esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Power QC-LDPC Decoder Based on Token Ring Architecture
## LDPC Codes and Their Decoding--Preliminaries
### QC-LDPC Codes  
The structure of the parity check matrix may be subject to additional constraints according to design requirements, in particular to the codec implementation method. For example, in order to facilitate the hardware implementation of LDPC decoders, the matrix structured into Quasi-Cyclic (QC) form, as shown in the example in Figure 1, is commonly used. In this illustration, the matrix \(\mathbf{H}\) of size \(8\times 16\) is divided into square submatrices of size \(4\times 4\), where each submatrix is either a zero matrix or a matrix formed by cyclically shifting the columns of the identity matrix (CPM--Circulant Permutation Matrix [35]). In general, the parity check matrix of a QC-LDPC code is partitioned with square submatrices \(\mathbf{P}\), where each is either a zero matrix of size \(P\times P\) or a CPM of size \(P\times P\). QC-LDPC codes are particularly attractive to systems implemented in hardware, because the block-circulant parity check matrix structure enables fast and efficient message routing in a decoder implementation.  
The parity check matrix \(\mathbf{H}\) of QC-LDPC code can be specified by a table (matrix) of cyclic shifts in CPMs (an example is shown in Figure 1), in which the nonnegative integers (from 0 to \(P\)) represent the number of cyclic shift positions in CPM, while the values \"\(-1\)\" indicate null submatrices. Such a representation of the parity check matrix \(\mathbf{H}\) is convenient because it allows \(\mathbf{H}\) to be stored in a compressed form in the memory of the decoder. All possible shifts in the CPMs with \(P=4\) are shown in Figure 2.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology
### Ship Trajectory Tracking Based on Multi-Object Tracking Algorithm  
After performing vessel detection, we extract visual features of the vessels from a single frame image. To track vessels across video frames, it is essential to establish associations between vessels in consecutive frames. The vessel tracking process consists of three main stages: detection, association, and trajectory update. In the detection stage, the object detection algorithm identifies vessels in each frame, generating bounding boxes and associated confidence scores. Vessel trajectory tracking in this study is based on the detection results obtained from a one-stage object detection algorithm. First, the object detection algorithm processes the input image \(\rm{I_{t}}\), producing multiple detection boxes, \(\rm{S_{t}}\), where each detection box contains the following information:  
\[\rm{S_{t}}=\left\{\left(x_{i},y_{i},w_{i},h_{i},c_{i}\right)\right\}_{i=1}^{N} \tag{24}\]  
where \(\rm{x_{i}}\) and \(\rm{y_{i}}\) represent the center coordinates of the \(\rm{i}\)-th detection box, \(\rm{w_{i}}\) and \(\rm{h_{i}}\) denote the width and height of the \(\rm{i}\)-th detection box, and \(\rm{c_{i}}\) indicates the probability that the \(\rm{i}\)-th detection box corresponds to a valid detection. This probability, typically output through the object detection model, takes values in the range [0,1]. The Kalman filter predicts the next state of the target using the state transition model:  
\[\rm{S_{i|t-1}}=F\rm{S_{t-1}}+w_{t} \tag{25}\]  
\[\rm{P_{t|t-1}}=FP_{t-1}F^{T}+Q \tag{26}\]  
where F is the state transition matrix, \(\rm{w_{t}}\) is the process noise, and P is the covariance matrix. In the data association phase, the Hungarian algorithm is used to solve the matching problem between detection results and predicted trajectories. The key to the matching process lies in the computation of the cost matrix C, which is obtained through a weighted calculation of motion information and appearance features:  
\[\rm{C_{ij}}=\lambda\cdot IoU\left(b_{i},b_{j}\right)+(1-\lambda)\cdot d_{ cosine}\left(f_{i},f_{j}\right) \tag{27}\]  
where \(\rm{IoU}(b_{i},b_{j})\) represents the intersection over union (IoU) between the detection and prediction boxes, which is used to measure the positional matching accuracy; \(\rm{d_{cosine}}\left(f_{i},f_{j}\right)\) is the cosine distance of the deep features between the detection target and the tracked target, used to measure the similarity of appearance features. The weight parameter \(\lambda\) determines the relative importance of these two types of information. Finally, through continuous trajectory updates, the complete trajectory of each target in the video is output.
## 3 Methodology
### PPG Signal Preprocessing
#### 3.1.2 Asymmetric Least Squares  
ALS is a useful baseline correction technique [42]. Motion interferences were removed by the help of ALS, which is useful in correcting data with relatively narrow peaks. ALS is useful in making the systolic and diastolic peaks more prominent, which are crucial attributes for feature extraction. The baseline correction through ALS improved the overall signal quality as the fluctuations between peaks were decreased.  
However, some high-frequency noise could still be observed in signals processed through ALS, as seen in Supplementary Material Figure S7. ALS was followed by a smoothing process applied through the Gaussian filter on the signal to remove existing high-frequency noise. A preprocessing model with ALS as a first stage and Gaussian filter as the second stage significantly improves the quality of the acquired PPG signals. This can be seen in Figure 6, which shows a raw PPG signal cleaned with Gaussian filter and corrected using ALS. All the signals were normalized from 0 to 1 F.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 5 Discussion  
### About Data Compensation Method for Environment Vibration  
The data compensation method for environment vibration is effective for small and sudden vibration. When the vibration of the laser transmitter module is large and continues for a while, the spot position in the reference monitoring image acquisition modules will change and reflect the vibration; then, the system will stop the measurement for the measured points until the vibration of the laser transmitter module stops, filtering out the influence of the vibration on the accuracy of the system.
# Fast 3D Semantic Mapping in Road Scenes +
## 3 Problem Formulation
### Framework  
Our target is to build a 3D semantic map with semi-dense and consistent label information online while the image sequences are captured by a forward-moving monocular camera. Given an image sequence, the inference of the 3D semantic map is regarded as:  
\[\mathcal{M}^{*}=\text{argmax}_{\mathcal{M}}P(\mathcal{M}|\mathcal{G}), \tag{1}\]  
which can be estimated by the maximum a-posterior (MAP). Compared to the model used in [28], our measures are continually updated with new keyframes. Thus, we adopt an incremental fusion strategy to estimate the 3D semantic map by incorporating new estimation of pose, depth, and semantic information. Correspondingly, the approach is decoupled into three separately running processes as shown in Figure 2.  
In the system, the monocular SLAM process maintains and tracks on a global map of the environment, which contains a number of keyframes connected by pose-pose constraints with associated probabilistic depth maps. It runs in real-time on a CPU. Represented as point clouds, the map gives a semi-dense and highly accurate 3D reconstruction of the environment. Meanwhile, the second process of the 2D semantic segmentation generates the pixel-level classification on the extracted keyframes. A fast deep CNN model is explored to predict the semantic information on a GPU. In addition, an incremental fusion process for the semantic label optimization is operated in a parallel way. It builds a local optimal correspondence between semantic labeling and 3D points in the point cloud. To obtain a globally optimal 3D semantic segmentation, we attempt to make use  
Figure 2: **Framework of our method:** The input is the sequence of RGB frames I. There are three separate processes, a keyframe selection process, a 2D semantic segmentation process, and a 3D reconstruction with semantic optimization process. Keyframe, denoted as K, is conditionally extracted from the sequence based on the distance between the poses \(\mathbf{T}\). The following frames refine the inverse depth map D and the variance map V of each keyframe until new keyframe is extracted. The 2D semantic segmentation module predicts the pixel-level class with scores S of the new arriving keyframe. Finally, the keyframes are incrementally explored to reconstruct the 3D map with semantic labeling and then it is regularized by a dense Conditional Random Field (CRF).  
of information of neighboring 3D points, involving the distance, color similarity, and semantic label. It optimizes the point cloud and semantic labels to generate a globally consistent 3D semantic map.
## 3 Simulation Setup and Performance Evaluation
### End-to-End Delay  
End-to-end delay is another significant parameter for analyzing the performance of QoS-based routing protocols. We show the end-to-end delay of each routing protocol in Figure 8. Based on the results, we observe that when the time interval increases then the end-to-end delay performance of each routing protocol is affected.  
Figure 6: Average delivery ratios _vs._ different node failure probability.  
Figure 7: Average energy consumption _vs._ node failure probability.  
In this experiment, variable packet sizes are used for the arrival rate at the sender side. One of the interesting measurements is to deal with both non-real time and real time data traffic. Finally, based on the results, we observed that PT shows slightly lower end-to-end delay as compared with other routing protocols. However, the end-to-end delay of PT can also be considered as higher within the scope of the routing performance. MIMO has higher end-to-end delay as compared with other routing protocols.
###### Abstract  
The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles' charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (_Itsse_). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method.  
distributed generation (DG); electric vehicle (EV); kernel extreme learning machine (KELM); voltage stability evaluation index (_Itsse_); capacity selection; total investment cost; power loss
# A High-Sensitivity Cesium Atomic Magnetometer Based on A Cesium Spectral Lamp
## 6 Conclusions  
In response to the demand for geomagnetic measurements under challenging environments, a high-sensitivity cesium atomic magnetometer with a wide operating temperature range was developed in this study. The quantum limit sensitivity of the cesium atomic magnetometer was estimated by calculating the relaxation rate and quadratic Zeeman effect in detail. After improving the cesium lamp module, the output performance of the light source showed advanced stability and rigidness for operating temperatures of \(-50\)\({}^{\circ}\)C to +70 \({}^{\circ}\)C. Under a background magnetic field of 50 \(\mu\)T at room temperature, the measured sensitivity of the cesium atomic magnetometer reached 140 fT/\(\sqrt{\mathrm{Hz}}\) at 1 Hz in an optimized working condition, surpassing the performance of the current leading commercial product.  
Figure 14: Measured sensitivity of the cesium atomic magnetometer under \(B_{0}=50\)\(\mu\)T. Intrinsic sensitivity estimated by the measured system noise floor: 100.3 fT/\(\sqrt{\mathrm{Hz}}\). Intrinsic sensitivity estimated by the photon-shot noise: 91.5 fT/\(\sqrt{\mathrm{Hz}}\). Fundamental quantum limited noise estimated. The measurement data were normalized to a 1 Hz bandwidth.  
**Author Contributions:** Conceptualization, Y.-T.L., L.-S.L., W.-H.Z., W.-M.L. and X.-J.Z.; methodology, Y.-T.L. and Y.-Q.S.; formal analysis, T.-F.Z.; writing--original draft preparation, Y.-T.L.; writing--review and editing, Y.-Q.S., T.Z. and T.-F.Z.; supervision, X.-J.Z.; funding acquisition, W.-H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2021 YFB3900202, 2021 YFA1400900, 2021 YFA0718300, and 2021 YFA1402100, the NSFC under grant numbers 61835013, 12174461, and 12234012, and the Science and Disruptive Technology Research Pilot Fund of the Aerospace Information Research Institute, grant number E1Z21003.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data is unavailable due to privacy.  
**Acknowledgments:** The authors acknowledge [PERSON], [PERSON], and [PERSON] from the University of Fribourg in Switzerland for their insightful discussions and invaluable help in characterizing the cesium atomic vapor cell.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Shear Flow Device  
In our experiment, an air blowing scheme is employed to design the shear flow device. As shown in Figure 4a,b, the body of the device is divided into two layers, and the corresponding nozzles have different surface structures. Therefore, the flows with different Mach numbers are jetted and mixed, and the shear flow is generated in the test section. To improve the contraction ratio of the nozzles and provide a flow with high quality, a scheme with contracting in three dimensions is employed to design the nozzle. In this device, the Mach number of the primary flow from the high-speed nozzle is 3.0, and the operating mode is Mach 3.0-2.0 during the experiment. To ensure the two nozzle outlets have the same pressures of about 23.5 kPa, the corresponding total pressures of the two layers at the settling chamber are 804 kPa and 194 kPa. The Reynolds numbers at the outlets of the high-speed nozzle and low-speed nozzle are 2.99 and 1.99, respectively. The four walls of the optical test section are made of planar quartz glass so that the optical measurement window of this device is large enough. In addition, Figure 4c shows the numerical simulation of the shear flow, from which the stable shock waves and the fine structure of the shear flow can be observed clearly. The primary flow and secondary flow are mixed in the initial position of the device, and the small-scale vortex structures are caused by the instability and transition of the mixing layer. Due to the entrainment effect, the vortex structures gradually evolve into larger-scale structures. Here, the power spectrum of the density fluctuation at the blue point in Figure 4c is also analyzed. The numerical calculation result is shown in Figure 4d and the peak frequency is about 80 kHz.  
Figure 4: (**a**) Schematic diagram and (**b**) image of shear flow device. (**c**) Numerical simulation of the shear flow. (**d**) Simulation result of the density fluctuation at the location of blue point.  
Figure 3: (**a**) Schlieren image of the ionization point caused by a femtosecond laser pulse. (**b**) Time-varying signal with a sampling time range of 50 ms. (**c**) Power spectrum of the density fluctuation.
## 3 Examples
### Synthetic Signal Example  
We consider a synthetic signal as  
\[g(t)=\cos(180\pi t+30\times\sin 4\pi t), \tag{21}\]  
and its instantaneous frequencies can be expressed as  
\[f(t)=90+60\times\cos 4\pi t. \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time-Sample Intervals** & \(\Delta t\) = 2 ms & \(\Delta t\) = 4 ms \\ \hline
**Methods** & **PSST** & **HSST** & **PSST** & **HSST** \\ \hline Time consuming (s) & P1 = 16.282 & H1 = 16.288 & P2 = 9.868 & H2 = 9.872 \\ Ratios & H1/M1 = 1.019; H1/C1 = 1.004 & H2/M2 = 1.019; H2/C2 = 1.004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time-consuming of PSST and HSST for the 50 Hz harmonic signal with 4 s length.  
Figure 2: 50 Hz harmonic signal: (**a**) Time-frequency principal instantaneous phases; Time-frequency instantaneous amplitudes obtained by PSST with (**b**) \(\Delta t\) = 2 ms and (**c**) \(\Delta t\) = 4 ms. Time-frequency instantaneous amplitudes obtained by HSST with (**d**) \(\Delta t\) = 2 ms and (**e**) \(\Delta t\) = 4 ms.  
Figure 3a,b show the synthetic signal and its instantaneous frequencies, respectively. Figure 4a-e show time-frequency instantaneous amplitudes obtained by MSTFT, CSST, ASST, PSST, and HSST with \(\Delta t\) = 1 ms, respectively; Figure 4f shows the differences between Figure 4d,e. We can see that the peak frequencies in Figure 4a,c,e are almost equal to the instantaneous frequencies shown in Figure 3b, whereas the peak frequencies in Figure 4b show some deviations from the instantaneous frequencies shown in Figure 3b, and these deviations become more obvious with the increasing instantaneous frequencies. A comparison of Figure 4a-c,e shows that Figure 4c  
Figure 4d has the same results as Figure 4e, except for time-points corresponding to period-jumps of time-frequency principal instantaneous phases; thus, Figure 4e has higher precision and better continuity than Figure 4d. Above all, HSST has higher precision and resolution than MSTFT, CSST, and PSST, and almost the same results as ASST.
# The Impact of Using Mobile Phones on Gait Characteristics: A Narrative Review
## 5 The Impacts of Other Independent Variables  
It is well known that age has a great impact on gait. Compared to young adults, the influences of using a mobile during walking were more significant for older adults, particularly for people aged more than 49 years [15]. Additionally, the increased step width variability indicated that participants were distracted during the walking tasks, due to the cognitive demands and visual interference. This could contribute to the higher risk of falling among the elderly.  
Environment factors also affect gait. In the real environment, subjects needed to occupy more cognition to scan their surroundings compared to the lab environment, which reduced their gait velocity. Additionally, the brightness of the test environment also affected gait [37], but further studies are still needed in this area. Gender and speed were also considered to be independent variables, but there were no significant differences between the groups [28].
## 3 Results and Discussion
### Effects of Gas Components
#### 3.3.2 Effects of NO  
As shown in Figure 10, the introduction of 500 ppm NO into pure N\({}_{2}\) fuse gas led to a significant improvement of Hg\({}^{0}\) removal efficiency, which can be accounted for by the capability of NO to react with the surface oxygen species of CoO\({}_{x}\) and FeO\({}_{x}\) from reactive NOx species. This promotes the Hg\({}^{0}\) heterogeneous oxidation occurring on the adsorbent surface [48]. However, in the context of O\({}_{2}\)- and NO-containing flue gas, the impressive removal efficiency of Hg\({}^{0}\), close to 100%, was achievable. It is suggested that NO is conducive to the removal of Hg\({}^{0}\), regardless of the presence of O\({}_{2}\), which is supposed to result from O\({}_{2}\) accelerating the formation of reactive NOx species. Thus, the enhancement of Hg\({}^{0}\) removal efficiency is achieved [35].
## 5 Summary and Conclusions  
The objective of this research was to determine the feasibility of using commercially available NDT methods such as GPR and PAU in detecting FRP bars embedded in concrete. These two NDT methods were tested over several parameters, such as FRP type, bar diameter, bar direction, and bar depths to determine their limitations and detection capabilities. Detecting the FRP bars embedded in concrete is the first obstacle to be overcome before being able to detect damages for ensuring their structural safety. Thus, the long-term objective of this study is to promote the research on NDT methods applicable to embedded FRP bars, which could help in proliferating their use in the construction industry. In conclusion, the results of this study show that NDT methods that rely on electromagnetic waves such as Ground Penetrating Radar (GPR) become less effective, if not obsolete for non-metallic/non-conductive embedded bars. However, with the increase in center frequency of the GPR device, the detectability of FRP bars can be fairly improved. Similarly, other NDT methods such as Ultrasonic Testing (UT) or Phased Array Ultrasonic (PAU) that are based on stress waves have some capability for detecting steel reinforcement, but they perform poorly for the detection of the most commonly used FRP embedded bars (GFRP bars) but are good for detecting FRP strands. However, there are some limitations related to the research conducted in this paper, which can be further explored and incorporated in the future studies related to NDT methods for the inspection of FRP-reinforced concrete elements. The scope of this study was limited to the real time test results obtained from post processing tools embedded within the devices using GPR and PAU, which were originally tuned for the detection of steel bars in traditional reinforced concrete elements. The detectability of the FRP bars embedded in concrete can be further verified by the use of advanced post processing algorithms such as SAFT (synthetic aperture focusing technique) or FMC/TFM (full matrix capture/total focusing method) in a follow up future study. Similarly, this study is limited to only three specimens with limited variation in depth of bars up to 3 in., which can be overcome in future studies by either conducting experimental verification on several specimens or by conducting numerical simulation analysis with adequate test parameters to collect more data for a statistically sound validation. Conceptualization, P.M., S.S.K.D. and A.B.M. ; methodology, P.M., S.S.K.D. and J.D.O. ; software, P.M., S.S.K.D. and K.D. ; validation, A.B.M., A.N. and K.D. ; formal analysis, A.B.M. and A.N. ; investigation, P.M., S.S.K.D. and K.D. ; resources, A.B.M., A.N. and K.D. ; data curation, P.M., S.S.K.D. and J.D.O. ; writing--original draft preparation, P.M.; writing--review and editing, A.B.M., A.N., S.S.K.D. and J.D.O. ; visualization, P.M., S.S.K.D. and J.D.O. ; supervision, A.B.M. and A.N. ; project administration, A.B.M. and A.N. ; funding acquisition, A.B.M. and A.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding.