esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Frost Heave and Thaw Settlement Characteristics of Sanya Estuary Channel Soil Layer
## 6 Conclusions  
This paper provides a detailed account of the frost heaving and thaw settlement tests of typical soil layers in the Sanya Estuary Channel Project, monitoring the displacement evolution during freezing and thawing processes, and calculating the frost heaving rate and thaw settlement coefficient. The main findings are as follows:  
1. During the freezing test, the displacement changes in the soil layers can be divided into four stages: frost shrinkage stage, rapid frost heaving stage, slow frost heaving stage, and stable frost heaving stage. The displacement changes mainly occur in the rapid frost heaving stage and the slow frost heaving stage.
2. During the thawing test, the displacement changes in the soil layers can be divided into three stages: slow thaw settlement stage, rapid thaw settlement stage, and stable thaw settlement stage. The displacement changes mainly occur in the rapid thaw settlement stage.
3. Among the fine sand, silty clay, gravel sand, and clay at natural water content, the silty clay exhibited the highest frost heaving rate and thaw settlement coefficient. At a freezing temperature of \(-28^{\circ}\)C, the frost heaving rate and thaw settlement coefficient of the silty clay were 4.51% and 5.88%, respectively, making it the most unfavorable soil layer.
4. The frost heaving rate and thaw settlement coefficient of the silty clay increase (decrease) with increasing (decreasing) water content. Additionally, soil samples with higher water content experience greater mechanical performance degradation and are more prone to damage after testing.  
X.W.: Resources, Writing--original draft, and Writing--review and editing. J.H.: Investigation, Supervision. J.S.: Writing--review and editing, Methodology. H.X.: Formal analysis, Investigation. J.X.: Resources, Project administration. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 52471281), the Hainan University Collaborative Innovation Center Project (Grant No. XTCX2022 STB09), the High Technology Direction Project of the Key Research and Development Science and Technology of Hainan Province, China (Grant No. ZDYF2024 GXJ8001), the Key Research and Development Projects of the Haikou Science and Technology Plan for the Year 2023 (2023-012), and the Hainan Provincial Innovation Foundation for Postgraduate (Ohyb2023-117).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Hainan Hongjing Architectural Design Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Comprehensive Review on Music Transcription
## 2 Overview of Music Transcription
### Notation-Level Transcription  
Notation-level transcription is the higher level of transcription which is the process of converting audio recordings into a traditional musical notation format such as sheet music or a musical score. Figure 1 illustrates an example of notation-level transcription. The visualization for this transcription has been carried out using AnthemScore [35] from a wave file (opera_male5.wav) of the ADC2004 vocal melody dataset. This transcription maps the audio signals to specific symbols, notes, rhythms, and other notation elements that can be read and performed by musicians. It focuses on capturing the basic structure, melody, and harmony, making it suitable for less complex music. It provides a quick overview of the musical content which can be easily understood by individuals with limited musical training. However, it is not suitable for in-depth musical analysis or the complex composition of the music. Notation-level transcription is often used to preserve and communicate musical compositions and performances in a standardized form. The transcription in this level requires a deeper understanding of musical structures, including harmonic, rhythmic, and stream structures. Transcribers use specialized music notation software to create the symbolic representation of the music. The popular software for notation-level transcription include Finale V27 ([[https://www.finalemusic.com/](https://www.finalemusic.com/)]([https://www.finalemusic.com/](https://www.finalemusic.com/)), accessed on 5 October 2023), Sibelius for desktop and mobile version 2023.8 ([[https://www.avid.com/sibelius](https://www.avid.com/sibelius)]([https://www.avid.com/sibelius](https://www.avid.com/sibelius)), accessed on 5 October 2023), MuseScore 4.x ([[https://musescore.org/](https://musescore.org/)]([https://musescore.org/](https://musescore.org/)), accessed on 5 October 2023), LilyPond 2.24.2 ([[http://lilypond.org/](http://lilypond.org/)]([http://lilypond.org/](http://lilypond.org/)), accessed on 5 October 2023), AnthemScore version 4 [35], and more. These tools provide a user-friendly interface for inputting and editing musical elements.
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 2 Materials and Methods
### Spikelet Instance Segmentation Method
#### 2.2.2 Image Augmentation  
To enhance the model performance and robustness, this study implemented image augmentation for the wheat spike dataset. Every image had a certain probability of passing through the following data augmentation strategy: affine (scaling, translation, rotation, and shear), horizontal flip, brightness, contrast, Gaussian blur, and Gaussian noise.  
1. Scaling: Randomly scaling the image within a range of 0.8 to 1.2 times its original size;
2. Translation: Randomly translating the image horizontally and vertically up to 20% of pixels;
3. Rotation: Randomly rotating the image within a range of \(-\)15 to +15 degrees;
4. Shear: Randomly applying shear transformations up to 10 degrees;
5. Horizontal Flip: Each image had a 50% chance of being flipped horizontally;
6. Brightness: Randomly adjusting the brightness by a factor ranging from 0.8 to 1.2;
7. Contrast: Randomly adjusting the contrast by a factor ranging from 0.8 to 1.2;
8. Gaussian Blur: Applying Gaussian blur with a kernel size of 5 \(\times\) 5 with a probability of 30%;
9. Gaussian Noise: Adding Gaussian noise with a variance of 0.01 with a probability of 30%.  
Augmentation was executed three times with random probability. The origin dataset was augmented from 450 images to 1800 images, which means every variety of wheat spike increased from 150 images to 600 images.
## 5 Discussion
### Variability in Time Measurements  
One of the notable aspects encountered during the experimental procedure was the observed variability in time measurements recorded through smartphone audio recordings. While the methodology utilizing the smartphone's onboard microphone for time measurements proved effective, some degree of variability was evident across the conducted experiments. Several factors could contribute to this variability in time measurements. Ambient noise, variations in the smartphone's microphone sensitivity, and the inherent limitations of audio-based time measurement systems might have influenced the accuracy and consistency of the recorded time intervals. Additionally, variations in the acoustic signals generated by the switch actuation and the impact of the steel ball on the ground could have contributed to the observed variability. Despite this variability, it is crucial to note that the overall trend and consistency in the time measurements remained within an acceptable range across the experimental trials. Rigorous data-collection procedures and averaging methodologies were implemented to mitigate individual inconsistencies and fluctuations, resulting in reliable averaged data used for analysis and calculation of \(g\). Furthermore, the small magnitude of variability observed in the time measurements did not significantly impact the final calculated \(g\) value. The methodology's robustness was evident in the consistent alignment between theoretical and experimental values, showcasing a high degree of correlation despite the observed variability in individual time measurements. To address and potentially minimize this variability in future experiments, the standardization of experimental conditions, such as controlling ambient noise levels and optimizing smartphone recording settings, could be considered. Additionally, exploring advanced signal processing techniques or utilizing external microphone accessories to enhance signal clarity and precision might offer avenues for reducing variability in time measurements.
## 3 Materials and Methods  
The _hypothesis_ of this work is that a WSN can be protected from jamming using a mechanism for its detection, which is based on performance metrics and uses cooperative and collaborative schemes for any density of nodes. To accept or reject this hypothesis, we have designed a _jamming detection mechanism_. The mechanism is showed in Figure 3 and is carried out in two directions. First, the _down arrows_ indicate the way _simulations_ and _experiments_ with jammers bring information on how jamming attacks impact performance metrics. Second, the _up arrows_ indicate the way in which a _jamming detection algorithm_ obtains the type of jammer that is attacking the network.  
Figure 2: Schemes for WSNs.  
The jamming detection mechanism includes the following elements for simulations and the experiment:  
* _WSN configuration_, which takes into account the _density_ of nodes, consists of the number of nodes and the geographic area in which a WSN is being applied, and the _immunity_ of the WSN, which refers to how much the network configuration protects the network from attacks. The simulations and the experiment are carried out under cooperative and collaborative schemes.
* The selected jamming metrics or _symptons_ which are outputs or _dependent variables_ that the network presents in a steady state. The selected metrics (PDR, RSSI and Energy) were described in Section 2 and are provided by the results of the simulations and the experiment.
* The _aggressor_, which is the input or independent variable as it refers to the type of jamming attack. The four elementary types of jamming attacks shown in Section 2 are simulated, which are the _aggressors_ who attack the network, affecting its performance metrics.  
The _jamming detection algorithm_ will follow the opposite route to simulations and the experiment. The obtained values for the performance metrics in the simulations and the experiment are integrated into a data history to detect the type of jamming. The algorithm for jamming detection is presented below.
###### Abstract  
We propose a multi-temporal-scale analysis of ground deformation data using both high-rate tilt and GNSS measurements and the DInSAR and daily GNSS solutions in order to investigate a sequence of four paroxysmal episodes of the Voragine crater occurring in December 2015 at Mt. Etna (Italy). The analysis aimed at inferring the magma sources feeding a sequence of very violent eruptions, in order to understand the dynamics and to image the shallow feeding system of the volcano that enabled such a rapid magma accumulation and discharge. The high-rate data allowed us to constrain the sources responsible for the fast and violent dynamics of each paroxysmal, while the cumulated deformation measured by DInSAR and daily GNSS solutions, over a period of 12 days encompassing the entire eruptive sequence, also showed the deeper part of the source involved in the considered period, where magma was stored. We defined the dynamics and rates of the magma transfer, with a middle-depth storage of gas-rich magma that charges, more or less continuously, a shallower level where magma stops temporarily, accumulating pressure due to the gas essolution. This machine-gun-like mechanism could represent a general conceptual model for similar events at Etna and at all volcanoes.  
volcano geodesy; multidisciplinary monitoring; paroxysms; lava fountain; volcanic eruption; modeling; tilt; GPS; InSAR; ground deformation +
Footnote †: journal: Applied Sciences
## 3 Analysis and Discussion
### Measurement and Monitoring of Soil Moisture Dynamics
#### 3.2.1 Remote Sensing: Satellite Dataset for Monitoring Soil Moisture Dynamics
* _Ranulysis products_: European ReAnalysis (ERA5-Land) (0.1\({}^{\circ}\) spatial resolution--global) [66], NASA Global Land Data Assimilation System Version 2 (GLDAS-Noah) (0.25\({}^{\circ}\) spatial resolution--global), Global Land Surface Satellite Soil Moisture product (GLASS SM) (1 km spatial resolution--global) [67]. Table 1 synthesizes satellite options for SM measurement, highlighting how the choice of an instrument and associated model depends on specific project needs, including desired resolution, geographical coverage, and operational limitations.
## 3 Results  
### Quantitative Evaluation  
The results of the quantitative evaluation of the TLG and ALG are presented in Table 1. For the pre-test, the mean scores were 98.73 \(\pm\) 4.66 for the TLG and 98.14 \(\pm\) 6.30 for the ALG, with no significant difference observed (\(p\) = 0.676). Similarly, the post-test scores showed a mean of 98.78 \(\pm\) 5.15 for the TLG and 98.55 \(\pm\) 4.86 for the ALG, indicating no statistically significant difference (\(p\) = 0.879)  
In terms of learning duration, the TLG recorded an average of 119.53 \(\pm\) 16.15 min, while the ALG averaged 117.20 \(\pm\) 27.43 min. The difference between the two groups was not significant (\(p\) = 0.716). For course satisfaction, the TLG reported a mean score of 4.90 \(\pm\) 0.43, while the ALG achieved 4.91 \(\pm\) 0.39. The difference was minimal and not statistically significant (\(p\) = 0.909).  
The statistical analysis demonstrated a substantial effect size, with a [PERSON]'s d value of 0.86, indicating a strong practical significance for the intervention. This effect size corresponds to an 86% improvement, emphasizing the robust impact of the teaching method on learning outcomes and its potential to significantly enhance educational effectiveness.  
Overall, these findings demonstrate that both groups performed similarly across all measured parameters, including pre-test and post-test scores, learning duration, and course satisfaction. The results suggest that the intervention methods applied to both groups yielded comparable outcomes without significant discrepancies (Table 3).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Groups** & **M \(\pm\) SD** & **t** & **P** \\ \hline Pre-test & TLG & 98.73 \(\pm\) 4.66 & 0.420 & 0.676 \\  & ALG & 98.14 \(\pm\) 6.30 & 0.420 & 0.676 \\  & TLG & 98.78 \(\pm\) 5.15 & & \\ Post-test & ALG & 98.55 \(\pm\) 4.86 & 0.154 & 0.879 \\ Learning & TLG & 119.53 \(\pm\) 16.15 & & \\ Duration & ALG & 117.20 \(\pm\) 27.43 & 0.368 & 0.716 \\ Course & TLG & 4.90 \(\pm\) 0.43 & & \\ Satisfaction & ALG & 4.91 \(\pm\) 0.39 & \(-\)0.115 & 0.909 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation results for the TLG and ALG (n = 86).
## 3 Results
### The Relative Contribution Rate of Influencing Factors
From the quantitative calculation results of the elastic net model, it can be seen that PRE, SM, and DEM are the most important determinants of NPP changes in the Songnen Plain. Among them, PRE and SM, which influence water availability, show interannual variation. Their driving characteristic on NPP is different from that of DEM with long-term stability. Therefore, interannual trends of PRE and SM in the Songnen Plain from 2001 to 2020 were analyzed (Figure 17). Based on the analysis results, the changes in NPP from 2001 to 2020 were divided into three periods, namely, the period of significant increase, the period of continuous increase, and the period of stable increase. The first period spans from 2001 to 2005, during which NPP rose most significantly, with an average growth rate of 26.34%. The reason is that in this period, both PRE and SM showed a significant increase, alleviating water stress. During the second period, which extends from 2006 to 2014, NPP showed continuous and stable growth with an average growth rate of 11.21%, but the increase was slowed down. PRE still showed an increasing trend, but SM gradually stabilized, indicating that PRE played a dominant role in driving the observed increase in NPP. In the third period spanning from 2015 to 2020, the NPP values stabilized above 380 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) and the growth rate gradually stabilized with an average of 9.96%. PRE showed the most significant increase among the three periods, but SM gradually decreased. Despite the decreasing trend in SM, the joint influence of PRE and SM still contributed to an upward trend in NPP. However, the magnitude of the increase was not as pronounced as that in the first two periods. To more intuitively reflect the relationship between NPP variation and land use transformation, NPP statistics were conducted for the regions that experienced interconversion between different land types from 2001 to 2020. The results showed (Figure 18) that the GRA maintenance region exhibited the most significant growth of NPP per unit area (154.33 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), followed by the GRA to SWA conversion region (148.87 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)). The SWA to FOR conversion region exhibited the lowest growth in NPP growth per unit area (14.97 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)). These results indicated that the interconversion among land types affects NPP increase. Figure 16: Contribution of each impact factor of NPP in the Songnen Plain in each land cover type.
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 6 Conclusions  
In this paper, we presented the principle of a persistent homology approach for the detection of the targets (more specifically, a person) from the 2D delay-Doppler images. We presented our unique millimeter-wave measurement test-bed, which is able to continuously transmit and receive communication signals in an unlicensed 60 GHz band. This setup was used to transmit the OTFS signals with a bandwidth of \(\approx\) 500 MHz, and the received signals reflected from the person in the environment were used to generate the delay-Doppler data for detection. We compared the performance of the proposed detector with a detector based on the standard CFAR approach. In the selected scenario of various movements of one person in real conditions, the results show the superiority of the persistent homology over the conventional CFAR detector. So far, we have used a simple implementation of the persistent homology detector, with higher computational demands than required by the conventional CFAR detector. An in-depth optimization of the detector structure and parameters would be an interesting topic for future work. An important research question is a search for the best pre-processing of data fed to the input of the detector. We presented our first attempts in this direction by showing the detector performance on two types of input data--the rough observations in the delay-Doppler domain, and the SNR map computed from the delay-Doppler domain images.  
Conceptualization, J.B. and R.M.; methodology, R.M., E.Z. and G.G.; software, [PERSON] ([PERSON]) and J.V.; validation, J.V., [PERSON] ([PERSON]), [PERSON] ([PERSON]) and R.M.; formal analysis, G.G.; writing, review and editing R.M., J.B., E.Z. and R.Z. ([PERSON]); project administration, R.M.; funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
The research presented in this paper was financed by the Ministry of the Interior of the Czech Republic project No. VJ01030008 _Passive crowd monitoring system with privacy by design_ prepared under the IMPAKT BV IMP3/1 VS program.  
Not applicable.  
The complete data set is not publicly available due to the data size (28 GB for 14 s of measurements). Sample delay-Doppler matrices are available on request.  
The authors declare no conflict of interest.  
Figure 12: Analysis of target detector performance for various activities: walking (**top**), jumping (**middle**), and running (**bottom**).
## 4 Application of the Concepts: Design of an In-Line Measuring Machine  
The development of manufacturing metrology is a highly competitive field that continuously incorporates technology to improve the accuracy and resolution of measurement equipment [16; 17]. Recently, the need for metrology techniques that are efficient at getting data from parts at the production facilities has been recognized [20; 22]. [PERSON] et al. [40] proposed the use of intelligent systems to promote a paradigm shift from dedicated off-line metrology to in-line metrology. They also pointed out that the most important challenge for the introduction of in-line techniques is the validation of the system's capabilities in terms of accuracy, repeatability, and measurement time. [PERSON] et al. [41] discussed how I4.0 could impact the design of metrology equipment in terms of speed, flexibility, and reliability, and stated that protocols for safe and efficient connection are needed. A further argument of this work was that technologies associated with Industry 4.0 could be used to improve the value of in-line measurement systems. [PERSON] et al. [42] proposed the use of computer tomography to inspect parts quickly in an I4.0 environment. They mentioned the need to inspect parts completely, at speed to match production rates. Their system measured parts in batches with an accuracy comparable to that of a CMM, at a fraction of the time.  
This work proposes that in-line metrology systems should have specific characteristics suitable for the production environment: Flexibility, Accuracy, Robustness, Speed, and Functional Intelligence and Connectivity. All the components of an in-line machine interact to achieve these characteristics. Nevertheless, the first four characteristics are mostly associated with the hardware of the machine, while Functional Intelligence and Connectivity are exclusively addressed by the design of the machine control. As will be shown, these principles were applied to the design of an in-line measuring machine for the inspection of die castings.
## 6 Discussion and Future Directions
### Performance Evaluation Metrics  
For most applications of IoV, some of the metrics used to evaluate a blockchain performance include time, cost, key transfer time, power, reputation value, detection rate, utility, detection accuracy, throughput, end-to-end delay, packet loss ratio, energy, and network congestion [28]. Time is the more favored metric for BC performance, and the interval with the average execution rate is 70-80 s [30]. Particularly for data and information-sharing applications where data privacy, learning accuracy, and the participation of the vehicles are explored, the performance criteria that researchers try to evaluate include model accuracy, computational overhead, and the effectiveness of the incentive mechanism [143]. The apprehensiveness of a consensus mechanism can be evaluated by two approaches; thefirst is the communication rounds and accuracy approach, and the other approach is by evaluating the block confirmation delay [153]. Other evaluation metrics for blockchain and ML integrated applications include security, communication cost, anonymity, attack probability, average transaction confirmation time, and average transaction speed [148].