esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
###### Abstract  
Since 2015, China has successfully launched five experimental BeiDou global navigation system (BDS-3) satellites for expanding the regional system to global coverage. An initial performance assessment and characterization analysis of the BDS-3 is presented. Twenty days of tracking data have been collected from eleven monitoring stations. The tracking characteristics and measurement quality are analyzed and compared with the regional BDS (BDS-2) in terms of observed carrier-to-noise density ratio, pseudo-range multipath, and noise. The preliminary results suggest that the measurement quality of BDS-3 outperforms the BDS-2 for the same type of satellites. In addition, the analysis of multipath combinations reveals that the problem of satellite-induced code biases found in BDS-2 seems to have been solved for BDS-3. Precise orbit and clock determination are carried out and evaluated. The orbit overlap comparison show a precision of 2-6 dm in 3D root mean square (RMS) and 6-14 cm in the radial component for experimental BDS-3 satellites. External validations with satellite laser ranging (SLR) show residual RMS on the level of 1-3 dm. Finally, the performance of the new-generation onboard atomic clocks is evaluated and results confirm an increased stability compared to BDS-2 satellite clocks.  
BeiDou; multipath combination; code pseudo-range; precise orbit determination; clock stability +
Footnote †: journal: Sensors
# Correction of Aero-Optical Effect with Blow-Suction Control for Hypersonic Vehicles
## 1 Introduction
To summarize, it is evident that previous studies have employed a relatively simplistic experimental method, with limited involvement of hypersonic speeds in numerical simulations of Mach numbers. In the context of aero-optical effects at higher Mach numbers, there is a dearth of numerical simulations with enhanced precision and more efficient jet cooling strategies. Furthermore, there is a lack of an effective measure to mitigate the additional wavefront distortion introduced by jet injection. To address this research gap, the present study employs the DDES method to accurately simulate the flow field. Additionally, a comprehensive blowing-suction flow control approach with MVGs is proposed for correcting aero-optical distortions, thereby effectively mitigating wavefront aberration. The proposed method improves the uniformity of temperature distribution across the optical window, effectively mitigating the impact of aerothermal effects and offering a novel solution for aero-optical correction. The remainder of this paper is organized as follows: In Section 2.1, a seeker model similar to THAAD (Terminal High Altitude Area Defense) is proposed as the basis for all subsequent studies. In Section 2.2, a series of correction methods for aero-optical effects are presented. In Section 3, the evaluation parameters for aero-optical effects are introduced, followed by a presentation of the governing equations of the numerical method. In Sections 3.3 and 3.4, the numerical method validation and grid-independent analysis are carried out. Section 4 compares the influence of different jet modes on both window cooling and the formation of flow field structures near the window. The impact on aero-optical effect correction is then examined through comparison after incorporating MVGs. Furthermore, Section 4.3 investigates changes in refractive index within the flow field and explores improvements in imaging effects resulting from blow-suction control. Finally, the conclusions drawn from this paper are summarized in Section 5.
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 2 Method
### Shuffle Attention Model  
Shuffle Attention (SA) introduces a novel mechanism to enhance feature representation learning by dynamically reallocating channel-wise attention weights. Unlike conventional attention modules that employ uniform channel scaling (e.g., SE-Blocks [50]) or sequential channel-spatial processing (e.g., CBAM), SA partitions feature channels into non-overlapping subgroups and computes attention weights in parallel across these subgroups. This group-wise attention computation significantly reduces computational complexity while preserving feature discriminability.  
The proposed method comprises two sequential operations, as shown in Figure 6: intra-group attention calibration and inter-group feature shuffling. After parallel attention weighting within each subgroup, a stochastic channel shuffle operation is performed to redistribute features across subgroups. This procedure achieves two key advantages: it promotes feature diversity by enabling cross-group information exchange, and it mitigates overfitting risks through inherent stochasticity in feature redistribution. Experimental evaluations demonstrate that the shuffle operation effectively reduces model reliance on dominant features, thereby enhancing generalization capability [51].  
When integrated into the YOLOv8 detection framework, SA yields substantial performance improvements, particularly in complex scenarios. During upsampling stages, the module bridges semantic gaps between multi-scale features through enhanced cross-channel interaction, leading to superior feature fusion quality. Quantitative results show notable detection accuracy gains for small objects and in cluttered environments, attributed to the module's dual benefits of computational efficiency and adaptive feature sampling.  
Figure 5: Structure diagram of ASPP module.
## 7 Embedded Board Application and Time Required  
In this section, we checked the data processing time by applying the algorithm to an embedded board to confirm the purpose of quickly inferring using machine learning instead of the DNN method, which has high time complexity, to classify narcotic IMS data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1\_score** \\ \hline Threshold & 0.362 & 0.667 & 0.362 & 0.415 (8) \\ KNN (\(k=1\)) & 0.881 & 1 & 0.881 & 0.936 (2) \\ KNN (\(k=2\)) & 0.813 & 1 & 0.813 & 0.896 (3) \\ KNN (\(k=3\)) & 0.754 & 1 & 0.754 & 0.858 (4) \\ KNN (\(k=4\)) & 0.713 & 1 & 0.713 & 0.831 (5) \\ TSF (\(c=0.9\)) & 0.41 & 1 & 0.41 & 0.572 (7) \\ TSF (\(c=0.8\)) & 0.56 & 1 & 0.56 & 0.731 (6) \\ ROCKET & 0.953 & 1 & 0.953 & 0.974 (1) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy, precision, recall and F1_score evaluation results of each algorithm when SAA is not applied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1\_score** \\ \hline SAA(KNN (\(k=1\))) & 0.970 & 1 & 0.970 & 0.989 (3) \\ SAA(KNN (\(k=2\))) & 0.975 & 1 & 0.975 & 0.986 (5) \\ SAA(KNN (\(k=3\))) & 0.970 & 1 & 0.970 & 0.988 (4) \\ SAA(KNN (\(k=4\))) & 0.984 & 1 & 0.984 & 0.991 (2) \\ SAA(TSF (\(c=0.9\))) & 0.913 & 1 & 0.913 & 0.953 (7) \\ SAA(TSF (\(c=0.8\))) & 0.972 & 1 & 0.972 & 0.985 (6) \\ SAA (ROCKET) & 0.993 & 1 & 0.993 & 0.996 (1) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy, precision, recall and F1_score evaluation results of each algorithm when SSA is applied.  
Embedded Board Specifications and Operating Time  
The embedded board for classifying narcotic IMS data was operated on a board equipped with NVIDIA's TX2. The algorithms operated were KNN, TSF, and ROCKET applied to the system application algorithm. Looking at Table 8, the KNN method showed an average operation time of 2.7 s, which was the fastest performance. Meanwhile, the TSF algorithm had an average operating time of about 5 s, whereas the ROCKET algorithm had an average operating time of about 30 s due to convolution operations. In this regard, KNN can construct a system with an accuracy of 0.98 within 2 s on average when k is set to 4.
# A Survey on Blockchain-Based Federated Learning
## 2 Preliminary
### Threats and Challenges of FL  
Since the concept of FL was proposed, it has quickly attracted widespread attention and research in the academic community. However, there are still many threats and challenges that urgently need to be addressed in this research direction. The most core issues include single point of failure [29; 30; 31], lack of incentive mechanisms [20; 32], poisoning attacks [33; 34; 35; 36; 37], defects in privacy policies [9; 10; 11; 12; 14] and low communication efficiency [16; 17; 38]. These issues have greatly limited the further development and application of FL.  
**Single Point of Failure**: The central server in FL is susceptible to malicious updates, causing defects in the global model update. This affects all local model updates and reduces their accuracy. Additionally, FL requires local devices to upload local model updates to the central server. When too many devices are transmitting models simultaneously, it can lead to network overload.  
**Lack of Incentive Mechanism**: FL generally assumes that each local device willingly contributes data resources to the global model. However, this does not align with reality. The lack of an incentive mechanism affects participants' motivation to contribute, and some participants may even obtain rewards without contributing data, leading to unfair economic compensation.  
**Poisoning Attacks**: Malicious users may deliberately upload carefully calculated malicious local training models to affect global model training, intentionally sabotaging predictive outcomes of machine learning. This is mainly because FL lacks the ability to monitor and diagnose malicious users or malicious model updates.  
**Defects in Privacy Policies**: Despite training data resources being stored on local devices, the FL framework may still lead to a leakage of training data privacy. In a real network environment, it is challenging to assess the motivations of participating clients in the training process, and ensuring the trustworthiness of the central server is equally difficult. Relying solely on model updates to protect user privacy appears to be insufficient.  
**Low Communication Efficiency**: Since FL requires communication between clients and servers to transmit local learning models and perform multiple rounds of model training iterations for local or global model updates, the communication efficiency between the client and server, as well as the model training efficiency, can also affect FL performance.
## 1 Introduction
### Analyses of CO\({}_{3}\)\({}^{2-}\) Vibration in FTIR Spectra  
Several researchers have used FTIR to determine the level of carbonation and to qualitatively distinguish between different samples. [PERSON] et al. [37] identified asymmetric stretching (v\({}^{3}\)), out-of-plane (v\({}^{2}\)), and in-plane bending of CO at 1471 cm\({}^{-1}\), 856 cm\({}^{-1}\), and 715 cm\({}^{-1}\), respectively. These identified bands are assigned to calcium carbonates, calcite, and aragonites, respectively [91; 92]. The vibration of linear C-O bond from calcite  
Figure 11: The effect of NiO\({}_{2}\)/SiO\({}_{2}\) on Si-O-T asymmetric vibrations in FTIR for (**a**) OH/HOH and (**b**) Si-O-T bands (From ACS Ref. [45] and used with permission).  
Figure 10: The role of aluminum in the asymmetric stretching of Si-O-T (in NC, NA2, and NA4 containing Al(NO\({}_{3}\))\({}_{3}\)). From Elsevier Ref. [65] and used with permission.  
(CaCO\({}_{3}\)) could be found between 1475-1390 cm\({}^{-1}\) with strong vibration [53] within the binder [91]. [PERSON] et al. [19] also assigned C-O (CO\({}_{3}\)\({}^{2-}\)) bond asymmetric stretching vibration to wavenumber 1421-1458 cm\({}^{-1}\). [PERSON] et al. [27] observed CO\({}_{3}\)\({}^{2-}\) asymmetrical stretching frequency around 1410 cm\({}^{-1}\), while [PERSON] et al. [80] claimed that the interaction of atmospheric CO\({}_{2}\) with the hydroxyl of cations caused the formation of calcite, as shown in Equation (8).  
\[\text{CO}_{2}+\text{Ca}\left(\text{OH}\right)_{2}\rightarrow\text{CaCO}_{3}+ \text{H}_{2}\text{O} \tag{8}\]  
Furthermore, the carbonate band has been reported to be more pronounced in OPC and AAB spectra compared to GP products, as shown in Figure 5. [PERSON] et al. [93] reported that GP binder exposure to CO\({}_{2}\) causes an increase in the Si/Al ratio due to silicate unit alteration by the infusion of CO\({}_{2}\) from the atmosphere compared to what is obtainable in the seal-up samples. They also noticed that the incorporation of MgO into a GP product could reduce CO\({}_{2}\) diffusivity thereby increasing the compressive strength of the binder. The atmospheric CO\({}_{2}\) has been reported to have a stretching band at 2350 cm\({}^{-1}\) and bending at 660 cm\({}^{-1}\)[92, 94]. Hence, FTIR spectra can be used to monitor the level of carbonation of aluminosilicate and OPC binders. Carbonate stretching vibration occurred at 1390-1420 cm\({}^{-1}\), while CO\({}_{3}\)\({}^{2-}\) from CaCO\({}_{3}\) was noted to vibrate around 871- 874 cm\({}^{-1}\) and 1792-2516 cm\({}^{-1}\).
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
## 2 Methodology
### Geometry Configuration  
The geometric structure of the GNSS-S radar system is shown in Figure 3, which includes two antennas and is a bistatic radar configuration [29]. The direct antenna receives the direct signal of the GNSS satellite, and the scattering antenna receives the GNSS signal scattered by the target. The detection of the target is realized by the distance difference between the direct signal and the scattering signal. The distance from the GNSS satellite to the GNSS-S receiver is \(D\), the distance from the GNSS satellite to the target is \(d_{1}\), and the distance from the target to the GNSS-S receiver is \(d_{2}\). The bistatic angle between the target, satellite, and receiver is \(\alpha\). The bistatic distance between the GNSS-S receiver and the target is the propagation path difference \(\Delta R\) between the direct signal and the scattering signal, which is given by Equation (1).  
\[\Delta R=d_{1}+d_{2}-D \tag{1}\]  
According to the path difference \(\Delta R\), the time difference \(\Delta t\) between the scattering signal and the direct signal can be calculated, as shown in Formula (2), where \(c\) is the propagation speed of the electromagnetic wave, which is 299,792,458 m/s.  
\[\Delta t=\frac{\Delta R}{c}=\frac{d_{1}+d_{2}-D}{c} \tag{2}\]
## 3 Results and Discussion
### Molecular Docking Studies
#### 3.4.2. Docking Studies with Additional Ligands Implicated in Alzheimer's Disease
In previous work, it has been shown that A\(\beta\) (1-42) is a surface-active peptide; its hydrophilic parts encompass residues Asp1, Ala2, Arg5 and residues 24-28. Lys28 residue of A\(\beta\) (1-42) plays a critical role in binding to lipid membranes, while the C-terminal residues (29-42) consist primarily of hydrophobic residues [103]. These results indicate that the TauRP (1-4), TauRP (1-8) and TauRP (1-14) peptides may be able to bind to A\(\beta\) (1-42) and plausibly compete with its membrane interaction capabilities. In a study conducted by [PERSON] and co-workers, it was shown that lipid membranes modulate the rate of A\(\beta\) (1-42) self-assembly, and that charged lipid vesicles such as 1,2-Dioleoyl-sn-glycero-3-phosphocholine (DOPC) vesicles expedite the growth and aggregation of A\(\beta\) (1-42) fibrils [104]. Therefore, if A\(\beta\) (1-42) can bind to TauRP peptides, it might reduce interactions with membranes and lower the aggregation of A\(\beta\) (1-42) fibrils. However, whether or not this affects A\(\beta\) (1-42) clearance would require additional studies beyond the scope of this manuscript, which primarily focuses on interactions with PHF Tau. In a study conducted by [PERSON] and co-workers, it was shown that endogenous LRP1 contributes to the generation of A\(\beta\) and simultaneously acts as a receptor for A\(\beta\) clearance. They found that if LRP1 expression  
Figure 5: Left to right shows docked images of TauRP (1–2), TauRP (1–4), TauRP (1–8) and TauRP (1–14) with (**a**) MMP-13, (**b**) ADAMTS-4, (**c**) receptor-binding domain of ApoE4, (**d**) A\(\beta\) (1–42) fibrils. was lessened, it may reduce A\(\beta\) clearance, but it also might upregulate non-amyloidogenic pathways for APP processing through alpha-secretase instead of beta-secretase, thereby culminating in the reduction in the formation of A\(\beta\) and leading to a decrease in amyloid beta plaque formation in the brain [105]. Thus, even if the TauRP peptides bind to A\(\beta\) and possibly reduce clearance, this may also trigger non-amyloidogenic pathways. However, a thorough study with A\(\beta\) peptides will be required to determine these possibilities.
# Adaptive Sensor Array Error Calibration Based Impact Localization on Composite Structure
###### Abstract  
Gains and phases delay induced by sensor position error would significantly degrade the performance of high-resolution two-dimensional multiple signal classification (2D-MUSIC) algorithm, which resulting in low positioning estimation accuracy and poor imaging. In this study, adaptive piezoelectric sensor array calibration based method is proposed for impact localization on composite structure. First, observed signal vector from the sensor array is represented by error calibration matrix with unknown gains and phases, and then it used to construct the cost function including sensor array parameters. Second, a 2D-MUSIC algorithm based on linear attenuation calibration is applied for estimating the initial estimate of impact location. Finally, substituting the initial estimate, the cost function is minimized by adaptive iterative to calculate the sensor array error parameters and the exact location of the impact source. Both finite element method (FEM) simulation and experimental results on carbon-fiber composite panel demonstrate the validity and effectiveness of the proposed method.  
array error calibration; 2D-MUSIC algorithm; composite structure; impact location +
Footnote †: journal: Applied Sciences
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
###### Abstract  
Periodic bridge inspections are required every several years to determine the state of a bridge. Most commonly, the inspection is performed using specialized trucks allowing human inspectors to review the conditions underneath the bridge, which requires a road closure. The aim of this paper was to use aerial manipulators to mount sensors on the bridge to collect the necessary data, thus eliminating the need for the road closure. To do so, a two-step approach is proposed: an unmanned aerial vehicle (UAV) equipped with a pressurized canister sprays the first glue component onto the target area; afterward, the aerial manipulator detects the precise location of the sprayed area, and mounts the required sensor coated with the second glue component. The visual detection is based on an Red Green Blue - Depth (RGB-D) sensor and provides the target position and orientation. A trajectory is then planned based on the detected contact point, and it is executed through the adaptive impedance control capable of achieving and maintaining a desired force reference. Such an approach allows for the two glue components to form a solid bond. The described pipeline is validated in a simulation environment while the visual detection is tested in an experimental environment.  
aerial robotics; inspection and maintenance; aerial manipulation; multirotor control Vol. 2021, 11, 8279. [[https://doi.org/10.3390/app11188279](https://doi.org/10.3390/app11188279)]([https://doi.org/10.3390/app11188279](https://doi.org/10.3390/app11188279))
## 3 Results and Discussion  
### Test Results  
#### 3.1.1 Strain and Stress  
The load-strain curve at each strain measuring point was derived from the static load test. Representative curves for the strain gauge (W\({}_{2}\) and W\({}_{9}\)) and strain rosette (W\({}_{5}\) and W\({}_{12}\)) are shown in Figure 9, where W\({}_{2}\) and W\({}_{9}\), W\({}_{12}\), and W\({}_{5}\) reflect the strain status of the outer web at the top end, middle range, and bottom end of the anchor box-support plate weldment, respectively. For the strain rosette, \(\varepsilon\)0, \(\varepsilon\)45, 90 are the measured strains in the horizontal, oblique, and vertical directions. Figure 9 shows that the relationship between measured strains and test load is essentially linear, and data dispersion is within a reasonable range, demonstrating that the specimen was in a linear elastic stage during the test.  
Figure 10 shows the time history of maximum stress at representative measuring points on girder webs during the fatigue verification test. Because of improper storage, test results for 40 \(\times\) 10\({}^{4}\) cycles were lost. For the strain rosette, the maximum principal stress calculated from the measured strain components is used. The results from finite element analysis are also shown in the figure, indicated by the suffix \"FE\" added to the series names in the figure legend.  
Figure 9: Load-strain diagram from static load test: (**a**) Measuring point W\({}_{2}\); (**b**) Measuring point W\({}_{9}\); (**c**) Measuring point W\({}_{5}\); (**d**) Measuring point W\({}_{12}\).  
We can observe from Figure 10 that the maximum stress at various measuring points is maintained at a reasonably stable level as the number of loading cycles increases with fluctuations within 3 MPa. For most measuring points, the difference between the analytical results and the average of the experimental results is not significant. However, a higher discrepancy exists at point N\({}_{3}\), mostly likely because of the more severe local stress concentration caused by the unexpected welding discontinuity of the nearby stiffener in the test specimen. In general, the experimental data agree well with the results from finite element analysis, indicating the reliability of the numerical simulation of stress distribution.