esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hierarchical Semi-Separable Compression
### Basic Relations and Algorithmic Steps  
A system of linear algebraic equations in the form \(Ax=b\) is solved with HSS in three steps.  
1. _Hierarchical semi-separable compression_. The matrix \(A\) is divided in four blocks, and the off-diagonal blocks are approximated by a product of three matrices (also called generators) as follows: \[A\approx\begin{pmatrix}D_{1}&U_{1}B_{1,2}V_{2}\\ U_{2}B_{2,1}V1&D_{2}\end{pmatrix}\] For a suitable matrix \(A\) (with low off-diagonal blocks), \(U\) generators have few columns, \(B\) are small and square or rectangular, and \(V\) have few rows. The diagonal blocks \(D\) can also be compressed in a similar way and so on recursively until a threshold for the smallest block to be compressed is reached. The maximum rank \(r\) of the off-diagonal blocks occurring in the HSS compression of \(A\) is calculated. The computational efficiency of the compression can be roughly measured by comparing \(r\) to the size of the matrix \(N\). For suitable problems \(r\ll N\). In general, HSS compression is approximate, where random sampling is applied to calculate the generators. The user must supply a relative threshold \(\epsilon_{\text{rel}}\) and an absolute one \(\epsilon_{\text{abs}}\). The computational complexity of the HSS compression is \(O(rN^{2})\).
2. _ULV-like factorization_. A special form of LU factorization called ULV-like factorization can be applied to the thus compressed matrix \(H\). The compression implemented within STRUMPACK uses the structure of the generators, unlike the original ULV factorization, which uses orthogonal transformations [PERSON] (2010). The computational complexity of ULV-like factorization is \(O(r^{2}N)\).
3. _Solving a factorized matrix system_. A system of linear algebraic equations with matrix \(H\) can be solved by ULV-like factorization with computational complexity \(O(rN)\).  
The compressed form of the matrix allows faster performance of linear algebraic operations. Thus, multiplying a vector by \(H\) has a computational complexity of \(O(rN)\). Itshould also be noted that HSS compression is sensitive to the order of the unknowns. In this work, we use the recursive bisection algorithm to obtain a more suitable off-diagonal structure. This choice is based on results from [31; 32].
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 4 Modeling and Control
### Navigation Modes
#### 4.2.2 Obstacle Avoidance  
The programming of the autonomous obstacle avoidance controller was carried out following the fundamental idea of reactive behavior. This is described as an action-reaction system. An immediate response to a stimulus received from its environment [23]. This obstacle evasion function responds to the necessity of maneuvering in complicated environments. For this, the proportional control law detailed below has been used:  
\[\text{Command}=Offset+k\cdot Error \tag{18}\]  
\(Offset\) is a controller command parameter to send to the servos, \(k\) is the regulator gain determined by the Ziegler-Nichols method, and the \(Error\) is the difference between the distance set as safe and the obstacle to evade (Safe distance has been taken as 0.5 m). In relation to this, if we define the distance that the sensor measured towards the obstacle as \(D_{Obs}\), and the safety distance of the platform as \(D_{Saf}\), we can describe two possible scenarios with lateral obstacles in the following way:  
1. \(D_{Obs}>2D_{Saf}\). When the distance to the obstacle measured by the sensor is greater than twice the safety limit, there is no reaction from the system.  
2. \(2D_{Saf}>D_{Obs}>D_{Saf}\). Between the safety distance and its double, the response is proportional, namely, as the obstacle to evade approaches, the rudder updates its position in real time proportionally until the maximum rotation angle is reached. In this way, there is a constant regulation of the rotation angle of the platform with an optimal response for each situation.  
In case of a frontal obstacle, the system will retreat, turn, and move away until a safe distance is reached to resume its new direction.
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 3 Results and Discussion  
### Temporal Heatmap Analysis  
In this study, the spatio-temporal statistics of all the UA events triggered by Chinese civil aircraft in January 2018 were analyzed. A temporal heatmap is presented in Figure 1 to show the temporal distribution characterization of UA events. The results indicate that UA was highly likely to occur from the 8 th to the 10 th or in late January, and the occurrence time was mainly between 11 am to 5 pm. Observe that this period was just one week before Chinese New Year, known as \"Spring Festival travel season\", so the number of flights could be the highest of the year. Furthermore, the period (11 am to 5 pm) corresponds to the busiest time with the most flights landing during a day. The results indicate that UA events should be noted for busy airports, but fail to provide detailed information for better risk management. Thus, spatial analyses with long time series data are needed for future studies.
# Enhancements and Challenges in CoAP--A Survey
## 6 Open Challenges
### Congestion Control  
As CoAP is initially designed to cope with the congestion problem in IoT networks. However, as millions of devices join the IoT networks every year, it is therefore not possible to control the congestion in dense network scenarios. In this regard, other similar protocols exhibiting the properties of CoAP with some new feature is introduced [6]. Those protocols consist of CoCoA and CoCoA+. The new protocols somehow reduce the effect of congestion on the network. However, still, some challenges exist such as backward compatibility, real-time implementation, complex calculation of RTTs, etc. As the CoAP is designed for tiny devices such as sensors, motes, etc. therefore performing complex calculations periodically may drain the energy eventually. Therefore, it is necessary to design protocols considering the battery-powered nature of IoT devices.
# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
## 2 Material and Methods
### Gazzano Cave  
The Gazzano cave (125 Pi/CN--Regional cave cadastre) is located in the Maritime Alps in the municipality of Garesso, Northwestern Italy (44\({}^{\circ}\)11\({}^{\prime}\)52.18\({}^{\prime\prime}\) N, 8\({}^{\circ}\)0\({}^{\prime}\)40.74\({}^{\prime\prime}\) E) at 574 m above sea level elevation (Figure 2). The cave was accidentally discovered during the excavation of a quarry and explored in 1898. In the past, several peculiar features were looted and, nowadays, the marks of these actions are still visible. The Gazzano cave is an important natural site for the preservation of biological diversity because it is a bat (_Rhinolopus_ spp.) hibernation site and a natural refuge for European cave salamanders (_Speleomantes_ spp.).  
The cave, excavated in dolomitic limestone, is characterized by a unique corridor approximately 105 m long that ends in a chamber 37 m high with a waterfall from its top (Figure 2a); the water forms a small lake in the chamber then streams through the whole cave length until the exit (Figure 2b). The cave is accessible after a short walk. Initially, approximately in the first third of its length, the cave is characterized by a reduced height--minimum \(\sim\)1 m hampering the accessibility and operation with bulky instrumentation; then the height increases allowing one to walk almost comfortably until the terminal chamber.
# GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
## 6 Application to New Monitoring Data  
We tested the effectiveness of this new method with new monitoring data by establishing a real-time array data noise processing procedure. First, we retrieved the original waveform data of the MKAR array beamform and filtered them with the previously described beamforming parameters, and detected signals with STA/LTA on several beamformed passbands. Next, we selected the maximum SNR as the final detection, obtained the optimal azimuth and slowness intervals, and calculated the accurate azimuth and slowness by f-k analysis. For each detection, we truncated the data 5 s before and 10 s after the arrival time, re-beamformed them with the azimuth and slowness information estimated by f-k analysis, filtered them over the 0.8-3.5 Hz passband, and normalized them. We input the normalized beamformed data into the trained network model and tested them, with a probability threshold of 0.67, to determine whether the detected signals were seismic phases or noises. Then we applied these phases in event association to form events. Figure 12 shows the real-time monitoring data processing procedure.  
We tested the model with the half-year phase data of 2019, involving 5376 seismic phases and 6439 noise signals. Besides seismic phases used in training above, 27 types of phases, including PKP and PKPbc, were used. Figure 13 shows the top ten phase types by the number of phases. We processed the half-year data with the above method, and the result was an identification rate of 90.8% for seismic phases and 97.8% for noise signals. Figure 14 shows the identification results of some of the seismic phases.  
Figure 12: Real-time monitoring array data processing procedure.  
Figure 13: The distribution of top 10 phases of MKAR in 2019 half year events.  
In order to further test the performance of our method for the automatic detection signals, we compared our method with the traditional identification method in IDC on the part of the MKAR automatic detection dataset in 2019. There are 17,060 samples of automatic signal detection, and the identified results of the IDC automatic identification algorithm are 10,676 seismic phases and 6930 noise signals. There are 7674 seismic phases and 9836 noise signals identified by our method, 2456 more noise signals than IDC. Most of these 2456 noise signals are identified as seismic phases in the IDC method, and some of these noise signals are used to form automatic events in IDC. However, compared with the seismic phases associated with interactive analysis events (LEB), 9023 out of 9386 noise signals identified by our method are not used to form real seismic events, account for 96.13% of the total noise signals. It shows that our method is able to identify more noise signals, which are misidentified as seismic phases and used to form false events in IDC. It also shows that the use of this method for automatic seismic phase identification in automatic processing reduces the false events in the automatic bulletin.
## 5 Background Noise Elimination Method  
As mentioned in Section 4, the periodic impulse of the experimental signal caused by the fault is submerged by the background noise, and the corresponding fault feature frequencies are difficult to identify in the frequency domain. In this section, a novel parameter-adaptive variational mode decomposition method is proposed to eliminate the background noise, while avoiding the disadvantage of choosing decomposed parameters manually by human experience in VMD.
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 3 Experiment and Results
### Experimental Datasets  
#### 3.1.1 The Plain Fabric Dataset  
The plain fabric dataset has 1054 images and overall 1164 defects labeled in PASCAL VOC [27] format. The image size is \(128\times 256\) pixels. There are five categories of defects which are oil stains, coarse warp, long coarse weft, short coarse weft and mispick, respectively, as shown in Figure 5. It can be seen that most defects are tiny and the shape of the defects are diverse. Table 1 gives the number of each defect category. All defects except oil stains are tiny. Long coarse weft, coarse warp and mispick are the extreme aspect ratio defects, which made up 54.4% of the total defects.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Category**} & \multicolumn{1}{c}{**Oil Stains**} & \multicolumn{1}{c}{**Coarse Warp**} & \multicolumn{1}{c}{**Long Coarse**} & \multicolumn{1}{c}{**Short Coarse**} & \multicolumn{1}{c}{**Mispick**} & \multicolumn{1}{c}{**Total**} \\ \hline \multirow{3}{*}{**Number of defects**} & Train & 39 & 145 & 252 & 304 & 25 & 765 \\  & Validation & 9 & 36 & 62 & 76 & 6 & 189 \\  & Test & 16 & 32 & 64 & 87 & 11 & 210 \\  & Total & 64 & 213 & 378 & 467 & 42 & 1164 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of each category defect of the plain fabric dataset.  
Figure 4: The architecture of the classification network in the PRAN-Net.  
Figure 5: Samples of the defects in the plain fabric images. (**a**) oil stain, (**b**) long coarse weft, (**c**) long coarse warp, (**d**) long coarse weft, (**e**) short coarse weft, and (**f**) mispick.
# Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming
## 2 Materials and Methods
### Gaussian Distribution Model Conceptualization
#### 2.2.4 2D Visualization of the Gaussian Distribution Model  
For a better visual perception of the presented model, the Gaussian distribution curves that describe each of the three possible operating conditions of the fish farming were stacked sequentially according to their standard deviation and the image formed by the top view of these curves creates the visualization of the Gaussian distribution model proposed.
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 3 GCPV Systems with Adaptive Sizing Ratio  
As suggested in Equation (1), the sizing ratio value of a GCPV system depends on both the nominal power of the PVG and the rated power of the central inverter. Therefore, we can define up to three different kinds of GCPV systems with adaptive sizing ratio, depending on which of these powers will be modified dynamically. However, in practice, there are only two types of reconfigurable systems:  
Figure 3: \(R_{S\ OPT}\) value of a GCPV located in Brussels.  
* MIX systems: These systems can modify the value of the central inverter rated power connected to a PVG.
* Team systems: These systems can modify the nominal power of the PVG connected to a central inverter.  
The next subsections are devoted to a brief description of the operating principles of these two reconfigurable PV systems.
## 4 Results
### Simulated Results
#### 4.2.1 Virtual Resistance Effect on the Transference  
For this analysis, a transference is forced to 50 ms time. A main switch to segregate the mains from the microgrid with a turn-on/off delay of 40/120 ms has been assumed.  
In Figure 9, the effect of the \(R_{\wp}\) value on the voltage \(u_{w^{\prime\prime}n^{\prime\prime}}\) and the delivered current \(i_{L2}\) can be observed when a grid-connected to grid-disconnected transference takes place. In the initial situation, \(P_{u}\)* is set at 30 kW before t = 50 ms and a local load of 1.81 \(\Omega\) is connected at any time. The use of an initial \(R_{\wp}\) allows for smoothing the transference in terms of voltage and currents.  
Figure 8: Converter installed in the ATENEA experimental microgrid.  
Figure 9: \(R_{\wp}\) (0.2 \(\Omega\) initially) effect during grid-connected to grid-disconnected transference at \(t\) = 50 ms.  
In Figure 10, the effect of the \(R_{v}\) value on the voltage \(u_{u^{\prime\prime}n^{\prime\prime}}\) and the delivered current \(i_{L2}\) can be observed when a reconnection transference takes place. In this case, in the initial situation, a load of 1.81 \(\Omega\) is connected and maintained after the reconnection. No \(PQ\) references are considered in this case. As in the disconnection case, the use of an initial \(R_{v}\) smooths the transference in terms of voltage and currents. The use of high \(R_{v}\) values helps to extinguish the \(i_{L2}\) current faster but creates a voltage dip. However, this virtual resistance allows for making the system less sensitive due to interfacing with a virtual current limiter during the reconnection process. Then, a trade-off \(R_{v}\) value has to be used when the reconnection occurs. According to the mentioned reasons, an \(R_{v}\) equal to 1 \(\Omega\) is suggested as a proper compromise value, also used in the experimental validation in Section 4.3.1.
# Spectra Prediction for WLEDs with High TLCI
## 1 Introduction
However, the mathematical model proposed did not consider the problem of spectral shift caused by the increase in the driving current. [PERSON] et al. [24] also neglected this. Due to the fact that the emission spectrum of the phosphor varies, the deviation of the final predicted color coordinates is relatively high. In this paper, to obtain WLEDs with a high TLCI, a spectral prediction model for dual-phosphor WLEDs was proposed to predict the emission spectrum. The model has considered the reabsorption and re-emission of phosphors and the spectral shift with the driving current. To validate the model, WLEDs were packaged by mixing red and green phosphors. The spectra were measured and compared with the predicted results. The result demonstrates that the proposed model can predict the emission spectra of WLEDs with a high degree of accuracy. We packaged high-TLCI WLEDs with a target color temperature that was achieved by adjusting the ratio of the phosphor. This paper is expected to provide a method for quickly packing high-TLCI WLEDs.
# Effect of Subsequent Subgrade on Seismic Response of the High-Speed Railway Track-Bridge System
## 6 Transfer of Bridge Seismic Risk  
During seismic events, the bridge structure will have a more pronounced displacement response compared to the flexible subgrade structure [36]. The track structure, serving as a longitudinal connector between the bridge and subgrade structure, plays a role in restraining the bridge's displacement to a certain degree [37]. However, this restriction comes at a cost: the track structure in the transition section of the subgrade structure will generate large internal forces and deformations. In other words, a portion of the seismic force on the bridge structure is transferred to the subgrade structure through the track structure, with dissipation occurring at the subgrade-bridge transition section. Despite its importance, the confinement effect of the track is often overlooked in bridge design. This oversight results in underutilization of the potential to harmonize the differences in seismic responses between the beam-beam and beam-subgrade interactions. Therefore, the protective capabilities of track structures in safeguarding bridges have not been prominently recognized or discussed in past practical engineering applications.  
In this study, a risk-transferring connecting beam device (RTCBD) is used, which can enhance the longitudinal connection effect between beams [38; 39; 40]. This device not only enhances the bond between beams but also significantly boosts the track structure's capability in transmitting seismic forces. Consequently, this facilitates the transfer of seismic risk from the bridge structure to the subgrade structure, effectively redistributing the seismic load [41]. The detailed arrangement and construction of this risk-transferring connecting beam device are shown in Figure 15.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 5 Discussion
### Urban Planning and Clustering  
By employing machine learning methods such as k-means clustering, hierarchical clustering, GMM, and SOM, this study highlights the ability of clustering to reveal patterns and similarities among cities based on their smartness. These techniques help policymakers understand how different cities compare in terms of dimensions like smart living, smart mobility, and smart governance. The research emphasizes that cities in the same cluster share common strengths, offering insights into which areas need improvement and which cities can serve as models for others. This approach allows for more informed, data-driven urban planning.  
The future trends in urban planning will increasingly rely on advanced AI techniques, such as clustering, to make sense of the vast amounts of urban data generated by smart cities. These techniques enable city planners to prioritize interventions, allocate resources efficiently, and focus on improving areas that most significantly contribute to urban quality of life, such as healthcare, transportation, and environmental sustainability. The ability to cluster cities and assess their performance across different dimensions allows for more targeted and strategic urban policies that can be tailored to specific urban contexts.  
Visualization and user interfaces (UI) will play a crucial role in transforming these complex data analyses into actionable insights for decision-makers. Effective visualization tools can turn data from clustering models into intuitive dashboards, enabling urban planners and policymakers to quickly grasp the performance of their city across various metrics. This helps in identifying which policies are working and where adjustments are needed. For instance, cities that score highly in smart living but lag in smart mobility can be easily identified through well-designed visual dashboards, allowing for immediate strategic focus.  
Furthermore, the integration of these data insights into decision-making platforms enables real-time monitoring of urban performance. Future smart city strategies will benefit from the development of user-friendly interfaces that not only visualize these clusters and trends but also allow city administrators to simulate potential interventions and see their predicted outcomes. By visualizing data in accessible ways, city leaders can better engage with stakeholders, including citizens and businesses, to co-create solutions that enhance the overall urban environment.  
Clustering in urban planning offers a method to analyze and group cities, driving better-targeted interventions and policies. Future trends point to more sophisticated AI-driven analyses supported by strong visualization tools and intuitive user interfaces, which will become essential for informed decision-making and effective management of smart city initiatives. This approach will enable cities to become more adaptive, resilient, and responsive to the evolving needs of their populations.
# A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration
###### Abstract  
This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a fuzzy inference system (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility, and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules, and one output. The FIS is responsible for monitoring the restoration results, grading their validity, and choosing the one that yields to a better grade. These grades are produced autonomously by analyzing results of a Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 \(\upmu\)m wire probe were compared with the restorations. Main aims are therefore: (a) Provide unsupervised deblurring for device independent EB measurement; (b) improve the reliability of the process; and (c) apply deblurring without knowing the probe size. These further facilitate the deployment and manufacturing of EB probes as well as facilitate accurate and probe-independent EB characterization. This paper's findings also makes restoration of previously collected EB measurements easier where the probe sizes are not known nor recorded.  
fuzzy inference system; fuzzy logics; linear motion blur; fuzzy deblurring; electron beam calibration; signal and image processing +
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