esa-sceva/satcom-chunk-collection huggingface dataset:
# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 5 Conclusions  
The algorithm presented in this article combines deep learning and starts from multiple angles such as brightness and color information to effectively separate overlapping fruits and achieve rapid and accurate labeling of target fruits. From the detection and labeling results, the accuracy of the proposed algorithm is higher than that of [17] based on K-means and GA-RBF-LMS neural network detection in [19], based on the YOLO deep convolutional neural network algorithm, and in [23], the detection accuracy of CenterNet combined with the DeepSnake Algorithm was 14.29%, 9.17% and 36.01% higher, respec  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Unobstructed** & **Overlapped** & **Branches** & **Poor Light** & **Set of Plastic** & **With Water** & **Patterned** \\  & **Overlap** & **Fruit** & **Shade** & **Conditions** & **Bags** & **Droplets** & **Label** \\ \hline Precision & 99.12\% & 94.78\% & 90.71\% & 94.46\% & 91.67\% & 98.08\% & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of the test results was measured by the method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **The Average IOU** &
\begin{tabular}{c} **The Standard** \\ **Deviation of IOU** \\ \end{tabular} \\ \hline Proposed method & 95.66\% & 0.85 & 0.14 \\ GA-RBF-LMS [17] & 81.37\% & 0.70 & 0.29 \\ YOLO v3 [19] & 86.49\% & 0.72 & 0.33 \\ YOLO v5 [20] & 87.52\% & 0.78 & 0.34 \\ mask R-CNN [21] & 81.39\% & 0.72 & 0.28 \\ CenterNet + DeepSnake [24] & 59.65\% & 0.56 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of target fruit detection algorithms in 1036 test data sets.  
tively. From the processing time point of view, the algorithm in this paper can quickly identify and detect the target fruits, which provides the possibility for further application to picking robots. In the future, we will try to replace other neural networks in the DeepSnake algorithm to further improve the detection accuracy and further expand the data set.  
Conceptualization, W.J. and W.L.; methodology, W.L.; formal analysis, Z.Y.; investigation, H.Y.; resources, Z.R.; writing-original draft preparation, W.L.; writing-review and editing, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number (61806123 and 41871325); National Key R&D Program of China(2019 YFD0900805).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Methodology  
### Simulation Setup  
Finite element analysis is carried out using ABAQUS-FEA(tm) (Dassault Systems, RI, USA). The single-cell trapping simulation model consists of fluid channels (the Eulerian part) and a yeast cell, modeled as a 3D deformable elastic sphere (Figure 3A,B). The fluid part was represented by two microchannels the main channel and the trap channel, which were modeled as a 3D Eulerian explicit EC3 DR and an eight-node linear Eulerian brick, assigned with water properties (density, equation of state, and viscosity). The yeast cell was modeled as a sphere-shaped elastic 3D standard solid deformable C3D8R and an eight-node linear brick 3D part with yeast cell properties (Young's modulus, Poisson's ratio, and density) obtained from the literature [51; 52; 53; 54; 55; 56; 57]. The appropriate channel's geometry to trap a 5\(\upmu\)m, single-particle yeast cell in the specified design was studied. The finite element single-cell trapping model is focused on a single trap channel for geometry optimization due to the complexity and high processing time required for the analysis.  
The parts assembly is demonstrated in Figure 3C, showing the initial position of the yeast cell before simulation. The fluid channel and yeast cell model were assembled to develop the single cell trapping model. The initial position of the yeast cell in the channel was fixed (the distance between the cell and the trap channel) throughout all simulations. The interaction for cell and water was defined as general contact with rough tangential behavior. Both the fluid channel and the cell were meshed using hexahedron mesh type and total mesh elements for the cell trapping model ranged from 28,043 to 100,803 elements. Eulerian boundary conditions applied to the channel's wall were defined as no-inflow and non-reflecting outflow. A constant fluid inflow velocity of 1.0 \(\upmu\)m\(\cdot\)s\({}^{-1}\) was applied to the inlet and the channel outlets were defined as free outflow.  
The simulation analysis could be divided into two parts: T-channel single particle/cell trapping ability and the effects of different \(Rh_{\text{Main}}/Rh_{\text{Trap}}\) ratio and \(W_{\text{Hole}}\). A T-channel model with a trap hole width of 3.0 \(\upmu\)m was used to analyze trapping ability via a simulation analysis. To study the effects of \(Rh_{\text{Main}}/Rh_{\text{Trap}}\) ratio and \(W_{\text{Hole}}\) on cell trapping, various \(L_{\text{Main}}\) values ranging from 50 to 580 \(\upmu\)m and \(W_{\text{Hole}}\) values ranging from 1 to 5 were analyzed. The height of the main channel, trap channel, and trap hole were uniform (\(H_{\text{Channel}}\)) at 7 \(\upmu\)m throughout the analysis.
###### Abstract  
The resilience of cities has received worldwide attention. An accurate and rapid assessment of seismic damage, economic loss, and post-event repair time can provide an important reference for emergency rescue and post-earthquake recovery. Based on city-scale nonlinear time-history analysis (THA) and regional seismic loss prediction, a real-time city-scale time-history analysis method is proposed in this work. In this method, the actual ground motion records obtained from seismic stations are input into the building models of the earthquake-stricken area, and the nonlinear time-history analysis of these models is subsequently performed using a high-performance computing platform. The seismic damage to the buildings in the target region subjected to this earthquake is evaluated according to the analysis results. The economic loss and repair time of the earthquake-stricken areas are calculated using the engineering demand parameters obtained from the time-history analysis. A program named, \"Real-time Earthquake Damage Assessment using City-scale Time-history analysis\" (\"RED-ACT\" for short) was developed to automatically implement the above workflow. The method proposed in this work has been applied in many earthquake events, and provides a useful reference for scientific decision making for earthquake disaster relief, which is of great significance to enhancing the resilience of earthquake-stricken areas.  
city-scale nonlinear time-history analysis; real-time city-scale time-history analysis; resilience-oriented earthquake emergency response; multiple-degree-of-freedom (MDOF) model; ground motion records +
Footnote †: journal: Applied
## 6 Engineering Application  
In order to further validate the applicability of the algorithm, it was implemented in the identification of effective microseismic signals in practical engineering scenarios. The data originated from a microseismic monitoring project conducted in the Jiu Mining area of Hebi City, Henan Province, China. The observation system comprised 16 geophones with a spacing of 60 m between channels. Given the project's nature of long-term monitoring, a substantial volume of microseismic signal data was collected daily. In this study, the effective signal identification was conducted solely on 15 sets totaling 240 microseismic signals collected over a period of three hours. Both the raw signals and their processed results for one of the sets are depicted in Figures 10 and 11, respectively.  
Figure 10: Original microseismic records of actual engineering data.  
Figure 9: The root-mean-square errors of typical signals collected in the field before and after processing by the algorithm.  
From Figures 10 and 11, it is evident that invalid signals in the data are transformed into non-signals through algorithmic processing, while noise within the valid signals is effectively suppressed. Among the 15 sets of microseismic signals, as is shown in Table 2, there are 135 valid microseismic signals, of which the algorithm successfully identifies 132. The accuracy reaches 98.75%, and the recall reaches 97.78%. These results demonstrate that the algorithm can achieve satisfactory performance in practical engineering applications, effectively accomplishing the task of identifying valid microseismic signals.
## 3 Results and Discussion
### Case Studies
The shear correction factor for the case \(\theta=0^{\circ}\) assumes a constant value of 0.7692. Overall, from these figures, it is possible to conclude that, when the ratio \(\eta 1\) increases, the shear correction factor also increases. For the same angle \(\theta\) and the same ratio \(\eta 3\), these variations are less significant for smaller angle absolute values, e.g., \(\theta=-15^{\circ}\) and \(\theta=15^{\circ}\), which are very close to the rectangular cell configuration (\(\theta=0^{\circ}\)). It is also visible that higher values of \(\eta 3\) lead to higher values of the shear correction factor, regardless of the angle \(\theta\) and the ratio \(\eta 1\) values. This means that, when the walls of the unit cell are thicker, this translates into a higher shear correction factor. Figure 11: Evolution of shear correction factor as a function of \(\theta\), \(\eta 1\), and \(\eta 3\) for the set of negative angles of the discrete domain.
# A Novel L-Shaped Metalens for Ultra-Wide Band (UWB) Antenna Gain Improvement
###### Abstract  
In this work, a novel metamaterial lens (metalens) is designed and optimized to improve the radiation performance of an antipodal Vivaldi antenna for wideband applications. The metalens is integrated into the antenna substrate, and placed close to the tapered slot in the end-fire direction, allowing the preservation of the lightweight and compactness of the antenna. The prototype has been fabricated and characterized, demonstrating good agreement with the simulations. The insertion of the metalens allows, with respect to the pristine Vivaldi, a measured maximum gain of \(G_{max}=14.2\)  dB, increased by about \(\Delta G_{max}=4.8\) dB; an operating bandwidth of \(f=3\div 147\) GHz, increased by \(\Delta f=1.2\) GHz; and a radiation pattern with a maximum reduction in half-power beamwidth of \(\Delta HPBW_{max}=31.3^{\circ}\), more symmetrical in the E and H planes.  
metamaterials; microstrip antennas; ultra-wide band antennas; Vivaldi antennas +
Footnote †: journal: Applied
## 2 Materials and Methods
### Metrics  
To quantitatively compare the proposed GPP method with ones presented in Section 2.2, some metrics are introduced. In particular, the three metrics exploited within this work are meant to evaluate the informativeness of the path and its capacity to cover a wider portion of the target area.  
* **Distance Origin to End (02E)** This metric coincides with the variable adopted as contribution in the GPP utility function, indicated as \(d_{02e}\). It specifies how far the final point is from the starting one, providing a sense of how spread the path is over the target area.
* **Path Length (PL)** PL is the total length of a path, computed by adding all the distances between consecutive nodes. It is an indicator of how extended the path is, which has to be considered together with the O2E to avoid misleading conclusions. In particular, in those cases where the O2E results to be small, a high value of PL could mean an extended path and, thus, a wider coverage of the area to inspect. On the contrary, for the same O2E value, a small value of PL entails a short path.
* **Mean Entropy (ME)** ME calculates the line integral of the DE of the points along the whole path. This is done refining Equation (3) and sampling the path with a resolution \(r\). The ME calculation is defined as \[ME=\frac{\int_{P}h(x)dx}{PL/r}[bits]\] (5) where \(P\) is the evaluated path and \(PL\) represents the path length. In this work, the resolution of the path sampling \(r\) is set to 0.2 m. Note that, generally \(r\) can differs from \(d_{min}\), and it is recommendable to always have \(r<d_{min}\) to increase the accuracy in the computation of the metric ME. Moreover, it is important to highlight that the computation of the metric ME is performed offline. Thus, \(r\) can be set according to the desired accuracy of the ME metric without influencing the computational cost of the planners. Therefore, the authors decided to maintain \(r\) equal for all the target areas. Considering IPP strategies in the IG scenario, the ME represents a decisive indicator about the planner performance, as it provides the amount of information acquired along a path. However, this metric alone could not be sufficient to assess the best path as the goal of the planner proposed in this work is to maximise both information and coverage of the inspected area. Therefore, PL and O2E are utilised to support the evaluation and are used as area coverage indexes. Higher PL and O2E values correspond to a wider coverage of the target area.
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 4 Experiments
### Comparisons with Other State-of-the-Art Methods  
#### 4.3.1 Results on UTDAC2020  
The experiment results obtained from the UTDAC2020 dataset are presented in Table 3. As this work focuses on model lightweighting research, we employed AP and AP50 to assess the model accuracy and used parameters, FLOPS, and model size to compare the model scale. The highest level is indicated by text in bold. To validate the effectiveness of our proposed method, we compared it with the YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l models, and other well-established detectors results from the paper [6]. As shown in Table 3, our method reduces the parameters, FLOPS, and model size of the YOLOv8s model by 23.42% (8.5 M vs. 11.1 M), 10.21% (25.5 B vs. 28.4 B), and 22.73% (17 MB vs. 22 MB), respectively. However, because our model is specifically optimized for underwater scenarios, its accuracy surpasses even that of the larger YOLOv8l model, achieving an AP of 52.12% and AP50 of 85.49%. This represents an improvement of 1.62% (52.12% vs. 50.50%) and 0.76% (85.49% vs. 84.73%) compared to YOLOv8s, respectively. The experimental results confirm the effectiveness of our method in underwater scenarios and its high precision. To further enhance the model accuracy, especially considering the prevalence of small objects in underwater scenarios, we increased the image input of the longer side to 1280 pixels. This led to a further improvement in model accuracy, with an AP of 53.18% and an AP50 of 86.21%. We conducted processing speed tests using a single GeForce RTX 2080 Ti, testing over 1000 images from the UTDAC2020 dataset and averaging the results. Our method, at 640, achieved a processing speed of 68.03 frames per second (FPS), while at 1280, our method achieved 41.49 FPS. Despite a decrease in processing speed when increasing the input image size, both methods maintain real-time performance (30 FPS or better) [38]. Users can choose the most appropriate method based on the specific underwater scenario. To the best of our knowledge, our method achieves state-of-the-art (SOTA) performance on the UTDAC2020 dataset. Thanks to the prediction head for small objects, DCNv2, and coordinate attention, false positives and false negatives in the Echinus and Starfish categories significantly decreased. To accurately illustrate the differences, the baseline YOLOv8s used the image with a longer side of 640 pixels as the input, while our method used input images with longer sides of 640 pixels and 1280 pixels, respectively. We visualized the detection results for the underwater UTDAC2020 dataset. As shown in Figure 5, the YOLOv8s model exhibits some instances of false positives (yellow boxes) and false negatives (blue boxes), especially with small objects.
# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 4 Experiments
### Speech Experiment
#### 4.1.2 Setup for Speech Experiment  
We used the spectrogram magnitude of the \(16\) kHz single-channel audio signal as the model input. Spectrograms can be obtained through a short-time Fourier transform (STFT) with Hanning window of \(320\) samples and \(50\%\) overlap. The number of frames in the input spectrogram is \(256\). The learning rate was \(0.001\) and the optimizer used Adam [48]. The batch size was \(16\) and the number of epochs was \(20\). The validation dataset was tested at every epoch and the model with the best signal-to-distortion ratio (SDR) [49] was finally selected and tested. To evaluate speech enhancement performance, we performed a subjective speech quality test. In addition, we also calculated objective measures. As objective measures, SDR, which is the signal separation performance, and perceptual evaluation of speech quality (PESQ) [50], which is performance related to speech quality, and short-time objective intelligibility (STOI) [51], normalized-covariance measure (NCM) [52], coherence-based speech intelligibility index (CSII) [53], which are speech intelligibility measures, were computed. PESQ ranges from \(-0.5\) to \(4.5\), STOI, NCM, and CSII range from \(0\) to \(1\), and SDR had no fixed range. Larger values of the performance evaluation indicators represented better performance. In addition, to evaluate speech recognition performance, we used the nnet3 (chain) model of the Kaldi toolkit [54]. The Kaldi speech recognition model was trained with the WSJ1's clean speech, the SI-284 training dataset. The speech recognition performance is computed by word error rate (WER), and lower WER indicates better performance.
## 4 Discussion  
### Differences of Sound Levels and Loudness  
The results reported in Section 3.1 described the differences of sound levels and loudness recorded between different days of the week, different living rooms, and different time slots of the day. To the knowledge of the authors, this was the first study to monitor multiple nursing homes over a one-week period.  
The fact that no statistically significant differences were observed in terms of sound levels and loudness in the living rooms between different days of the week was expected, to some extent. It reflects the methodical routine of both staff members and residents in such facilities. Everyday activities (and the sound levels they generate) occur more or less always with the same intensity and over the same periods of time, regardless of the day, in all the nursing homes. This approach seems to be encouraged at a planning and management level in the nursing homes, since the regular temporal variations in the sound environments provide indicators of \"audible safety\", which is a crucial construct for residents affected by BPSD [1].  
When checking for the living room factor, differences of sound levels and loudness did indeed emerge, with some living rooms noisier than others, confirming that each facility has its own \"acoustic climate\". In general, the noisier living rooms tended to be the medium-sized and slightly over-reverberant ones (see also Table 1 and Figures 8 and 9); however, it was not possible to experimentally control for these factors in particular, since geometries and materials were not modifiable. This suggests that the room acoustics condition is affecting the sound levels, so an acoustic retrofit might actually be a viable strategy to control the sound environment of the living rooms.  
The differences in sound levels between different moments of the day were also found to be statistically significant, considering all nursing homes together, with higher levels occurring typically around the breakfast/lunch/dinner times due to higher rates of activity both at the social (e.g., people talking) and functional level (e.g., staff working with kitchenware and serving food). This sound level variability reflects the daily pattern of the living rooms, which is also desirable for its perceptual implications of hints of audible safety [1; 19], as mentioned above.
# Pore Structure of Coals by Mercury Intrusion, N\({}_{2}\) Adsorption and NMR: A Comparative Study
## 3 Results
### The Porosity and Pore Size Distribution by Nuclear Magnetic Resonance (NMR)  
#### 3.3.1 Determination of Optimum Centrifugal Force  
In NMR experiment, the determination of the optimal centrifugal force is helpful for the accurate calculation of NMR T\({}_{\text{2C}}\) value that reflects the accuracy of pore size distribution (PSD). The NMR signal of the saturated water coals under different centrifugal forces are used to calculate and compare the change of water saturation to acquire the optimal centrifugal force. The changes of water saturation of the 16 coal samples are shown in Figure 4. The results show that the water saturation of coals keep decreasing when the centrifugal force increases, and the decrease range of water saturation is 13.85%-42.10% when the centrifugal force increases to 1.61 MPa, with an average of 27.08%, which indicate that the pores mainly contain bound water. When the centrifugal force increases from 1.38 MPa to 1.61 MPa, the decrease range of water saturation is between 0.51% and 1.01% with an average of 0.71%, which is basically negligible. Therefore, it can be determined that 1.38 MPa (200 psi) can be considered as the optimal centrifugal force to make saturated coal samples of different coal rank to reach the state of bound water.  
The water in pores is subjected to capillary pressure under the effect of centrifugal force, which is similar to the process of mercury withdrawal in MIP experiment. Therefore, the development characteristics of the pore in coals can also be studied by centrifugation [8]. However, the centrifugal pressure points selected in this experiment are scarce to characterize the pore development in detail.