysis is crucial for the suitability of the method. In general, the phenomena involving soil movements may be characterized by different evolution in time. A too short observation time makes it difficult to detect precursors of warning trends, since the phenomena of interest may not yet have completely developed; on the other hand, a too long observation time may mask the warning trend. Therefore, the best approach is to run in parallel several monitoring systems with different observation times. The choice of the most suitable set of observation times may be led by the manager of the monitoring system.  
The flowchart diagram of the warning system is depicted in Figure 1. An observation time is assumed to be initially preset. The diagram describes the procedures of both training (synthesis) and recall (monitoring). In the synthesis step, an SOM neural network is trained to cluster the timeseries on the basis of their similitude. In the monitoring step, the timeseries are fed to the trained SOM, which assigns each one to the corresponding cluster. An alarm is raised when a timeseries is assigned to a warning cluster. The synthesis and monitoring procedures are described below in detail.