esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study  
### Study Area  
The NCP is one of the most important agricultural production areas in China. Based on the expansion of inorganic fertilizers since the 1070s [45] and the increase in irrigation [46], the wheat and corn production of the NCP agricultural production system accounted for 61% and 39% of China's total agricultural production in 2012, respectively [47]. The annual chemical nitrogen fertilizer input was estimated to be as high as 400-600 kg\(\cdot\)hm\({}^{-2}\)[48]. Excessive application of nitrogen fertilizers is one of the important factors leading to the increasing nitrate concentration in groundwater in the NCP region. The Lake Baiyangdian watershed is a typical NCP region. The main crops in the region are cotton, vegetables, fruit trees, winter wheat, and summer maize, which are rotated annually. The water deficit is balanced by groundwater pumping. Because of intensive agricultural activities and fertilizer application, nitrate has become the main chemical pollutant input in farmland. To mitigate the increasing nitrate pollution of the groundwater, it is important to assess groundwater vulnerability.  
The plain area of the Baiyangdian watershed (16,102.6 km\({}^{2}\)) is in the western part of the NCP (114\({}^{\circ}\)18\({}^{\prime}\)45\({}^{\prime\prime}\)-116\({}^{\circ}\)29\({}^{\prime}\)59\({}^{\prime\prime}\) E, 38\({}^{\circ}\)01\({}^{\prime}\)18\({}^{\prime\prime}\)-39\({}^{\circ}\)51\({}^{\prime}\)27\({}^{\prime\prime}\) N; Figure 1). The climate is semiarid continental monsoon with a perennial mean precipitation of 503 mm; 75% of the rainfall occurs from June to September. The annual evaporation is 1369 mm [49]. The topography of the plain area of the Baiyangdian watershed inclines from west to east and includes a piedmont plain (composed of alluvial fans and plains) and a depression. The entire area is flat and the slope of the terrain only changes on dry riverbeds or gullies. The alluvial fans and paleochannels in the study area were formed by rivers and lake depressions in the Late Pleistocene [50].  
Unconsolidated Quaternary sediments constitute the main stratigraphy of the plain area (Supplementary Figures S3 and S4). The sediments include cobbles, gravel, sand, laminated or lensed clay, and interbedded sand and clay. Based on the stratigraphic features, the aquifers can be divided into four aquifer groups: the first and second aquifer groups (I, II), that is, Holocene Q\({}_{\text{h}}\) aquifers with a depth of 10-50 m and upper Pleistocene Q\({}^{3}_{\text{P}}\) aquifers with a depth of 80-160 m; the third aquifer group (III), that is, the mid-Pleistocene Q\({}^{2}_{\text{P}}\) with a depth of 330-400 m; and the fourth aquifer group (IV), that is, the lower Pleistocene Q\({}^{1}_{\text{P}}\) aquifer group [49]. Shallow groundwater, which was selected as the target for the assessment of the vulnerability to nitrate contamination, includes the first and second aquifer groups (I, II). It is the major stratum used for the supply of water for domestic and agricultural use in the area.
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Cluster-Based  
Almomani et al. [139] advanced a cluster framework for planning preventive maintenance actions using the Group Technology (GT) concept. Gong et al. [140] used cluster analysis (CA) to establish the qualitative analysis models on the basis of the two-channel and differential dielectric spectroscopy (TD-DES) data and Fourier transform infrared (FTIR) spectroscopy data of the in-service lubricants. Da-Silva et al. [141] utilized most of CA techniques while clustering the chemical analysis of lubricant. In a recent study, CA was utilized to confirm fuel dilution in an engine oil [142]. A fuzzy clustering approach was proposed as a lubricant system fault diagnosis framework using fuzzy sets theory on the system characteristics [143].
# Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts
## 1 Introduction  
### Motivation  
In 2021, NASA will have spent sixty years conducting human spaceflight operations. From Mercury onwards, including the current International Space Station (ISS) programs, astronauts and flight controllers in Mission Control Center (MCC) have had near real-time communications. Even during the Apollo program, the landing sites selected enabled line-of-sight communication with Earth [1]. Space-to-Ground communication between these two teams is essential and allows for astronauts to have real-time access to the vast knowledge and resources available on Earth. Flight controllers can supervise and manage the various subsystems of the spacecraft, allowing astronauts to focus on other tasks. If something goes awry in space, astronauts and flight controllers can collaborate to diagnose, repair, and recover from the issue. Astronauts can also receive advice, training, and guidance to complete scientific or maintenance tasks.  
The new challenge that NASA faces in human spaceflight operations for future long-duration exploration missions (LDEMs) stems from the dramatic change in communications between astronauts and ground. As missions explore farther afield from Earth, one-way light time communication transmissions increase. Communication latencies start from a few seconds on the Moon and grow to several minutes while in transit to Mars. Once on Mars, astronauts will encounter up to 44-min round trip communication delays [2]. Communication is unlikely to be constant, and MCC and astronauts will only be able to exchange data during certain times of the day. Depending on the length of the stay, astronauts will also contend with periods of time where there is no communication with Earth. As such, astronauts need to become more Earth-independent, completing assigned tasks and dealing with unexpected events more autonomously. This significant shift in operations requires innovative, new methods, tools, and processes that enable astronauts to conduct themselves autonomously from MCC. At NASA Ames Research Center (ARC), the Human-Computer Interaction (HCI) Group has started to investigate the use of emerging technologies to make crew autonomy possible under high communication latencies. Through a series of prototypes and user testing, we aim to identify how best to map these emerging technologies to tasks conducted in human spaceflight operations in order to design future systems with high user adoption and acceptable usability.
# Developing a Novel Design for a Tubular Solid Oxide Fuel Cell Current Collector
## 3 Numerical Methodology
### Model Validation  
In order to use the numerical model effectively, it is important to confirm that the results obtained from the numerical model are accurate and correct. This objective can be achieved by validating the numerical results with available experimental data. The polarization curve (I-V curve) is an effective way to compare the validation of the numerical model with the experimental data. The I-V curve can be constructed experimentally by fixing the voltage at certain values and then measuring the current density at each value. The same method can be followed to construct the I-V curve through the numerical model. The I-V curve obtained from the numerical model was compared with the I-V curve obtained experimentally by [PERSON] et al. [38] and [PERSON] et al. [39] at different operating conditions and cell configurations, as described in Table 5. The results demonstrated that the numerical model could obtain results nearly identical to those obtained experimentally under different operating conditions and also with different cell configurations (as shown in Figure 5), confirming the model's validity and its ability to simulate the real case, even when used with cells of different dimensions and configurations. Comparing the results of the numerical model with the experimental values obtained by [PERSON] et al. [38] and [PERSON] et al. [39], the maximum errors were 5.3% and 5.85% at 0.6 volts, respectively. These results ensure the validity of the numerical model using various geometrical configurations, boundaries, and operating conditions and that it can be utilized effectively to investigate and attain the purpose of this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mesh** & **A** & **B** \\ \hline Element size & 0.2 mm sizing & 0.125 mm sizing \\ \hline Mesh size & 1,057,997 & 1,617,846 \\ \hline No. of cores used & 7 & 7 \\ \hline Iteration time (sec) & 15 & 20 \\ \hline Amp. at 0.6 volt & 0.636838 & 0.635234 \\ \hline Error (\%) & 5.78 & 5.5466 \\ \hline No. of iterations & 17,650 & 17,650 \\ \hline Solving time (hr) & 73.54 & 98.06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mesh independency analysis.  
Figure 5: Validation of the numerical result with the experimental result.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 6 Privacy Preserving Search for the Nearest Bank/ATM
### Circuit for Computing the Nearest ATM  
Following the computation of distance between the mobile client and \(L\) ATM locations, it is necessary to find the nearest ATM, along with its distance. We use the approach of [PERSON] _et al._[21] to find the minimum value and its index, given a list of values. [PERSON] _et al._[21] have designed a \(MIN\) block to find the minimum of two input values - it uses the result of a comparator to multiplex the minimum value, as well as the corresponding index as shown in Figure 12. Table 4 shows the size of the circuit that computes the minimum of two values, along with its index. In our privacy preserving application, each distance is an \((l+1)=11+1=12\)-bit number, and each index that identifies an ATM using its East and South coordinates is an \(L_{ind}=2l=22\)-bit number.  
Figure 12: Finding minimum value and index.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Block** & **\#XOR Gates** & **\#AND Gates** \\ \hline \(2\times SUB\) & \(2\times 4l\) & \(2\times l\) \\ \(2\times INV\) & \(2\times l\) & \(0\) \\ \(ADD\) & \(4l\) & \(l\) \\ \(INC\) & \(l+1\) & \(l\) \\ \(Total\) & \(15l+1\) & \(4l\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Circuit size for Manhattan distance calculation.  
Given \(L\) values and their indices, using \((L-1)\)\(MIN\) blocks organized as a tree, the minimum value and the corresponding index are propagated from the leaves to the root. Figure 12 shows the computation of the minimum of 4 input values, \(D_{1}\), \(D_{2}\), \(D_{3}\), \(D_{4}\), and the corresponding index, \(i_{min(D_{1},D_{2},D_{3},D_{4})}\), as an example.  
Table 5 shows the number of \(XOR\) and \(AND\) gates in the complete circuit that computes the nearest ATM location. It shows that in our privacy preserving application of finding the nearest Chase or Wells Fargo ATM in Salt Lake City, the circuit has a total of 2596 \(XOR\) and 854 \(AND\) gates.
## 4 Experiments and Results
### Experimental Results

Table 4: Comparative evaluation of deep learning models for gym exercise recognition using accelerometer and gyroscope data (Scenario III). \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recognition Performance**} \\ \cline{2-4}  & **Accuracy** & **Loss** & **F1-score** \\ \hline CNN & 75.74\% (\(\pm\)0.51\%) & 0.93 (\(\pm\)0.02) & 8.00\% (\(\pm\)1.38\%) \\ LSTM & 78.05\% (\(\pm\)0.40\%) & 1.25 (\(\pm\)0.04) & 24.06\% (\(\pm\)1.45\%) \\ BiLSTM & 77.58\% (\(\pm\)0.34\%) & 1.83 (\(\pm\)0.07\%) & 22.04\% (\(\pm\)0.98\%) \\ GRU & 76.57\% (\(\pm\)0.85\%) & 1.37 (\(\pm\)0.03) & 18.82\% (\(\pm\)0.69\%) \\ BiGRU & 76.18\% (\(\pm\)0.41\%) & 2.14 (\(\pm\)0.06) & 16.55\% (\(\pm\)0.45\%) \\ \hline CNN-ResBiGRU & 91.53\% (\(\pm\)0.60\%) & 0.34 (\(\pm\)0.02) & 74.49\% (\(\pm\)1.74\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative evaluation of deep learning models for gym exercise recognition using EMG data (Scenario IV). The results indicate that combining IMU and EMG data leads to improved recognition performance across all deep learning models compared to using either type of data alone. The CNN-ResBiGRU model adeptly integrates spatial and temporal features from both IMU and EMG data, resulting in the highest accuracy and F1-score.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 3 Deep Learning
### Image Segmentation and Image Detection Models  
Image segmentation is one of the core tasks in computer vision, with the goal of dividing an image into semantically meaningful regions. Depending on the task requirements, image segmentation tasks can be further categorized into three types: semantic segmentation, instance segmentation, and panoptic segmentation. The purpose of semantic segmentation is to assign a category label to each pixel in an image, without distinguishing between different instances. Instance segmentation requires distinguishing between different instances of the same category. Finally, panoramic segmentation combines the characteristics of semantic segmentation and instance segmentation [29; 77]. Image detection models are also one of the core tasks in computer vision, aiming to identify objects in images and represent their locations using bounding boxes or masks. Unlike segmentation models, image detection models must simultaneously address the questions of \"what\" and \"where\". Detection models can be categorized into two types: two-stage models that first generate candidate regions and then classify and regress these regions, The other category consists of single-stage models that directly predict object categories and locations without requiring candidate region generation [78]. Unlike traditional methods that rely on manually designed features, deep learning provides detection models with powerful feature extraction capabilities and end-to-end training frameworks, enabling them to automatically learn the categories and locations of target objects. Deep learning-based image segmentation and detection are developing rapidly and hold great promise in the agricultural field. In [79], deep learning-based object detection and image segmentation techniques are used to assess tomato ripeness and locate stems. Detection accuracy of tomato stems in complex agricultural environments is improved, enabling efficient robotic autonomous harvesting. Additionally, these techniques can be applied to crop monitoring, pest and disease identification, automated harvesting, and irrigation management in precision agriculture [80; 81].
###### Abstract  
In space environments, radiation particles affect the stored values of SRAM cells, and these effects, such as single-event upsets (SEUs) and single-event multiple-node upsets (SEMNUs), pose a threat to the reliability of systems used in the space industry. To mitigate the impacts of SEUs and SEMNUs, this paper proposes the Read Stability Improved and Low Power (RSLP16T) SRAM cell. It was confirmed that in SEU-induced simulations, all nodes of the RSLP16T could be restored with a charge amount of less than 100 fC. Additionally, it was verified that a similar level of restoration was possible for SEMNUs occurring in pair of storage nodes. The proposed cell achieves a high level of read stability due to a high pull-down cell ratio (current ratio, CR) at the storage nodes and the fact that only a pair of nodes is in contact with the bit lines during read operations. Because all node paths use a stacking structure for internal transistor configuration and a relatively higher number of cells are composed of FMOS, it consumes the least hold power. While these improvements come at the cost of slightly increased delay time and area, performance evaluation revealed that the equivalent quality metric (EQM) was the highest, indicating that the benefits outweigh the drawbacks. The proposed integrated circuit is implemented in the 90 nm CMOS process and operated on \(1\) V supply voltage.  
critical charge; radiation-hardened; read stability; single-event multinode upset (SEMNU); single-event upset (SEU) +
Footnote †: journal: Applied sciences  
**Citation:** [PERSON]; [PERSON].  
Low-Power Radiation-Hardened Static Random Access Memory with Enhanced Read Stability for Space Applications. _Appls. Sci._**2024, 14**, 10961. [[https://doi.org/10.3390/app142310961](https://doi.org/10.3390/app142310961)]([https://doi.org/10.3390/app142310961](https://doi.org/10.3390/app142310961))  
Academic Editor: [PERSON]  
Received: 5 November 2024  
Revised: 21 November 2024  
Accepted: 23 November 2024  
Published: 26 November 2024
## 5 Result and Discussion
### Protocol Comparison: Throughput Metric  
The total number of bits delivered from a network to the nodes in its higher layer is known as throughput. The proposed OLSR has a higher throughput since it benefits from MPR nodes, hello message exchange, and TC messages. Through improved routing efficiency and a decrease in OLSR time, clustering increases throughput. Each cluster's cluster head serves as the forwarder node, and the source is aware of the cluster that contains the destination node. As a result, sending a packet to the target node without having to explore the entire network is made simple for the forwarder node. The improved OLSR illustrates significantly excessive throughput in contrast to standard OLSR utilized in [16] and AODV used in [26]. The value of throughput obtained is highlighted in Table 6 and the delay comparison graph is highlighted in Figure 9.
# Portable Prototype of Hydrogen Fuel Cells for Educational Training
## 4 Experimental Setup
### Evaluation of the Characterization Results
#### 4.9.1 Serially-Connected FC  
The mean consumption value is reduced by the leakage rate from the previous experiment, and the leakage rate is 0.8 mL/min, which means that 2.4 mL of hydrogen leaked out in three minutes. Therefore, according to the Equation (5), the energy efficiency of the fuel cell can be calculated:  
\[\eta_{fc}=\frac{W_{el}}{W_{ch}}=\frac{V\cdot I\cdot t+l^{2}\cdot R}{H_{ \mathrm{H_{2}}}\cdot Vol}=\frac{0.29\cdot 1\cdot 180+1^{2}\cdot 0.1}{11920 \cdot(45.33-2.4)\cdot 10^{-3}}=0.102 \tag{5}\]  
The equation shows that the energy efficiency of the cells on the model is 0.102 or 10.2%. Other measurements are made the same way. Only the current of the series-connected fuel cells is changed using a variable resistor. Energy efficiency is calculated according to the Equation (5). The working power of the fuel cell is calculated according to the equation.  
\[P_{fc}=I\cdot V\ \ [\ W] \tag{6}\]  
where \(P_{fc}\) is the delivered power of the fuel cell, \(I\) and \(V\) are the operating current and voltage, respectively. It can be concluded that mathematically speaking, the efficiency is most significant at lower currents. However, that area could be more interesting for practical applications since the goal is to achieve the highest possible power during operation and, therefore, a higher current load. Thus, the system's maximum efficiency is calculated according to the Equation (7) by taking the maximum value of the efficiency of the fuel cell and multiplying it by the maximum value of the efficiency of the electrolyzer.  
\[\eta_{t}=\eta_{fc}\cdot\eta_{el}=0.5166\cdot 0.9396=0.4854 \tag{7}\]  
The maximum efficiency of the series-connected fuel cell system is 48.54%.
## 4 Experiment
### Data Preparation
#### 4.1.3 Application of Image Augmentation Techniques  
The cameras typically used on construction sites capture high-resolution images but also pose substantial challenges, including motion blur, abrupt perspective changes, and frequent occlusions. Consequently, constructing a comprehensive dataset from these images for deep learning remains time-consuming and labor-intensive. To address this issue, image augmentation is employed to artificially expand the training set. In the present study, six distinct augmentation techniques were applied to both source- and target-domain datasets, with each technique targeting a specific concern, as summarized in Table 1; more detailed mathematical formulations are available in [34].  
Because construction-site images may be acquired under varied outdoor conditions, they often exhibit significant illumination variability caused by artificial lighting, weather, or time of day. Brightness augmentation compensates for these fluctuations by adjusting the overall light intensity, generating images that range from dim to bright [35]. Contrast augmentation modifies intensity differences among pixels, making the distinction between foreground objects and background more or less pronounced. Perspective transformation alters the homography matrix, helping ensure that construction equipment elements remain clearly identifiable across different viewing angles. Scale augmentation, an affine transformation, simulates varying distances while accommodating size discrepancies among heavy equipment. For scenarios where the camera is misaligned, rotation augmentation helps maintain accurate feature detection regardless of orientation. Lastly, when the camera is angled off-perpendicular, shear augmentation corrects the resulting skew by shifting pixel rows or columns along a predetermined axis [36].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Augmentation Techniques** & **Parameters** & **Ranges of Parameter Values** \\ \hline Brightness & \(\beta\) & \([-50,50]\) \\ Contrast & \(\alpha\) & \([0.3,2.0]\) \\ Scale & \(\alpha\) & \([0.3,2.3]\) \\ Perspective & x, y & \([0.75,1.25]\), \([0.75,1.25]\) \\ Rotation & \(a_{31},a_{32}\) & \([0.02,0.17]\) \\ Shearing & \(\theta\) & \([-30^{\circ},30^{\circ}]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The used augmentation techniques with parameters and their values.  
Figure 5: Examples of original dataset from surveillance cameras.  
The parameters in each augmentation techniques were established through a trial-and-error approach designed to produce realistic augmented images, as summarized in Table 1. Additionally, geometric transformations--scaling, rotation, perspective, and shearing--can shift portions of an image beyond its original boundaries, creating voids or overlapping pixels. Because deep learning typically requires inputs of fixed dimensions [37,38], any empty regions were filled with a pixel value of 255 (white), ensuring all augmented images maintain a uniform size. Figure 6 illustrates examples of original images alongside each type of augmentation.
## 3 Adaptive Time-Frequency Analysis
### Variational Mode Decomposition and Optimization Models  
Variational mode decomposition (VMD) is a relatively new and powerful signal decomposition method. It formulates the decomposition problem as a variational optimization problem, enabling the extraction of multiple intrinsic mode components from a signal. In this subsection, we will discuss VMD in detail, including variational mode decomposition, successive variational mode decomposition, variational mode extraction, and adaptive variational mode decomposition. We will explore how these methods operate, their advantages in handling different types of signals, and their potential applications across various disciplines.