esa-sceva/satcom-chunk-collection huggingface dataset:
# A Conversation History-Based Q&A Cache Mechanism for Multi-Layered Chatbot Services
## 1 Introduction  
Due to development in Internet technologies, online learning has grown from Internet-propelled distance education to online higher education enrollments. Online education has such benefits as saving money and time, choosing an area of interest and a course of study individually [1]. One of the ways of learning online is using video-based online courses (MOOC, Coursera, edX, and others) [2; 3; 4]. The demand for online lectures is increasing, and accordingly, the combination of MOOC and chatbot is becoming more useful [5]. It helps overcome one of the limitations and weaknesses of MOOC, which is taking a long time to receive feedback [6]. One of the examples of the combination of MOOC and chatbot systems is a video tutoring assistant for online video tutoring [7]. The video tutoring assistant for online video tutoring is a chatbot service that plays the role of an assistant and provides real-time feedback to user questions. Thus, the main goal of this chatbot is to answer specific user questions to shorten the time it takes to receive feedback from a lecturer.  
In this paper, a multi-layered multi-chatbot system architecture is proposed for the video tutoring assistant to improve the coverage of Q&A that the system deals with. It uses multiple chatbots for each video instead of using a single chatbot. This strategy, however, may decrease the speed of the system, and a cache mechanism is necessarily required consequently to overcome such decline of the speed. The concept of layers comes from edge computing in cloud technologies. This technology helps to ease the system and its process. The cache mechanism is one of the ways to use edge computing.  
The cache in CPU utilizes the locality of references within binary code executions [8]; web cache policy is created based on the location of the documents [9]. The cache mechanism proposed in this paper uses the frequency and relevance information which potentially exists within the set of Q&A pairs.  
The remainder of this paper is organized as follows. Section 2 introduces the problem definition and previous approaches to solve this problem. In Section 3, we introduce the architecture and algorithm of the caching mechanism proposed in this paper. In Section 4, we discuss experiments and the experimental results. Section 5 concludes the paper with a summary of our main findings and contributions.
## 2 Materials and Methods
### Global Path Smoothing Optimization  
#### 2.2.1 Problems with the Global Path of the Traditional A* Algorithm  
The traditional A* algorithm grids the global environment and explores the surrounding grids, starting to traverse from the grid where the target is located. As shown in Figure 3, in the traditional algorithm, there are mainly four or eight directions for traversal search. However, the complexity of the actual global environment is much higher; therefore, even if the eight-direction search algorithm is used, the calculated global path is only composed of continuous line segments in eight directions, making it difficult to satisfy the requirements of the actual environment. When other angular directions appear, the traditional A* algorithm combines them according to the existing directions, thereby causing additional turning points to appear in the generated paths. This increases the complexity of the global path and affects the stability and accuracy of the navigation phase of the USV.  
Figure 3: Traversal search of traditional A* algorithm.  
Figure 2: Traditional A* and dynamic window fusion algorithm.
## 4 Conclusions and Future Works  
This study shows it is possible to combine the proposed method to extract and select two important features and unsupervised machine learning algorithms to cluster squats of different levels. In detail, the following conclusions are drawn:  
* The presented signal processing method is effective and promising to extract useful information from the vibration signal.
* It is possible to only utilise features from SAWP from the vibration signal to identify different degrees of squat defects of the S&Cs.
* The number of peaks and the total power are the two most important features that can be utilised to estimate the squat levels.
* Both k-means and agglomerative hierarchical clustering provide similar good results.
* The DBSCAN clustering encounters some challenges and clusters the 4 mm depth case as anomalies; therefore, it is not suitable for using this algorithm to process such a data set.  
The conclusions above show it is promising to utilise the features extracted from SWAP time series to determine the level of squats with unsupervised learning algorithms such as k-means or agglomerative hierarchical clustering. The presented method of measuring the vibration signals at the point machine could also possibly be used for detecting other types of defects in the S&C such as wear, point machinery defects and vehicle wheel defects such as wheel flats. There are some limitations of this study. First, the experiments were performed under a low-speed condition (<2 m/s) with a constant load. Second, the data set size is small. Future studies will address these two limitations.  
It is possible to perform an extra parametric study similar to what [PERSON] et al. [36] did and study the influence of different speeds and loads on the collected vibration signals. Combining the approach presented in this study and the emerging technique of federated learning could potentially construct a nationwide condition-monitoring system for S&Cs. Another possible future study is to collect more data from a real railway system to further verify the method presented in this study. One last future study could be to study the influence of parameters such as different train types, variant load and different operating speeds on the vibration signals and further improve the current algorithm to include these factors.  
Figure 11: Agglomerative hierarchical clusters.  
**Author Contributions:** Conceptualization, Y.Z. and J.L.; methodology, Y.Z., M.R. and P.C.; software, Y.Z. and formal analysis, Y.Z.; data acquisition, J.L. writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., M.R., J.L. and P.C.; visualization, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Great thanks to [PERSON] for help designing and building of the test rig and collecting all data.  
**Conflicts of Interest:** The authors declare that there are no conflict of interest.
## 2 Materials and Methods
The aqueous-phase degradation of MG and MB gauged the photocatalytic activity of powder films at an initial dye concentration of 10 ppm. A 200 mL photoreceptor, equipped with UV (36 W, 315-400 nm emission range) and visible (150 W LED, V-Tac, emitting 5700 K white, 420-700 nm) lamps, along with a magnetic stirrer (500 rpm) was used for the photocatalytic tests. Experiments were conducted at 23 \(\pm\) 2 \({}^{\circ}\)C. The concentration of MG and MB, as a function of irradiation time, was estimated from absorbance measurements at 615 and 666 nm, respectively, on an UV-vis spectrophotometer (Evolution 300 Thermo Scientific (Madison, WI, USA) with a calibration curve. The data were converted into dye removal rate constants via the widely used ln(C/C\({}_{0}\)) pseudo-first-order rate dependence. Residual silver, released by Ag(I) leeching from the catalysts, was measured via a Flame Atomic Absorption Spectrometer (FAAS) on Perkin and Elmer AAnalyst spectrometer, equipped with a hollow cathode Ag lamp and air/acetylene. Calibration was performed at three concentrations of water standard solutions: 0.1, 0.5, and 1.0 mg/L, at an RSD of 2-4%. Sample solutions, taken from the dye aqueous phase during experiments, were concentrated 5-fold according to a previously developed procedure [28]. Figure 1: Schematic process for the preparation of TiO\({}_{2}\) and Ag/TiO\({}_{2}\) films. The total organic carbon in the treated dye solutions was measured using an Elementar Vario Select TOC analyzer (Langenselbold, Germany), based on catalytic oxidation. All measurements were performed in triplicates.
## 2 Literature Review
### InSAR Monitoring Technology
[21] used PS-InSAR technology to monitor the Beijing Capital Airport and found that the Shunyi fracture developed by the Shunyi-Liangxiang fracture structure is the main cause of the development of differential settlement at the airport. [PERSON] et al. [16] analyzed the surface deformation related to tunneling using the PS-InSAR and Sentinel-1B SAR data, and compared the PS-InSAR results with those from the conventional leveling method. [PERSON] et al. [41] proposed a cross-comparison method using SBAS-InSAR and PS-InSAR technology that refined the deformation features and verified the reliability of the InSAR results, which helps in the comprehensive identification and further mapping of landslides. Real-time and accurate structural deformation monitoring plays a vital role in preventing traffic accidents, which makes PS-InSAR technology more and more widely used in linear transportation infrastructure. [PERSON] et al. [42] calculated the vertical surface displacements from 2010 to 2016 according to the TerraSAR-X images and discussed the relationship between groundwater level, temperature, and age with the seasonal deformation of the overpass. The conclusions show that the seasonal vertical deformation in the study area is influenced by the interaction of groundwater level, temperature, compressible layers and the age of the overpass. The seasonal deformation trends of the overpasses were consistent with the temperature trends. The authors also found that the thicker compressible soil thickness provided a conducive geologic environment.
## 5 Conclusions  
We proposed and evaluated an algebraic identification approach for the on-line estimation of the natural frequencies and damping ratios for an experimental case study of a particular wind turbine blade. We computed the natural frequencies and damping ratios based on the coefficients of the characteristic polynomial.  
We used only measurements of a single velocity output or test location for the construction of the on-line algebraic estimator. It is important to mention that the approach presented can be easily extended for situations where acceleration measurements are preferred, since the characteristic polynomial is the same for both cases. The algebraic modal parameter identification method was evaluated on a wind turbine blade in a wind tunnel facility, excited by a turbulent wind flow that produces a change of its initial position and condition. The non-linearity indicator tested is easy to program and test for having a numeric and graphic indicator of the effects of the non-linearities present in the system due to its inner nature, e.g., construction based on composed materials, geometric non-linearities, etc.  
On the other hand, it is necessary to carry out a precise analysis of the numerical methods applied to the original data to have a more rational criterion for the final determination of the presence of non-linearities; that is, care must be taken when defining an acceptable value of \(\eta\) to consider the system as non-linear. Here, we have assumed a value of \(\eta\leq 0.9\) to establish that a given system is dominantly non-linear.  
Figure 17: Spectrogram of the velocity measurements.  
**Author Contributions:** Conceptualization, L.G.T.-F. and H.F.A.-F.; methodology, L.G.T.-F., H.F.A.-F. and R.C.-A.; experimental validation, L.G.T.-F., R.C.-A. and R.G.-M.; writing--original draft, L.G.T.-F. and H.F.A.-F.; writing--review and editing, R.G.-M., A.I.M.-P. and A.C.-A.; visualization, A.I.M.-P. and A.C.-A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 5 Conclusion and Future Work
In particular, differences between environments regarding size and complexity (i.e., containing static objects) and the corresponding impact on classification accuracies will need to be thoroughly examined. Additionally, we will also need to look into the accuracy of the system if a target is not present in one of the exact fingerprinting locations. The relationship between this accuracy and the number of fingerprinting locations for different types of environments will be an important topic of research. Methods will need to be developed to take all of these aspects on both localization and identification into account. Finally, we would like to note that RF-based device-free localization has become an increasingly popular topic of research [4] with a variety of potential applications [6; 24; 25]. In particular, the rise of the Internet-of-Things has led to significant interest in the use of \"signals of opportunity\" from already existing network infrastructure for DFL applications [26]. These types of systems would be very rapidly deployable, but it would not be possible to select the specific RF signals and node locations yourself. Therefore, in order to aid the further development of this field, there will be a need for public datasets from a variety of different environments, experimental setups and signals. The lack of public datasets is a major problem within the research field of RF-based DFL in general and we have previously discussed this in our earlier work [4]. Additionally, in the context of large-scale device-free crowd estimation, we have published one such dataset ourselves [27]. To lead by example, the data which was collected during the experiments described in this paper can be found through the link provided in the header of this paper. This dataset is accompanied by a small example in MATLAB code. Conceptualization, S.D. ; Data curation, S.D. and A.K. ; Formal analysis, S.D. ; Investigation, S.D. ; Methodology, A.K. ; Software, S.D. and A.K. ; Supervision, R.B. and M.W. ; Writing--original draft, S.D. ; Writing--review & editing, R.B. and M.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 2 Materials and Methods
### Ground Target Geolocation Error Analysis and Systematic Error Correction
#### 2.4.2 Systematic Error Correction Using Control Points  
Considering the installation error of the integrated navigation system and the installation error of the imaging coordinate frame, the conversion matrix \(\left(M_{p}^{b}\right)^{\prime}\) and \(\left(M_{b}^{v}\right)^{\prime}\) are used instead of \(M_{p}^{b}\) and \(M_{b}^{v}\), so Equation (6) can be expressed as follows:  
\[\overset{\rightarrow}{T}_{g}-\overset{\rightarrow}{P}_{g}=M_{g}^{p}\left(M_{ p}^{b}\right)^{\prime}\cdot\left(M_{p}^{v}\right)^{\prime}\left[\begin{array}{c}0 \\ 0\\ Rng\end{array}\right] \tag{13}\]  
Figure 6: Error items that affect the accuracy of target positioning.
## 4 Comparison with Event of 10 to 11 October 2024  
### Solar Wind and Geomagnetic Activity Indices  
The second most important storm (so far) of the present solar cycle 25 started on 10 October 2024 and reached a minimum of Dst = \(-\)335 nT on 11 October 2024. It was preceded by another storm with a minimum of Dst = \(-\)153 nT on 8 October, as illustrated  
Figure 9: **(Top panel)**: midnight plasmapause proxy derived from the magnetic and plasma observations of the low-Earth orbiting Swarm satellites from 9 to 12 May 2024. (**Bottom panel**): observed Bartels geomagnetic activity Kp index. ([[https://swe.ssa.esa.int/elte-plasma-federated](https://swe.ssa.esa.int/elte-plasma-federated)]([https://swe.ssa.esa.int/elte-plasma-federated](https://swe.ssa.esa.int/elte-plasma-federated)), accessed on 5 November 2024).  
in Figure 10 (bottom panel) together with the Kp index (5 th panel) and the solar wind characteristics that generated these storms: density (1 st panel), bulk speed (2 nd panel), and temperature (3 rd panel). The 8 October storm, associated with an X9 flare on 3 October, is mainly due to the arrival of high-density but low-energy particles, while the 11 October storm (associated with an X1.8 flare on 9 October and an X2.1 flare on 7 October) is due to a density peak of energetic particles, as can be seen in the temperature and bulk speed observations. The velocity of the solar wind particles exceeded 700 km/s for this last event. The negative Bz of the interplanetary magnetic field (see 4 th panel) explains the high geomagnetic perturbations induced on the terrestrial magnetosphere.  
In Figure 10, the arrival of the energetic solar wind particles initiating the strong geomagnetic storm is well visible in the evening of 10 October 2024 (Dst = \(-\)335 nT, Kp = 9\(-\)), preceded by a smaller event (Dst = \(-\)153 nT, Kp = 7) starting on 7 October due to a peak of solar wind density. Both give effects visible in the ionosphere.
# Detection of Pest Feeding Traces on Industrial Wood Surfaces with 3D Imaging
## 2 Materials and Methods
The imaging resolution in the X- and Y-axes can be adjusted to reduce the number of points to be analysed or reduce the imaging time. However, reducing the number of points had a negative effect; the image of wormholes was represented by a smaller number of measuring points, which reduced the visibility of such defects on the surface. Four different values of the resolution \(\Delta\)X = \(\Delta\)Y = (0.125; 0.20; 0.25; 0.5) were investigated in the preliminary research. Figure 3a shows the laser line projected onto the wood surface. The laser line scatters and refracts in the wormholes. It is visible in the form of its discontinuities (Figure 3a). The imaging results showed that the images acquired with a resolution of \(\Delta\)X = \(\Delta\)Y = 0.125 gave a good representation of the holes and the micro-roughness on the surface of the wood (Figure 3b). An area of 1 mm\({}^{2}\) is described by 64 pixels in this image. The image recorded with a resolution of \(\Delta\)X = \(\Delta\)Y = 0.2 mm uses 25 points to describe an area of 1 mm\({}^{2}\) and sufficiently represents defects in the form of wormholes visible on the wood (Figure 3c). The visibility of these defects already decreases at a resolution of 0.25 mm, where an area of 1 mm\({}^{2}\) is described by 16 points (Figure 3d). The visibility of the defects decreased significantly in images acquired at a resolution of \(\Delta\)X = \(\Delta\)Y = 0.5 mm, where an area of 1 mm\({}^{2}\) is described by four measurement points (Figure 3e). In this image, red circles mark wormholes on the wood surface that are not visible in the 3D image.
## 3 Results
### Comparison of Prediction Accuracy for All Variable Selection Approaches
#### 3.3.1 Comparison of Classical Variable Selection Methods  
Figure 14 compares the predictive performance of BSS, BE, and FS across different sample sizes and SNRs in both low- (upper panel) and high (lower panel)-correlation settings. In low-correlation settings, all three methods produced similar prediction accuracy. In high-correlation settings, BSS and BE produced nearly identical results, with only minor differences at large sample sizes and high SNR (bottom-right panel), while FS showed slightly better performance than both BSS and BE. Therefore, we compare the results of BE and FS with those of the penalized methods.
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 2 Antenna Theory: Classical and Quantum Radiation Scenarios
On the other hand, a traditional classical antenna is essentially a \"one-point physical process\" in which a generic source point \(x^{\prime}=(x^{\prime},t^{\prime})\) radiates into another arbitrary observation point \(x=({\bf x},t)\), where the total radiation is just the linear superposition of all contributions emanating from all relevant individual non-interacting spacetime points [45]. The most obvious and rigorous way to understand the classical process of radiation and reception of electromagnetic signals is through the two fundamental Green's functions of the antenna system, the source or current Green's function \(\overline{\bf F}({\bf x},{\bf x}^{\prime},t-t^{\prime})\)[28, 46, 47, 48], and the radiation Green's function \(\overline{\bf G}({\bf x}-{\bf x}^{\prime},t-t^{\prime})\)[49, 50, 51]. These two are essentially defined via their crucial superposition-like integral summation rules (A1) and (A2); see Appendix A, where the [PERSON]'s function approach to classical antenna theory is briefly reviewed and additional references are given. However, when we next move toward examining the emerging theory of quantum antenna systems, the natural question is whether this generalized structure of classical antennas persists. It turns out that radiation formulas similar to the [PERSON]'s function-based expressions (A1)-(A3), Appendix A, can _not_ in general be maintained in the case of _quantum_ radiation. However, in this paper, we focus on ways to retain as much classical structure as possible in the new quantum antenna theory. This turns out to be possible for the case of _linear_ quantum antenna systems, which will be investigated in details below. To summarize, we find that the following general situation holds in antenna theory:  
1. Classical electromagnetic radiation in free space or linear materials, when viewed from the perspective of its ultimate source (external field), is inherently _linear_.2
2. On the other hand, quantum antennas involving higher-order processes (many-particle interactions, \(n\)-point processes with \(n>1\), etc.) are intrinsically _nonlinear_ radiation problems (due to the many-body nature of interactions in quantum field theory). To be more precise, it can be shown that only _first_-order quantum radiation processes may lead to linear operator radiation relations similar to (A1)-(A3). This is the process that corresponds to what we call _linear quantum antennas_, which will be studied in more details in this paper, while a more complete theory of relativistic nonlinear quantum antennas is relegated to future publications. All other higher-order processes, which are ultimately due to the many-body quantum interactions between source points, can be shown to lead to _nonlinear_ contributions to the total probability amplitude of the quantum radiation emitted by the source system.
## 5 Current Treatments to Avoid or Reduce PPO Activity in Fresh-Cut Produce
### Antioxidants and Antimicrobials
#### 5.4.5 Whey Permeate  
In order to avoid chlorinated water (and other chemical treatments) due to the formation of carcinogenic compounds or other health concerns, many alternatives (especially natural products) have been studied to reduce microbial counts or improve shelf-life in fresh-cut produce. That is the case of whey permeate, which is a by-product of the dairy industry. In that sense, the use of wastes from other industries could contribute to the concept of circular economy, which is highly rated nowadays.This washing treatment has been proved to be effective when it comes to microbial growth. However, not many studies about its application in minimal processing or its effect on PPO activity have been carried out. Nevertheless, whey permeate was a suitable treatment for fresh-cut lettuce and carrot. Namely, its PPO activity inhibition was outstanding, being more effective when concentration was increased up to 3%. Moreover, no sensory differences were found for treated samples, which is essential for a treatment to be applied in minimal processing [20]. The optimum use of whey protein concentrate in order to inhibit PPO activity was proved to be competitive (28 mg/L) in fresh-cut lettuce (60% inhibition) even though its effect was not as effective as in the case of cysteine [109].