esa-sceva/satcom-chunk-collection huggingface dataset:
# The HD(CP)\({}^{2}\) Data Archive for Atmospheric Measurement Data
## 1 Introduction
There is already a wide spectrum of archives for atmospheric data in central Europe. Some archives focus on specific instruments or products, such as ACTRIS [8], which is concentrated mainly on LIDAR (light detection and ranging) data, WDC-RSAT [9], which specializes in satellite data, or Cloudnet [10], which offers data products resulting out of instrument combination for cloud specification. Other archives are more general such as PANGAEA [11], which is for georeferenced data or CERA [12], which is mainly for climate model output data; both are well established. These data archives were designed for specific instruments/instrument combinations or model output data. The wide range of instruments in HD(CP)2 is not sufficiently covered in any of these existing archives. Furthermore, an intensive measurement campaign, planned for HD(CP)2, with additional and partly new developed instruments and products, has indicated a need for a new data archive. Finally, the new archive contains data standardized to a high extent. The data standard used in the project was designed especially for the archive and supports an easy and fast data exchange between project scientists. In the following we will describe the genesis of the archive in more detail. Section 2 shows an overview of the different kinds of observation data in the HD(CP)2 archive. In Section 3, a short introduction into the binding conventions for each dataset is given. Section 4 describes the technical infrastructure the archive is built on, followed by the description of value-added services in Section 5 and some future perspectives in Section 6.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 4 Vibration Evaluation Metrics
### SEAT Value  
Seat Effective Amplitude Transmissibility (SEAT) is also an evaluation method used to assess the seat comfort through the Power Spectral Density (PSD) [38]. It is used to reflect the performance of seating suspension as a predictor of the comfort of the seated occupant. Its mathematical representation is given by Equation (3)  
\[SEAT=\frac{\left(\ddot{x}_{w}\right)_{RMS}}{\left(\ddot{x}_{sw}\right)_{RMS}}, \tag{3}\]  
where \(\ddot{x}_{sw}\) is the frequency weighted root mean square value of vehicle floor input acceleration; \(\ddot{x}_{w}\) is the frequency weighted root mean square value of the seat output acceleration.
## 4 Discussion  
### The Influence of Image Block Size  
When image blocks are extracted according to pixel points in fundus retinal images, the sizes of image blocks affect the effect of vascular segmentation. In order to obtain the optimal image block size, we organized experiments in the DRIVE and CHASE_DB1 datasets, whose sizes of extracted image blocks ranged from 9 \(\times\) 9 to 37 \(\times\) 37. The experimental results are shown in Figure 7.  
It can be seen from the Figure 7, when the results with the sizes of the extracted image block increases, the segmentation effect of retinal blood vessels increases first and then decreases. In the DRIVE dataset, when the size of the extracted image block is 23 \(\times\) 23, the Acc is highest, reaching 96.63%, SE reached 82.06%. In the CHASE_DB1 dataset, when the extracted image block size is 31 \(\times\) 31, Acc is highest, reaching 97.25%, and SE reaching 76.84%.  
Figure 7: The influence of the image block size in the segmentation effect in the DRIVE and CHASE_DB1 datasets. (**a**) The influence of image block scale in Acc in the DRIVE dataset; (**b**) the influence of image block scale in Acc in the CHASE_DB1 dataset; (**c**) the influence of image block scale in SE in the DRIVE dataset; (**d**) the influence of image block scale in SE in the CHASE dataset; (**e**) the influence of image block scale in SP in the DRIVE dataset; (**f**) the influence of image block scale in SP in the CHASE_DB1 dataset.  
Therefore, the optimal sizes of the image blocks extracted from DRIVE and CHASE_DB1 datasets are 23 \(\times\) 23 and 31 \(\times\) 31, respectively.
## 2 Current Supervisory or Regulatory Technologies
### Three Stages of RegTech Development  
The development of RegTech has gone through three stages [14]. RegTech 1.0 refers to the early stages of RegTech development when companies started leveraging technology to address regulatory challenges. RegTech solutions primarily focused on automating manual processes, improving data management, and ensuring compliance with existing regulations during this period. It developed risk management practices by introducing new technologies to monitor regulations, risks, or specific processes before the 2008 global crisis [15].  
RegTech 2.0 applications, driven by industry and regulators to improve regulatory compliance and efficiency after the global crisis, facilitated companies improvements in consumer protection and supervisory activities [16]. Regulators seek to enhance their capacity to analyze the rising volumes of data generated by institutions required by the post-GFC (global financial crisis) reporting obligations in 2008. It involves more advanced and sophisticated solutions that leverage emerging technologies like artificial intelligence, machine learning, natural language processing, and data analytics. These technologies strengthen risk assessment, real-time monitoring, predictive analytics, and regulatory reporting. It enables financial institutions to control costs and predict risks more effectively, saving regulatory funds and creating new opportunities for the financial industry. It also allows regulators to identify problems using continuous monitoring tools and spend less time conducting compliance breach investigations. Without RegTech, regulators will face significant challenges such as \"fundamental information asymmetry, higher regulatory arbitrage, and more serious systemic risks\" [17].  
In the third phase, more advanced technologies are adopted in applications to foresee risks with predictive algorithms [18] and tools shifting the focus from KYC (Know Your Customer) [19] to KYD (Know Your Data) [20]. This stage involves further advancements in automation, integration, and scalability of RegTech solutions. It incorporates technologies like blockchain, cloud computing, and decentralized applications to address compliance challenges more efficiently and securely.
## 3 System Descriptions and Assumptions
### DESCO Network Model
Additionally, any user's request satisfies \(\mu^{t}_{n}\in\{\mathrm{K}\cup\{0\}\}\). The set \(\mathrm{P}^{d,t}=\{p^{d,t}_{1},p^{d,t}_{2},...,p^{d,t}_{K}\}\) is utilized to denote the application data retained in the cache pool of the MEC server node \(d\in\Delta\) at time \(t\). The element in the set satisfies \(p^{d,t}_{k}\in\{0,1\}\); \(0\) and \(1\), respectively, represent the absence or presence of application data \begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline \(\mathrm{T},\tau\) & Set of time sequence; time frame length \\ \(\theta_{k},\omega_{k},\phi_{k}\) & Real-time data volume of \(k\); application data size of \(k\); necessary FLOPs of task \\ \(\Delta,D\) & MEC server set; number of MEC server \\ \(\mathrm{N}d\), \(N\) & User request under server \(d\); request of user \(n\) \\ \(o_{d,n}\) & MUs’ local compute power \\ \(\mathrm{K}\), NID, KID & Service feature set; MEC server IP address set; application identifier set \\ \(U^{t}_{N_{d}},\mu^{t}_{n}\) & User request under server \(d\); request of user \(n\) \\ \(b\) & Wireless subchannel bandwidth of server \(d\) \\ \(\mu^{d,t}_{n}\) & Minimum efficient uplink transmission rate of user \(k\) in range \(d\) \\ \(\psi_{n}\) & Transmit power of user \(n\) \\ \(\forall d,t\) & RCO decision of server \(d\) in slot \(t\) \\ \(v^{d,t}_{n}\) & RCO policy of user \(n\) \\ \(\mathrm{P}^{d,t}\) & Application cache replacement operation of \(d\) \\ \(\rho^{d,t}_{k}\) & Operation of task \(k\) in server \(d\) at time \(t\) \\ \(\Upsilon_{n,d,k}\) & Energy consumption of user \(n\) in range \(d\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation summary. Figure 2: DESCO network architecture. in the cache pool of server node d. Meanwhile, the set \(\text{P}_{d,t}=\{\rho_{1}^{d,t},\rho_{2}^{d,t},...,\rho_{K}^{d,t}\}\) represents the cache replacement strategy. Each \(\rho_{k}^{d,t}\) can assume one of three values: \(-1\), \(0\), or \(1\), corresponding to the deletion, maintenance, or addition operation. Based on this, the cache replacement operation for application \(k\) on server \(d\) should adhere to  
\[p_{k}^{d,t}=p_{k}^{d,t-1}+\rho_{k}^{d,t-1},\forall p_{k}^{d,t}\geq 0. \tag{1}\]  
The inequality \(\forall p_{k}^{d,t}\geq 0\) represents the MEC server unable to delete data that do not exist in their cache pool. Further, considering the cache pool capacity S of server \(d\), the system's cache status and replacement decisions at any given moment must subject to  
\[\sum_{k=1}^{K}\omega_{k}(p_{k}^{d,t}+\rho_{k}^{d,t})\leq\text{S},\forall t\in T,\forall d\in\Delta. \tag{2}\]  
This ensures the integrity and efficiency of data management in the DESCO system.
# Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LDA** & **Fencing** & **Walk** & **Punch** & **Smoke** & **Cartwheel** \\ \hline Brush\_hair & HOF: 0.6591 & HOF: 0.6920 & HOF: 0.6180 & HOF: 0.6288 & HOF: 0.7752 \\ \cline{2-6}  & Variance: & Variance: & Variance: & Variance: & Variance: \\  & q = 3: 0.8492 & q = 3: 0.7143 & q = 3: **0.8333** & q = 3: 0.7460 & q = 3: 0.9365 \\  & q = 5: 0.7302 & q = 5: 0.7540 & q = 5: 0.8016 & q = 5: 0.7778 & q = 5: 0.9603 \\  & q = 10: 0.8809 & q = 10: 0.7698 & q = 10: 0.7699 & q = 10: 0.6826 & q = 10: 0.9365 \\ \cline{2-6}  & More info: & More info: & More info: & More info: \\  & q = 3: 0.9445 & q = 3: 0.6984 & q = 3: 0.8174 & q = 3: 0.7699 & q = 3: **0.9921** \\  & q = 5: **0.9683** & q = 5: 0.7540 & q = 5: 0.7936 & q = 5: 0.7936 & q = 5: **0.9921** \\  & q = 10: **0.9683** & q = 10: **0.7857** & q = 10: 0.8016 & q = 10: **0.8254** & q = 10: **0.9921** \\ \hline  & & HOF: 0.6414 & HOF: 0.7083 & HOF: 0.7803 & HOF: 0.7984 \\ \cline{2-6}  & & Variance: & Variance: & Variance: & Variance: \\  & q = 3: 0.6522 & q = 3: 0.7174 & q = 3: 0.6818 & q = 3: 0.8571 \\  & q = 5: 0.6377 & q = 5: 0.6957 & q = 5: 0.6667 & q = 5: 0.8651 \\ Fencing & & q = 10: 0.6739 & q = 10: 0.7246 & q = 10: 0.6212 & q = 10: 0.9048 \\ \cline{2-6}  & & More info: & More info: & More info: & More info: \\  & q = 3: **0.8188** & q = 3: 0.8768 & q = 3: 0.8030 & q = 3: **0.9921** \\  & q = 5: 0.7826 & q = 5: **0.9058** & q = 5: 0.7651 & q = 5: 0.9286 \\  & q = 10: **0.8188** & q = 10: 0.8406 & q = 10: **0.8182** & q = 10: 0.9603 \\ \hline  & & HOF: 0.6546 & HOF: 0.6287 & HOF: 0.8889 \\ \cline{2-6}  & & Variance: & Variance: & Variance: \\  & q = 3: 0.6000 & q = 3: 0.5379 & q = 3: 0.7381 \\  & q = 5: 0.6267 & q = 5: 0.5303 & q = 5: 0.7778 \\ Walk & & q = 10: 0.6067 & q = 10: 0.6061 & q = 10: 0.7460 \\ \cline{2-6}  & & More info: & More info: & More info: \\  & q = 3: **0.7000** & q = 3: 0.6136 & q = 3: 0.9524 \\  & q = 5: 0.6667 & q = 5: **0.7046** & q = 5: 0.9524 \\  & q = 10: 0.5600 & q = 10: 0.6515 & q = 10: **0.9603** \\ \hline  & & HOF: **0.7847** & HOF: 0.9007 \\ \cline{2-6}  & & Variance: & Variance: \\  & q = 3: 0.5757 & q = 3: 0.8969 \\  & q = 5: 0.6667 & q = 5: 0.9127 \\  & q = 10: 0.5076 & q = 10: 0.8413 \\ \cline{2-6}  & & More info: & More info: \\  & q = 3: 0.7046 & q = 3: 0.9841 \\  & q = 5: 0.7197 & q = 5: **0.9921** \\  & q = 10: 0.7273 & q = 10: 0.9445 \\ \hline  & & HOF: 0.7985 \\ \cline{2-6}  & & Variance: & Variance: \\  & q = 3: 0.7778 \\  & q = 5: 0.7698 \\  & q = 10: 0.8412 \\ \hline  & & More info: & & More info: \\  & q = 3: **0.9841** \\  & q = 5: 0.9762 \\  & q = 10: 0.9683 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Linear Discriminant Analysis (LDA) classifier results.
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 4 Practical Challenge--Content Dependency and Possible Approaches to Its Analysis
### Power Measurement  
Another basic approach is to measure the power of the received signal in a selected interval and compare it to some threshold. To evaluate this approach, 3 cases were simulated using the MATLAB computational environment and are illustrated in Figure 10. A 5G signal was generated with a duration of 20 ms. White Gaussian noise was added. The presence of the target in the 200 m range and of velocity 10 m/s was simulated. First (Figure 10a), small network traffic was simulated (10% occupancy of the resources). The measured power was 14.4 dBm. Then, (Figure 10c) the transmitted power of the data channel was scaled up, and the measured power equaled \(-\)7.9 dBm. In the last case (Figure 10e, the transmitted power of the data channel was set again as in the first case, but the occupancy of the resources was increased to 70%, so the measured power again reached approximately \(-\)7.5 dBm. Plots of the related cross-ambiguity functions show that in the first case there is no target in the range-Doppler map. In the second case, the improvement is hardly visible, as the peak at the place of the object is a few decibels above noise level. Only in the last case could the target be clearly seen, 24 dB above noise. Thresholding the measured power would select both the second and third signals for radar processing, however, only big traffic (case 3) makes the signal useful from the radar processing perspective. Thus, the trivial method based on power measurements is not sufficient.  
Figure 10: Evaluation of power measurement method. Spectrograms and CAF plots for different amount of content in real-life 5G signal. (**a**) Spectrogram for 10% of content filling. (**b**) CAF for 10% of content filling. (**c**) Spectrogram for 10% of content filling with larger transmission power. (**d**) CAF for 10% of content filling with larger transmission power. (**e**) Spectrogram for 70% content filling. (**f**) CAF for 70% content filling.
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 3 Model and Methodology
### Data Processing  
Using raw AIS data, we account for anomalous data resulting from issues with the recording and reporting of some ships. The data must therefore be filtered and de-noised. By analysing the recording time of a vessel's AIS data, if the vessel's AIS signal briefly disappears from the experimental range and then returns within a short time, the ship is judged to have been within the field. In contrast, the vessel is considered to have left the designated range.  
The emissions estimate depends on the distance travelled between consecutive position updates by each vessel. The AIS reporting interval is typically between a few seconds and one minute. As a result, significant deviation locations can be removed from the route-planning process. Moreover, only some items of AIS data require calculation. As a result, we determined that choosing 5 min as the average period would reduce the number of measures, while maintaining accuracy. We utilised this method to remove irrelevant data and conduct data cleansing. After an initial screening, and removing considerably  
Figure 1: Flow chart for calculating ship emissions.  
misplaced duplicate reports, some missing AIS data were appropriately eliminated. We utilised the data purification methodology of Goldsworthy and Goldsworthy (2016) and [PERSON] (2014) by comparing the shipping speed reported by AIS to the computed speed. Thus, utterances with abnormally rapid speeds could be discovered and eliminated. The implementation of filtering data is abstractly formulated as follows:  
\[v_{i}=\frac{\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}}}{ \Delta t} \tag{1}\]  
\[v_{i}-d\Delta t\leq v_{i}\leq v_{i}+a\Delta t \tag{2}\]  
where \(x_{1}\) and \(y_{1}\), respectively, denote the longitude and latitude of the ship entering the measurement range. \(x_{2}\) and \(y_{2}\), respectively, denote the longitude and latitude of the ship leaving the measurement range. \(\Delta t\) denotes the time difference between entry and departure. \(v_{i}\) represents the average speed between these two positions. d and a are deceleration and acceleration, and specific values are used based on data from the research of [PERSON] et al. (2015) and our experience analysis, which is 0.1 kn/min-0.9 kn/min.  
After filtering the data, it is necessary to estimate the operating status and trajectory of the vessel. The AIS recording times should be evaluated to determine the vessel's course. AIS data typically includes the points' latitude, longitude, and speed. In particular, longitudes and latitudes with significant variants must be excluded. In constructing the vessel trajectory, we referred to one study from [PERSON] et al. (2016) to analyse and eliminate time- and position-related inaccurate data.
# Research on Prediction Method of Bolt Tightening for Aviation Components Based on Neural Network
## 3 Aviation Component Bolt Tightening
### Verification of the Tightening Scheme Based on a Finite Element Simulation Method
The mean values of the residual preload of the four single-bolt tightening sequences were 62,884.9 N, 62,956.1 N, 62,966.6 N, and 63,006.3 N, respectively. The mean values of the residual preload of the three kinds of two-bolt tightening sequence were 65,090.9 N, 65,504.6 N, and 65,208.1 N, respectively. The average value of the residual preload was the highest when two bolts were tightened at an interval simultaneously. The corresponding variance was 450.3, which was the smallest of all the tightening sequences. Therefore, when the gap value was 0.8 mm and the axis inclination angle was 0.15\({}^{\circ}\), the two-bolt tightening interval at the same time was the best sequence. The two optimal tightening sequences of the aviation component are shown in Figure 4:6 It can be concluded that the optimal tightening sequence for the aviation component was different under different conditions. 4 Optimal Tightening Sequence Prediction for the Aviation Component Based on a Neural Network Method  
From the conclusions of Section 3, it can be seen that the optimal tightening sequence of the aviation component was different under different conditions. Therefore, the optimal tightening sequence for the aviation component under different joint gaps should be calculated separately. As the finite element simulation method takes a long time to calculate, each simulation needed more than 7 h to complete the calculation. If we used the finite element method to plan the bolt tightening sequence of the aviation component, the tightening efficiency would be as low as possible. Therefore, a surrogate model method was proposed to reduce the calculation time and improve the efficiency of the whole tightening process. The radial basis function (RBF) neural network algorithm has strong robustness and fault tolerance. It uses a parallel processing method to improve the speed of computing, and it has a strong information synthesis ability. Compared with other types of neural network, RBF neural networks have a strong approximation ability. It can approximate any accuracy of the nonlinear function. It also has a global search ability, and its convergence speed is fast. The RBF neural network has a strong input and output mapping function, and it is the forward network that has the best mapping function. Based on the above advantages, this paper used the RBF neural network to establish a surrogate model of the tightening system of the aviation component. This was used to rapidly predict the optimal tightening sequence for the aviation component in the case of different docking gaps and axis inclination angles.
## 7 Conclusions  
A novel methodology of a vibration control system for the high-frequency vibration of the superstructure of pumped storage power stations was proposed in this study. A spring plate was used to replace the spring elements of traditional tuned mass dampers to form a new TMD system that is suitable for high-frequency vibration control. The vibration problem of higher-order modes was simplified to a single mode vibration problem, and then the optimal design of a single-degree-of-freedom system was applied to the single-mode vibration to solve the high-frequency vibration problem of the structure. The feasibility of the proposed method was investigated by experimental and numerical simulations.  
The experimental results show that compared with the uncontrolled structure, the time-history response of the plant structure using the TMD was significantly reduced, indicating that the TMD could effectively reduce the high-frequency vibration response of the structure. With the increased TMDs, the damping effect gradually increased, and the maximum RMSA attenuation rate reached 80.73%. In addition, the control effect of the TMD at the bottom of the structure was better than that at the top layer. Numerical results verified the accuracy of the experiment. The TMD with non-optimal tuning parameters could not only reduce the high-frequency vibration of the structure effectively, but also amplified the vibration further due to its failure. In addition, the vibration velocity of the TMD system increased as it approached the tuning frequency ratio, so that the vibration absorption capacity reached the optimal level. As the number of TMDs increased, the damping effect increased and the tuning band of the TMDs gradually widened, showing good robustness and ensuring controller performance when the excitation was unstable.  
From a practical perspective, passive structural control, especially using tuned mass dampers, is a robust, relatively inexpensive solution to reduce the risk of plant vibration in the PSP environment. Using TMDs is a relatively new consideration for PSPPs, but in many civil engineering applications, the benefit of vibration reduction has been determined to be greater than the cost of TMDs. Therefore, although the feasibility of practical engineering application of PSPPs was not the focus of this paper, the significant vibration reduction demonstrated here highlights the potential for TMDs as an effective mechanism to reduce the vibrations of plant structures and improve the reliability of PSPPs.  
Conceptualization, T.Z. and X.F.; methodology, [PERSON] and X.F.; software, T.Z.; validation, T.Z. and Y.Z.; formal analysis, T.Z.; investigation, T.Z.; resources, X.F. and J.Z.; data curation, X.F.; writing--original draft preparation, T.Z.; writing--review and editing, X.F.; visualization, T.Z.; supervision, X.F. and J.Z.; project administration, X.F. and J.Z.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (grant no. 91215301). The authors declare no conflict of interest.