esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
In pavement distress detection, differences between the source domain and the target domain can be summarized into three types: background differences, viewpoint differences, and distribution biases. Background differences refer to the variability in the non-road surface elements of the images, caused by a variety of factors, including distress collection locations, road conditions, and types of roads. Viewpoint differences arise from the different angles between the camera orientation and the road surface. Existing pavement image viewpoints primarily encompass wide view, oblique view, and top-down view. These varying viewpoints dictate differences in the road surface coverage, details of distresses, and the extent of perspective distortion, where generally, a top-down view is advantageous for capturing a comprehensive view of pavement distresses. Furthermore, even within the same data source, external interferences such as weather, lighting, and shadows can introduce systematic biases during the collection process. For instance, rain can cause image noise, while shadows from buildings or other obstacles can affect the images, constituting a distribution bias. This distribution bias results in the deviation of the dataset's distribution from the real distribution and can also impact the generalization ability of detection algorithms in practical applications.  
To tackle these three differences, three modules and a domain prior generator were developed to improve the model's performance in the target domain: (1) Domain Prior Generator: This module utilizes a convolutional neural network for self-supervised learning on pavement distress data, extracting features of pavement distress data from both the source domain and the target domain. (2) Viewpoint Transformation: This module employs the domain prior generator to generate perspective information for the target domain images and perform viewpoint transformation on the images to a unified viewpoint for recognition. (3) Foreground Focus: This module uses the domain prior generator to generate attention weight information for the target domain images. By using the attention weight information, pavement distress detection can focus on the foreground of the image and emphasize more on foreground distress information while ignoring non-uniform background information. (4) Adaptive Normalization: This module utilizes the domain prior generator to generate the mean and variance of the target domain image's distribution and then normalize the image and its features to adapt to the distribution of the target domain. By utilizing the domain prior generator and three modules, the gap between the target domain and the source domain can be reduced, and the pavement distress detection model can better adapt to the distribution of test data in the target domain, thereby improving the detection results of the model in the target domain. The framework of this paper is illustrated in Figure 3.
## 1 Introduction
These detected images are not large enough; that is, for a high-precision large image to detect fine or micro defects, there will be two problems: one is whether large images can participate training and whether the detected speed can meet the actual needs of production, while the other is that when the proportion of micro defects in the image is large, they can be detected. This paper will give partial solutions to these. The feature of the detection object in this paper is the micro-fine defect, highlighted on the surface of the metal shaft; when the image resolution reaches 16,384 \(\times\) 4096,a small group of pixels with a minimum of 80 points or fine lines with a width of 4 pixels can be detected. The classification of this defect proportion has not been studied before. According to the previous research results, it is still challenging to use existing deep learning technology for such features to achieve good classification results. The following solutions are adopted in the research. Firstly, the data set of surface defects of the metal shafts was collected in the project, and the image of highlighted shaft surface defects was obtained by line scanning; secondly, the proposed ResNet [39] convolvutional neural network feature extraction is combined with the Faster-R-CNN [40] object detection model for the detection of metal shaft surface defects, and the structure and parameter of the ResNet model is adjusted and optimized for defect detection; third, we realize the detection and classification of a 16,384 \(\times\) 4096 largeimageof a small object by screen capture; fourth, the ResNet convolutional neural network is embedded in the Faster-R-CNN program framework, which can be changed later by replacing the convolutional neural network, realizing the scalability application of practical object detection and classification. In addition, due to the disadvantages of positive and negative sample screening methods, a method to increase the number of positive samples is proposed base on IoU multiple values; finally, the method of improving the recall, precision and accuracy rate is analyzed based on the experimental results. A limiting condition is proposed in this paper when using an existing deep learning network system in industry. The rest of the paper is organized as follows; The system construction of the industry application and Faster R-CNN is described in Section 2. Data collection is introduced in Section 3. The system framework design is shown in Section 4. The experiment parameters setting, operation, ablation experiments, and performance evaluations are discussed in Section 5. Finally, the conclusions are presented in Section 6.
###### Abstract  
In this paper, new schemes have been proposed for the estimation of the additive white Gaussian noise (AWGN) channel with intersymbol interference (ISI) in an iterative equalization and decoding system using low-density parity check (LDPC) codes. This article explores the use of the least squares algorithm in various scenarios. For example, the impulse response of the AWGN channel h was initially estimated using a training sequence. Subsequently, the impulse response was calculated based on the training sequence and then re-estimated once using the sequence estimated from the output of the LDPC decoder. Lastly, the impulse response was calculated based on the training sequence and re-estimated twice using the sequence estimated from the output of the LDPC decoder. Comparisons were made between the performances of the three mentioned situations, with the situation in which a perfect estimate of the impulse response of the channel is assumed. The performance analysis focused on how the bit error rate changes in relation to the signal-to-noise ratio. The BER performance comes close to the scenario of having a perfect estimate of the impulse response when the estimation is performed based on the training sequence and then re-estimated twice from the sequence obtained from the output of the LDPC decoder.  
intersymbol interference; impulse response; least squares; low-density parity check codes; training sequence +
Footnote †: journal: _Entropy_  
0000-0002-4670-387X]C. Acolemente: C.  
was described in detail in [7], a system used further in this work to estimate the \(h\) of the AWGN channel with ISI. We proposed in [8] new LS estimators for LDPC and polar codes, where the equalization process was achieved independently from the decoding.  
As we mentioned before, our main objectives in this work using the system proposed in [7] are as follows:  
Creating a new scheme for estimating the \(h\) of the AWGN channel based on a training sequence.  
* Creating a new scheme for estimating \(h\) involving a training sequence and re-estimating it only once using the estimated sequence produced by the decoder. This estimated sequence from the output of the decoder is then used to re-estimate \(h\) twice more. This approach helps to improve the accuracy of the \(h\) estimation.
* Performing the performance analysis regarding the BER against SNR of the new schemes contrasted with the ideal situation where we assumed a perfect estimate of \(h\). The simulations carried out in Section 4 demonstrated that BER performance approaches the case where we have a perfect estimate of impulse response when it was estimated based on the training sequence and re-estimated twice using the estimated sequence produced by the min-sum LDPC decoder.
## 2 Materials and Methods
### Model Reduction and Optimization
#### 2.2.2 Genetic Algorithm Optimization Framework  
The Genetic Algorithm component is configured to optimize both model reduction accuracy and controller performance. The optimization framework employs the following parameters: The genetic algorithm parameters used in this optimization are shown in Table 1:  
The fitness function is defined as follows:  
\[\text{F}=1/(\text{ITAE}+\lambda\sum\left|\text{ei}\right|) \tag{10}\]  
where  
- ei represents control errors  
- \(\lambda\) is a weighting factor  
- ITAE is the integral time absolute error  
The integration of Genetic Algorithms in PID tuning introduces significant computational complexity that requires careful consideration, especially in resource-constrained environments. Our analysis focuses on the balance between optimization accuracy and computational resource utilization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline Population Size & 50 & Number of candidate solutions \\ Chromosome Length & n & Based on parameter count \\ Mutation Rate & 0.1 & Genetic diversity factor \\ Crossover Rate & 0.8 & Solution combination rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Genetic Algorithm configuration parameters.  
The Genetic Algorithm's computational complexity (T = G \(\times\) P \(\times\) (Cf + Ce)) depends on the number of generations (G), population size (P), and associated calculation costs. Traditional implementations with large parameters (G = 100, P = 50) can lead to excessive computational overhead. Our analysis shows that reducing these parameters impacts optimization accuracy, but through strategic parameter adjustment, we maintain acceptable performance levels while significantly reducing resource usage.  
We implement three key optimization strategies: adaptive population sizing, early termination criteria, and efficient computation distribution. These optimizations are managed through a resource monitoring system that dynamically adjusts algorithm parameters based on available computational resources. Our simulation results demonstrate that these optimizations achieve a 35% reduction in computational overhead and a 60% reduction in memory usage while maintaining 95% of optimal performance in resource-constrained environments.  
This framework ensures practical applicability of Genetic Algorithm-based PID tuning across various industrial applications while balancing performance requirements with resource limitations.
## 3 Method
### Generating a Simulated Environment  
An RGB image \(I_{rgb}\) and a depth image \(I_{dpt}\) of a robot working scenario are captured from an RGB-D camera. As is shown in Figure 1a, a semantic image \(I_{sem}\) is segmented from \(I_{rgb}\) based on fully convolutional networks (FCN) [46]. To conveniently and effectively construct a task-relevant simulated environment for policy learning, some simplifications are made for semantic image \(I_{sem}\) to create a simulated environment, as is shown in Figure 2. The target object is simplified to its geometry center. The robot (for navigation task) or gripper (for manipulation task) is simplified to be a solid ball, the center and diameter of which are calculated from its semantic pixel region contour. The obstacles are completely preserved, and other irrelevant objects that cannot be obstacles are ignored. Given depth image \(I_{dpt}\), our method transforms each pixel position \([u_{i},v_{i}]^{T}\) of the RGB image from the image coordinate system to the robot coordinate system with the following coordinate transformation equation:  
\[p_{ri}=\left[\begin{array}{c}x_{ri}\\ y_{ri}\\ z_{ri}\end{array}\right]=Rz_{ci}M_{in}^{-1}\left[\begin{array}{c}u_{i}\\ v_{i}\\ 1\end{array}\right]+T, \tag{5}\]  
where \(i=1,2,\cdots,T\), \(p_{ri}\) is the transformed position under the robot coordinate system, \(M_{in}\) is the camera inner parameter matrix, \(z_{ci}\) is the depth value with respect to the pixel position \([u_{i},v_{i}]^{T}\), and \(R\) and \(T\) are the calibrated rotation matrix and transformation vector from the camera coordinate system to the robot coordinate system. As a result, we obtain the pose information of the corresponding objects under the robot coordinate system.  
Consequently, we get a task-relevant simulated environment, which is an abstraction of the real-world scene, and meanwhile keep the information related to training the desired control policy. In the policy training period, the solid red ball corresponding to the robot (or gripper) represents the virtual agent, the solid green circle corresponding to the target object denotes the target position, the blue area corresponds to the obstacle area being unreachable for the virtual agent, and the black area corresponds to the background and irrelevant objects area being reachable for the virtual agent.  
Figure 1: Illustration of the proposed real–sim–real (RSR) transfer learning pipeline. (**a**) Real-to-sim training phase. A task-related simulated environment is generated based on coordinate transformation and semantic images of real-world robot working scenarios. A policy network is trained in the constructed simulated environment with the deep reinforcement learning (DRL) method. (**b**) Sim-to-real inference phase. The trained policy takes simulated-like synthetic images as input and outputs actions to control real-world robots via an ROS (robot operation system).  
The initial positions of the virtual agent and target object, and the shape and number of obstacles can be changed randomly in the simulated environment.
## 2 Materials and Methods
### Statistical Analysis  
The exports of FIB were estimated for each watershed by multiplying the FIB concentration (MPN 100 mL\({}^{-1}\)) by stream discharge (L s\({}^{-1}\)). Normalization by watershed area was performed to account for variability in discharge that may be related to differences in watershed size. Thus, stream discharge and FIB export reported in the current study was area normalized; henceforth, \"normalized exports\" and \"normalized discharge\" will be referred to as \"exports\" and \"discharge\", respectively. Concentrations and exports of FIB were compiled into comparison groups based on septic system density and wastewater treatment approach. FIB concentrations may vary seasonally due to changes in temperature [15; 24; 31; 32; 33], thus watersheds were grouped by summer (June-September) and winter (December-March) seasons (\(n=56\), 8 samples per watershed) to compare data based on the largestdifferences in temperatures, which may affect FIB survivability. Summer and winter months are henceforth referred to as \"warm\" and \"cold\", respectively. Data figures were developed using the \"ggplot2\" and \"cowplot\" packages developed for R software [34].  
A Shapiro-Wilks normality test was employed to determine if data exhibited normality. Data were not normally distributed, even after transformation. Non-parametric statistics were employed to determine if significant differences (\(p\leq 0.05\)) existed between CS, SF, and SEW watersheds based on the wastewater treatment approach or septic system density. Kruskal-Wallis \(H\) tests were employed to test if significant differences existed in FIB concentrations and exports between: (i) pooled data from watersheds using CS, SF, or SEW wastewater treatment; and (ii) septic system density regardless of the type of septic system. If a significant \(p\)-value was determined via Kruskal-Wallis, post hoc Mann-Whitney rank sum tests with Bonferroni correction were used to isolate differences among comparison groups. Statistical tests were conducted in the R statistical framework [34].
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 5 Results and Discussion
### Communication Delays
One-way delay in Figure 8 is the time taken to send a message from the HEMS controller through the Hanadu gateway to the lamp/sensor in one direction. Various network sizes ranging from 1-50 smart appliances are investigated. As seen in Figure 8, although 6 LoPLC allows us to adapt PLC for low-rate applications, such as HEMS, that comes at a price in terms of additional delay, which could be as high as 131.7512 ms in the case of lamp control signalling (gateway \(\rightarrow\) lamp) and 109.6414 ms for monitoring (sensor \(\rightarrow\) gateway). As seen in Figure 1, the CoAP client is resident on a standard network node (a laptop) with full IPv6 capability. The latency performance described above is therefore logical, given that fragments received from the HEMS controller have to wait in the buffer until all bits belonging to the same flow are received before reassembly and further processes are carried out. The significance of this result is that for planning and design purposes, apart from application data size shown in Figure 7, large number of nodes per controller can remarkably increase delay. Therefore, the network size of HEMS must be carefully chosen to ensure the latency is within acceptable limits.
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
###### Abstract  
Neutron stars (NSs) are compact objects that host the densest forms of matter in the observable universe, providing unique opportunities to study the behaviour of matter at extreme densities. While precision measurements of NS masses through pulsar timing have imposed effective constraints on the equation of state (EoS) of dense matter, accurately determining the radius or moment of inertia (Mol) of an NS remains a major challenge. This article presents a detailed review on measuring the Lense-Thirring (LT) precession effect in the orbit of binary pulsars, which would give access to the MoI of NSs and offer further constraints on the EoS. We discuss the suitability of certain classes of binary pulsars for measuring the LT precession from the perspective of binary star evolution and highlight five pulsars that exhibit properties promising to realise these goals in the near future. Finally, discoveries of compact binaries with shorter orbital periods hold the potential to greatly enhance measurements of the Mol of NSs. The MoI measurements of binary pulsars are pivotal to advancing our understanding of matter at supranuclear densities, as well as improving the precision of gravity tests, such as the orbital decay due to gravitational wave emission, and of tests of alternative gravity theories.  
neutron stars; pulsars; moment of inertia; dense matter equation of state +
Footnote †: journal: Physics Letters
# Simulation Model for Correction and Modeling of Probe Head Errors in Five-Axis Coordinate Systems
## 2 Developed Simulative Model and Steps to Its Implementation
### Correction of Probe Head Errors Using the Developed Model  
To perform a simulation and correction of probe head errors during the measurements performed in five-axis mode, the \(A\), \(B\) and \(\alpha\) angles of all points that were measured have to be recorded. Then the simulations according to the presented methodology have to be performed \(n\) times for all of these points. The mean from \(n\) simulated values is taken as the probe head error (PE) for the considered point (depending on \(\alpha\)). To perform the correction of this error, the \(\mathrm{PE}_{x}\), \(\mathrm{PE}_{y}\) and \(\mathrm{PE}_{z}\) components have to be calculated using Equations (2)-(4). Then, the values of these components should be subtracted from the actual values of point coordinates \(x\), \(y\), \(z\), giving the corrected point coordinates \(x_{\mathrm{corr}}\), \(y_{\mathrm{corr}}\) and \(z_{\mathrm{corr}}\) from Equations (7)-(9).  
\[x_{\mathrm{corr}} = x-\mathrm{PE}_{x} \tag{7}\] \[y_{\mathrm{corr}} = y-\mathrm{PE}_{y}\] (8) \[z_{\mathrm{corr}} = z-\mathrm{PE}_{z} \tag{9}\]  
All measured features and relations should be calculated once again using the corrected point coordinates \(x_{\mathrm{corr}}\), \(y_{\mathrm{corr}}\) and \(z_{\mathrm{corr}}\). In the presented research, the process of correction of the probe head errors was assisted by a script written in the Python programming language cooperating with  
Figure 3: Measurements of standard ring in one of the selected positions.  
macro prepared in Modus software. The raw measurement data including point coordinates, approach vectors and stylus orientation angles was sent to the Python script, which performed the correction of probe head errors and sent back corrected point coordinates to the metrological software, in which the calculation of measured features was done once again.
## 4 Influence Factors on Damage Location Identification
### Influence of Noise on Damage Identification  
During the on-site measurement and inspection process, due to the influence of the measurement environment and the measurement system, noise is inevitable, and the noise will \"contaminate\" the measurement data, which will have a greater impact on the later data processing and analysis. In order to study the effect of noise on the damage recognition effect of the method, white noise was superimposed on the acceleration response of the vehicle body, and the expression is as follows:  
\[a_{n}=a+E_{n}N_{oise}\sigma(a) \tag{30}\]  
where \(a_{n}\) is the acceleration response with noise, a is the original acceleration response, \(E_{n}\) is the noise level, \(N_{oise}\) is the noise obeying the standard normal distribution \(N\) (0, 1), and \(\sigma(a)\) is the standard deviation of the original acceleration response.  
The noise level was 3%, 5%, and 7% in sequence, and the above Section 3.3 working condition 3 (single damage; damage location: 20 m; damage degree 30%) was selected as the damage condition, and the damage identification of the bridge by noise was studied. The influence of this is shown in Figures 13 and 14.  
Figure 13: Wavelet transform coefficient (d1) under different noise levels.  
When the sensor records the acceleration of the vehicle body, it will inevitably be affected by environmental noise. The frequency and energy of these noises will affect the wavelet transform, which may cause the results of the wavelet transform to be irregular, and the damage location cannot be identified. It can be seen from Figure 14 that when selecting the low-frequency area (d5) for damage identification, the wavelet transform coefficients under different levels of noise are basically the same as those under noise-free conditions, indicating that selecting the d5 coefficient for damage location identification has good anti-noise qualities. It can be seen from Figure 13 that when the high-frequency area (d1) is selected for damage identification, it cannot be identified at all in the presence of noise, and the amplitude of the wavelet coefficient increases with the increase in the noise level. This is mainly because the high-frequency energy of noise is higher than the low-frequency energy, which affects the damage identification of wavelet transform coefficients in the high-frequency region. However, the original signal can be denoised. The original signal is first decomposed in multiple layers, high-frequency coefficients are thresholded, and low-frequency coefficients are retained. Then, the signal is reconstructed through wavelet inverse transform to reduce the influence of noise on damage identification.