esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Full-Scale Tests for Tunnel Boring Machine (TBM) Tunneling
### Artificial Rock Mass  
Full-scale tests were performed through the preparation of artificial rock mass using concrete and mortar due to the problems of size, handling, and expenses in using the real rock mass block having a volume of 87.5 m\({}^{3}\). A total of four tests cases were planned according to the UCS values of rock mass as shown in Table 2. These tests were carried out in the artificial rock mass having UCS ranging from 20 MPa to 100 MPa. Achieving 50 MPa strength of artificial rock mass in Test# 2 was planned in order to analyze the material characteristics of artificial rock mass affected by the performance of TBM using different materials, but it was deduced that there was no special difference between using concrete or mortar on the results. So, all the 50 MPa case results using concrete and mortar were combined afterwards for analysis. The measured UCS values after testing were slightly different than those planned for each case. Hence, all the four UCS cases can be summarized in the form of planned and measured values; (Test# 1) 20 MPa (measured 27 MPa) using mortar, (Test# 2) 50 MPa (measured average 57 MPa) using concrete and mortar, (Test# 3) 70 MPa (measured 70 MPa) using concrete, and (Test# 4) 100 MPa (measured 99 MPa) using mortar.  
Artificial rock mass was designed using Portland cement by controlling the cement-water ratio as well as the percentage of fine aggregates and admixture. Quality of the rock mass was ensured by conducting the strength tests after 28 days of curing as well as through sampling from the rock mass after tests. A rock box made by steel structure with dimensions of 5 m height, 5 m width, and 3.5 m length was prepared, as shown in Figure 5, for making an assembly of artificial rock mass without joints having 87.5 m\({}^{3}\) volume. An important part of this study was the focus on analyzing the effect of rock mass UCS on the performance of TBM in homogeneous and intact rock condition without having any joints.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Cases** & **Planned UCS(MPa)** & **Measured UCS(MPa)** & **Material** \\ \hline Test\# 1 & 20 & 27 & Mortar \\ Test\# 2 & 50 & 57 (average) & Concrete \\ Test\# 3 & 70 & 70 & Concrete \\ Test\# 4 & 100 & 99 & Mortar \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test cases having variation of UCS in this study.  
Figure 4: Cutterhead drive capacity expressed by relationships between torque and rotational speed of cutterhead.
###### Abstract  
In this paper, an asynchronous collision-tolerant ACRDA scheme based on satellite-selection collaboration-beamforming (SC-ACRDA) is proposed to solve the avalanche effect caused by packet collision under random access (RA) high load in the low earth orbit (LEO) satellite Internet of Things (IoT) networks. A non-convex optimization problem is formulated to realize the satellite selection problem in multi-satellite collaboration-beamforming. To solve this problem, we employ the Charnes-Cooper transformation to transform a convex optimization problem. In addition, an iterative binary search algorithm is also designed to obtain the optimization parameter. Furthermore, we present a signal processing flow combined with ACRDA protocol and serial interference cancellation (SIC) to solve the packet collision problem effectively in the gateway station. Simulation results show that the proposed SC-ACRDA scheme can effectively solve the avalanche effect and improve the performance of the RA protocol in LEO satellite IoT networks compared with benchmark problems.  
LEO satellite IoT networks; random access; collaboration-beamforming; satellite-selection; collision-tolerant 2023233549.
## 2 Materials and Methods
### Sensor Formula Optimization  
#### 2.2.1 General Sensor Preparation  
The sol-gel synthetic procedure followed three basic steps: hydrolysis, condensation, and gelation (solidification). In this formulation, 100 uL of an inorganic precursor--TEOS--was mixed with 480 uL of either MeOH or DI water in a 2 mL microtube. The TEOS molecules underwent hydrolysis with the addition of 34 uL TFA (0.1 M in methanol) acting as an acid catalyst. This solution was homogenized by vortexing for 3 min, following the addition of each compound, and then left to react overnight (~19 h). Thereafter, condensation of the TEOS polymer was induced with the addition of a base catalyst, ammonium hydroxide (NH\({}_{4}\)OH; 0.25 M in DI water). The mixture was vortexed for 1 min and gelation followed shortly after the addition of the base. The molar ratio between TEOS:water:TFA (0.1 M in methanol):NH\({}_{4}\)OH (0.25 M in DI water) was maintained at 1:56:1.8:5.8-17.5. Prior to complete gelation, 5 uL drops of the sol-gel mixture were deposited onto the chosen substrate, Whatman No. 1 filter paper (Sigma Aldrich, St. Louis, MO, USA). The paper was left to air dry for 30 min and was then placed in an oven at 70 \({}^{\circ}\)C for at least 30 min to remove any remaining solvent. Some parameters for this formulation--such as gelation time, indicator used, and indicator concentration--were optimized. Thereafter, the colorimetric indicator was included into the initial TEOS/solvent mixture for the sol-gel. To prepare the indicator solution, 0.01 N NaOH was added in a dropwise fashion to 10 mg of the pHindicator dye until it was dissolved in a 25 mL volumetric flask. This solution was then diluted to the mark with water. Methanol was initially used as a solvent/porogenic agent for the sol-gel material; however, it was replaced with DI water due to the dye solubility.
## 3 Contributions of the Proposed Work
A major concern with the weighted sum method is that a small change in weights may result in big changes in the criteria's importance and significantly different weights may produce nearly similar decisions [34]. The TOPSIS method, on the other hand, does not suffer from most of the above concerns. The TOPSIS approach is a computationally efficient method. The method is simple and able to consider a non limited number of alternatives--a limitation of the AHP method [35]. The method has been effectively used to solve a number of decision problems. Furthermore, the TOPSIS method does not require involvement of the decision-maker for defining upper and/or lower limits of criteria, as is required in fuzzy logic, goal programming, or the weighted sum method. Noting these characteristics, the key contributions of the work proposed herein are enumerated as follows:  
1. A TOPSIS-based approach is proposed for the turbine selection problem. To the best of our knowledge, this study is the first attempt to solve the turbine selection problem using the TOPSIS method. 2. A multi-criteria decision approach is employed using important and relevant criteria. These criteria include hub height, wind speed, percentage of zero power, percentage of rated power, and annual energy production. The importance of these criteria is endorsed by several previous studies [18; 21; 29; 36; 37]. Furthermore, the approach is robust and scalable, and can be easily extended to accommodate more selection criteria. 3. As opposed to many previous studies, the proposed method was tested on real data collected from two potential sites in Saudi Arabia, namely, Turaif and Wejh. However, the approach is generic and is broadly applicable to any real site around the globe. 4. To demonstrate the impact and diversity of the TOPSIS approach in different scenarios, a comparative analysis on the performance of the proposed TOPSIS approach is provided with regard to the two diverse potential sites. Both sites have different topographic and climatic conditions. 5. Comparison of the TOPSIS approach is done with three other MCDM techniques that were employed for turbine selection problem in previous studies [18; 19; 20; 21; 22; 23]. These techniques include the weighted sum method, fuzzy logic, and goal programming. The comparison aims at analyzing the trends of a turbine type for the two sites when TOPSIS is used and when all four techniques are used in an aggregated manner.
## 2 Methodology
### Simulation
#### 2.3.1 Ray-Tracing Model  
The simulation model in COMSOL(r) consists of a 2D ray-tracing model. A 2D simulation can be used to design the best shape for a section of the reflector geometry. There is no need for a 3D ray-tracing simulation model as a result of the simple geometry of a halogen tube lamp. The designed shape for a section can simply been extruded over the total length of the heat source. The simulation model exists of the halogen tube lamp, the reflector shape and an object on which the incident radiation will be measured. Figure 4 visualises the outcome of a ray-tracing simulation in ideal circumstances.  
Figure 3: (**Left**) This figure shows a geometrical representation in order to calculate the amount of the radiation not being reflected by the elliptical reflector. The left image represents the complete set-up with the halogen tube lamp in point O and the focal point in point F. \(\overline{E_{2}E}\) represents the width of the illuminated region by direct rays leaving the tube lamp. The right image can be used to determine the angles needed to calculate the unwanted illuminated area. Representation of the optimised reflector shape and the related ray trajectories. (**Middle**) Optimised reflector shape established through ray-tracing simulations. The optimised heating source consists of an external reflector in the shape of an ellipse and additional straight reflectors in the middle. The halogen tube lamp is placed in the upper focal point of the ellipse. (**Right**) Ray trajectories according to ray-tracing simulation. The vast majority of the electromagnetic rays are focussed in the second focal point of the ellipse. The minority that intersects with the sample outside the focal point are reflected from the additional reflectors in the heat source. The heating power coming from these rays is negligible.
## 4 Classification of PPDA Protocols
### Homogenous protocols
#### 4.2.1 Perturbation
The perturbation technique is applied to hide the actual individual readings and the actual aggregate results sent by sensor nodes. For this, every sensor node is preloaded with a unique secret number which is known exclusively by the sink and the node itself. Sensor nodes and the sink form a tree. The basic idea of PHA is to generalize the values of data transmitted in a WSN, such that although individual data content cannot be decrypted, the aggregator can still obtain an accurate estimate of the histogram of data distribution and thereby approximate the aggregates. In particular, before transmission, each sensor node first uses an integer range to replace the raw data. Next, with a certain granularity, the aggregator plots the histogram for data collected and then estimates aggregates such as _MIN_, _MAX_, _Median_ and _Histogram_. Although the PHA supports many data aggregation functions, it has the following disadvantages. First, the final aggregated result is an approximation value of the sensor data rather than the real data. Secondly, the PHA requires a large size payload (message/data) because all sensor data need to be replaced by an integer range. Moreover, the bandwidth consumption of this protocol increases as the number of ranges increases. Finally, storing interval ranges to replace the original data consumes a significant amount of memory.
# Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition
## 2 Method Overview
### Noise Reduction Principle Based on MRSVD  
For any discrete digital signal containing noise \(y(n)\), it can be expressed as \(y(n)=x(n)+\delta(n)\)\(n=1,2,\cdots N\). Where \(x(n)\) is the original signal and \(\delta(n)\) is the noise, the specific noise reduction process is as follows:  
1. The signal containing noise \(y(n)\) is constructed to a matrix  
noise signal. It can be seen from Equation (4) that the larger the signal to noise ratio, the larger the power of the original signal and the smaller the noise power.  
(2) Mean square error. The MSE is the expected value of the square of the difference between the parameter estimate and the true value of the parameter. The smaller the value of the MSE indicates that the prediction model has better accuracy in describing the experimental data. In this paper, the MSE reflects the magnitude of the noise power. The smaller the value indicates that the less noise in the signal and the better the noise reduction effect. The specific calculation formula is as shown in Equation (5):  
\[MSE=\frac{1}{N}\sum_{n=1}^{N}\left(x(n)-\hat{\bar{x}}(n)\right)^{2} \tag{5}\]
## 4 Data Smoothing Algorithm
### Detection Accuracy Results and Analysis of Inertial Track Geometry State Detector
#### 5.3.4 Test Results Comparison  
Since 2013, many similar experiments have been conducted on the same test site, and the experimental results have been published in the literature [27; 28; 29]. After using the new filter proposed here, the measurement accuracy of the track geometry parameters of the track detector was improved. In order to better compare results, different algorithms were used to calculate the same data. The comparison is shown in Table 2.  
Figure 7: Repeatability comparison showing data from three experimental replicates for track geometry parameter measurements of test lines: (**a**) track direction; (**b**) high and low; (**c**) horizontal (ultra-high).  
Figure 8: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Track Geometry** & **Measurement Accuracy by** & **Measurement Accuracy by** \\
**Parameters** & **Centralized Filtering** & **Federated Filtering** \\ \hline Track direction & \(\leq\)0.33 mm & \(\leq\)0.2 mm \\ High and low & \(\leq\)0.45 mm & \(\leq\)0.2 mm \\ Horizontal (ultra-high) & \(\leq\)0.31 mm & \(\leq\)0.2 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of measurement accuracy of different filtering methods.  
The following are prospects for further work to build on our findings:  
1. Continue to study the inertial/GNSS/odometer integrated navigation post-filtering algorithm. This algorithm is the core of the inertial/GNSS/odometer integrated navigation system and the key to improving its accuracy. The attitude calculation accuracy of the integrated navigation system has already been improved.
2. The next step is to carry out research on the detection and correction methods of data outliers and noise of the four types of sensors (gyroscope, accelerometer, satellite positioning system, and odometer), control the noise from the source, and improve the accuracy of the inertial/GNSS/odometer integrated navigation system.
3. In the future, the federated filtering algorithm should be tested for computing power and fault tolerance using longer line sets and more data.
## 1 Introduction
Therefore, a hybrid prediction model based on a back propagation neural network (BPNN), modified particle swarm optimization (MPSO), principal component analysis (PCA), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD) for corrosion rate prediction was developed. This model integrates advanced techniques such as machine learning benchmark models, optimization algorithms, and data decomposition algorithms to eliminate noise in the prediction process, capture the main features of the original data, and obtain ideal prediction results. The established model consists of three parts: data processing, optimization, and prediction. In this model, the input parameters are first decomposed into multiple Intrinsic Mode Functions (IMFs) and a residual by CEEMDAN. Then, Variational Mode Decomposition (VMD) is adopted to further process the high-frequency subsequences within the multiple IMFs, decomposing them into low-frequency subsequences to obtain more stable sequence components, thus effectively reducing the non-stationarity of the original data. Subsequently, PCA is applied to reduce the dimensionality of the patterns obtained from the residual and low-frequency subsequences and the processed results are used as input features. Then, a corrosion rate prediction is carried out using a BPNN optimized by MPSO. Finally, an evaluation module is established to comprehensively evaluate the established hybrid model. The evaluation module includes R\({}^{2}\), MAPE, RMSE, MAE, U1, U2, RE, forecasting effectiveness, comparing the results with existing literature, Grey Relational Analysis, and interpretability research. Compared with previous methods, the original contributions of the present research study are as follows: (1) CEEMDAN-VMD is innovatively adopted to solve the forecasting difficulties of high-frequency signals so that the main features of the original data series can be successfully identified and extracted. (2) The use of MPSO is proposed to solve the short
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 4 Experiments  
### Dataset  
As there is no dataset that has a sequence in which the person moves primitive-shaped objects, we created the dataset using Kinect v.1 to evaluate our pose estimation method. In this paper, we did not conduct the evaluation using the synthetic dataset, because the effectiveness of the proposed method can only be validated using the data captured in the real environment. The main task of this paper is to robustly estimate the pose of primitive shaped objects in the case that the object point clouds cannot be accurately segmented. Although the sensor noise can be added to the acquired synthetic data, it is difficult to simulate the miss-segmentation.  
Four primitive-shaped objects were used in the experiments. The objects used in the experiment were cube (length = 20 cm), tall cylinder (radius = 7.5 cm, height = 40 cm), wide cylinder (radius = 15 cm, height = 5 cm), and half sphere (radius = 10 cm). These objects are illustrated in Figure 4. In the dataset, there are 10 scenes included, and each scene comprised approximately 220 frames. At each scene, the user lifted, piled, and moved each object. The examples of frames in the scene are depicted in Figure 5.  
The ground truth pose is needed to evaluate our proposed method. We used Cloudcompare [33] to annotate the ground truth pose. First, the synthesized primitive-shaped objects are generated. The detailed explanation to generate the object point cloud is explained in the later section. For example, we set superquadric parameter \((a_{1},a_{2},a_{3},\varepsilon_{1},\varepsilon_{2})=(10.0,10.0,10.0,0.1,0.1)\) for generating a cube, and \((a_{1},a_{2},a_{3},\varepsilon_{1},\varepsilon_{2})=(7.5,7.5,20.0,0.1,1.0)\) for generating a tall cylinder. Third, we manually align the generated model to the point cloud of each frame. By extracting the transformation matrix after the alignment, we can obtain the ground truth pose of each primitive-shaped object.  
Figure 4: Primitive-shaped objects used in the experiment (**left**) and the generated point clouds (**right**) for each object. From left to right, cuboid, tall cylinder, sphere, and wide cylinder. The generated point clouds are used just as the evaluation and annotation of ground-truth pose.  
Figure 5: Examples of frames included in the dataset.
## 4 Results and Discussion
### PV Installations Connected to the Grid: Surplus of Annual Energy  
Depending on the agronomic management of irrigation, the amount of water demanded by certain crops, the climatic conditions, and the state of the soil, the energy required by crops can vary considerably. As previously discussed, most crops require irrigation during specific periods of the year and their demand can be considered as seasonal. For example, for the case study, the months  
Figure 8: Description of electricity rates for different time periods in the Spanish electricity system.  
Figure 9: Example of PV generation and surplus of energy. Costs and benefits for the Spanish electricity system.  
are limited to May, June, July, August, and September. Therefore, an important part of the potentially generated annual power is initially wasted. Under this hypothesis, the energy generated for the case study has been quantified-based on the selected PV configuration and the corresponding 1500 and 3000 m\({}^{3}\)/ha crop water needs, which represents vineyard crops and a mosaic of vegetation and vineyard for typical areas of the case study. Figure 10 shows the surplus energy gradient based on the surface in hectares and the aquifer depth for the different PV configurations summarized in Figure 7. As shown in these results, greater depth aquifer and greater crop water need would imply more power required by the system, and consequently, the potential annual generated energy will be higher. This is due to the fact that both parameters have a relevant influence on the preliminary estimation of the PV solar pumping installation. Nevertheless, the investments are related to size of the PV power plant, and subsequently, the higher the PV system the higher the annual profits. A more detailed economic analysis, including investments, should be conducted to estimate the best solution. A recent detailed economic analysis carried out by the authors can be found in [59].
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 6 Effects of Noise and Interference  
In practical applications, the target environment and system components will degrade the processing performance by adding noise. Simultaneously, it is expected that transmissions from other GPS systems may interfere with desired signals and reduce SNR. In this section, we address concerns related to interference, thermal noise, and measurement errors.