esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Over the past few decades, rapid economic development has led to the establishment of numerous monitoring systems, resulting in the accumulation of vast amounts of monitoring data. Among these data, dynamic acceleration data stand out prominently. However, the quality of collected acceleration data is often compromised due to factors such as challenging operational environments and sensor malfunctions. This severely hampers the value extracted from the data. Although manual identification and classification of data anomalies are more reliable, they are time consuming and labor intensive. To address the challenge of identifying and classifying anomalies in massive acceleration data, this paper proposes a two-stage model for intelligent data cleaning. Firstly, raw acceleration data are transformed into IPDF and PSD features, and a one-dimensional convolutional neural network is trained to preliminarily identify and classify acceleration data anomalies. Subsequently, the RPV indicator is extracted from the original data of the normal and outlier categories to achieve precise classification based on threshold values. The proposed method is successfully validated using acceleration monitoring data from a large-span arch bridge, achieving an accuracy of over 99%. Furthermore, compared to directly employing a one-dimensional CNN classification model, the approach significantly enhances the model's perception of local significant disturbances.  
bridge structural health monitoring; data cleaning; one-dimensional convolutional neural network; two-stage classification model +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# GA-PSO Algorithm for Microseismic Source Location
## 3 Experiments and Results
### Validation Through Engineering Case Studies
#### 3.2.2 Engineering Experiment Results  
The positioning results of the blasting experiment were analyzed using five established methods: GA, PSO, PSO-ACO, NM-PSO, and the GA-PSO algorithm. The waveform propagation speed was set to 5000 m/s. The results are summarized in Table 8 below.  
In this engineering experiment, the PSO-ACO algorithm exhibited the largest error (53.20 m), indicating that it is not suitable for medium- to large-scale seismic events. Although the pheromone update mechanism of the ACO algorithm performs well in combinatorial optimization and path planning, its discrete nature is ill suited for continuous optimization problems in medium- to large-scale localization tasks. On the other hand, although the NM-PSO algorithm achieved a 33% improvement in accuracy compared to the PSO-ACO algorithm, it only improved by approximately 0.23% (from 35.52 m to 35.44 m) compared to the traditional PSO algorithm, indicating that the NM-PSO algorithm does not  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Number** & **X/m** & **Y/m** & **Z/m** & **P-Wave** \\  & & & & **Arrival/ms** \\ \hline
1 & 8716 & 6614 & 522 & 34.9 \\
2 & 8737 & 6609 & 565 & 36.6 \\
3 & 8666 & 6600 & 520 & 39.3 \\
4 & 8668 & 6599 & 565 & 41.1 \\
5 & 8641 & 6515 & 520 & 42.3 \\
6 & 8691 & 6684 & 520 & 44.5 \\
7 & 8721 & 6449 & 520 & 47.8 \\
8 & 8702 & 6604 & 647 & 50.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Detector position and P-wave arrival.  
Figure 5: (**a**) Variation in absolute error with iteration times; (**b**) variation in fitness value with iteration times.  
achieve satisfactory results in real data tests. As a result, when confronted with complex real seismic data, the NM-PSO algorithm's localization performance cannot be substantially improved (with an error of 35.44 m).  
The results indicate that the GA-PSO algorithm effectively integrates the strengths of both the GA and PSO algorithms. It achieves an improved balance between global exploration in the early stages and local refinement in the later stages. Under the experimental conditions, the GA-PSO algorithm resulted in an average positioning error of 20.08 m, representing an improvement of approximately 59% over the GA (49.31 m) and a 43% improvement over the PSO algorithm (35.52 m). Moreover, the GA-PSO algorithm outperforms the other two hybrid optimization algorithms, demonstrating superior accuracy and robustness.
## 4 Experiment Results and Analysis
### Ablation Experiment  
In order to further explore the role of each improved part of the Dense Pyramidal Net model, an ablation experiment was designed. Each improvement is added sequentially from DenseNet to determine the impact of each part on the overall model. The results are shown in Table 1.  
It can be seen from Table 1 that the improved residual block has a partial impact on the original network, and the use of the pyramid structure increases the accuracy of model recognition. The parallel computing of the DC Block significantly enhances the model's ability to recognize images.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods
### Multi-Granularity Partition of the Multi-Agent Negotiation Model  
The framework in this paper is decentralization. In the blockchain technology, there are several consistency algorithms, including RAFT [22], PBFT [23], DPOS [24], RIPPLE [24], POW [25], POS [26], etc. We applied RAFT, PBFT, and RIPPLE in this paper. In RAFT, there are three roles of leader, candidate, and follower. At the start, all nodes in the net are followers. Then some nodes become candidates and initiate a vote. After voting, only one leader is elected, who manages the net by synchronizing logs and connects all followers by heartbeat. Followers only replicate the log. Differently from RAFT, PBFT introduces a result verification mechanism. It can avoid a byzantine fault by  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Stages** & **Discovery** & **Identification** & **Confirmation** & **Tracking** & **Monitoring** \\ \hline
**profit** & 0.25 & 0.1 & 0.15 & 0.25 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial profits \(p_{j}^{ini}\) for each stage of one mission.  
using the processes of REQUEST, PRE-PREPARE, PREPARE, COMMIT, and REPLY. In RIPPLE, more leaders are elected. The consistent process is divided into two parts: consistency in leader group and global consistency.  
There are three roles of satellites set in this system: leader node (_Leader_), temporary leader node (_TLeader_), and follower node (_Follower_). The _Leader_ is elected to organize the negotiation. The _Leader_ periodically processes the mission requests from _Follower_ and releases negotiation results to _Followers_. The _Leader_ establishes a leader group consisting of itself and _TLeaders_. They firstly reach an agreement in the group by group vote. After being verified by _TLeaders_ the negotiation result can be sent to _Followers_. _Followers_ originate missions themselves and negotiate with each other under the _Leader_'s control.
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 2 Related Works
### Localization and Tracking Technology  
There is increasing interest in various location-based service (LBS) technologies to estimate the exact point of an accident and the location of a fire in a disaster scene. Generally, the global positioning system (GPS) enables positioning in any part of the world, but in indoor or underground spaces object tracking is difficult, due to the restriction of signal arrival. In particular, GPS devices cannot be used in tunnels due to their closed structure, so other radio frequency (RF)-based solutions should be utilized. The most representative examples are positioning methods using WiFi or Bluetooth. [PERSON] et al. (2019) proposed a WiFi-based peer-assisted localization approach to obtain accurate ranging estimation among peer devices and then map their locations. The ranging error showed about 2-m accuracy. [PERSON] et al. (2019) proposed a Bluetooth-based indoor localization approach that jointly uses iBeacon ([PERSON] et al., 2019) and inertial sensors in large open areas. Their performance showed 90% errors within 3.5-m ranging accuracy. In addition, UWB-based solutions ([PERSON] et al., 2019; [PERSON] et al., 2019) are being developed for more accurate indoor positioning. Because these positioning solutions have different strengths and weaknesses depending on their respective frequency bands, bandwidths, and installation costs, it is necessary to understand the disaster environment to be addressed in advance and to select the appropriate method or combination of methods.
## 5 Conclusions  
In this study, we developed a suitable AGB model for Xinjiang grasslands based on the random forest algorithm, using AGB observation data, remote sensing vegetation indices, and meteorological data. We analyzed the grassland AGB spatiotemporal changes and further explored the response of AGB to variations in climatic factors. The main conclusions are as follows:  
The grassland AGB model based on the random forest algorithm fit well, with a coefficient of determination and RMSE of 0.55 and 64.33 g\(\cdot\)m\({}^{-2}\), respectively. The estimated values of the model were close to the actual values, making them suitable for monitoring changes in grassland AGB.  
From 2000 to 2022, the spatial distribution of grassland AGB in Xinjiang showed high levels in the northwest and low values in the southeast, with an overall upward trend. All grassland types, except montane meadows, showed increasing trends, with lowland meadows showing the fastest growth rate (0.65 g\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\)). Additionally, 16.15% of the total area showed a significant increase in AGB, mainly located in the periphery of the Tarim Basin and southeastern part of Xinjiang.  
Xinjiang's climate is experiencing warming and increased humidity, leading to a positive impact on AGB growth due to increased precipitation and warming. Furthermore, temperature and precipitation were positively correlated with AGB, but precipitation correlates better with AGB than temperature.  
[PERSON] was responsible for the research design, analysis, manuscript design, and review; [PERSON] drafted the manuscript and was responsible for data preparation; experiments and analyses were conducted by [PERSON]; [PERSON] was responsible for funding acquisition and resources; [PERSON] was responsible for the research design field survey; and data curation was completed by [PERSON]; [PERSON] was responsible for the research review and editing. All authors contributed to the editing and reviewing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China: \"Study on Change Characteristics of Grassland Productivity and Carbon Source/Sink Mechanism in Xinjiang Based on Improved CoLM Land Surface Model\" (Grant No. 42161024) and the Autonomous Region Finance Forest and Grass Science and Technology Project funded \"Integrated Application of Natural Grassland Monitoring and Evaluation Data in Xinjiang\" (Grant No: XJLYKJ-2023-18).  
Dataset available on request from the authors.  
The authors are grateful to the editors and the anonymous reviewers for their insightful comments and helpful suggestions.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Tuning Resolution
Figure 5: (**a**) Experimental results illustrating 1 st mode resonant frequency as a function of filling individual cavities along the middle row with W, Mo, and Silica powders, (**b**) results of simulated FEM versus experimental results of W powder, and (**c**) image of filled row. The order of filling the columns did not significantly influence the resonant frequency because the lateral location has little impact on the 1 st frequency mode, so the effective change in frequency was due to the change in overall mass. However, filling consecutive rows should have an influence as per Equation (2). The experimental results of the 1 st mode frequency as a function of filling rows of cavities in both directions (free end to tethered end, and tethered end to free end) for W-filled cavities are illustrated in Figure 7. The colored space between both curves represents the possible frequency values that could be achieved by filling the cavities. The results show that any specific frequency can be achieved within the given range. Mismatching different types of fillers could also be used to enhance resolution even more--for example, you could fill H45 with W to reduce the frequency, and then fill cavity H41 with Silica to obtain a large range with high resolution.
## 2 Results and Discussion
### In Vitro Antioxidant Activity of SBP-III-3
#### 2.3.2 DPPH\(\bullet\) Scavenging Activity  
DPPH\(\bullet\) scavenging assay is popular and efficient in predicting the antioxidant activities of protein hydrolysates and peptides. It has a deep violet colour in solution and generates a strong absorption band at about 517 nm. The solution becomes colourless or pale yellow following the reduction of the absorption value at 517 nm when the radicals are neutralized [21]. Therefore, DPPH\(\bullet\) scavenging activity of SBP-III-3 was measured and shown in Figure 2B. SBP-III-3 scavenged DPPH\(\bullet\) in a concentration-effect manner with EC\({}_{50}\) of 0.895 mg/mL, but its activity was lower than the positive control of ascorbic acid. In addition, the EC\({}_{50}\) of SBP-III-3 was lower than those of PSYV (17.0 mg/mL) [18], Phe-Leu-Asn-Glu-Phe-Leu-His-Val (FLNEFLFHV) (4.950 mg/mL) [22], Thr-Thr-Ala-Asn-Ile-Glu-Asp-Arg-Arg (TTANIEDRR) (2.503 mg/mL) [23], FIMGPY (2.60 mg/mL), GPAGDY (3.48 mg/mL) and IVAGPQ (3.93 mg/mL) [10], PYSFK (1.575 mg/mL) [20], and Leu-Leu-Pro-Phe (LLPF) (1.084 mg/mL) [24] from hydrolysates of loach, blue mussel, bluefin leatherjacket, salmon pectoral fin, skate cartilage, grass carp skin, and corn gluten meal, but it was higher than those of Gly-Ser-Gln (GSQ) (0.61 mg/mL) [25], Pro-Ile-Ile-Val-Tyr-Trp-Lys (PIIVYWK) (0.713 mg/mL) [20], His-Phe-Gly-Asp-Pro-Phe-His (HFGBPFH) (0.20 mg/mL) [26], Phe-Leu-Pro-Phe (FLPF) (0.789 mg/mL), and Leu-Pro-Phe (LPF) (0.777 mg/mL) [24] from protein hydrolysates of Chinese leek, blue mussel, grass carp skin, mussel sauce and corn gluten meal. Therefore, these results indicated that SBP-III-3 had the strong ability to donate an electron or hydrogen radical for inhibiting the DPPH\(\bullet\) reaction.
## 4 Simulation Results
### Containment Control Based on L1 Adaptive Control with Actuator Time Delay  
For containment control, the communication between the agents through the DDS middleware is shown in Figure 8 by considering the same policies of QoS in Table 6. This communication structure is maintained during all the tests shown in Figures 9-14.  
Two cases have been included for containment control results based on the modified L1 controller.  
* AUVs-UAVs containment without disturbance and uncertain parameters: The set of five AUVs can be constituted in a certain topology inside their UAVs leaders in 2D space and 3D space without disturbance and uncertainty in the inertia matrix, which is indicated in Figures 9 and 10.
* AUVs-UAVs containment with disturbance and uncertain parameters: The disturbance and the parameter uncertainty were not considered in Figures 9 and 10, but now it is considered. Figures 11 and 12 show the 2D space and 3D space of the set of five AUVs with disturbance and uncertainty in water density and the inertia matrix. Figures 13 and 14 illustrate the containment control of five AUVs and four UAVs avoiding some obstacles during their movement in 2D and 3D space. One can see that during this maneuver the controller maintain the containment and the formation of the followers within their leaders  
Figure 10: The containment control of five AUVs in star formation within their four UAVs leaders in square formation without disturbance and uncertainty in 3D space.  
Figure 11: The containment control of five AUVs in star formation with their four UAVs leaders in square formation under disturbance and uncertainty in 2D space.  
Figure 12: The containment control of five AUVs in star formation within their four UAVs leaders in a square formation under disturbance and uncertainty in 3D space. The dashed lines project the containment area in the space of AUVs.
# A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE
## 5 Conclusions  
This article proposes and studies an efficient and low complex receiver that jointly performs channel estimation based on superimposed pilots, and data detection, optimized for massive MIMO. 5G Communications will support mm-Wave, which will enable much higher throughputs and facilitate the employment of Massive MIMO.  
The use of superimposed pilots avoids the overheads associated with channel estimation based on conventional pilot symbols, which tends to be more demanding in the case of m-MIMO, and therefore, this technique achieves an improvement of spectral efficiency.  
The proposed receiver uses two low complex algorithms: the MRC and the EGC. These algorithms are compared with the ZF. The advantage of the MRC and EGC relies on the fact that the ZF requires  
Figure 10: BER results for \(4\times 32\) MIMO with superimposed pilots, with a block of 5 versus 10 data symbols.  
Figure 9: BER results for \(4\times 32\) MIMO with superimposed pilots, with pilot power of 0 dB versus \(-3\) dB.  
the computation of the pseudo-inverse of the channel matrix for each frequency component, processing not required with MRC/EGC, keeping the complexity requirements at a low level.  
It was viewed that the MRC algorithm implemented in the proposed iterative receiver, which performs channel estimation using superimposed pilots and data detection for m-MIMO, achieves a performance very close to the MFB, just after few iterations. It was also viewed that the MRC achieves better performance than the ZF, even with a reduction of complexity, which occurs since ZF introduces noise enhancement.  
All authors contributed equally to the article. All authors have read and agreed to the published version of the manuscript.  
This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020.  
We acknowledge the support of FCT/MCTES, as described above in funding.  
The authors declare no conflict of interest.
# Correcting Ski Resort Trajectories Extracted from Video
## 1 Introduction
[PERSON] [9] provided an overview of content-aware video analysis techniques, essential for processing large volumes of sports footage. [PERSON] et al. [15] extended this discussion by reviewing video action recognition in sports, highlighting the challenges and advancements in the field. [PERSON] et al. [16] demonstrated the application of neural networks to enhance the accuracy and speed of data acquisition in sports, a technique highly relevant for analyzing skiing videos. In the context of trajectory analysis, [PERSON] and [PERSON] [8] and [PERSON] et al. [10] used tools like PeTrack to extract and analyze skier trajectories, forming the basis for the trajectory correction methods employed in this study. Further advancements in video analysis tools, such as those discussed by [PERSON] and [PERSON] [17], provide crucial improvements in tracking and analyzing skier movements in real-time. The use of scaling factors and reference systems in trajectory analysis is well-supported by existing research. [PERSON] and [PERSON] [18] developed algorithms for real-time event detection in sports video, and [PERSON] et al. [19] proposed a framework for semantic annotation of sports video, which enhances the contextual analysis of skier trajectories. [PERSON] et al. [20] explored gesture recognition in low-resolution sports video, providing methodologies that can be adapted for analyzing skier movements. These approaches offer a robust framework for understanding the dynamic movements of skiers and developing strategies to improve safety. Moreover, the integration of machine learning with traditional data analysis methods, as discussed by [PERSON] et al. [21], provides a comprehensive approach to sports video analysis. This is further complemented by recent advancements in AI-assisted video tracking systems, which have shown potential for real-time application in dynamic sports environments such as skiing [22]. The visualization and interpretation of sports data have also been significant areas of research. [PERSON] et al. [23] surveyed visualization and visual analytics approaches for image and video datasets, emphasizing the importance of translating raw data into actionable insights. [PERSON] and [PERSON] [24] utilized data mining and image feature retrieval in tennis match analysis, offering techniques that could be applied to skiing. The effective visualization of sports data is critical for understanding complex movement patterns and communicating findings to practitioners in the field. Additionally, the importance of visual analytics in safety assessment is highlighted by studies such as [PERSON] et al. [25], who applied video analysis to assess physical activity intensity in sports. In the following, we first describe the problems faced dealing with video footage where relevant information on the perspective is missing or only approximately known. We then introduce a method for perspective correction, describe its principles, and explain how it is applied to correct the trajectory data. Finally, we discuss our findings and possible improvements.