esa-sceva/satcom-chunk-collection huggingface dataset:
# ATOSE: Audio Tagging with One-Sided Joint Embedding
## 4 Experiments
### MagnaTagATune (MTAT) Dataset  
The MagnaTagATune (MTAT) dataset [22] is a well-known dataset used for experiments with music auto-tagging tasks. The dataset consists of about 25,000 files with tags, and each file has a length of 29 s and indicates whether it contains each of the 188 tags. In this paper, we adopt the experimental settings employed in previous studies [9; 12; 13; 14], and we focused our experiments on the top 50 most frequently used tags and utilized data segmentation techniques.  
In the music datasets, notable correlations can often be observed, such as the correlation between genre and associated instrument tags. This observation suggests that the proposed one-sided joint embedding technique, which takes advantage of tag correlations, has the potential to enhance the performance of classification tasks. Figure 2 shows the correlations among the top 10 highly correlated tags in relation to the \"classical\" tag.  
Figure 2: The correlation of tags in the MTAT dataset.  
We utilized the ground-truth tags from the MTAT dataset and calculated the correlations between tags. Note that we observed strong correlations between certain tags. For instance, the tag \"classical\" exhibited a high correlation of 0.44 with \"strings\", 0.41 with \"violin\", and 0.41 with \"harpischord\". Similarly, the tag \"strings\" demonstrated a significant correlation of 0.46 with \"violin\", 0.44 with \"classical\", and 0.28 with \"cello\". Furthermore, the tag \"violin\" displayed a substantial correlation of 0.46 with \"strings\", 0.41 with \"classical\", and 0.41 with \"cello\". These correlations are shown in the provided figure, which depicts numerous tags that exhibit strong inter-correlations.  
We used the area under the receiver operating characteristic curve (ROC-AUC) and the area under the precision-recall curve (PR-AUC) as evaluation metrics to measure the model's performance with the MTAT dataset. The experimental results presented in Table 1 demonstrate the performance improvements achieved by all models incorporating joint embedding with the MTAT dataset. Specifically, the models showed enhancements in terms of the ROC-AUC, with improvements ranging from 1.45% p to 2.15% p. Additionally, in terms of the PR-AUC, the models exhibited improvements ranging from 2.85% p to 6.11% p. Notably, our HarmonicCNN model leveraging our joint embedding technique achieved the best performance, with a PR-AUC of 91.27% and ROC-AUC of 53.78%. These results show the effectiveness of our joint embedding approach in the music auto-tagging task.
# Theoretical and Experimental Analysis for Cleaning Ice Cores from EtisolTM 140 Drill Liquid
###### Abstract
For the project BEOI, an aliphatic ester called EstisolTM 140, has been selected as it meets many criteria, such as density, freezing point, no toxicity (to nature and humans), non-polarity, low pour point (\(-90\)\({}^{\circ}\)C), and low viscosity. To limit contamination of the core by the fluid, the EstisolTM 140 should be removed from the ice cores after drilling. Most of the fluid will be mechanically removed from the ice core surface by a special vacuum-cleaner unit and wiped down with paper towels immediately after drilling. Prompted by experiences in Greenland deep drilling projects, where it was observed that the cores after one week of storage on open shelves in a snow cave appeared much \"drier\" than fresh cores, the BEOI project planned to store the fresh cores on open shelves in a ventilated room. In order to speed up the \"drying\" process by evaporation of drilling fluid, a ventilation air flux of 40 m\({}^{3}\)/h air flux has been suggested. The main aim of this paper is to discuss how storage time, degree of ventilation and temperature influence the removal of residual fluid from the cores, i.e., the \"drying\" process. We will discuss: (1) the physical processes of ice sublimation and drilling fluid evaporation under ventilation conditions; (2) calculations of how much ice could be lost and how much drilling fluid could be removed at maximum ventilation; (3) simple tests of fluid removal, and (4) how to optimize the storage conditions of ice cores to preserve their scientific usefulness even after long time in storage. We were most curious about the rate between the evaporation of EstisolTM 140 and the sublimation of the ice. Evaporation and sublimation processes will be discussed theoretically first, followed by an analysis of the particular properties of ice and the chosen drilling fluid. The evaluation of ice losses and fluid evaporation will be finally compared with specific tests on real ice cores, in order to get an evaluation of the theoretical findings. The overall aim is to provide input to the logistical planning and engineering of the drill site under development for the BEOI.
## 3 Proposed System Architecture
### System Model  
In our study, we investigate a two-tier configuration of a HetNet illustrated in Figure 1.  
Figure 1: Proposed HetNet system model.  
This configuration includes seven LTE-A base stations (BSs), each with three sectors, and an ultra-dense collection of 5G BSs. The LTE-A BSs are positioned at the center of the MC and cover the surrounding area with three sectors. The 5G BSs are located at the center of the SCs and exhibit omni-directional propagation. SCs are distributed evenly and randomly. UEs are also uniformly placed within the coverage areas of the MCs. It is worth mentioning that we have only considered users within one MC for thorough investigation. The mobility of the UEs is characterized by two parameters: the direction and speed of the UEs. The UEs move in parallel to each other, following the directions determined by their initial positions. During the simulation, UEs positioned on the positive \(x\)-axis move to the left, whereas those on the negative \(x\)-axis move to the right. This is because the simulation steps are not limited by the size of the simulation environment.
## 3 Results and Discussion
### Foregeability  
The influence of load and Si\({}_{3}\)N\({}_{4}\) on the forgeability property is described in Figure 15. The graph demonstrates the relationship between forgeability, load, and Si\({}_{3}\)N\({}_{4}\) reinforcement  
Figure 14: Flexural strength and Young’s modulus of AZ91/x-Si\({}_{3}\)N\({}_{4}\) composites.  
Figure 13: Effect of Si\({}_{3}\)N\({}_{4}\) on impact strength of AZ91/x-Si\({}_{3}\)N\({}_{4}\) composites.  
ment. It shows that as the amount of Si\({}_{3}\)N\({}_{4}\) reinforcement increases, the forgeability of the composites tends to improve. This improvement can be attributed to the presence of hard ceramic particles, such as Si\({}_{3}\)N\({}_{4}\), which can enhance the strength and deformation resistance of the composite. The reinforcing particles act as barriers to dislocation movement, thus improving the material's ability to withstand deformation without fracturing. On the other hand, the graph also illustrates that increasing the applied load during forging has a negative effect on forgeability. Higher loads impose greater stress on the material, leading to increased deformation and strain. This results in a larger change in height (deformation) relative to the initial height, which ultimately reduces the forgeability value. In other words, excessive loads can lead to increased susceptibility to cracking or failure during forging.
# Sc/SiC/Al Multilayer Optimization for Li K Spectroscopy
###### Abstract
The same system was also designed, fabricated, and characterized for a grazing angle of 30\({}^{\circ}\)[11], which is more suitable for WDS applications in the Li K emission range. However, the use of Be is not recommended in practical designs due to safety precautions, because of the toxicity of inhaled beryllium-containing dust [17]. Similarly, Co/Mg multilayers were developed to work with 50 eV radiation at 45\({}^{\circ}\), and this system reaches a reflectance of 40% [18]. In our research, we are studying the Sc/SiC/Al periodic multilayer. Multilayers containing Sc and Al have already been introduced for applications in attosecond physics, coherent light sources (synchrotrons, EUV lasers, and high-harmonic generation, etc. ), and astrophysics [13]. These multilayers show a high reflectance, ranging from 42 to 56% for energies from 30 to 24 eV (40-51 nm) at near normal incidence (5\({}^{\circ}\)) with a bandwidth between 2.2 and 2.6 eV. The aging properties of Al/Sc multilayers were also studied, showing a good temporal stability after a period of time of nine months [13]. In the present work, we are interested in the design of multilayer systems for energies from 45 to 55 eV (wavelength between 27.5 and 22.5 nm), corresponding to the lithium K spectral range. We show that the new proposed Sc/SiC/Al multilayer can work efficiently for spectroscopic purposes in the Li K spectral range and avoid the use of previously designed Be/Si/Al multilayers, for which toxicity could be a problem during their preparation, owing to the use of beryllium. Indeed, nowadays, lithium is a major element in the domain of energy [19; 20], but its routine spectroscopic characterization on widely used apparatus, such as scanning electron microscopes or electron microprobes, is not yet possible. In fact, these kinds of apparatus are equipped with energy-dispersive spectrometers that cannot detect low-energy photonsas one of the Li K\(\alpha\) emissions or wavelength-dispersive spectrometers (WDS) equipped with periodic multilayers [11; 21]. However, up-to-date WDS apparatus is not available with multilayers optimized for the Li K\(\alpha\) range. Thus, any progress to obtain spectroscopy in this spectral range is welcome, because it will make it possible to determine the chemical state of lithium atoms, as well as to perform quantification.
# Underwater Acoustic Target Tracking: A Review
## 4 Theory of Target Tracking  
The general process of target tracking including target detection, position determination, target model construction, state filtering and prediction is introduced in this section. For the integrity of the article, the process of tracking based on images is also covered.  
Target tracking refers to the process of processing the measurements obtained from sensors and maintaining an estimate of the current state of the target. The state mainly contains the position, velocity, and other kinematic components. The measurements refer to the observation information about the target, including direct distance estimation, slope distance, direction of arrival (DOA), received signal strength indication (RSSI), time of arrival (TOA) or time difference of arrival (TDOA), as well as the frequency difference between sensors caused by the Doppler frequency shift. When sensors detect the presence of the target, the observation information can be obtained by sampling the sensed signals (e.g., light, sound, image, or video). Then the position of the target can be computed mathematically by relating this information to the target state. Finally, the tracking process is completed by determining the position of the target at each step. Due to the existence of noise in measurements, an extra component, filtering process should be included to improve the tracking accuracy. Filtering algorithms are also used to predict the position of the target according to the target motion model to accelerate the tracking speed. The process described previously is the general tracking process in non-visual tracking which refers to using TASA or UWSNs.  
In visual tracking, which refers to using acoustic imaging sensors, the tracking process is a little different. Target tracking based on acoustic imaging sensors involves determining the exact location of the moving target in the next frame quickly by minimizing the search scope of the maneuvering, which can be summarized as shown in Figure 3. Firstly, the initial position of the target is determined by automatic detection or manual selection and the template of the image sequence is established. Secondly, according to the established target motion model and the selected filtering algorithm, the state of the moving target in the next frame such as the speed and position can be estimated, which can be used to determine the search area while avoiding the time consumption of global searching. Then, the target detection is carried out in the search area to obtain the accurate observation value of the moving target. Finally, the estimated value of the target is corrected and the target information is updated to start the next tracking cycle.  
In conclusion, the tracking process both in visual tracking and non-visual tracking has some important components such as target detection, position determination, target model construction and state filtering and prediction.
## 4 Results  
The computation of the shape parameter was straightforward, and the results are shown in Figure 4. The scale for the shape factor is shown on the left. Colors indicate locations, and symbols represent velocity classes. Time series with \(s_{p}^{2}<2\) were grayed out, and will not be considered further in the following analysis. For purposes of comparison, shape factors from a particular velocity measurement ([25], \(\bar{U}=7.6\) m/s, \(u^{\prime}=1.36\) m/s) were included (black line) as well.  
The general behavior of both shape parameter functions for velocity and pressure was similar, whereas absolute values differed significantly. This behavior was also observed in reference [26]. Surprisingly, most of the pressure measurements resulted in \(s_{p}^{2}\) close to zero.  
We noted differences between the measurements of velocity and pressure concerning sampling rates and length of the time series.  
In Figure 4, offshore curves are distinguished with numbers in brackets. They were all recorded on the same day with a short delay at the following times:  
Pressure time series were recorded for a period of 100 s. For the first data set (1) \(s_{p}^{2}\) was high. However, then, the pressure data resulted in \(s_{p}^{2}\approx 0\) until the values increased eight minutes later at  
Figure 4: Shape parameters for all collected measurements. Colors represent locations: offshore, onshore with wind coming from land or sea side. The symbols represent the velocity classes for the wind speed. Measurements with \(max(s_{p}^{2})<2\) are shown in grey. For reasons of comparison, [PERSON]’s and [PERSON]’s time scales are given in 5 msec and 0.05 msec, respectively. The sensor’s resolution goes down to approximately 0.3 msec only.  
(2). The time series (2), (3), and (4) were consecutive, so it can be said that the shape parameter for the turbulent pressure fluctuations did not remain constant in time for this interval. When all time series from 8:44 a.m. to 8:57 a.m.were concentrated into one and evaluated according to a common \(s_{p}^{2}\), a curve similar to the curve for velocity fluctuations was obtained. This has already been presented in reference [14].  
We now try to relate these different statistical behaviors to the atmospheric boundary layer state. With our estimation of the Richardson number, a stability state was determined. Selected data from all our 119 measured data sets are shown in Tables 1 and 2. Measurements with \(s_{p}^{2}>0\) are marked with a star in the last column.  
As can be seen from Table 1, high \(s_{p}^{2}\) (\(s_{p}^{2}>0\)) behavior corresponded to unstable to neutral Atmospheric Boundary Layer (ABL) conditions: \(-0.13\leq Ri\leq 0.01\). However, a significant correlation with the Richardson number was not found.
## 5 Comparative Analysis of Hydrocarbon Source Rock Indicators
### Hydrocarbon Source Rock Type  
The Silurian hydrocarbon source rocks of North Africa, the Middle East's \"Pan-Cratonic Basin\", and the Yangtze Platform in South China are primarily characterized by mud shale hydrocarbon source rocks (Figure 9). These regions share remarkably consistent hydrocarbon source rock features [74]. Specifically, the rock formations in North Africa's Triassic/Ghadames and Illizi Basins, as well as the Middle East's Central Arabian and Rub' al Khali Basins, are characterized by dark-gray to black graptolitic shales (Figure 10a) [75]. Moreover, the Sichuan Basin on the Yangtze Platform boasts hydrocarbon source rocks, including rich, black carbonaceous-siliceous shales abundant in graptolite fossils (Figure 10b).  
The analytical data of this study are based on the \"13 th Five-Year Plan\": Study on Hydrocarbon Accumulation Regularity and Favorable Exploration Direction in Key Basins of North Africa. These Silurian hydrocarbon source rocks across the North Africa-Middle East-South China nexus are distinguished by their high natural gamma radiation and elevated TOC levels, typically exceeding 2.0% [76]. This makes them highly comparable, as illustrated in Figure 9. However, the thickness of these hydrocarbon source rocks can vary significantly between regions, generally falling within the 30-100 m range.  
Figure 9: Comparison of Silurian hydrocarbon source rock characteristics between North Africa and South China (Modified from [77]).
## 3 Results and Discussion
### Outdoor Experimental Results
#### 3.2.3 Tomato Fresh Yield  
As shown in Table 2, the subsurface drip irrigation system resulted in higher tomato production. The seedling transplanting experiments under outdoor conditions had problems under local weather conditions, and rain occurred several times, which could not be adjusted for the soil-water balance method. However, in arid weather conditions, the developed system can be implemented with a long-range controlling system by adjusting weather parameters. The yield obtained by implementing this system was lower than the production level of growers producing tomatoes in Japan [40]. The low yield was mainly because the cutter pillar once attacked the tomato plants in the field. As shown in Table 3, the subsurface drip irrigation system resulted in higher tomato production. Each plant produced approximately 1243 g and 1061 g in the subsurface and surface drip irrigation systems, respectively. However, for agronomical cases, in the outdoors, further research on transplanting in different years can be conducted.
## 3 Data and Methods
### Logging Calculation of In Situ Stress  
Various methods exist for determining the orientation of in situ stress, including the strike of borehole breakouts, drilling-induced fractures, and hydraulic fracturing, all of which are derived from imaging logging [39, 40]. The azimuthal anisotropy of shear waves provides an alternative method to determine the orientation of in situ stress. Considering the available data, this research plotted rose diagrams of borehole breakout strikes and drilling-induced fractures to determine the orientation of maximum (\(\theta_{\text{hmax}}\)) and minimum horizontal stress (\(\sigma_{\text{hmin}}\)).  
In situ stress magnitude may vary due to external factors, but the stress gradient tendency should be similar within a specific area. Logging offers advantages for calculating in situ stress, including low cost, straightforward data acquisition, and continuous evaluation (Figure 6). Several models are used for stress magnitude calculation, with the most commonly employed being as follows:  
\[\sigma_{v}=\int_{0}^{H}\rho(H)gdH \tag{5}\]  
\[\sigma_{hmin}=\frac{\mu}{1-\mu}\sigma_{v}+\frac{1-2\mu}{1-\mu}\alpha p_{p}+ \frac{E}{1-\mu^{2}}\varepsilon_{h}+\frac{E\mu}{1-\mu^{2}}\varepsilon_{H} \tag{6}\]  
\[\sigma_{hmax}=\frac{\mu}{1-\mu}\sigma_{v}+\frac{1-2\mu}{1-\mu}\alpha p_{p}+ \frac{E}{1-\mu^{2}}\varepsilon_{H}+\frac{E\mu}{1-\mu^{2}}\varepsilon_{h} \tag{7}\]  
where \(\sigma_{v}\) is the vertical stress, MPa; \(\sigma_{\text{hmin}}\) is the maximum horizontal stress, MPa; \(\sigma_{\text{hmax}}\) is the horizontal minimum stress, MPa; \(\sigma(\text{H})\) is the bulk density varying with depth, g/cm\({}^{3}\); E is the [PERSON]'s modulus, GPa; \(\mu\) is the [PERSON]'s ratio; \(\varepsilon_{\text{h}}\) is the \(\sigma_{\text{hmin}}\) correction coefficient; \(\varepsilon_{\text{H}}\) is the \(\sigma_{\text{hmax}}\) correction coefficient; \(\alpha\) is the Biot coefficient; and \(\text{p}_{\text{P}}\) is the pore fluid pressure, MPa.
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 4 Experiment and Discussion
### Ablation Experiments
#### 4.2.3 Evaluation of Combining DGA and UBR  
The proposed DGANet network combines DGA with the UBR module to improve detector performance in remote sensing images. The comparison results are listed in Table 3.  
Compared with a single module improvement, the DGANet network performance is more advantageous. The mean average precision rose further to 0.971 and the mean recall reached 0.987. Compared with the original network, the accuracy increased by 2.9% and the mRecall increased by 1.9%. We improved the Centernet with a dynamic gradient adjustment mechanism, since it significantly increased the ability of the network to learn effective features and almost no reduction of the network operation speed. DGANet achieved a better convergence speed compared with the original network.
# Experimental Study on Horizontal Pipeline Transportation Characteristics of Coarse Particle
## 1 Introduction
Current research still faces problems such as small parameter ranges and difficulty in meeting accuracy requirements. This study focuses on the challenges associated with pipeline conveyance and the transfer of coarse-grained dredged materials during dredging operations, with a particular emphasis on investigating the motion characteristics of coarse particle groups in horizontal pipeline hydraulic conveying systems. The research begins by establishing an energy theory model for solid-liquid two-phase flow, which is subsequently validated through experimental observations. By analyzing the motion states of coarse particles, the study systematically examines the influence of the mixed average velocity, material volume concentration, and particle size on the motion characteristics of coarse particles during pipeline hydraulic conveying. Based on the findings, the research provides practical recommendations for optimizing flow parameters in the pipeline hydraulic conveying of coarse particles, offering valuable insights for enhancing the efficiency and effectiveness of dredging construction processes.
## 4 Simulations
### Impact of Some Parameters on the Invulnerability Performance
#### 4.2.1 Effect of Capacity Regulation Parameters  
In Section 3.2, the overload tolerance factor \(\lambda\) and the exponential adjustment parameter \(\beta\) have an effect on the node capacity. To explore their respective effects on invulnerability performance, we let \(\sigma=2\) to conduct the experiment.  
As shown in Figure 4, from (a), when \(\lambda=0.9\), \(M\) oscillates with the increase of \(\beta\). When \(\beta>0.8\), \(M\) no longer increases. When \(\beta=0.8\), the efficiency of the network and the number of surviving nodes are optimized. The conclusion of (b) is similar to (a), except that \(M\) does not increase as fast as (a), which means that the network performance is more sensitive to \(\beta\). Meanwhile, it is unreasonable to blindly increase the capacity factor of the network and increase the cost of network construction for nothing. This conclusion is consistent in planar coupled networks [40] and single-layer networks [26].