esa-sceva/satcom-chunk-collection huggingface dataset:
# A BDGIM-Based Phase-Smoothed Pseudorange Algorithm for BDS-3 High-Precision Time Transfer
###### Abstract  
Single point positioning (SPP) can meet the requirements of the majority of real-time time transfer applications. Meanwhile, a single-frequency (SF) receiver is cheaper than a dual-frequency receiver. However, SPP performance can be greatly affected by large pseudorange observation noise. Phase smoothing the pseudorange is an effective approach to reduce pseudorange noise. Since the classical phase-smoothed pseudorange algorithm does not account for the effect of ionosphere delay, we propose a BDGIM-based phase-smoothed pseudorange algorithm to eliminate the ionospheric delay and apply it to BeiDou Navigation Satellite System (BDS-3) SPP time transfer. In this paper, we first evaluate the performance of the BeiDou global ionospheric delay correction model (BDGIM) and compare it with that of the BeiDou Klobuchar model to determine if it is practical to incorporate the BDGIM into our suggested method. The performance of the BDGIM is better than that of the Klobuchar model. The mean RMS value of the BDGIM is 2.6 Total Electron Content Unit (TECU). The average ionospheric correction rate of the BDGIM is 75.5%. Then, we investigate the performance of the improved SF SPP time transfer. The performance of the improved SPP time transfer is much better than that of the traditional SPP time transfer. Compared with the traditional time transfer, the average Type A uncertainty of the improved time transfer is 2.08 ns, which is reduced by about 11.1% from the time transfer without it. Regarding frequency stability, the modified Allan deviations of the improved time transfer are 1.43E-12 and 1.68E-13 at 960 s and 61,440 s, with improvements of 51.2% and 59.9%, respectively.  
BDGIM; phase-smoothed pseudorange; single point positioning; time transfer 2022
## 3 Materials and Methods
### Available Topographic Monitoring: Data Analysis  
The most important dataset comes from topographic measurements (active from 2006) and laser distance meters (active from 2009) made and collected by ARPA Piemonte. All active monitoring instruments are installed only on block A (Figure 3). ARPA installed four topographic benchmarks at this site: three are located on the parapet of the courtyard (T1, T2, and T3), and one in the unstable sector of the courtyard (T0). Two adjunctive benchmarks are located in a stable area near the sanctuary and they are used as reference points. The benchmarks have been measured about 2-3 times per year since 2006. The two laser distance meters are located across the K4 fracture on the north side of the courtyard, and they provide about two measurements per year since 2009.  
Available satellite Interferometric Synthetic Aperture Radar (InSAR) data are not very significant. On the unstable blocks, only one persistent scatterer (PS) of the Radarsat satellite (ascending geometry, Omega dataset) provided by ARPA Piemonte is available and covers the period (2003-2009). However, the PS InSAR was taken into consideration because the estimation of displacement rate on the long-term is quite accurate (+/-2 mm/year). In addition, to improve the accuracy of the measure, we used a PS located in the nearby stable area as a local benchmark. Available monitoring data (Table 1) have been re-processed. The re-processing of the data consisted into recalculating for each type of data (topographic, Laser distance meter and InSAR) the linear regression of displacement for a better comparison of velocity and time series. The results of monitoring analysis were used firstly to choose the best place to install the new GNSS. In a further phase, monitoring data of ARPA Piemonte were used for validation/comparison of GNSS data for the first period of measurement (December 2018-May 2019).  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**CODE** & **Name** & **Type** & **from–to** & **Active** \\ \hline L7 MDSA0 & D1 & Laser distance meters & 2009–2018 & yes \\ L7 MDSA1 & D2 & Laser distance meters & 2009–2018 & yes \\ T7 MDSA3 & T0 & Topographic Benchmark & 2006–2019 & yes \\ T7 MDSA2 & T1 & Topographic Benchmark & 2006–2019 & yes \\ T7 MDSA1 & T2 & Topographic Benchmark & 2006–2019 & yes \\ T7 MDSA0 & T3 & Topographic Benchmark & 2006–2019 & yes \\ AO7 ND & PS1 & InSAR data (RADARSAT) & 2003–2009 & no \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of ARPA Piemonte monitoring data used for this work.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 1 Introduction
In surface defect control, the accurate classification of defects in each image is often more important than precise defect localization. To overcome the challenge of limited sample quantities in deep learning, the proposed method is designed in two stages: the first stage implements a segmentation network for pixel-wise defect localization, and the second stage involves a supplementary network built on top of the segmentation network and utilizes segmentation outputs and features for binary image classification. The main contributions of this work include:  
1. Introducing an efficient encoder-decoder architecture based on the MBConv block, which is a module that combines depthwise separable convolution and an inverted residual structure. This architecture significantly reduces the number of parameters and amount of computational resources while optimizing the network's learning capability and feature extraction efficiency through precise adjustments of the convolution kernels and expansion ratios;
2. Employing a multi-layer fusion-improved loss function in the decoder, which enhances the segmentation accuracy for objects at different scales by better learning convolutional feature information from various levels;
3. Proposing a two-stage method for industrial product surface defect detection. In the first stage, a designed lightweight U-Net network generates a mask for the defect area, and in the second stage, a compact decision network construction method is used, which is combined with the refined mask area from the first stage to detect and assess defects.
## 3 System Setup
### Tx/Rx Dsp  
The TX and RX DSP blocks using FDMA and TDMA aggregation schemes are shown in Figures 2 and 3, respectively. We implemented the generation of \(n\) NR uplink channels, each made by an OFDM modulation on 4096 point Fast Fourier Transform ( FFT )sampled at sampling rates for the different types of SCs described in Table 1. In all the cases, each channel is composed of PUCCH generated using QPSK modulation and a PUSCH part generated using 64 QAM modulation format. To fit the DAC sampling rate of 12 GSa/s from the transmitter DSP output of both aggregation techniques, the number of NR channels \(n\) to be aggregated varies depending on the type of SCs. For instance, in our scenario, \(n\) is 72, 36, 18, and 9 for SCs type 0, 1, 2, and 3, respectively.
## 3 Results and Discussion
### Traffic  
For the summer period, the weekday traffic volume in Site-1 was slightly higher than that of the weekend (mean weekday traffic = 1352 vehicles-(30 min)\({}^{-1}\) vs. mean weekend traffic = 1061 vehicles-(30 min)\({}^{-1}\)). The total traffic volume at Site-2 was lower than at Site-1 (weekday = 1041 vehicles-(30 min)\({}^{-1}\)), but due to the narrow road width (Table 1), frequent congestion occurred during the daytime. Winter traffic volumes at Site-1 and Site-2 were similar to those in summer. At Site-3, weekday and weekend traffic volumes were similar, with a mean of 1199 and 1046 vehicles-(30 min)\({}^{-1}\), respectively (Figure 4a). Site-4 had minimal traffic, with no traffic access from 11:00 to 13:00. Thus, no traffic record was obtained for this pedestrianized street.  
Figure 4: Weekday traffic characteristics at three sub-sites for the entire monitoring period. (**a**) Diurnal variations in the traffic flow rate (top), mean vehicle speed (middle), and traffic density (bottom; i.e., traffic flow rate divided by traffic speed). Dark gray circles represent Site-1, black triangles represent Site-2, and gray squares represent Site-3 (winter only). (**b**) Mean fleet composition (%) at each sub-site (traffic speed is provided by Transport Operation & Information Service managed by Seoul; [[https://topis.seoul.go.kr/refRoom/openRefRoom_1.do](https://topis.seoul.go.kr/refRoom/openRefRoom_1.do)]([https://topis.seoul.go.kr/refRoom/openRefRoom_1.do](https://topis.seoul.go.kr/refRoom/openRefRoom_1.do)) (accessed on 23 June 2021)).  
Figure 3: Air pollutant concentrations (at Site\(-1\) in summer and Site\(-2\) in winter) as a function of wind speed (blue circles: ground wind speed; black circles: prevailing wind speed). Error bars denote standard deviations. (**a–d**) CO, NO\({}_{2}\), O\({}_{3}\), and PM\({}_{2.5}\) in summer (at Site-1), (**e–h**) CO, NO\({}_{2}\), O\({}_{3}\), and PM\({}_{2.5}\) in winter (at Site-2).  
The major constituents of the traffic flow at all monitoring sites were sedans (gasoline) (32-42%), taxis (LNG) (24-27%), and SUVs (diesel) (17-23%), accounting for 76-84% of the total traffic. Diesel trucks and motorcycles accounted for 5-6% of the traffic flow at all monitoring sites. There was a meaningful volume of public buses (CNG) only at Site-4 (11%) (Figure 4b).
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 3 Results
### Horn Antenna
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Geometric Parameter** & \(\mathbf{a_{p}}\) & \(\mathbf{b_{p}}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{a_{r}}\) & \(\mathbf{b_{r}}\) & \(\mathbf{l}\) & \(\mathbf{l_{r}}\) \\ \hline Nominal value (mm) & 80.00 & 60.00 & 23.00 & 10.00 & 23.00 & 10.00 & 150.00 & 10.00 \\ \hline Tolerance (mm) & \(\pm 0.30\) & \(\pm 0.30\) & \(\pm 0.05\) & \(\pm 0.05\) & – & – & \(\pm 0.50\) & – \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Nominal values and tolerances of the horn antenna internal dimensions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \(\mathbf{a_{p}}\) & \(\mathbf{b_{p}}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{l}\) \\ \hline
1 & 80.00 & 60.00 & 23.00 & 10.00 & 150.00 \\ \hline
2 & 79.70 & 60.00 & 23.00 & 10.00 & 150.00 \\ \hline
3 & 80.30 & 60.00 & 23.00 & 10.00 & 150.00 \\ \hline
4 & 80.00 & 59.70 & 23.00 & 10.00 & 150.00 \\ \hline
5 & 80.00 & 60.30 & 23.00 & 10.00 & 150.00 \\ \hline
6 & 80.00 & 60.00 & 22.95 & 10.00 & 150.00 \\ \hline
7 & 80.00 & 60.00 & 23.05 & 10.00 & 150.00 \\ \hline
8 & 80.00 & 60.00 & 23.00 & 9.95 & 150.00 \\ \hline
9 & 80.00 & 60.00 & 23.00 & 10.05 & 150.00 \\ \hline
10 & 80.00 & 60.00 & 23.00 & 10.00 & 149.50 \\ \hline
11 & 80.00 & 60.00 & 23.00 & 10.00 & 150.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Statistical characteristics of horn antenna parameters in Wire-Grid.
## 3 System Overview
### Patch Connectivity
The considered sensing procedure is similar to those in the literature [7; 11]. The ECG signal acquisition is facilitated by using several electrodes. The bio-signals are collected by using a capacitance generated from the electrodes. Then, the signal passes through the instrumentation amplifier. This amplifier reduces the unwanted noise generated in the circuit. In addition, the acquired bio-signal can be naturally weak owing to the regular movement of patients, which is another reason for using this amplifier. In addition, high- and low-pass filters are exploited to pass the signals within a targeted frequency, and a bandstop filter is used to attenuate unwanted frequencies. Thereafter, the signals pass through the analog-to-digital converter (ADC), which presents the digitalized signals. Figure 1 depicts the overall data acquisition procedure. The ADC is connected with the BLE chip and LED driver circuit. A NAR is generated based on the patient's condition. The NAR determines whether the bio-signal will be mounted to the BLE chip or the LED driver. At the LED side, the bio-signal is modulated by a small microcontroller circuit. The LED array switching is controlled by a metal-oxide-semiconductor field-effect transistor.
# A Self-Powered Basketball Training Sensor Based on Triboelectric Nanogenerator
## 4 Conclusions and Discussions  
This research proposes a self-powered basketball training sensor based on triboelectric nanogenerator, and which can automatically count the number of basketball impacts in a self-powered model. Test results show that the working frequency of the sensor is 0 to 5 Hz, the working environment temperature should be less than 75 \({}^{\circ}\)C, the working environment humidity should be less than 95%, and which has high reliability. Further tests show that the maximum output voltage, current, and power of the sensor can reach about 52 V, 4 uA, and 26.5 uW with a 10 M\(\Omega\) resistance in series, respectively, and the output power can light up 12 LED lights in real-time. Therefore, children can judge whether they impact the target area correctly according to whether the LED lights are lit during training, and then adjust their training posture.  
The designed sensor meets the basic requirements for beginner children basketball training, but the energy harvesting efficiency is low, resulting in a small amount of output power and unable to meet the higher demands, as follows. The current output power of the sensor can only light up 12 LED lights in real-time--that is, the sensor lights up 12 LED lights once if a basketball impacts the sensor once, but the brightness of the 12 LED lights is low and which is easily misjudged by beginner children as not being lit when the light is brighter in the day. Therefore, the next step can start from increasing the number of friction layers and synthesizing nanowires to increase the output power of the sensor, to light up more high-brightness LED lights in real-time to meet the needs under different light conditions.  
Conceptualization, Z.Z. and C.W.; methodology, C.W. and Q.Z.; investigation, Z.Z. and Q.Z.; data curation, Z.Z. and Q.Z.; writing--review and editing, Z.Z., C.W. and Q.Z.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangdong Basic and Applied Basic Research Foundation (2019A15110634); Open Foundation of Hubei Intelligent Geological Equipment Engineering Technology Research Center (DZZB202004).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: The output circuit of the sensor. (**a**) Energy processing circuit; (**b**) low-voltage output energy processing circuit.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Gaps in Underwater SLAM Research  
#### 9.1.1 Limitations of Existing Underwater SLAM Datasets  
While datasets such as Aqualoc [150] and the black smoker field dataset [152] have provided valuable resources for training and validating SLAM algorithms, critical challenges still remain:  
* **Limited Diversity:** Current datasets often cover a narrow range of underwater conditions and depth profiles. Factors such as turbidity, lighting variations, and diverse seafloor terrains are not always comprehensively represented, limiting the robustness of deep learning-based SLAM methods when deployed in new environments.
* **Insufficient Annotations:** Many underwater datasets lack detailed ground-truth information, particularly for semantic segmentation or dynamic object tracking. This hampers the development of advanced DL techniques that rely on precise, fine-grained annotations.
* **Specialized Use Cases:** Some datasets focus on specific tasks such as archaeological surveys or sensor configurations like monocular cameras only, reducing their general applicability. This specialization can make it difficult to benchmark SLAM algorithms intended for multi-sensor or cooperative AUV scenarios.  
To advance the field, there is a pressing need for more extensive, standardized datasets that capture the full spectrum of underwater environments that could help train robust SLAM models capable of generalizing to unseen conditions. Furthermore, improved annotations such as semantic labels and precise depth maps would enable the application of cutting-edge deep learning techniques that go beyond basic pose estimation and mapping to semantic and dynamic scene understanding.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme
### Performance Analysis
This proves that there is lower delay for circular partition with TSR-DARDA scheme than other partition schemes.
###### Abstract
[22] reconstructed coal bodies in three dimensions using X-ray imaging and calculated the volume fractal dimensions of coal fractures. [PERSON] et al. [23] performed uniaxial compression experiments on coal samples, producing visualized three-dimensional fracture networks through CT scanning. Their findings demonstrated that the fractal dimension of coal fractures increases with stress, with initial fractures extending along dominant directions while transverse microcracks emerge, eventually forming main cracks. During mining operations, the advancing face of the coal seam is subjected to repeated disturbances from drilling and blasting, as well as cyclic cutting by mining machines and roof collapses. These factors impose cyclic loading and unloading stress on the coal-rock mass, leading to continuous fracture propagation and heightened risk of instability and failure in the coal-rock system [4,24,25]. Studies have shown that, under cyclic loading, coal-rock masses experience stress-induced failure more readily than under monotonic loading. This results in significant alterations to mechanical properties, such as Young's modulus, Poisson's ratio, and permeability, which are attributed to fatigue damage [5,26,27,28,29]. The deformation and failure of coal-rock masses under cyclic loading reflect the progressive accumulation of internal damage, culminating in failure. This process is influenced by the morphological characteristics of the primary damage structure. According to damage mechanics theory, rock damage represents the irreversible deterioration of mechanical properties caused by microstructural features such as microcracks, voids, and dislocations, which arise from environmental or external loading effects. While macroscopic damage variables have been used effectively to describe rock behavior [30,31,32], these modelsoften treat damage as a continuous scalar field, which fails to capture the heterogeneity and discontinuity of rock materials. Consequently, they are inadequate for revealing three-dimensional fracture field evolution mechanisms during rock failure. This study aims to address these limitations by investigating the physical changes in microstructures during cyclic loading using advanced microscopic observation techniques, including high-resolution three-dimensional imaging systems (XRM) and scanning electron microscopy (SEM). The research explores the damage mechanisms of coal-rock materials and analyzes the effects of internal material composition, physical structure, and other microstructural characteristics on the evolution of macroscopic three-dimensional fracture fields.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 3 Performance Analysis  
In this section, we analyse some important factors of the proposed scheme RSCS-IM, including the SE, complexity and BER performance of the desired position. Meanwhile, the factors mentioned above of the scheme's \"prime subcarrier set plus randomization procedure (PSS Plus RP)\" proposed in [18] were also analyzed for comparison.
# Study on the Wellbore Flow for Carbon Dioxide Fracturing
## 3 Solution Procedure
### Physical Properties of CO\({}_{2}\)  
The properties of CO\({}_{2}\) are sensitive to the change of temperature and pressure. Tiny changes in temperature and pressure may cause significant fluctuations in CO\({}_{2}\) properties, and the phase change of CO\({}_{2}\) could also be reflected by fluctuations in CO\({}_{2}\) properties, so it is important to calculate properties of CO\({}_{2}\) accurately.  
The Span-Wagner equation [PERSON] (1961) has high accuracy in calculating thermodynamic properties of CO\({}_{2}\), especially in supercritical areas. Therefore, the Span-Wagner equation is chosen to calculate thermodynamic properties of CO\({}_{2}\) including density, specific heat capacity, Joule-Thomson coefficient, and specific enthalpy in this paper. For transfer properties of CO\({}_{2}\), the Vesovic model [PERSON] (1961) is used to calculate the heat conductivity of CO\({}_{2}\), and the Fenghour model Fenghour (1961) is used to calculate the viscosity of CO\({}_{2}\).
## 5 Simulation
### Experimental Results and Analysis
#### 5.2.1 IoT Local Training Performance  
Figure 3 presents a comparative analysis of local model training accuracy and loss across 50 training rounds for both our framework and the HierFAVG method. The results highlight the convergence characteristics and stability of our proposed framework in comparison to the widely used HierFAVG approach.  
The training accuracy for both methods demonstrates effective convergence by the 50 th round, with negligible differences in the final performance. Specifically, our framework achieves a final accuracy of 98.5%, closely aligning with HierFAVG's 98.7%. The observed fluctuations in the early rounds for our method are attributed to the asynchronous nature of the aggregation, which allows IoT devices to update the global model independently of synchronization constraints. Despite these fluctuations, the model stabilizes effectively as training progresses.  
Similarly, the training loss for both methods converges to comparable values. By the 50 th round, our framework achieves a final training loss of 0.045, slightly higher than HierFAVG's 0.042. The early-round variance in loss for our framework is consistent with the observed accuracy fluctuations and reflects the impact of asynchronous updates. However, the convergence rates for both methods are nearly identical, confirming that our hierarchical aggregation strategy does not compromise the stability of local model training.  
The negligible differences in accuracy and loss between our framework and HierFAVG are expected, as our focus is on scalability, efficiency, and privacy rather than model accuracy. The consistent convergence of both methods demonstrates the robustness of our approach in heterogeneous IoT environments. Furthermore, the asynchronous aggregation mechanism highlights the framework's ability to handle varying device capabilities and communication latencies without sacrificing training performance.  
Figure 3: Comparison of local model training accuracy and loss across 50 rounds. (**a**) Training accuracy with our framework. (**b**) Training accuracy with HierFAVG. (**c**) Training loss with our framework. (**d**) Training loss with HierFAVG.  
The local training performance of our framework is on par with HierFAVG, with comparable convergence rates and outcomes. The results underscore the stability and reliability of our framework in achieving effective training under the constraints of real-world IoT environments.