esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Deep Learning for Data Caching in Edge Network
## 4 Deep Learning for Data Caching
### Auto Encoder  
Generally, the auto encoder is utilized to learn efficient representation or extract features of raw data in an unsupervised manner. The work in Reference [73] considers the cache replacement in wireless sensor network (WSN) based on content popularity. Considering sparse auto encoder (SAE) can extract representative expression of input data, the authors employ an SAE followed by a classifier where the input contains collecting user content requests and the output represents the contents popularity level. The authors also think about the implementation in a distributed way by SDN/NFV technical, i.e., the input layer is deployed on the sink node, while the rest layers are implemented on the main controller. A related work applying auto encoder in 5G network proactive caching can be found in Reference [74]. In Reference [75], two auto encoders are utilized for extracting the features of users and content, respectively. Then, the extracted information is explored to estimate popularity at the core network. Similarly, the auto encoder in Reference [76] is for spatio-temporal popularity features extraction and auto encoders work collaboratively in Reference [77] to predict top K popular videos.
# Radiometric Identification of Signals by Matched Whitening Transform
## 4 Results
### Radiometric Identification  
We now apply the proposed radiometric identification method to the signals generated by the following waveform generators or standards: Agilent [54], Viast EBEM [55], Teledyne Paradise [56], KRATOS Real Time Channel Simulator (RTsim) [57], and USRP [58]. The data have QPSK modulation sampled at 2.95 MHz for a total of 35 million symbols per model. Figure 8a,b show signal constellations that are affected by varying amount of smearing. Figure 8b is a particularly sever case due to the large \(f_{d}T\) product causing symbols to rotate potentially multiples of \(2\pi\). Following the estimation of \(f_{d}T\) and derotation of symbols, the original constellation is restored in Figure 8c. Figure 9 is a close up of six constellations after all phase and frequency offsets have been removed. The task now is to attribute the signals to individual sources. Given the similarity of the constellations in  
Figure 6: The offset frequency \(f_{d}\) is the slope of the phase trajectory \(\theta=2\pi f_{d}t\). The least squares fit to the estimated phase values is used to estimate the slope. \(\hat{f}_{d}=0.0505\) with \(f_{d}=0.05\). This approach works for nonlinear phase trajectory as well. (**a**) SNR = 20 dB, (**b**) SNR = 10 dB, (**c**) Quadratic fit to time varying phase angle.  
Figure 7: Phase offset histogram is unimodal for small \(f_{d}T\), (**a**). Longer data blocks cause phase smearing illustrated by a broad phase angle histogram, (**b**). (**a**) Small phase offset, \(6^{\circ}\), (**b**) Large phase offset, \(26^{\circ}\).  
structure and features, it is clear that radiometric identification is a much more challenging problem than conventional signal classification based on modulation information.
## 3 Results
### Model Validation  
The calibrated model was used to predict GPP (GPP\({}_{\text{predicted}}\)). The results were compared with an independent validation data set corresponding to 20% of the original GPP sample (GPP\({}_{\text{observed}}\)). The model resulted in an MAE of 0.52 gC m\({}^{-2}\) d\({}^{-1}\), RMSE of 0.63 gC m\({}^{-2}\) d\({}^{-1}\) and Sest equal to 0.27. The latest indicated that the model predictions had a standard deviation of 27% with respect to the GPP observations.  
The scatterplots of observed versus estimated values of GPP for the validation phase (Figure 7) show that the slope of the predicted-observed relationship did not differ significantly from 1 (0.97), indicating that the model predictions neither under nor overestimated the observed values of GPP.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 3 Study on Synchronization Noise Immunity
### Results Analysis  
This subsection summarizes the results of the analog-discrete and discrete-analog synchronization noise immunity study; the results are grouped by the oscillators. The results using the approximated curves for the RC chaos oscillator are presented in Figure 16 and the results for the Vilnius chaos oscillator are presented in Figure 17.  
Figures 16 and 17 demonstrate the change of correlation coefficient \(\beta\) over the noise levels. As expected, the correlation level is mostly 0.8 to 0.95 for 30 dB SNR, and the correlation level decreases with the decrease in SNR (i.e., increase of the noise level) down to 0.1 to 0.2 for 0 dB SNR. Analyzing the results compiled in the two figures, the match  
Figure 16: Analog-discrete (**a**) and discrete-analog (**b**) synchronization noise immunity of RC chaos oscillator.  
Figure 17: Analog-discrete (**a**) and discrete-analog (**b**) synchronization noise immunity of Vilnius chaos oscillator.  
between the LTspice and hardware results is exceptionally close, with the differences explained by the additional noises and mismatches introduced in hardware. The overall close match between the LTspice and hardware means that the LTspice is precise in terms of synchronization performance in hardware. The difference between the \(x\) and \(z\) in terms of noise immunity was expected due to the difference in waveforms of the signals.  
The RC chaos oscillator results in Figure 16a,b show little difference in the behavior of \(x\) and \(z\) state variables and close match between the analog-discrete and discrete-analog chaotic synchronization performance. The Vilnius results in Figure 17a,b shows that the \(z\) state variable has lower correlation than \(x\) for the given noise levels. This means that this state variable is more sensitive to noise due to the signal's waveform. Comparing the results of the two chaos oscillators, the RC chaos oscillator demonstrates better synchronization for the two state variables.
###### Abstract  
Restricting the use of chemical pesticides in forestry requires the search for alternative solutions. These could be volatile organic compounds produced by three investigated species of bacteria (_Bacillus amyloliquefaciens_ (ex Fukumoto) [PERSON], _B. subtilis_ ([PERSON]) [PERSON] and _B. thriingiensis_ [PERSON]), which inhibit the growth of the pathogen _F. oxysporum_ Schltdl. emend. Snyder & Hansen in forest nurseries. The highest inhibition of fungal growth (70%) was observed with _B. amyloliquefaciens_ after 24 h of antagonism test, which had a higher content of carbonyl compounds (46.83 \(\pm\) 8.41%) than _B. subtilis_ (41.50 \(\pm\) 6.45%) or _B. thriingiensis_ (34.62 \(\pm\) 4.77%). Only in the volatile emissions of _B. amyloliquefaciens_ were 3-hydroxybutan-2-one, undecan-2-one, dodecan-5-one and tetradecan-5-one found. In contrast, the main components of the volatile emissions of _F. oxysporum_ were chlorinated derivatives of benzaldehyde (e.g., 3,5-dichloro-4-methoxybenzaldehyde) and chlorinated derivatives of benzene (e.g., 1,4-dichloro-2,5-dimethoxybenzene), as well as carbonyl compounds (e.g., benzaldehyde) and alcohols (e.g., benzyl alcohol). Further compounds were found in the interactions between _B. amyloliquefaciens_ and _F. oxysporum_ (e.g., _a_-cubehene,linalo, undecan-2-ol, decan-2-one and 2,6-dichloroanisole). Specific substances were found for _B. amyloliquefaciens_ (limonene, nonan-2-ol, phenethyl alcohol, heptan-2-one and tridecan-2-one) and for _F. oxysporum_ (propan-1-ol, propan-2-ol, heptan-2-one and tridecan-2-one). The amounts of volatile chemical compounds found in _B. amyloliquefaciens_ or in the bacterium-fungus interaction can be used for further research to limit the pathogenic fungus. In the future, one should focus on the compounds that were found exclusively in interactions and whose content was higher than in isolated bacteria. In order to conquer an ecological niche, bacteria increase the production of secondary metabolites, including specific chemical compounds. The results presented are a prerequisite for creating an alternative solution or supplementing the currently used methods of plant protection against _F. oxysporum_. Understanding and applying the volatile organic compounds produced by bacteria can complement chemical plant protection against the pathogen, especially in greenhouses or tunnels where plants grow in conditions that favour fungal growth.  
keywords: secondary metabolites; antagonism tests; _B. amyloliqueficiens; B. subtilis; B. thuringiensis; damping off; chemical compounds_
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Features
#### 4.2.6 Activity Based  
Activity-based techniques focus on specific activities present in the video, such as lunch at the office, sunset at the beach, drinks after work, friends playing tennis, bowling, cooking, etc. [PERSON] et al. [66] proposed a summarization system that can be used with any kind of video in any domain. From each keyframe, 25 semantic concepts are detected for categories of visual scenes that cover people, areas, settings, objects, events, and activities that are sufficient to represent a keyframe in a semantic space. From each video, relevance scores are assigned for a set of semantic concepts. The score shows the relevance between a particular semantic concept and a shot. [PERSON] [20] proposed a system that accepts from the user an image, text, or video query, then retrieves from the memories small subshots stored in the database, and produces the summary according to the user's activity preferences. The following studies [17; 19; 58; 67] are also based on activities.  
The distribution of the papers reported in Table 1 is shown in Figure 6 according to the type of features of the personalized summary. According to the distribution, most tasks perform a personalized object-based summarization at a percentage of around 39%. With a difference of 10% and a percentage of around 29%, those tasks follow in which the personalized video summary is event-based. Next, we have the tasks whose personalized summary is an area based on a percentage of around 10%. In fourth place are personalized perception-based and activity-based summaries with the same number of tasks and with a percentage of around 9%. The smallest number of works is the one with a percentage of around 4%, whose abstract is based on motion and color.
However, the authors [38] noted that the use of these techniques can result in the appearance of false contours in the compressed images, which in turn significantly impairs their quality. This is particularly evident in images where the quantization accuracy has been reduced. Additionally, quantization methods can often discard colors corresponding to low frequencies and select inappropriate colors when the image contains many colors with similar frequencies [39]. These drawbacks limit the direct application of non-transform methods [40]. Furthermore, reducing the number of bits used in encoding brightness levels can cause critical distortion at high compression ratios, making it impossible to restore the original image. An alternative approach to reduce the dynamic range of signals is to determine a signal's deviation function from a specific basis function. Coordinated group signal transformation (CGST) is a non-transform-based approach that reduces the average power of source signals through signal prediction. It has already been proven to be effective for processing low-speed data [41; 42]. Unlike existing difference coding methods that predict separate signals [43; 44], CGST predicts multiple correlated signals together, simplifying the coding scheme in a multi-channel system, such as the codec structure. The hypothesis behind CGST for image compression is that an image's color channels can be considered a correlated data source since they describe a single object. In this study, we evaluate the performance of the CGST codec in image compression using the Earth remote sensing color images dataset as an example. Using CGST combined with transform-based methods significantly reduces a transmitted signal's dynamic range, leading to lower energy consumption at the transmitter and extra image compression. To ensure the maximum image quality of the reconstructed image, we apply additional image processing techniques, including a modified dynamic range reduction algorithm to prevent unwanted brightness \"zeroing\" and postprocessing using a neural network to combat image blurring. This paper is organized as follows. In the second section, the basics of CGST are explained, and a block diagram of the CGST-based image compression algorithm is pro vided. The third section discusses the results of the proposed algorithm simulation for PNG images, including an analysis of resulting distortions and ways to reduce them. This section also presents the results of the joint use of our proposed algorithm and the JPEG method. Finally, in the \"Discussion\" section, the developed algorithm's key features and development prospects are examined.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 4 Fault Prediction Results and Discussion
### Fault Prediction Results and Discussion of Vibration Signals
#### 4.1.2 The Results of LSSVM Optimized by QGA  
The LSSVM optimized by QGA (QGA_LSSVM) is also adopted to predict the kurtosis of misalignment fault. The same first 45 samples are used to train the LSSVM model. The other 15 samples are used as the testing set. The results are compared with the LSSVM optimized by GA (GA_LSSVM) and the LSSVM optimized by Grid Search (Grid Search_LSSVM). The parameters \([\gamma,\sigma^{2}]\) obtained by the three prediction models are listed in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Date Set** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **Time(s)** \\ \hline \multirow{2}{*}{MGM (1,9)} & Fitted values & 0.1946 & 0.1313 & 0.9071 & \multirow{2}{*}{1.8776} \\  & **Predicted values** & **0.5047** & **0.4086** & **0.3392** & \\ \hline \multirow{2}{*}{IMGM (1,9)} & Fitted values & 0.1959 & 0.1232 & 0.9053 & \multirow{2}{*}{1.6780} \\  & **Predicted values** & **0.4349** & **0.3358** & **0.5673** & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of forecasting evaluation indexes of MGM (1,9) and IMGM (1,9) for the vibration signal.  
Figure 4: The result of MGM (1,9) and IMGM (1,9) for vibration signal. (**a**) The result of MGM (1,9); (**b**) the result of IMGM (1,9).  
The three forecasting models are established based on the optimized parameters in Table 7. The corresponding forecasting evaluation indexes are listed in Table 8.  
In Table 8, for the training set, the indexes of Grid Search_LSSVM show that it has smaller prediction errors of training set than GA_LSSVM and QGA_LSSVM. For the testing set, the RMSE and MAE of QGA_LSSVM are smaller than those of GA_LSSVM and Grid Search_LSSVM. The \(R^{2}\) of QGA_LSSVM is the largest and its running time is the shortest. Therefore, compared with the results of Grid Search_LSSVM and GA_LSSVM, QGA_LSSVM has the best prediction accuracy and the shortest running time, so it can be one of the single forecasting models input to the combined forecasting model for vibration signal.
# Analyzing Momentum Shifts in Tennis: A Machine-Learning Approach to Predicting Match Outcomes
## 2 Materials and Models
### Momentum Concept  
In the domain of competitive sports, particularly in strategic games like tennis, match outcomes are influenced by a complex interplay of variables. Among these, \"momentum\" emerges as a critical yet abstract factor [14; 15]. It refers to a player's accumulating advantage, both in scoring and psychological dominance, through consecutive successful plays. This phenomenon can enhance confidence, extend score leads, disrupt the opponent's rhythm, and shift the game's trajectory. Conversely, losing momentum due to errors or unfavorable conditions can negatively affect performance and potentially reverse match outcomes. Previous literature has proved the significance and effectiveness of momentum in affecting the outcome of a tennis match [16].  
The existing literature highlights several factors that shape momentum in sports. Studies have integrated the concept of momentum into predictive models for table tennis, demonstrating its utility in forecasting game trends. In tennis, variables like court surface type, service rights, and rally length significantly impact match outcomes [6]. Given that this study focuses exclusively on data from post-third-round men's singles matches at Wimbledon 2023, played on grass courts, the effect of the court surface was excluded. Instead, emphasis was placed on service rights and rally duration to comprehensively define and analyze momentum.
## 4 Bandwidth Study
The attenuation of the S\({}_{21}\) response curve at 27.3 GHz is less than 3 dB for a waveguide link with a splice length of 2.1 m. As a result, the BLP of the waveguide under the overfilled launch condition is greater than 57.3 GHz\(\cdot\)m. The measured result agreed well with the simulated ones. The measured BLP of the waveguide link spliced by 20 cm-long polymer waveguides fabricated by the same process at a wavelength of 780 nm is about 42 GHz\(\cdot\)m using an optical sampling technique [31]. The difference between the two results may result in both a large reduction in the number of connections in the waveguide link and the difference in wavelength. Figure 13: Experimental setup for the S\({}_{21}\) response measurement.
# Safety Challenges in Battery Swapping Operations of Electric Underground Mining Trucks
## 5 Conclusions  
The transition to electric vehicles in the underground mining of mineral resources presents a compelling opportunity for enhanced sustainability and reduced operational costs. However, as described in this study, this transition introduces significant safety challenges that require careful consideration and proactive mitigation strategies. The contrasting approaches of two major OEMs to battery swapping highlight the diverse technological solutions available but also emphasise the inherent complexities of operating in confined, high-risk environments.  
Examination of these distinct approaches reveals a trade-off between simplicity and compactness. Company 1's crane-based system offers a simpler, potentially more robust solution, but with increased space requirements and the added risk associated with lifting equipment. Company 2's integrated system minimises the need for external equipment but introduces potential mechanical failures and relies on a smaller auxiliary battery for manoeuvring, creating other potential hazards.  
Comprehensive hazard analysis underscores the importance of a meticulous safety plan. The identified hazards, ranging from crushing injuries to electrical risks and confined space issues, highlight the potential for serious incidents. The use of a risk matrix effectively prioritiises these hazards, enabling the allocation of resources to the most critical areas. The proposed mitigation strategies, emphasising training, clear instructions, access control, and regular maintenance, provide a roadmap for minimising risks.  
Ultimately, the successful integration of electric vehicles in underground mining demands a holistic approach that considers both the technological advancements and the inherent safety challenges. A rigorous risk assessment, coupled with robust safety protocols and continuous monitoring, is essential to ensuring efficient, sustainable, and above all, safe operations.  
Conceptualisation and methodology, V.M., O.P. and A.M.; data collection and analysis, V.M. and O.P.; writing--original draft preparation, V.M., O.P. and A.M.; writing--review and editing, V.M., O.P., A.M., M.D., P.P.O. and A.C.; supervision, V.M.; project administration and funding acquisition, A.M. and V.M. All authors have read and agreed to the published version of the manuscript.  
The research presented in this paper was conducted with the financial support of the Ministry of Science, Technology, and Innovation of the Republic of Serbia, within the framework of funding for scientific research at the University of Belgrade, Faculty of Mining and Geology in Belgrade, under contract number 451-03-65/2024-03/200126.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results
### Ab Inititio Melting Curve of hcp-Cr  
The six melting points of hcp-Cr that we obtained are listed in Table 2.  
The best fit to these six hcp-Cr points gives the melting curve of hcp-Cr in the Simon-Glatzel form (\(T_{m}\) in K, \(P\) in GPa):  
\[T_{m}(P)=1205\left(1+\frac{P}{15.4}\right)^{0.50}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!  
The other two solid structures that we considered, namely, fcc-Cr and dhcp-Cr, melt below bcc at lower \(P\) and below hcp at higher \(P\).  
The melting curves of bcc-Cr and hcp-Cr cross each other at (\(P\) in GPa, \(T\) in K) \((P,T)=(1370.9,\,11433)\) which is the bcc-hcp-liquid triple point. The choice of \(T(P)=a(710-P)^{b},\ 0<b<1\) as a functional form for the bcc-hcp solid-solid phase transition boundary leads to:  
\[T(P)\ =\ 33.1\,(P\ -\ 710)^{0.90}, \tag{5}\]  
which crosses the triple point and lies within the bounds imposed by the solidification simulations using the inverse Z method. We discuss these inverse Z simulations in the following section. Note that the value \(b=0.9\) for Cr is exactly the same as for both of its PT partners Mo and W [4].  
Although the rigorous derivation of the thermal equations of state of bcc-Cr and hcp-Cr goes beyond the scope of this work, we note that the finite-\(T\) counterparts of the above two EOSs can be written approximately as \(P(\rho,T)=P(\rho)+\alpha\,T\), where \(\alpha_{\rm bcc}=7.9\cdot 10^{-3}\) and \(a_{\rm hcp}=1.9\cdot 10^{-2}\). The resulting \"approximate\" thermal EOSs turn out to be quite accurate. For example, for the six bcc-Cr melting points in Table 1, the corresponding thermal EOS gives pressures of \(-5.11,\,47.9,\,125.1,\,265.6,\,525.7\) and \(815.5\), which are basically identical to those in the third column of Table 1. For the six hcp-Cr melting points in Table 2, the six \(P\) values are 1.693, 129.7, 347.2, 691.9, 1094.2 and 1745.4, in excellent agreement with those in the fourth column of Table 2.
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 2 Hybrid Algorithm in TTF Diagnosis System
### Differential Negative-Sequence Protection  
The fourth sub algorithm is the differential negative sequence protection. The application of the negative-sequence algorithm for the protection of PTs has attracted great attention in the last years and several variants of the method have been proposed. This algorithm measures the different values of the negative-sequence current over a finite time. During an internal fault, the negative current sequence generates a current and the differential current (3) increases so the differential current and negative-sequence current can fuse. This algorithm uses the differential current (refer to Equation (4)) to obtain a negative-sequence component using Equation (3). The inputs from Equation (3) are substituted by the differential current value for each phase. The differential negative sequence can be expressed as Equation (7).  
\[\mathrm{I_{d2}} = \tfrac{1}{2}\left(\mathrm{I_{da}}+\mathrm{a^{2}I_{db}}+\mathrm{ aI_{dc}}\right) \tag{7}\]