esa-sceva/satcom-chunk-collection huggingface dataset:
# Total Least-Squares Iterative Closest Point Algorithm Based on Lie Algebra
###### Abstract  
In geodetic surveying, input data from two coordinates are needed to compute rigid transformations. A common solution is a least-squares algorithm based on a Gauss-Markov model, called iterative closest point (ICP). However, the error in the ICP algorithm only exists in target coordinates, and the algorithm does not consider the source model's error. A total least-squares (TLS) algorithm based on an errors-in-variables (EIV) model is proposed to solve this problem. Previous total least-squares ICP algorithms used a Euler angle parameterization method, which is easily affected by a gimbal lock problem. Lie algebra is more suitable than the Euler angle for interpolation during an iterative optimization process. In this paper, Lie algebra is used to parameterize the rotation matrix, and we re-derive the TLS algorithm based on a GHM (Gauss-Helmert model) using Lie algebra. We present two TLS-ICP models based on Lie algebra. Our method is more robust than previous TLS algorithms, and it suits all kinds of transformation matrices.  
Keywords:total least-squares; ICP; lie algebra; Euler angle; 3D space transformation +
Footnote †: journal: Applied Sciences
###### Abstract  
The present work analyses broadband noise scattering from permeable trailing edges with identical micro-structure but under a change of temperature. Experiments are performed in an anechoic wind tunnel using a NACA0018 airfoil at chord-based Reynolds numbers between \(1.88\times 10^{5}\) and \(3.14\times 10^{5}\) and no incidence. A microphone array is used to determine far-field sound pressure level changes upon trailing edge heating. It is found that broadband noise emission can be actively controlled by varying the temperature of the porous trailing edge inserts. Specifically, the electrically heated inserts yield a noise level variation of up to 2.5 dB with the heated one being noisier compared to a baseline, unheated material with similar micro-structure. Resistivity measurements of permeable samples with varying temperature show that flow resistivity increases with the fluid temperature which is in agreement with the reported trailing edge noise increase.  
porous materials; turbulent boundary layer trailing edge noise; microphone array; flow resistivity +
Footnote †: journal: Applied Physics Letters
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 2 Overview of mmWave HetNets
### MmWave HetNet Model  
According to the features of 5G devices and networks described above, we consider an mmWave HetNet consisting of a macro base station (MBS) and several small-cell base stations (SBSs), WiFi APs, and mmWave APs, as shown in Figure 1. The whole network is within the coverage of the MBS. Due to the high complexity of such HetNets, we utilize a hierarchical structure to manage the network. In particular, several nearby SBSs, mmWave APs and WiFi APs are clustered into one group and are associated with a subordinate controller. Meanwhile, all subordinate controllers are connected with one superior controller. In our system, subordinate controllers are utilized to perform resource management and user association in the corresponding groups, whereas the superior controller collects information from all subordinate controllers and makes comprehensive decisions in a centralized way.
## 4 Results
### Outage Probability  
Figure 11 shows the relationship between the outage probability and transmission distance, based on (26), and the simulation curves. From (26), it can be seen that the received optical power is highly correlated with the threshold SNR, which is a function of the threshold data transmission rate. Therefore, the threshold of the data transmission rate has a great influence on the outage performance of the V2I-VLC system. Figure 11a,b depict the outage performance when the threshold is 200 kb/s and 500 kb/s, respectively. It is observed that the higher the threshold data transfer rate is set, the higher the outage probability under the same weather and transmission distance conditions. For example, for the same moderate fog conditions and \(I_{V_{1}-PD}=30\) m, the outage probability at a threshold data transfer rate of 200 kb/s is about \(5.1\times 10^{-3}\), and 0.11 at 500 kb/s. In addition, it can also be seen from Figure 11 that the outage probability increases rapidly as the transmission distance increases. When considering the weather factor, the outage probability curves for different weather conditions are relatively close to each other, regardless of whether the threshold data transmission rate is 200 kb/s or 500 kb/s. The results indicate that weather conditions have less impact on the outage performance compared to transmission distance.  
Figure 11: Outage probability for different weather conditions with threshold data transfer rates of (a) 200 kb/s and (b) 500 kb/s.  
Figure 10: Channel capacity corresponding to the proposed V2I-VLC propagation model for different weather conditions.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Hydrodynamics in Submerged HF Membranes  
Enhanced surface shear, for example caused by an increased cross-flow velocity, is a common strategy to control concentration polarization and fouling in submerged HF systems [138; 198; 199]. Unsteady-state shear, such as two-phase flow (i.e., gas bubbles or fluidized particles) and vibration, is more energy-efficient than steady-state shear [123]. Air bubbling is particularly attractive in MBRs for aeration, mixing, and augmenting liquid flows [14; 82; 200; 201].
## 6 Conclusion  
In this paper, we have proposed a novel method for the coupling of the antenna type and decoupling structure to the operating frequency, and the MC value. The results demonstrate that the current study was able to achieve a relatively improved MC reduction over a wide range of frequencies. It should be noted that the research studies in [39; 43] also achieve similar results and a relatively higher reduction in MC for [43], but within a narrow band of frequencies (3.1-11 GHz for studies in [43]).  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Authors** & **Antenna Type and Design** & **MC** & \
\begin{tabular}{c} **Operating** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Impedance** \\ **Bandwidth** \\ \end{tabular} \\ \hline
[39] &
\begin{tabular}{c} A mushroom-like EBG was employed for suppression \\ between two rectangular UWB MIMO antenna. \\ \end{tabular} & \(-23\) dB & \(5.8\) GHz & NA \\ \hline
[40] &
\begin{tabular}{c} Two long protruding ground stubs were added to the \\ ground plane of a compact MIMO-UWB planar \\ monopole antenna for MC reduction. \\ \end{tabular} & \(-15\) dB & NA & \(3.1\)–\(10.6\) GHz \\ \hline
[41] &
\begin{tabular}{c} Array antenna with EBG with High Impedance Surface \\ (SHI) structure for MC reduction for WiMAX system. \\ \end{tabular} & \(-10\) dB & \(3.5\) GHz & \(3.3\)–\(3.7\) GHz \\ \hline
[42] &
\begin{tabular}{c} A T-shaped ground stub and a slot were employed for \\ MC reduction in a MIMO antenna with a strip-line was \\ used as the feed. \\ \end{tabular} & \(-20\) dB & \(6.85\) GHz & \(3.1\)–\(10.6\) GHz \\ \hline
[43] &
\begin{tabular}{c} A compact Uni-planar MIMO antenna with partial \\ ground stub and a single column EBG structure for MC \\ reduction was investigated. \\ \end{tabular} & \(-25\) dB & NA & \(3.1\)–\(11\) GHz \\ \hline
[44] &
\begin{tabular}{c} A Minkowski fractal geometry with four EBG elements \\ between the antenna array was utilized for MC reduction \\ \end{tabular} & \(-22\) dB & \(5.6\) GHz & \(3.1\)–\(10.6\) GHz \\ \hline
[45] &
\begin{tabular}{c} Stub loading technique for MC reduction between \\ UWB-MIMO antenna elements was presented. \\ \end{tabular} & \(-20\) dB & \(5.8\) GHz & \(2.6\)–\(12\) GHz \\ \hline
[46] & \
\begin{tabular}{c} A dual split CSRR EBG was utilized for MC reduction in \\ rectangular inset feed MIMO. antenna \\ \end{tabular} & \(-18.8\) dB & \(2.45\) GHz & \(3.1\)–\(10.6\) GHz \\ \hline Current study &
\begin{tabular}{c} A Modified EBG was used with beveled rectangular \\ radiating MIMO antenna. \\ \end{tabular} & \(-23\) dB & \(6.85\) GHz & \(3.1\)–\(13.5\) GHz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the current study for MC reduction with some related studies.  
Figure 15: MIMO\(-\)UWB antenna \(\mathcal{S}-\)parameter (\(\mathcal{S}_{12}\)) using HFSS and CST software (**a**) with MEBG, (**b**) without MEBG.
## 6 Conclusions  
This study investigates the MAEOS scheduling problem, described by an MDP model. In the MDP, the characteristics of the learning environment were presented by static and dynamic elements of the state. The action space was decomposed into two subspaces corresponding to the task sequence and resource allocation subproblems, and a new reward function was designed. Based on the MDP, we proposed a TPHO framework. An encoder-decoder network was designed to determine the task scheduling sequence, and a heuristic rule, MRC, was employed to allocate the resource. Comprehensive experiments were conducted to examine the performance of the TPHO framework. The results indicated that the proposed encoder-decoder network could converge on a stable policy. The proposed TPHO framework with the MRC rule significantly outperforms other algorithms, including two advanced algorithms (A-ALNS and SA). The computational results also showed that the TPHO-based algorithms showed high time efficiency for the MAEOSSP.  
Owing to the considerable time efficiency and good generalization ability, the TPHO-MRC integrated with the target decomposition technique can be applied to MAEOSSP for area targets and mobile targets. Another promising direction involves using the proposed methods to address online scheduling or reactive scheduling. The proposed TPHO framework is a DRL-based general framework whose training stability and generalization ability are limited to the environment. Thus, it may not be suitable for more complex situations.  
Conceptualization, D.L. and G.Z.; methodology, D.L.; software, D.L. and Z.J.; validation, D.L; formal analysis, D.L. and Z.J.; investigation, D.L.; resources, D.L. and G.Z.; data curation, D.L. and Z.J.; writing--original draft preparation, D.L. and Z.J.; writing--review and editing, D.L. and G.Z.; visualization, D.L. and Z.J.; supervision, D.L. and G.Z.; project administration, D.L. and G.Z. 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 Nos. 72301273, 91538113, 72071195, and 71402176), the China Postdoctoral Science Foundation (Grant No. 2022M723106), the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2019171), and the Fundamental Research Funds for the Central Universities.  
The data used in this paper are generated by simulation, the detailed generation process has been described in this paper, and the data are also available from the author upon request.  
The authors are grateful for the support and help of their lab classmates.  
The authors declare no conflicts of interest.
## 3 The Proposed IoT-Based Master-Slave Regionalized Intelligent LED Light-Controlling System
### Energy Consumption Calculation
Therefore, the annual electric energy \(E_{\rm py}\) consumed by the proposed IoT-based intelligent LED light is:  
\[E_{\rm py}=\sum E_{\rm ph}=E_{\rm ph}\ \times\ 8760\ \text{kW\ h} \tag{3}\]  
Compared with the general non-inductive lamp: 15 W T8 LED tube (only 15 W in full light mode), the electric energy consumed by each lamp per year is (15 W/1000) \(\times\) 8760 = 131.4 kW h, we can calculate if the general non-induction lamp 15 W T8 LED tube is changed to the proposed IoT-based intelligent light (new induction lamp), its energy-saving rate \(ESR\), the amount of electricity saved per lamp per year \(D_{\rm py}\), and the annual saving cost \(M_{\rm py}\) (assuming 4 TWD per kilowatt-hour of electricity), the carbon emission reduction \(C_{\rm py}\) in a year, etc. are calculated as follows (each kilowatt hour (kW\(\cdot\)h) can generate 0.623 kgCO\({}_{2}\)e carbon emissions:  
\[ESR=(15-E_{\rm ph})/15*100\% \tag{4}\]  
\[D_{\rm py}=131.4-E_{\rm ph}*8760\ \text{kW\ h} \tag{5}\]  
\[M_{\rm py}=(131.4-E_{\rm ph}*8760)\ \text{kW\ h}*4\ \text{TWD/kW\ h} \tag{6}\]  
\[C_{\rm py}=(131.4-E_{\rm ph}*8760)\ \text{kW\ h}*0.623\ \text{kgCO}_{2}\text{e/kW\ h} \tag{7}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline T & The number of seconds that the high-light mode is maintained when someone passes by; the proposed IoT-based intelligent LED light \\  & defaults to 30 s \\ \hline F & The number of pedestrians passing by per hour; that is, the frequency of the high-light mode being activated per hour \\ \hline W\({}_{\rm H}\) & Power in high-light mode; the default power of the proposed IoT-based intelligent LED light is 15 W in high-light mode (default value is 100\%) \\ \hline W\({}_{\rm L}\) & The power in low-light mode; the default power of the proposed IoT-based intelligent LED light is 1.5 W in low-light mode (default value is 10\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameter descriptions.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 4 Battery and Energy Harvesting Technologies for Sensing and Monitoring in Buildings
### Potential of Building Energy Harvesting Technologies
In this regard, PV energy harvesting is regarded as the most viable of all technologies at present due to the frequent availability of light (both natural and artificial) on a daily basis (up to 15-16 h a day), easy installations, no requirement for replacements and maintenance, and the overall maturity and variety of PV panel technologies in general, factors which make it the most attractive candidate of all for harvesting energy in buildings [117].
# Anti-Collision System for Small Civil Aircraft
## 4 Accuracy of Determining the Position of Flying Objects by Anti-Collision System
### Verification of the Functionality of Derived Algorithms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **X (km)** & **Y (km)** & **Z (km)** & **Lat. (\({}^{\circ}\))** & **Long. (\({}^{\circ}\))** & **Height ASL (m)** \\ \hline LOT5 MF & 3930.301 & 1520.361 & 4776.659 & 48.7712N & 21.1476E & 3784 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The initial position of FO was obtained by calculation according to derived algorithms. The figure shows that the errors in determining the coordinates \({}_{\Delta}\)X, \({}_{\Delta}\)Y, and \({}_{\Delta}\)Z of the anti-collision system are in the range of \(\pm\)1.0 \(\times\) 10\({}^{-9}\) m. The mean value of the positioning error of the anti-collision system \({}_{\Delta}\)P is equal to 0.1 \(\times\) 10\({}^{-5}\) m. The dispersion of the positioning error of the anti-collision system is equal to 0.9 \(\times\) 10\({}^{-12}\) m\({}^{2}\). The results of both simulations confirmed that the derived algorithms for determining the position of the anti-collision system show approximately the same methodological error, which, given the required accuracy of positioning by the anti-collision system, can be considered negligible. Based on the simulation results, Equations (1) to (27) can be used to design an anti-collision system for small civil aircraft.
# Nitrogen Removal Using a Membrane Bioreactor with Rubber Particles as the Fouling Reducer
## 3 Results & Discussion
### Nutrient Removal by a Reactor with Intermittent Aeration
#### 3.2.2 Nitrogen Removal  
Figure 8 illustrates the variations in the T-N concentration of the MBR with intermittent aeration in the presence of 0.5% rubber particles. According to the figure, NH4\({}^{+}\) was completely oxidized to NO3\({}^{-}\) over the experimental period, whereas the T-N removal efficiency varied according to the aeration/non-aeration period. With aeration/non-aeration periods of 40 min/20 min or 30 min/30 min, ammonia was oxidized to nitrate; however, denitrification was not satisfactorily accomplished. With aeration/non-aeration periods of 20 min/40 min or 10 min/50 min, the average effluent T-N concentration was 3.2 or 1.6 mg/L, respectively. The T-N removal efficiencies with aeration/non-aeration periods of 40 min/20 min, 30 min/30 min, 20 min/40 min, and 10 min/50 min were 36.8, 43.1, 87.0, and 93.3%, respectively. This indicates that increasing the non-aeration period of the intermittent aeration period effectively improved the T-N removal. At the same time, changes in anoxic duration did not have a notable effect on ammonia oxidation to nitrate. It can be concluded that the intermittent aeration time is important for achieving T-N removal, and the aeration/non-aeration period of 10 min/50 min is the most suitable for simultaneous COD and T-N removal with an influent COD/T-N ratio of 2.7.  
Figure 8: T-N concentration in MBR with intermittent aeration.  
Figure 7: COD concentration in MBR with intermittent aeration.
## 2 Time-Frequency Image Feature-Based HI
### Cwtcnn-Hi  
In this study, we construct a CNN for image-based RUL prediction of the bearing. [PERSON] et al. [30] proposed 12 CNN models based on the classical CNN, LeNet-5, and compared performances. The best CNN structure has shown that it can classify bearing faults with an accuracy of 99.79%. Therefore, we designed the model by referring to the best CNN structure derived from [PERSON] et al. [30]. The structure of the proposed CWTCNN-HI model is shown in Figure 3. To use this model, the input data is prepared by converting the CWT coefficients into a 2-D image. Then, four convolutional layers and four pooling layers are connected alternately for feature extraction. The filter size of the convolutional layer is \(3\times 3\), and the filter size of the max pooling layer is \(2\times 2\). The channels of the four convolutional layers are 32, 64, 128, and 256. The last layers of the model comprise a flatten layer, two fully connected layers, and one output layer. After the convolutional layers, the 2-D feature map is flattened and connected to the fully connected layer. In the fully connected layers, 2560 and 768 nodes are selected, respectively. Finally, the last fully connected layer and one output neuron are connected to obtain the CWTCNN-HI. The ReLU function is applied to the convolutional and fully connected layers, and the sigmoid function is used on the output layer, normalizing the output HI from 0 to 1. After parameter setting, the model is trained to minimize the loss function  
\[LOSS=\frac{1}{T}\sum_{t=0}^{T}\left(\hat{Y}_{t}-Y_{t}\right)^{2}, \tag{7}\]  
where \(\hat{Y}_{t}\) is a predicted value of the CWTCNN-HI model, \(Y_{t}\) is an actual value at time \(t\), and \(T\) is a failure time. The Adam optimizer [34] is employed with mini batches to optimize the loss function. The batch normalization is used to accelerate the training process and overcome the overfitting problem of the model. For the experiment, we implemented the Python-based Keras deep learning framework [35], which uses Tensorflow on the back-end to build the CWTCNN-HI model [36]. The parameters used for model training such as optimizer, loss function, and normalization method were chosen by a trial and error method.
)analyzed the response characteristics of a TRUM under a high-overload environment and mentioned that the wave spring and stainless steel gasket were effective for the anti-overloaded actuator [11], but the process of establishing the impact dynamics model and the performance of the TRUM was not described in detail. All the above research theoretically analyzed the feasibility of TRUM application under high overload environments. However, the influence of the high overload on the performance of the TRUM, which is vital for its application, is rarely reported. There is still a lack of studies which could be beneficial to the reliability prediction of TRUM and to its optimal design to improve the ability of high overload resistance. For the above purposes, based on the PMR40 ultrasonic motor (a kind of TRUM made by NUAA Super Control Technology Co. Ltd., Nanjing, China), the rotor structure is optimized, four motors are designed, and the impact dynamics model of the TRUM with high acceleration of 10,000 g is established using the finite element (FE) software Automatic Dynamic Incremental Non-Linear Analysis (ADINA). Force situations during the impact of the main parts and contact surfaces of the TRUMs are analyzed, and motors' performances before and after the impact are compared in experiments. Finally, a TRUM structure with the strongest anti-overload capability and the best reliability is obtained. The actual high-overload test will cause large loss to the motors and high cost. By establishing the impact dynamics model, the impact process is simulated, the results are analyzed, and the motor structure is optimized. This work is of important guiding significance to the design and application of ultrasonic motor in the high-overload environment. Structure Improvement Design of Traveling-Wave Rotary Ultrasonic Motor (TRUM) in High-Overload Environments
## 3 Materials and Methods  
### Specim Specification  
The experimental specimens used in this study are the same CFRP laminates previously examined by [PERSON] et al. [38] and [PERSON] et al. [39]. Two different thicknesses of CFRP samples were used in this study: a thin laminate and a thick laminate. Both sample types were manufactured using unidirectional pre-preg material (IMS-977-2) with a symmetrical stacking sequence of \([45/-45/90/0/90/0/90/-45/45]\). The thin laminate consisted of 9 plies, resulting in an average cured thickness of 1.65 mm, while the thick laminate was made by stacking the same sequence in a symmetric fashion (denoted as \([45/-45/90/0/90/0/90/-45/45]_{s}\)), producing a total of 18 plies with a cured thickness of 3.8 mm. The corresponding per-ply thickness was approximately 0.183 mm for the thin laminates and 0.211 mm for the thick laminates, accounting for slight variations due to manufacturing tolerances. The laminates were cut into rectangular samples measuring 100 mm \(\times\) 150 mm [38].  
The specimens were subjected to BVID, created using a 13 mm steel hemispherical impactor and following the ASTM impact testing standard. Impact energies ranged from 2.5 J to 20 J, with specific drop heights and impactor masses calibrated to achieve the desired energy levels [38]. This approach ensured controlled damage conditions for studying the application of vibrothermography.  
The experiment involved 10 samples: five thin samples (1.6 mm thickness) and five thick samples (3.8 mm thickness). For the thin samples, one specimen received 2.5 J of impact energy, one received 4 J, two specimens received 8 J, and one received 12 J. For the thick samples, one specimen received 4 J, two received 8 J, one received 12 J, and one received 20 J of impact energy. Two samples were prepared at the 8 J impact level for both thicknesses to allow assessment of the repeatability and consistency of barely visible impact damage (BVID) under conditions considered critical for BVID formation in aerospace-grade CFRP. Figure 1 depicts one of the experimental samples.  
Figure 1: Photographs of the CFRP specimen: (**left**) front surface and (**right**) back surface, where the transducer was bonded for ultrasonic excitation; this surface also faced the infrared camera during testing.