esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Single Vertical Layer Stratum Model  
The results of Single Vertical Layer Stratum as shown in Figure 13. There was a large gradation zone in the single vertical layer stratum, and it was difficult to clarify the vertical layer; the vertical layer could be identified clearly within 8 M. As the R1 became closer to the R2 value, the gradation zone widened gradually, but the range outside the gradation zone was identical with the set value, as shown in Figure 13a. When there was noise interference in the detection process, the resistivity profile had a random jump. The random jump worsened as the noise increased, and the resistivity of the left and right layers was different from the set value, as shown in Figure 13b.
## 2 Materials and Methods
### Material Measure Calibration  
The material measure SPB panels for every deformation stage were calibrated through the measurement of the speckle pattern dimensions using a SmartScope Flash 200 multi-sensor optical measuring machine (OMM) (Rochester, NY, USA). The OMM had been calibrated with traceability by the National Institute of Standards and Technology (N.I.S.T), with the length measurement error being a function of the length measured (L), defined as  
\[Machine\ Error\ (mm)=\left(0.002+\frac{6L}{10^{6}}\right) \tag{1}\]  
Ten separate measurements of the total pattern length were made along the axis of deformation for each deformation stage panel and an average pattern length was calculated, shown in Table 2. OMM measurements were corrected for the machine error, calculated using Equation (1) for each stage.  
The results showed that each pattern appeared to be printed slightly longer than designed, by approximately 0.1-0.2 mm. It was necessary, therefore, to determine if the error was cumulative across the whole pattern, so that deformation values could be adjusted accordingly for the actual printed lengths.  
Figure 2: Speckle pattern board (SPB) design for (**a**) 0% and (**b**) 30% strain.  
The standard deviations for the pattern length measurements (Table 2) demonstrated that the metrology system was able to measure the pattern length to a high level of repeatability. However, inevitable bleeding of the printed ink on the paper substrate meant that there was error associated with identifying the edge of the pattern using the OMM. Determining the magnitude of this error would enable it to be accounted for in the calibration measurement.  
Distances between the incremental calibration lines printed around the perimeter of the patterns were measured on each board along the deformation axis. Measurements were made for the ten divisions created by the calibration lines and compared with the theoretical separation calculated as one tenth of the total pattern length. Any edge detection error incorporated as part of the whole pattern length measurement was divided by ten and was thus deemed negligible in the measurements. The errors in these measurements were found to be normally distributed (based on an Anderson-Darling statistical test, \(p<0.05\)), with a mean error of 0 mm and a standard deviation of 0.013 mm. Consequently, with a 95% confidence, the maximum error in a length measurement as a result of the edge detection error would be \(\pm 0.026\) mm which, at the print resolution of 1440 dpi, was the equivalent of approximately one pixel at either end of the pattern. These results also indicated that the error in the length of the printed pattern was evenly distributed across the pattern.  
Detailed calibration results for each deformation stage are presented in Table 3. The results show relatively consistent error across all strain states, with mean strain values within 0.1% of the desired strain. This is small relative to the total strain and for applications involving the measurement of these larger strains, the material measures developed can be considered suitable for measurement calibration.
# Tree-Structured Regression Model Using a Projection Pursuit Approach
###### Abstract  
In this paper, a new tree-structured regression model--the projection pursuit regression tree--is proposed. It combines the projection pursuit classification tree with the projection pursuit regression. The main advantage of the projection pursuit regression tree is exploring the independent variable space in each range of the dependent variable. Additionally, it retains the main properties of the projection pursuit classification tree. The projection pursuit regression tree provides several methods of assigning values to the final node, which enhances predictability. It shows better performance than CART in most cases and sometimes beats random forest with a single tree. This development makes it possible to find a better explainable model with reasonable predictability.  
regression tree; projection pursuit; exploratory data analysis; piecewise regression; recursive partition
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 8 Simulation Set-Up and Results
### Results  
Considering the episode with the highest possible reward, Figure 11 shows the plot of the Cumulative Complement Distribution Function (CCDF) of \(\delta_{eff}\), shown for the three algorithms FPA, tabular Q-learning, and the proposed DQL.  
As anticipated, the FPA strategy produces the least optimal performance due to its lack of power control or interference coordination. Tabular Q-learning exhibits slightly improved results over FPA, yet it still falls short when compared to the addition of power control mechanisms to the gNBs, under-performing FPA at approximately \(\delta_{eff}=9\) dB. On the other hand, the proposed DQL algorithm outperforms the other two algorithms, achieving better convergence to a solution. Unlike the tabular implementation, the convergence of DQL is not affected by the initial state-action value function. As \(\delta_{eff}\) approaches 13 dB, UAVs are situated in closer proximity to the gNB, resulting in comparable performance among all power control algorithms.  
We also analyse the link performance for each of the algorithms at different heights of the UAV. As explained in the previous sections, when the altitude increases, the interference also increases.  
The simulation is run for a duration of 120 s, with the UAVs flying at three different altitudes: 50 m, 100 m, and 120 m. To analyse the link performance, SINR and latency are compared for the different heights and the three different interference mitigation schemes.
## 3 Experimental Results and Analysis
### Experimental Environment and Parameter Settings  
Experimental hardware platform. The operating system is Windows 10-64 bit, the CPU is an 11 th Gen Intel(R) Core(TM) [EMAIL_ADDRESS] GHz (16 CPUs)-2.3 GHz, GPU 0 is an Intel(R) UHD Graphics, and GPU 1 is an NVIDIA GeForce RTX 3060 Laptop GPU. The software platform is Python 3.9.0 (64-bit), PyCharm Community Edition 2022.1.3, which is used to build the deep learning framework. Simulink in MATLAB R2023b is used to build the traction dynamics model. The interface between Python and MATLAB should be configured with particular attention to ensure compatibility between the two versions. In Python, the rail surface condition identification model, which has been developed, is loaded to recognize images and output class index values. These values are then called by MATLAB through the py function and stored in the MATLAB workspace. Subsequently, the set_param() function is used to transfer the stored values from the workspace to the Constant block within the Simulink model, where they serve as inputs to the traction dynamics model. This process facilitates the integration of both Python and MATLAB for co-simulation. A detailed flowchart of this procedure is shown in Figure 8. When the rail surface condition identification model is trained, the learning rate of the experiment is set to \(1\times 10^{-5}\), the batch size is set to 64, and the number of training rounds is set to 100 epoches. A smaller learning rate helps avoid oscillations or divergence during model training, batch size 64 provides a better balance between training stability and computational efficiency, and 100 epoches gives the model ample time to train. The classification function uses the Softmax function, the loss function uses the cross-entropy function, and the default framework parameters of the adaptive learning rate optimization algorithm Adam are used. These three are the classical configurations in deep learning classification tasks.
## 3 Network Architecture and Topology
### Distribution Subsystem
Even gateways are expensive devices which require cellular network interfaces, only a few of them are required to be placed close to cellular Base Stations (BS) and some cheaper nodes which connect the sensing nodes with the gateways must be deployed (see Figure 4). On the other hand, exploitation costs must also be taken into account since cellular operators bill the use of BS. This may lead to another choice either when it is not planned to pay for the use of BS or they are not available, which consists of using IMS (Internet Multimedia Subsystems) [11, 42] that provide access to the Distribution System through different radio technologies (2G, 2.5G, 3G, WLAN) regardless of the operator.
## 6 Experiments
### What-If Scenario
As for the previous experiment, we report the statistics collected during the optimization runs to compare the best values of the objective function (9) and the PSR of services in Tables 9 and 10. Looking at Table 9, it is easy to note how PPO can still find service component allocations that achieve an objective function score close to 4, without re-training the model. This seems to confirm the good performance of PPO for the service management problem discussed in this manuscript. Moreover, PPO can achieve this result after an average of 36 steps, i.e., each one corresponding to an evaluation of the objective function. This was the result of the longer training procedures that on-policy DRL algorithms require. On the other hand, as is visible in Table 7, the DQN showed a strong performance degradation, as the best solutions found during the 30 test episodes had an average of 1.60. Therefore, the DQN demonstrated lower adaptability when compared to PPO in solving the problem discussed in this work. With regard to the CI algorithms, the GA was the worst in terms of the average values of the objective functions, while all the other algorithms achieved average scores higher than 4.0. As for the average number of infeasible allocations, the constrained versions achieved remarkable results, especially QPSO-ECT and MSPO-ECT, where the number of infeasible allocations was zero or close to zero for GWO-ECT. Differently, the GA-ECT and PSO-ECCT could not minimize the number of infeasible allocations to zero. Overall, MPSO was the algorithm that achieved the highest score at the cost of a higher number of iterations. From a sample efficiency perspective, Table 9 shows that QPSO was the CI algorithm that achieved the best result in terms of the number of evaluations of the objective function. At the same time, MPSO-ECT showed that it found its best solution with an average of 1493 steps, which was considerably lower than the 7432 steps required by MPSO, which, in turn, found the average best solution overall. Finally, the GA and GA-ECT were the CI algorithms that required a larger number of steps to find their best solutions. Furthermore, looking at Table 10, we can see the reasons for the poor performance of the DQN: four out of six services had a PSR less than 50%. More specifically, the PSR for the safety service was 0%, and the one for healthcare was 5%. Even PPO was not great in terms of the PSR in the what-if scenario. On the other hand, all the CI algorithms, excluding the GA and GA-ECT, recorded PSR values above 90% for all services, thus demonstrating that the cold restart technique was effective at exploring the optimal solutions in the modified search space. Finally, we can conclude that PPO was demonstrated to be effective in exploiting the experience built upon the previous training even in the modified computing scenario. Contrarily, the DQN did not seem to be as effective as PPO in reallocating services' instances in
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 2 Cellular-Based Positioning Methods
### Assisted GPS  
A-GPS is designed to transfer almanac data from an assisted-positioning server to a mobile device through a network connection [11; 12]. This method requires less time than traditional GPS method, approximately thirty seconds, for searching for satellites and determining location.
## 2 Wettability Measurement Methods
### Methods That Consider the Characteristics of Liquid Absorption
#### 2.3.3 Thermogravimetric Analysis (TGA)  
The thermogravimetric method is based on the change in the sample's mass during heating. Under the influence of temperature, adsorbates will evaporate from the surface of the sample [33]. The result of TGA is a graph of the sample's mass loss as a function of time or temperature, as shown in Figure 9. This figure presents the TGA results of carbonate rock aged in oil and treated with various brines. The mass loss in the sample can be divided into three stages: physically adsorbed substances (30-120 \({}^{\circ}\)C), chemically adsorbed materials (210-400 \({}^{\circ}\)C), and rock decomposition (T \(>\) 570 \({}^{\circ}\)C). The smaller the change in the sample's mass, the more effectively the brine altered the rock's wettability [34,35]. TGA is considered an expensive and destructive method, thus limiting the possibility of experiment repeatability.
## 5 Results and Discussion
### Evaluation Metrics  
The quantitative evaluation of the proposed model and comparison experiments were conducted in two distinct ways: instant-level detection and event-level detection. Instant-level detection refers to evaluating each individual data point in a time series independently to determine whether it is normal or anomalous. This method treats data at each time instant as separate individual items without considering the relationship with preceding or succeeding data points. The primary goal is to identify anomalies by examining each point in isolation, ensuring that any deviation from normal behavior is detected at the moment it occurs. This method allows for real-time anomaly detection, which is particularly useful in applications where immediate identification of anomalies is critical. Conversely, event-level detection focuses the analysis on periods between two non-successive time instants, where each period contains either solely normal or anomalous data. For example, if an attack only occurs between the first and the twentieth time instants during one operation, this period is labeled as an attack event. In this work, if any instant within an attack event is identified as anomalous, then the entire event is considered a true positive. Conversely, if a time instant during a normal event is mistakenly identified as an anomaly, then the entire event is counted as a false positive. The consideration behind event-level detection is that detecting anomalies at every instant during an attack event is exceptionally difficult and normally unnecessary. In general, detecting one or a few anomalous data instances during an attack event is already sufficient for the purposes of raising an alert.  
More specifically, we use three evaluation metrics to describe detection performance quantitatively: recall, precision, and accuracy. These metrics are defined below using the values of true positive (TP ), true negative (TN), false positive (FP), and false negative (FN) [31].  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+TN} \tag{4}\]  
Recall represents the ratio of detected anomalies to all the abnormal samples in the ground truth dataset. Conversely, precision signifies the percentage of correctly predicted abnormal samples relative to the total samples predicted as positive. Accuracy in anomaly detection refers to the proportion of correct predictions among all the samples, including both normal samples and anomalies.
# A Virtual Reality Soldier Simulator with Body Area Networks for Team Training
## 2 System Description
### Sensor Calibration  
Since the accuracy of the orientation estimate depends heavily on the measurements, the null point, and the scale factor of each axis of the sensor, a calibration procedure should be performed before each practical use. To this end, the sensor calibration is described as follows:  
* Step 1: Given a fixed gesture, we measure the sensing data (i.e., the raw data) and calculate the measurement offsets.
* Step 2: Remove the offset and normalize the modified raw data to the maximum resolution of the sensor's analog-to-digital converter. In this work, the calibrated results (CR) of the sensors are described by Equations (4)-(6).  
Table 1 shows the means of the calibrated results in the x, y and z directions of sensor nodes, which include the accelerometer, the magnetometer, and the gyroscope. Note that the inertial sensor signals (e.g., \({}^{s}\)\({}^{m}_{t}\), \({}^{s}\)\({}^{m}_{t}\), and \({}^{s}\)\({}^{\omega}\)\({}^{m}_{t}\)) are measured without movement, and that a small bias or offset (e.g., \(b_{a,\,t}\) or \(b_{m,\,t}\) or \(b_{w,\,t}\)) in the measured signals can therefore be observed. In general, the additive offset that needs to be corrected for before calculating the estimated orientation is very small. For instance, the additive offset would be positive or negative in the signal output (e.g., the average \(b_{w,\,t}=-0.08\) degree/sec and \({}^{s}\)\({}^{\omega}\)\({}^{m}_{t}=0\) degree/sec) without movement.  
\[\text{CR}_{acc}=({}^{s}\)\({}^{m}_{t}\)\(\pm\)\(b_{a,\,t})/\text{max}({}^{s}\)\({}^{m}_{t}\) \tag{4}\]  
\[\text{CR}_{mag}=({}^{s}\)\({}^{m}_{t}\)\(\pm\)\(b_{m,\,t})/\text{max}({}^{s}\)\  
After bias error compensation for each sensor, the outputs of the sensors represented in (1)-(3) can be rewritten as \({}^{s}\omega_{t}^{m}={}^{s}\omega_{t}+e_{\omega}\), \(t\), \({}^{s}a_{t}^{m}\approx{}^{s}a_{b,t}+e_{a,\ t}\), and \({}^{s}m_{t}^{m}\approx{}^{s}m_{t}+e_{m,\ t}\), and they are then applied as inputs for the simulation system. In particular, the proposed algorithm can be good at handling zero-mean white noise. After the completion of modeling and calibration of the sensor measurements from the newly developed MARG, all measurements are then represented in the form of quaternions as inputs of the proposed algorithm.  
As shown in Figure 8, a typical run of the attitude angles of a sensor node placed on the upper arm when standing in a T-pose is recorded. In the first step, we stand with the arms naturally at the sides. Then, we stretch the arms horizontally with thumbs forward, and then we move back to the first step. This experiment shows that the sensor node is capable of tracking the motion curve directly.
# Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods
## 3 Results
### Similarity of Interpolation Results
\begin{table}
\begin{tabular}{c c c c c} \hline  & **IDW** & **OK** & **RBF** & **UK** \\ \hline IDW & & y = 0.956\(\times\) \(-\) 0.1146 & y = 1.0021x + 0.1355 & y = 0.9974x + 0.167 \\  & & R\({}^{2}\) = 0.9473 & R\({}^{2}\) = 0.9906 & R\({}^{2}\) = 0.9472 \\ OK & y = 0.7226x + 0.0046 & & y = 0.9698x + 0.0217 & y = 1.0037x + 0.0567 \\  & R\({}^{2}\) = 0.5327 & & R\({}^{2}\) = 0.9616 & R\({}^{2}\) = 0.994 \\ RBF & y = 0.8954x + 0.0049 & y = 0.7162x + 0.0003 & & y = 0.9981x + 0.0323 \\  & R\({}^{2}\) = 0.7465 & R\({}^{2}\) = 0.562 & & R\({}^{2}\) = 0.9615 \\ UK & y = 0.7084x + 0.005 & y = 0.8537x + 0.0013 & y = 0.6887x + 0.0008 & \\  & R\({}^{2}\) = 0.5445 & R\({}^{2}\) = 0.7752 & R\({}^{2}\) = 0.5528 1 & \\ \hline \end{tabular}
\end{table} 
Table 3: The functional relationship between PM\({}_{2.5}\) and elevation point data by different interpolation methods. of disparity. However, the R\({}^{2}\) value between OK and UK is 0.9940, indicating the smallest error difference and suggesting that the interpolation errors between OK and UK are most similar in the PM\({}_{2.5}\) concentration data. These results imply that interpolation methods of the same type (OK and UK) yield similar error outcomes. In the gully elevation data, the R\({}^{2}\) value between IDW and OK is 0.5327, the largest difference among the four interpolation methods, indicating greater variability in interpolation errors for these methods. The R\({}^{2}\) value between OK and UK interpolation is 0.7465, suggesting a smaller difference. For methods of the same type at identical densities, the interpolation error is more consistent. However, differences in point data density (PM\({}_{2.5}\) concentration and gully elevation) influence interpolation error, as demonstrated by the disparity in R\({}^{2}\) values between OK and UK for the two data types (0.9940 vs. 0.7465). This indicates that varying data densities affect interpolation accuracy. In the sample area of PM\({}_{2.5}\) concentration, with its lower data density, interpolation errors are smaller, and differences are less pronounced.
## 3 Compaction Effect Analysis  
### Void Ratio Variation  
During the vibratory compaction simulations, the settlement of the load plate and the void ratio were continuously monitored. Data were recorded at intervals of 1/8 cycle. The plate displacement increments between cycles were compared, and loading was terminated when the ratio fell below 0.1% for ten consecutive cycles, indicating volume stability.  
Figure 3 illustrates the settlement of the load plate over time for different samples with varying degrees of particle mixing. The results demonstrated that the sedimentation of the particle system can be divided into two main stages: a rapid change phase and a stable phase. In the initial period (\(t<25\)T), the sedimentation of the granular system experienced a significant increase, accompanied by a sharp decrease in the void ratio. Subsequently (\(t>25\)T), the sedimentation and the void ratio of the particle system tended to stabilize, reaching a state of relative constancy.  
Figure 4 presents the variations in void ratios and final values of the samples with different mixing degrees. The findings demonstrated that the uniformity of particle mixing had a substantial influence on densification under identical vibration loads. Specifically, the settlement decreased as the mixing degree increased for the same number of load cycles. Similarly, both the change in void ratio (\(\Delta e\)) and the final value (\(e_{\rm f}\)) decreased monotonically as the homogeneity of mixing improved.