esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Efficiency, Low-Power, Fully Integrated Neural Electrical Stimulation Circuit
###### Abstract  
This paper presents a highly efficient, low-power, fully integrated neural stimulation circuit implemented using solely low-voltage devices. The circuit primarily consists of a high-voltage-generation circuit, an output driver circuit, and a constant-current source, designed and simulated using a 180 nm low-voltage CMOS process. The high-voltage-generation circuit utilizes a negative-voltage-generation module together with a series-parallel capacitor charge pump circuit, which effectively reduces the number of charge pump stages by three, and saves 29% of the area compared to a conventional charge pump circuit. A bootstrap clock generation circuit was utilized to generate the control signal to ensure that all transistors work within their voltage limit. To realize the high-voltage output driver circuit using low-voltage devices, a stacked transistor structure with deep N-well (DNW) devices was utilized. The four different output voltage levels from the high-voltage-generation circuit were utilized to generate a different voltage domain of control signals and bias voltage for the stacked transistors, making sure that all transistors work within their voltage limit. Simulation results show that the high-voltage-generation circuit can generate an output of up to 12.69 V from a 1.65 V low input voltage, with a maximum output current of 1 mA, achieving 74.9% efficiency. The overall efficiency of the neural stimulation circuit, including the high-voltage-generation circuit, output driver circuit and constant-current source, reaches 74% under the voltage-controlled stimulation (VCS) mode and 59.5% under the current-controlled stimulation (CCS) mode, whereas the standby static power consumption is as low as 66 pW.  
negative voltage; high-voltage charge pump; neural stimulation; low power Article
## 1 Introduction
Comparisons of the general detection algorithms in the field of machine vision indicate that the design is more suitable for the characteristics of the SAR image ship target for data enhancement and model optimization. At the same time, it provides ideas for the design of other special target detectors. 3. The SAR image detection system requires continuous detection, and a fast, iterative system can reduce the training cost of the detection model. Surface ship detection is important in both civilian and military fields. It has great application value for managing ships entering and leaving ports, combating smuggling, and monitoring marine fisheries and ship oil spill pollution. These practical applications all require a simple and fast ship detection process. In real-world applications, the acquisition equipment continuously obtains new data that need to be relabeled and screened, requiring the detection system to train the model for a long time and be able to iterate rapidly and continuously to optimize the model. Therefore, the detection system requires continuous detection and continuous iteration to adapt to the updated data. If less data are needed during training, it can take less time to iterate faster and adapt to the updated data. The main contributions of this paper are as follows:  
1. Through balanced positive and negative data enhancement, in the case of the same amount of data, the data performance ability is improved so that the model can mine deeper data associations. When the amount of training data is reduced to 30%, the balanced positive and negative data enhancement can still maintain a detection effect close to the training result achieved with 100% of the dataset. 2. The accuracy of ship target detection is improved by using scale-adaptive loss. 3. Using the regional attention preadaptation mechanism based on statistical analysis to extract information, combined with the scale-adaptive loss, the detection accuracy of the model is improved. This paper is organized as follows: the second section introduces the related work in the field of ship target detection; the third section describes an improved data augmentation method to achieve ship target detection based on a small amount of data; proposes scale-adaptive loss; and a spatial attention mechanism based on statistical analysis to solve the problem of multiscale ship target detection. In Section 4, the experimental results are presented and discussed. After the results of the paper are presented, they are further discussed with concluding remarks in Section 5.
## 4 Experimental Results and Analysis
### Orthogonal Test Analysis
By correlation (Table 8) analysis, it was seen that the depth of the underwater vehicle dive and the wake buoyancy spread to the surface of the temperature difference had a significant correlation, with an absolute value greater than 0.8, indicating a negative correlation; the surge conditions and wake temperature difference correlation coefficient absolute value were less than 0.3, indicating a weak negative correlation. This further demonstrated that the influence of the dive depth of this underwater vehicle on the temperature difference between the buoyancy of the wake and the spread to the surface was larger than the effect of the seawater surge factor.
# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 4 Results and Analysis
### Objective Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image** & **SPD-MEF [19]** & **MRF [25]** & **DSIFT [28]** & **FEAF [7]** & **LE \({}^{*}\) [26]** & **B-LP [27]** & **LLF [14]** & **Ours** \\ \hline Balloons & 0.9640 & 0.9445 & 0.9621 & 0.8934 & 0.7704 & 0.8174 & 0.9512 & **0.9645** \\ Belgium house & 0.9702 & 0.9448 & 0.9723 & 0.9127 & 0.7346 & 0.7500 & **0.9747** & 0.9703 \\ Chinese garden & 0.9853 & 0.9820 & 0.9729 & 0.9640 & 0.9171 & 0.9062 & 0.9708 & **0.9873** \\ Church & 0.9924 & 0.9789 & **0.9922** & 0.9657 & - & 0.8787 & 0.9904 & 0.9849 \\ House & 0.9094 & 0.9213 & 0.9609 & 0.9419 & 0.6571 & 0.3661 & 0.9464 & **0.9621** \\ Kluki & 0.9704 & 0.9649 & **0.9800** & 0.9301 & 0.8514 & 0.8175 & 0.9722 & 0.9701 \\ Lamp & 0.9537 & 0.9306 & 0.9644 & 0.9254 & 0.5770 & 0.5510 & 0.9611 & **0.9675** \\ Landscape & 0.9731 & 0.9721 & 0.9720 & 0.9770 & 0.9008 & 0.9207 & 0.9916 & **0.9937** \\ Laurenziana & 0.9854 & 0.9760 & 0.9892 & 0.9690 & - & 0.8806 & 0.9867 & **0.9897** \\ Lighthouse & 0.9704 & 0.9529 & 0.9742 & 0.9679 & 0.7934 & 0.8845 & 0.9749 & **0.9813** \\ Mask & 0.9826 & 0.9814 & **0.9922** & 0.9696 & - & 0.8845 & 0.9883 & 0.9841 \\ Office & 0.9876 & 0.9722 & 0.9874 & 0.9614 & 0.8307 & 0.7674 & **0.9889** & 0.9780 \\ River & 0.9877 & 0.9733 & 0.9895 & 0.9814 & - & 0.8721 & 0.9885 & **0.9898** \\ Room & 0.9777 & 0.9722 & **0.9820** & 0.9439 & - & 0.8447 & 0.9777 & 0.9740 \\ Tower & 0.9860 & 0.9835 & 0.9870 & 0.9499 & 0.8979 & 0.8728 & 0.9861 & **0.9873** \\ Madison Capital & 0.9768 & 0.9182 & 0.9723 & 0.9268 & - & 0.5490 & 0.9772 & **0.9778** \\ Average & 0.9732 & 0.9605 & 0.9781 & 0.9487 & 0.7930 & 0.7852 & 0.9766 & **0.9789** \\ \hline \hline \end{tabular} \({}^{*}\)LE fusion images are provided by [26]. ‘-means results not included in [26]. Bold indicates the best fusion result.  
\end{table} 
Table 2: Comparison of image quality assessment (IQA) with existing MEF techniques.
###### Abstract  
This paper introduces Semantic Knowledge Advanced Tool for Extraction Browsing Organisation Annotation Retrieval and Discovery (SKATEBOARD), a tool designed to facilitate knowledge exploration through the application of semantic technologies. The demand for advanced solutions that streamline Knowledge Extraction, management, and visualisation, characterised by abundant information, has grown substantially in the current era. Graph-based representations have emerged as a robust approach for uncovering intricate data relationships, complementing the capabilities offered by AI models. Acknowledging the transparency and user control challenges faced by AI-driven solutions, SKATEBOARD offers a comprehensive framework encompassing Knowledge Extraction, ontology development, management, and interactive exploration. By adhering to Linked Data principles and adopting graph-based exploration, SKATEBOARD provides users with a clear view of data relationships and dependencies. Furthermore, it integrates recommendation systems and reasoning capabilities to augment the knowledge discovery process, thus introducing a serendipitous effect generated by the SKATEBOARD interface exploration. This paper elucidates SKATEBOARD's functionalities while emphasising its user-centric design. After reviewing related research, we provide an overview of the SKATEBOARD pipeline, demonstrating its capacity to bridge RDF and LPG representations. Subsequent sections delve into Knowledge Extraction and exploration, culminating in the evaluation of the tool. SKATEBOARD empowers users to make informed decisions and uncover valuable insights within their data domains, with the added dimension of serendipitous discoveries facilitated by its interface exploration capabilities.  
Semantic Web; knowledge graph; labelled property graph; extraction; browsing; organisation; annotation; retrieval; discovery Article
## 1 Introduction
### Motivation  
Aquaculture has grown considerably in Brazil over the past few decades, in line with global trends, to become a valuable source of income and employment [2; 14]. In 2022, Brazil produced 860,355 tons of farmed fish, based on data from the Brazilian Pisciculture Association. This represents an increase of 2.3% over the 841,005 tons produced in 2021. Since 2014, in fact, the annual production of farmed fish in Brazil has increased by almost half (281,555 tons or 48.6%) [15]. The majority of these fish are produced by smallholders in ponds with an area of less than 2 ha, a pattern similar to that observed in a number of prominent fish-farming countries in Asia [16].  
In addition to the possibility of generating a profit from aquaculture, smallholders are encouraged to invest in the production of aquatic organisms, such as fish, shrimp, frogs, oysters, and algae, by the demand from local and regional markets, the availability of water, land (including lakes, reservoirs, and coastal environments), and, in most Brazilian states, the access to raw materials. The vast majority of these producers initiate their aquaculture operations without any planning or preliminary survey, however, which has led many to abandon or downsize their operations [11]. There are a number of other weak links in the chain of production, such as the transfer of technology, technical support, the availability of credit, government policy, and the regulation of the sector [16].  
One other problem observed frequently in Brazil is the inadequate evaluation of the subsoil of sites earmarked for the excavation and construction of fish-farming ponds. A lack of adequate technology for the systematic assessment of the subsoil of sites earmarked for fish ponds has led to the failure of some projects, due to the instability of the infrastructure. Given the resources required to collect and analyze samples, and the delay in obtaining the final results, the need for a detailed on-site assessment may limit the viability of fish farming for many small- to medium-sized producers.  
Few rural landowners in the northern Brazilian state of Para--in particular in the geographic region of Braganca--are aware of the technology available to survey the subsoil of terrains earmarked for fish farming ponds and this lack of an adequate database has caused the failure of many projects. The reasons for failure range from inappropriate decision making on the types of terrain and aquaculture system to be used to the construction of inadequate installations. While reliable data on the structure of the subsoil are essential for the success of any aquaculture project, the high costs of obtaining and analyzing these data, as well as the time required to obtain the results, may represent a major limitation in many cases.
## 2 Materials and Methods
### Datasets  
Several datasets were used to evaluate the impact of selected artifacts removal methods on the image quality. The BreCaHAD dataset [43] includes 162 uncompressed histopathological RGB images showing tissue sections with breast cancer cells. The images are divided into 17 different cases. Each image was taken with 40\(\times\) magnification and then divided into smaller tiles saved as 1360 \(\times\) 1024 TIFF files. The proposed methods were also tested on the datasets commonly used in a task of compression artifacts removal: the test set of BSDS500 [44], LIVE1 [45], Kodak24 ([[https://r0k.us/graphics/kodak/](https://r0k.us/graphics/kodak/)]([https://r0k.us/graphics/kodak/](https://r0k.us/graphics/kodak/)), accessed on 4 February 2025) Set5 [46], and Set14 [47]. All of these datasets consist of uncompressed RGB images.  
Since the BreCaHAD dataset is not labeled for the nuclear segmentation task, two additional datasets were used: Kumar [41] and CPM-17 [40]. Both datasets contain compressed images of varying quality, with visible JPEG compression artifacts, but the information about the compression quality factor is not included. The Kumar dataset consists of 30 image tiles of size \(1000\times 1000\). Slides were acquired at \(40\times\) magnification and come from seven organs (6 breasts, 6 livers, 6 kidneys, 6 prostates, 2 bladders, 2 colons, and 2 stomachs) of The Cancer Genome Atlas (TCGA) database. We split this dataset into the test set with 14 images (2 prostates, 2 kidneys, 2 livers, 2 breasts, 2 bladders, 2 colons, and 2 stomachs) and the training set with 16 image tiles (4 prostates, 4 kidneys, 4 livers, and 4 breasts). The CPM-17 dataset includes 32 images from TCGA varying in size from \(500\times 500\) to \(600\times 600\) in both the training and test datasets. Images were taken at \(20\times\) and \(40\times\) magnification and included four different types of cancer. Note, we utilize the same split used in the [36] for both datasets.
## 4 Results and Discussion
### Isotopic Composition
Also, the increased temperature enhanced the evaporations, leading to isotopic depletion of \(\delta^{2}\)H and \(\delta^{18}\)O in the groundwater [47; 48; 49]. Generally, \(\delta^{2}\)H and \(\delta^{18}\)O showed either strong or weak negative correlations with all the measured parameters except for Ca and Mg (R\({}^{2}>0.5\)) (Table 3). This meant that any change in the fundamental chemistry of the water would affect the isotopic compositions of the groundwater. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & **Temp** & **pH** & **SAR** & **EC** & **TDS** & **AlkalinityNa** & **K** & **Ca** & **Mg** & **F** & **NO\({}_{3}\)** & **Cl** & **SO\({}_{4}\)** & **PO\({}_{4}\)** & **CO\({}_{3}\)** & **HCO\({}_{3}\)** & **8\({}^{3}\)O** & **8\({}^{3}\)H** \\ \hline
**Temp** & 1.00 & & & & & & & & & & & & & & & \\ pH & **0.58** & 1.00 & & & & & & & & & & & & & & \\
**SAR** & 0.01 & 0.19 & 1.00 & & & & & & & & & & & & & \\
**EC** & 0.27 & -0.07 & 0.41 & 1.00 & & & & & & & & & & & & \\
**TDS** & 0.48 & 0.03 & 0.42 & **0.95** & 1.00 & & & & & & & & & & & \\
**Alkalinity** & **0.81** & **0.62** & -0.31 & -0.23 & -0.01 & 1.00 & & & & & & & & & & \\ Na & 0.47 & 0.15 & 0.52 & **0.96** & **0.97** & -0.04 & 1.00 & & & & & & & & & \\ K & **0.50** & 0.00 & 0.40 & **0.92** & **0.92** & -0.04 & **0.94** & 1.00 & & & & & & & & \\
**Ca** & \(-0.23\) & -0.54 & -0.89 & -0.29 & -0.37 & 0.05 & -0.47 & -0.30 & 1.00 & & & & & & & \\ Mg & \(-0.47\) & -0.65 & -0.79 & -0.44 & -0.55 & -0.17 & -0.63 & -0.44 & **0.95** & 1.00 & & & & & & \\ F & **0.79** & 0.42 & -0.23 & 0.46 & **0.54** & **0.50** & **0.52** & **0.58** & -0.03 & -0.27 & 1.00 & & & & & \\
**NO\({}_{3}\)** & \(-0.14\) & -0.57 & -0.26 & 0.21 & 0.08 & -0.33 & 0.05 & 0.32 & 0.49 & **0.52** & 0.16 & 1.00 & & & & \\
**CI** & 0.02 & -0.28 & 0.31 & **0.91** & **0.83** & -0.43 & **0.79** & **0.74** & -0.16 & -0.26 & 0.33 & 0.33 & 1.00 & & & \\
**SO\({}_{4}\)** & 0.20 & -0.36 & **0.50** & **0.81** & **0.80** & -0.34 & **0.78** & **0.87** & -0.27 & -0.30 & 0.26 & 0.49 & **0.78** & 1.00 & & & \\
**PO\({}_{4}\)** & -0.35 & -0.54 & -0.07 & 0.32 & 0.13 & -0.65 & 0.13 & 0.31 & 0.25 & 0.34 & 0.12 & **0.87** & **0.51** & **0.52** & 1.00 & & & \\
**CO\({}_{3}\)** & **0.80** & **0.72** & -0.25 & -0.24 & -0.02 & **0.99** & -0.04 & -0.07 & -0.05 & -0.26 & **0.50** & -0.41 & -0.43 & -0.38 & -0.67 & 1.00 & & \\
**HCO\({}_{3}\)** & **0.81** & **0.58** & -0.33 & -0.23 & -0.01 & **1.00** & -0.04 & -0.03 & 0.08 & -0.14 & 0.49 & -0.31 & -0.43 & -0.33 & -0.64 & **0.98** & 1.00 & & \\
**8\({}^{3}\)O** & \(-0.24\) & -0.51 & -0.68 & -0.16 & -0.21 & -0.18 & -0.34 & -0.15 & **0.68** & **0.69** & 0.17 & **0.66** & 0.03 & -0.03 & **0.61** & -0.24 & -0.16 & 1.00 & & \\
**8\({}^{3}\)H** & \(-0.36\) & -0.37 & -0.61 & -0.35 & -0.43 & -0.06 & -0.46 & -0.35 & **0.69** & **0.71** & -0.28 & 0.09 & -0.41 & -0.45 & -0.07 & -0.14 & -0.03 & 0.42 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pearson correlation matrix of correlation among physicochemical and isotopic variables in geothermal water/boreholes.
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 6 Simulation Results and Discussion  
The Monte Carlo simulation results are presented in this section to figure out the performance of the proposed algorithms. The value of \(P_{max}\) is less than 20 dBm, whereas the circuit power dissipation at each node is assumed to be 10 dBm. The total number of subcarriers, \(K\), is set as 5 or 10. The Third-Generation Partnership Project (3 GPP) path-loss model is used in our simulations [23]. Additionally, both the Rayleigh fading, \(\sim\mathcal{CN}(0,1)\), and the log-normal shadowing effects, \(\sim\ln\mathcal{N}(0,8\text{ dB})\), are considered. The circuit and processing power dissipation values at each node are assumed to be 10 dBm, respectively, while the adjacent subcarriers frequency spacing is set to be 12 kHz. The thermal noise density is \(-174\text{ dBm}/\text{Hz}\). In the proposed EEM algorithm, the value of the maximum number of inner and outer iterations is 10, while we set the convergence tolerance value as \(10^{-5}\). The initial value of the penalty factor \(\Psi\) is 0.001. The distances from the source node to the relay node and from the relay node to the destination and eavesdropper nodes are denoted by \(d_{SR}\), \(d_{RD}\), and \(d_{RE}\), respectively. As benchmarks, we also simulate the performance of the EEM w/o E.  
Figure 2 illustrates the convergence behavior of the designed algorithms for a _single channel realization_ with \(K=5\), \(d_{SR}=d_{RD}=200\) m, \(d_{RE}=300\) m, and \(P_{max}=5\text{ dBm}\). It can be observed that the EE performance of the proposed algorithms and also EEM w/o E increases monotonically with the number of iterations, and the iterative algorithms converge within five iterations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Complexity** \\ \hline Optimal ES & \(\mathcal{O}\left(2^{2e}WLK!\left(V^{3}+1\right)\right)\) \\ EEM & \(\mathcal{O}\left(2^{2e}K^{2}WL\left(V^{3}+K+1\right)\right)\) \\ Suboptimal & \(\mathcal{O}\left(2^{2e}KWL\left(V^{3}+3\right)\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complexity comparison. ES: exhaustive search; EEM: energy efficiency maximization.
## 4 Results and Discussion
### Comparison between Simulations and Experimental Results  
Figure 11 shows the plot of the total charge in the EFC model for the three geometries. Using optimized parameters from Table 2, the circular EFC geometry was most favorable for charging the slurry electrodes at a low flow rate. The charging activity of the carbon particle was maximum near the current collecting boundary. The model also showed the inactivity of charging in the centers of the flow channels [49, 54] in terms of uncharged SP when moving away from the current-collector (CC) boundary.  
Figure 12 shows the charge distributions in the flow channel for the three geometries. The charging activity was more prominent near the CCs, leaving the central regions uncharged. The circular channel exhibited maximum charging and carbon utilization, as shown in Figure 12a-d. The charge diffusion becomes less effective for the carbon electrode in the box-shaped geometry with a linear and extended CC, slowing the rate of charging, as shown in Figure 12e-l. The progress of charging for each ge  
Figure 11: Total charge in three geometrical designs with optimized values of modeling parameters at the constant voltage of 0.75 V.  
Figure 10: \(\beta_{Na}\) as a function of SP size with \(D_{IA}=D_{Sp}\). Calculations were performed using the experimental reference data of EFC charging at a constant voltage of 0.5 V.  
plementary Materials: Figure S1 for the circular geometry design, Figure S2 for box-shaped geometry with a linear CC, and Figure S3 for box-shaped geometry with an extended CC.
# Design and Depth Control of a Buoyancy-Driven Profiling Float
## 3 Depth Control System
### Control Strategy
cycle time and the depth sensor's accuracy, the accuracy of the speed was about 0.01428 m/s. Floats can no longer accurately track the target speed with less than twice the speed accuracy. At this point, the float was about 0.8 m from the target depth, and the speed of the float was sufficiently low. So, depth PD control was adopted within the range of 0.8 m from the target depth. Due to the better robustness of the PID system, the PD parameters of two stages at different depths could achieve better depth control accuracy and speed tracking without adjustment in the same or similar sea area. In general, for segment PD control, in the initial stage of diving, the speed PD control with low inertia enables the float to dive at the expected speed and ensure that the speed is small enough when it is close to the target depth. Depth PD control was used in the area close to the target depth to avoid insufficient accuracy of differential velocity. The flow chart of the whole control process is shown in Figure 3. When the current position of the float was more than 0.8 m away from the target depth (\(e_{d}\) > 0.8), the float was controlled according to the red line in Figure 3. First, the depth error \(e_{d}\) was used to calculate the target velocity \(v_{d}\) through Equation (10), and then the difference between target speed \(v_{d}\) and actual speed \(v_{r}\) was used as the input of PD to control the float. When \(e_{d}\) < 0.8, the float was controlled according to the blue line in Figure 3. Then, the control strategy was equivalent to the traditional PD control.