esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Traffic Organisation in the Kiss-and-Fly Zone of Krakow Airport: Eye-Tracking Study
## 1 Introduction  
Based on the geometry range within which transport infrastructure is developed, it can be divided into linear infrastructure elements such as roads and point infrastructure elements such as ports and airports, which could be also considered as complex infrastructure objects [1, 2]. Although the second ones mainly act as connectors for the first ones, not only their appropriate planning and design but also the verification of their functionality for users are very important [3]. Moreover, some complex point infrastructure objects consist of few parts, which also often have a complex structure and consequently should be evaluated separately.  
As a research tool in the field of transport, the first application of eye-tracking technique was in the 1970s [4, 5]. Since that time, this technology strongly progressed, allowing us to cover both laboratory and field studies of different types of road users. In the first place, this method is widely used to understand drivers' behaviour reacting to various visual stimuli [6]. Also, with the same aim eye-tracking technology, research was conducted with cyclists [7] and pedestrians [8]. A common feature of most such studies was a focus on searching general patterns, dependencies and relationships in the behaviour of the selected participants' groups. There is quite a small amount of eye-tracking research works which have concentrated on such functional aspects of complex infrastructure objects or their parts as confusion points and problems met by road users when using them together and suggesting their further improvement. Here, the study at Krakow Glowny railway station can be mentioned as an example [9].  
Hence, the main purpose of this study was to assess and verify the eye-tracking technique with its standard metrics as an objective tool when searching and estimating confusions and problems of a complex infrastructure object/element faced by road users. The K&F zone of Krakow Airport was selected as a research object because of potential difficulties for drivers, which could be associated with the lack of accepted standards and regulations to design such an infrastructure element. The article is divided into six chapters. After the introduction, previous research works related to K&F zones themselves as well as the eye-tracking technique and its application to analyse road signage are discussed. Then, the organization and conduction of the field study including the description of selected area, equipment and participants are presented. Additionally, the approach to assess road signs is shown. Next, the outcomes concerning observations of road signs in the K&F zone are introduced in the form of an overview of road signs perception. Finally, each confusion point met by drivers in the K&F zone of Krakow Airport is discussed in detail. The article finishes with the conclusions.
# Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines
## 4 Participation Factor Analysis and Dynamic Interaction  
A participation factor analysis is a useful method for measuring the level of participation of the different state variables in the system's eigenvalues, and it is conducted to evaluate the sensitivity of controllers in a multi-VSC system having two VSCs (\(n=2\)) placed in parallel. This analysis is vital for identifying the dynamic states that most significantly influence the instability of identified eigenvalue modes. The participation factors are normalized to sum up to 1 in each scenario.  
In this context, dynamic states with the index \"1\" corresponded to \(VSC_{1}\), and dynamic states with the index \"2\" corresponded to \(VSC_{2}\). Initially, the analysis focused on how changes in the current controller's gain \(K_{P,CC1}\) of \(VSC_{1}\) influenced the system mode's instability. The results in Figure 10 indicate that the dynamic states of time delay \(x_{del,1d},1\) and d-axis current \(I_{Ld},1\) in the \(VSC_{1}\) were the most critical, particularly the time delay within the current controller's loop, which was identified as a primary instability factor following the controller design changes.  
Figure 8: Eigenvalue trace of PLL’s proportional gain variation K\({}_{P,PLL1}\), which varied from 0.1 (deep blue) to 20 (deep red) times K\({}_{P,PLL0}\). Green arrow means instability point.  
Figure 9: Eigenvalue trace of AVC’s integral gain variation K\({}_{La1}\), which varied from 0.1 (deep blue) to 200 (deep red) times K\({}_{La0}\). Green arrow means instability point.  
Furthermore, the analysis was extended to the mode affected by variations in the PLL's gain \(K_{P,PLL1}\) in \(VSC_{1}\). The results shown in Figure 11 reveal that the dynamic states of the PLL's output angle \(\theta_{PLL},1\) and the q-axis voltage at PCC \(V_{PCCq},1\) significantly influenced the mode's stability. However, the dynamic state of the q-axis output current \(l_{qq},2\) in the \(VSC_{2}\)'s control system had the most pronounced effect. This finding suggests that the instability arose from interactions between the two VSCs' control systems, especially when changes in one VSC's PLL bandwidth impacted synchronization with the grid.  
Lastly, when examining the mode affected by changes in the AVC's integral gain \(K_{I,\sigma 1}\) in \(VSC_{1}\), the results in Figure 12 show that the AVC controller's dynamic states \(q_{errac},1\) and  
Figure 11: Participation factors for critically unstable mode during variations of \(K_{P,PLL1}\).  
Figure 10: Participation factors for critically unstable mode during variations of \(K_{P,CC1}\).  
\(V_{M,LPF},1\) in \(VSC_{1}\) predominantly affected the mode's stability. This case emphasizes the crucial role of \(VSC_{1}\)'s AVC controller in the system mode stability.
# Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
## 4 Impact of the Timing Group Delay Bias  
The existence of the TGD bias is proven for BDS satellites. In this section, the impact of the TGD bias is assessed from the standpoints of the SISRE and SPP. For a comprehensive assessment, four schemes are applied:  
1. Scheme 1 (S1): Normal satellite TGD correction.
2. Scheme 2 (S2): Use of the satellite-dependent TGD bias calibrated from precise products.
3. Scheme 3 (S3): Use of the TGD bias between BDS-2 and BDS-3 calibrated from precise products.
4. Scheme 4 (S4): Use of the satellite-dependent TGD bias calibrated from network solution.  
When correcting the TGD bias, we substitute \(dtgd_{0}\) and \(dtgd_{1}\) into the satellite clock and TGD\({}_{1}\) respectively:  
\[\begin{array}{l}\overline{t}^{sat}=t^{sat}-dtgd_{0}\\ \overline{\delta}_{tgt1}=\delta_{tgt1}+dtgd_{1}\end{array} \tag{10}\]  
where \(\overline{t}^{sat}\) is the new satellite clock after \(dtgd_{0}\) correction for the code observation, and \(\overline{\delta}_{tgt1}\) is the new TGD\({}_{1}\) after \(dtgd_{1}\) correction.
## 3 Results  
This section presents a detailed evaluation of the results obtained from a complex algorithm for breast cancer diagnoses, which comprises a Deep MLP, a feature-fused autoencoder neural network, and a weight-tuned cross-validation decision tree. The hyperparameters used here were a batch size of 128, 256 hidden units, and a dropout of 0.45. The proposed model was applied to the Wisconsin Breast Cancer Database. Four evaluation measures were utilized to assess the prediction performance of the model: Accuracy (99.47%), Recall (99%), F1 Score (99%), and Precision (99%).  
Furthermore, the AUC values (the area under the ROC curve), illustrated in Figure 1(a), were always 0.996, highlighting the model's ability to discriminate across different thresholds. The confusion matrix shown in Figure 1(b) indicates balanced classification performance, which reduces false positives and negatives. Comparison with traditional machine learning techniques, which include Support Vector Machines (SVMs), Random Forests,and k-Nearest Neighbors (KNN), underscores the performance of the proposed model. It outperformed these strategies in terms of accuracy, precision, and overall performance balance. This superiority is attributed to the advanced feature extraction skills of the Deep MLP and the green dimensionality reduction facilitated by using the feature-fused autoencoder. Additionally, the Weight-Tuned Decision Tree classification method contributed to the model's robustness, mainly in addressing classification imbalances. This can also be attributed to the use of superior technology in gaining knowledge of strategies for enhancing the accuracy of breast cancer classification.  
Key factors that contributed to the model's success included comprehensive validation through K-fold cross-validation, careful hyperparameter optimization, and the use of superior neural network architectures. The study's findings suggest that the proposed model is a valuable diagnostic tool for clinical experts, and as such, may also facilitate early detection and improved outcomes for individuals affected by this condition. Future endeavors could focus on enhancing this model by exploring alternative neural network architectures and incorporating additional data resources, including genetic statistics and medical imaging records.
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 5 Experiment and Result Analysis
### Discussion of Results
In a 14-node network, the BPDM-GCN latency (12.1 ms) is only 6.2% faster than AREOR (12.9 ms), but the training cost is 20 times higher. The experimental setting is that the interval between topology changes is \(\geq\)1 s. When the frequency of link state mutations is \(>\)10 Hz (such as in ultra-low-latency 5 G scenarios), the success rate drops to 83.5%.
## 2 Materials and Methods
### Short Overview of Reliability Methods
#### 2.5.6 Direct Integration Method  
The reliability integral (Equation (13)) is approximated by the Riemann sum [36]. Integration boundaries for each variable in the standard normal space were defined on a closed interval \([-6,6]\), with the number of discretization levels m = 3000. Considering the form of the integrand, integration boundaries are determined empirically so that the points in their vicinity make a negligible contribution to the result, also taking into account the recommendation given by [PERSON] et al. [37]. They recommend defining the limits of integration in standard normal space within the \([-5,5]\) range, since this ensures a probability lesser than \(5.7\times 10^{-7}\) for uncertain input parameters to fall outside this range, which they consider to be negligible. The number of numerical integration points for a system with 3 random variables is approximately \(2.16\times 10^{11}\). The detailed integration procedure is described in [37]. Due to such a large number of integration points and mathematical complexity of the reliability integral, the integration was carried out on the computational cluster \"Isabella\". The cluster was founded in 1971 within the University of Zagreb Computing Centre (SRCE) and it is intended for high-performance computing (HPC) for research projects and education. The individual integration job was divided into segments. Each segment of the calculation was performed on a separate processor in parallel, which significantly accelerated execution time. To verify the direct integration algorithm, its results were compared to the results of the Monte Carlo method with a relatively large number of simulations (\(\approx 10^{4}/\text{p}_{\text{f}}\)). The difference in the results between the two methods was in the 5 th decimal place. Due to such a high degree of agreement of the results, it is considered that the direct integration algorithm is sufficiently accurate to assess the error of the other methods.
###### Abstract  
This paper presents a novel, high-speed, and low-complexity algorithm for pitch (F0) detection, along with a new dataset for testing and a comparison of some of the most effective existing techniques. The algorithm, called OneBitPitch (OBP), is based on a modified autocorrelation function applied to a single-bit signal for fast computation. The focus is explicitly on speed for real-time pitch detection applications in pitch detection. A testing procedure is proposed using a proprietary synthetic dataset (SYNTHPITCH) against three of the most widely used algorithms: YIN, SWIPE (Sawtooth Inspired Pitch Estimator) and NLS (Nonlinear-Least Squares-based). The results show how OBP is 9 times faster than the fastest of its alternatives, and 50 times faster than a gold standard like SWIPE, with a mean elapsed time of 4.6 ms, or 0.046 \(\times\) realtime. OBP is slightly less accurate for high-precision landmarks and noisy signals, but its performance in terms of acceptable error (<2%) is comparable to YIN and SWIPE. NLS emerges as the most accurate, but it is not flexible, being dependent on the input and requiring prior setup. OBP shows to be robust to octave errors while providing acceptable accuracies at ultra-high speeds, with a building nature suited for FPGA (Field-Programmable Gate Array) implementations.  
pitch detection; F0; algorithm; auto-tune; audio signal processing Article  
Algorithm Based on One-Bit
## 3 Simulation Example  
### Example 1: Single-Degree-of-Freedom System  
Here, we take the single-degree-of-freedom system as an example as shown in Figure 1. We put an external force \(f=15\sin(10t)\) on the system. The time interval between \(t_{1}\) and \(t_{2}\) is \(\Delta t=0.02\) s. We set \(m=2\) kg, \(k=20\) N/m, and \(c=0.05\). The initial state is 0, and observation lasts for 10 s. The identification results are as follows.  
From the Figure 5, we can see that the load identified by the algorithm is highly consistent with the real load applied to the system, with a deviation of less than 5%. The feasibility of this method for the load identification of the single-degree-of-freedom system is proven. There is no divergence of the results caused by accumulated errors in the time domain. Using the Newmark method [24] for load identification in the same load-identification model, the identification results are as follows.  
Figure 6 indicates that the load identified by the Newmark method has good identification accuracy in the first 5 s and then starts to diverge; the error exceeds 100% after 8 s. However, there are no corresponding problems in the function principle identification method; the load identification of each micro segment is independent of the others, and there is no accumulated error.  
Figure 4: Multi-load identification of multi-DOF system.  
Figure 5: Single-degree-of-freedom system load-identification results.
## 4 Results and Discussion
### Experimental Results
#### 4.2.3 Effect of Hyperparameters  
The prediction results of FedDeep under different hyperparameter configurations for the future 12 h forecasting on 133 sites are presented in Figure 5. When one hyperparameter was revised, the other hyperparameters maintained their optimal settings (\(N_{h}=4\), \(N_{d}=8\), \(L_{MHSA}=3\), \(L_{LSTM(ST)}=1\), and \(L_{LSTM(dec)}=1\)). From Figure 5a-c, we can observe that when the model structure was more complex, it was more likely to underfit, while when the model was more simple, it was easier to overfit. Figure 5d,e exhibit stacking one layer of LSTM in the spatio-temporal learning layer, and the decoder of seq2 seq accomplished the best accuracy. This was due to the fact that stacking too many LSTM layers causes error accumulation.  
Figure 4: \(PM_{2.5}\) Prediction results with the comparison models. (**a**) SVR for Pukou site of Nanjing city, (**b**) SVR for Nannen site of Suzhou city, (**c**) GAT-LSTM for Pukou site of Nanjing city, (**d**) GAT-LSTM for Nanmen site of Suzhou city, (**e**) FedDeep for Pukou site of Nanjing city, and (**f**) FedDeep for Nannen site of Suzhou city.  
Figure 5: Experimental results for different hyperparameter configurations. The vertical axis represents the values of MAE, RMSE, and \(R^{2}\).
## 4 On-Site Test Results and Analysis
### Correlation Analysis  
The deformation and stress release of the surrounding rock leads to a settlement in the upper area of the primary lining. According to the test data of the sinking rate of the vault settlement and the steel arch strain, the correlation characteristics between the vault settlement rate and the steel arch strain are explored in Figure 8.  
A rapid development of the vault settlement sinking rate and strain on the steel arch is observed within one week after the erection of the up-step steel arch because the shotcrete does not fully work yet. The surrounding rock pressure is mainly borne by the steel arch. When the shotcrete begins to work, the strength and stiffness of the primary lining are greatly increased, and its proportion for the surrounding rock pressure is also greatly increased. The strain growth rate of the steel arch slows down until stabilization. The supporting function of the steel arch and shotcrete effectively restrains the deformation of the surrounding rock. Thus, the vault settlement rate is greatly reduced. It is seen that (1) the value of the vault settlement rate in the left cavern shows that LM > RM. Additionally,  
Figure 8: Correlation analysis between the strain and the sinking rate of the primary lining. (**a**) Left cavern; (**b**) right cavern.  
the strain of the steel arch in the left cave shows that RS > LS. (2) The value of the vault settlement rate in the right cavern shows that RM > LM, and the strain of the steel arch in the right cavern shows that LS > RS.  
The reason for the above phenomenon can be concluded as follows. The values of the burial depth at the arch shoulders of each cavern are similar, although the burial depth for the left and right caverns is different (the left cavern is an open cavern where the burial depth of the arch shoulders is 0; the burial depth at the arch shoulders of the right cavern is about 6 m). Without considering any other influencing factors, the values of the surrounding rock pressure on both sides of the caverns are approximately equal. The settlement rate of the vault is related to the surrounding rock pressure. A larger value of the surrounding rock pressure will increase the deformation and settlement rate.  
The stress and strain of the steel arch are mainly caused by the surrounding rock pressure. An increasing strain at the steel arch indicates that more surrounding rock pressure is undertaken by the steel arch. The steel arch plays a supporting role and limits the displacement trend and the deformation rate of the surrounding rock.
## 2 Materials and Methods
### Methodology
#### 2.3.1 TSS Retrieval Model  
Based on different remote sensing data, combined with analysis, semi-analysis/semi-experienced and empirical methods, many experts and scholars [10; 14; 18; 22; 41; 42; 43] have calibrated a large number of remote sensing retrieval models for TSS in water bodies, and have obtained good retrieval accuracy with wide applications. In addition, there are many forms of TSS models, mainly including the single-band model, the band ratio model and the multi-band combination model. The band ratio model can effectively reduce the effect of particle size distribution and the bidirectional reflectance change of variable sediment [23]. The three-band model considers the interference of chlorophyll on the remote sensing reversion of the concentration of TSS [44]. Fully considering the optical complexity of the turbid case II waters and the interaction between the spectral characteristics of different water components (chlorophyll, suspended matter and colored dissolved organic matter) and minimizing the TSS concentration error in the remote sensing estimation, this paper mainly uses the band ratio and three-band TSS models in the forms  
\[\text{TSS}\propto\frac{R_{rs}(b_{2})}{R_{rs}(b_{1})} \tag{3}\]  
\[\text{TSS}\propto\left[R_{rs}(b_{1})^{-1}-R_{rs}(b_{2})^{-1}\right]\times R_{ rs}(b_{3}) \tag{4}\]  
where \(R_{rs}(b_{i})\) is the reflectance in spectral band \(b_{i}\).  
To find the best combination of band reflectances, a Pearson correlation analysis was used to investigate the strength of the association between the two variables (TSS concentration and spectral reflectance of the bands).
# MAToC: A Novel Match-Action Table Architecture on Corundum for 8 \(\times\) 25G Networking
## 3 Implementation
### Action Stage  
There are all kinds of functions that can be offloaded onto the NIC, e.g., checksum, PTP, and various protocol offloading; even data moving engines such as DMA and RDMA could be counted as offloads. Unlike FlowBlaze [15] and the other MAT solutions [11; 12; 14] whose execution units are ALU based implementations, here we use straightforward processing of packet headers to efficiently exploit the programmability of FPGA, and achieve a lower latency, as ALU based implementations require more cycles. [PERSON] et al. in [10] conclude the essential steps for the fast path in a router, which we adapt in our action stage, including decrement of time to live (TTL), rewriting of the destination and source  
Figure 2: The proposed TCAM update logic.  
mac address, and changing the output port according to a route table. The three steps are included in the proposed pipeline as the first, second and fourth actions. Furthermore, MAToC also offloads the virtual local area network (VLAN) extension of IEEE 802.1Q, operations including VLAN tag insertion, modification and removal. This is enabled by the capability of the variable length header merging of the deparser.  
As shown in Figure 3, the first action of the pipeline is TTL decrement. In the cases of IPv4 and IPv6, with and without VLAN, the position of TTL is separately handled according to the packet type information resolved from the parsers. The second action is the replacement of the destination and source MAC address, which is controlled by the operation code from the match stage. The third action is the operation of the VLAN tag. The fourth action is forwarding the packet to a corresponding channel, which might be one of the eight MAC ports or that to the slow path, i.e., the applications running on the processing system of SoC.  
Technically, the exact actions could be any packet processing offload engine in any order dedicated by users. As long as it does not block any incoming header frame while there is no backpressure signal asserting in the output, the throughput will not be affected.
### Literature Review
#### 1.2.1 Centralized vs. Distributed MPC Approaches  
MPC strategies, whether centralized or distributed, commonly focus on regulating the ACE, which manages both tie-line power flows and frequency deviations to uphold system stability. In centralized configurations, as discussed in [10], MPC functions in a supervisory capacity, setting optimal targets for local proportional-integral (PI) controllers. This supervisory role ensures that even during communication failures, local controllers can independently maintain ACE, providing a resilient control framework, as evidenced in studies like [11; 12]. This approach proves effective in scenarios where centralized control might be compromised by communication issues. However, centralized MPC implementation is often constrained by its high computational and communication demands, particularly in large-scale power systems [13]. To overcome these limitations, distributed model predictive control (DMPC) has been proposed, as detailed in works like [14; 10; 15; 16]. DMPC strategies decentralize the control process across multiple controllers, reducing the computational load and enhancing the scalability of the system. An in-depth review of various distributed MPC implementations for AGC can be found in [17]. A significant development within DMPC is the hierarchical MPC scheme introduced in [18], which extends control capabilities to include novel resources in smart grids. Despite this advancement, this approach overlooks the critical role of conventional turbine-governorsin frequency regulation, highlighting the need for more comprehensive control strategies that incorporate both traditional and modern resources.