esa-sceva/satcom-chunk-collection huggingface dataset:
# Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge
## 1 Introduction  
The metering of oil flow is critical for crude oil production and trade. As such, flow meters must be regularly verified to ensure accuracy. Standard pipe provers and volume methods are often used to test flow meters in varying conditions. Therefore, provers must be regularly checked against a traceable reference to demonstrate compliance with accuracy and repeatability requirements [1; 2].  
Water calibration is often used to verify pipe provers. The volume of a standard metal gauge includes the volume of the lower main body (which is fixed) and the volume of the neck. As a result, if the liquid level in the neck can be measured accurately, the volume can be calculated with high precision. According to the NIST standards [2], for volumetric field standards the maximum permissible errors should be \(\pm\)0.05% of the nominal capacity, and reading errors of the neck level should be \(\pm\)0.2 mm correspondingly. Because some normal level sensors cannot be used to measure the level due to being unfit in range, accuracy or installation, manual readings with Vernier calipersare used to measure the level [3], which prevents automated calculation of the water volume. In the two-color gauge recognition system designed by [PERSON] et al. [4; 5], a horizontal and vertical endpoint line (HVPL) identification technique was used to read scale numbers and line positioning was achieved using horizontal projection statistics. However, this approach did not consider measurement errors caused by differences in camera viewing angles, which reduced its accuracy and therefore did not meet the accuracy requirement of \(\pm\)0.2 mm. In the automatic verification system designed by [PERSON] [6], the automatic collection of liquid level is realized by image recognition, but the center of gravity of the concave meniscus of the liquid tube is used as the reading point in the liquid level, and the reading method is based on the level of the liquid level at two standard points, and thus the accuracy is lower than \(\pm\)0.5 mm, which does not meet the accuracy requirement of \(\pm\)0.2 mm.  
In this study, an automated measurement technique utilizing image recognition is proposed for neck level measurements. The inclusion of computer vision, rather than manual readings of liquid levels, compensates for the accuracy problems faced by conventional liquid level sensors. A camera-level follow-up control system was designed to achieve automated tracking of fluid levels using a camera, thereby improving level measurement accuracy other than inclined viewing angles. A curve-fitting algorithm was used to identify the lowest point of the meniscus. This increases the accuracy of level measurements and the efficiency of volumetric calculations for prover verification.
# May the Extensive Farming System of Small Ruminants Be Smart?
###### Abstract  
Precision Livestock Farming (PLF) applies a complex of sensor technology, algorithms, and multiple tools for individual, real-time livestock monitoring. In intensive livestock systems, PLF is now quite widespread, allowing for the optimisation of management, thanks to the early recognition of diseases and the possibility of monitoring animals' feeding and reproductive behaviour, with an overall improvement of their welfare. Similarly, PLF systems represent an opportunity to improve the profitability and sustainability of extensive farming systems, including those of small ruminants, rationalising the use of pastures by avoiding overgrazing and controlling animals. Despite the livestock distribution in several parts of the world, the low profit and the relatively high cost of the devices cause delays in implementing PLF systems in small ruminants compared to those in dairy cows. Applying these tools to animals in extensive systems requires customisation compared to their use in intensive systems. In many cases, the unit prices of sensors for small ruminants are higher than those developed for large animals due to miniaturisation and higher production costs associated with lower production numbers. Sheep and goat farms are often in mountainous and remote areas with poor technological infrastructure and ineffective electricity, telephone, and internet services. Moreover, small ruminant farming is usually associated with advanced age in farmers, contributing to poor local initiatives and delays in PLF implementation. A targeted literature analysis was carried out to identify technologies already applied or at an advanced stage of development for the management of grazing animals, particularly sheep and goats, and their effects on nutrition, production, and animal welfare. The current technological developments include wearable, non-wearable, and network technologies. The review of the technologies involved and the main fields of application can help identify the most suitable systems for managing grazing sheep and goats and contribute to selecting more sustainable and efficient solutions in line with current environmental and welfare concerns.  
sheep; goat; extensive system; Precision Livestock Farming (PLF); sensor 2025  
3
The measures obtained from twenty healthy young adults performing the tests revealed that the depth camera is a reliable instrument to parameterize both the MDRT and TUG test, though some issues were reported when employing depth cameras for the TUG test. In addition, with the increase of Microsoft Kinect popularity, Kinect Xbox 360 (Kinect v1) was used in the development of a standalone software to measure the FRT [34]. Concurrent validity of Kinect measures were assessed in comparison of measures obtained from a 3D motion-capture system. Implementation feasibility was also examined in a clinical setting where patients' feedback was collected. Findings showed moderate to good agreement between FRT measures made by the two systems and the patients valued favorably the Kinect system, which resulted its validity for measuring the FRT and the possibility of using it with gaming platforms. Similarly, [PERSON] et al. [35; 36] assessed the concurrent validity and reliability of reach test among other tests for evaluating standing balance and postural control. Firstly they used Kinect v1 and compared the data with a marker-based three dimensional motion analysis (3 DMA) system [35]. Then with the release of Kinect for Xbox One (Kinect v2), they updated the data with the Microsoft Kinect v2 measurements in comparison with data obtained by a multiple-camera 3D motion analysis as a benchmark [36]. In both studies, results from the Kinect and the benchmark system were comparable and findings suggested that Microsoft Kinect has the potential to be used as a balance assessment tool in the clinical setting. In these previous studies, the use of RGBD devices was validated to instrument the FRT and use it as an assessment tool. However, there is a lack of clear intention to translate the validation of the test into homes, whether by the physiotherapist assistance, or autonomously by the patient, which is becoming more needed nowadays. With the arrival of the late pandemic, it became even more important the validation of the use of RGBD devices at homes for balance and postural control assessment of individuals suffering balance problems. Thus, in this study, we present an experimental system (ES) based on Kinect v2 for measuring the Functional Reach Test (FRT) of the user. Kinect v2 provides a natural user interface which enables the users to control and interact with the applications through their body gestures and movements. Therefore, the same interaction system used for rehabilitation can be also used for assessment, hence saving the individual the hurdle of learning new interaction mechanism. We aim the validation of its use with the support of a physiotherapist, and autonomously by the user on their own. This validation acts as an initial phase of further experiments. The two experiments that we conducted, though held in lab settings, are meant as a step forward to deploy the system at home.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 4 Case Study
### Step 6: Produce Final Design Recommendations  
Are Stakeholder Requirements Met? Final recommendations for the case study conclude that stakeholders from the base command, tenant commands, local energy companies, microgrid provider, maintenance personnel, and chain of command all have their requirements met with the proposed nanogrid design. The nanogrid design validates top level functional requirements of: generate power, distribute power, control power, and store energy.  
Is Funding Available? Improving resilience by implementing nanogrids on critical loads within the current base microgrid structure is determined to be effective for a majority of the threat scenarios. We make the assumption that funding is available to implement the nanogrids rapidly and that no selection of specific nanogrids based on available funding needs to occur. Note that we explicitly did not conduct cost analysis within the proposed methodology. However, base energy managers often conduct cost studies when choosing between different potential energy system upgrades. The case study does not mention if funding is available because this is often encountered in the acquisition process of program upgrades and is beyond the scope of this research. This research focuses on how to improve military installation energy resilience.
## 3 Experiments and Results
### Motion Pattern Recognition Based on LSTM Neural Network
#### 3.2.3 Different Sliding Window Lengths and Different Feature Set Types
The confusion matrix corresponding to the data in Figure 12a is in Table A5 of Appendix A; the confusion matrix corresponding to the data in Figure 12b is in Table A6 of Appendix A. From the obtained results, it can be seen that, when the window length was 256 ms, recognition errors occurred in each motion mode. However, the main recognition errors occurred in the mode of stepping in place, walking flat, walking upstairs, and walking downstairs. This is because these several movement modes are relatively similar, and they all involve alternating movements of the left and right leg, using the same muscles of the lower limbs. The differences between them mainly lie in the activation time length and amplitude of the EMG signal. When the sliding window length is too short, these differences may not be obvious, which will cause misjudgment. Figure 11: Performance change of the LSTM model during the sliding window length of 512 ms. (**a**) Changes in accuracy. (**b**) Changes of the loss function value. Figure 12: Recognition results of the LSTM model in smaller window lengths. (**a**) Sliding window length of 256 ms. (**b**) Sliding window length of 512 ms.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Interpretation of Model Outputs
#### 3.2.2 Turbidity  
Turbidity, a measure of the cloudiness or haziness of a fluid, is another crucial factor influencing water quality. It is caused by the presence of suspended and dissolved solids, such as sediment, microscopic organisms, and pollutants. High turbidity levels can block light, affecting aquatic plants' photosynthesis, and can also smother aquatic organisms by clogging their gills [53; 54].  
The prominence of turbidity as a predictive factor in our models suggests that suspended solids, possibly from urban runoff and eroded soil in the stormwater, significantly impact the lake's water quality. This indicates the need for better management of stormwater and land use in the catchment area.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## A* Algorithm
### Environment Model  
As A* algorithm is a grid traversal search algorithm, we should first establish the grid model of the environment map by grid method. The grid method divides the map into adjacent grids of equal size. The size of the grid is determined by the size of the robot and it affects the efficiency and searching precision of the algorithm. The grid model of a two-dimensional environment map is as shown in Figure 1.  
Figure 1 represents the square with a side of length 50 m; it is divided into 50 \(\times\) 50 grids. Each grid represents a state where the robot can stay; it is connected to its eight neighbors. The black regions are known obstacle locations while the white regions are locations known to be in free space. The position coordinates of the initial state, \(S\), are \((2,4)\) and the position coordinates of the goal state, \(G\), are \((47,48)\). The task of path planning is to find an optimal path that leads the robot from \(S\) to \(G\).  
Figure 1: Environment model.
## 3 Background and Preliminaries  
### Wireless Sensor Network Architecture  
Before describing the high-level taxonomy of energy saving protocols, it is better to have an understanding of the node-level and network architecture for future reference. Figure 1 describes the components of a typical wireless sensor node and their interconnections.  
Figure 1: The structure of a typical wireless sensor node [1].  
A node consists of four main elements with two optional subsystems as follows:  
* A sensing unit, including one or several sensors equipped with analog-to-digital converters for data collection.
* A processing unit, including a microprocessor and memory which cooperate to process the sensed data locally.
* A radio unit used as a transmitter/receiver.
* A power supply unit, including one or more batteries.
* A global positioning system to find the sensors' locations (_optional_).
* A mobilizer unit to change their position (_optional_).  
It is worth mentioning that as indicated, the last two components are optional and may be used based on application requirements [1].
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### MRI Acceleration:  
The lengthy scanning process in which the samples are collected line-by-line in k-space (frequency domain and Fourier image space) is uncomfortable for the patients and becomes a reason for motion artifacts. The concept of accelerated MRI is crucial to tackling this issue. MRI is reconstructed from highly under-sampled (up to 20%) k-space data, especially in fetal, cardiac, functional MRI, multimodal acquisitions, and dynamic contrast enhancements. The acquisition time is lowered by less slice selection, reducing the spatial resolution. The sweep time can also be lessened by selecting a partial k-space and approximating the absent k-space points. A k-space and an image-space U-Net reconstruct the whole k-space matrix from under-sampled data [116]. The compressed sensing (CS) MRI scheme reduces the sweep time by considering a minor set of samples for image construction. Refine-GAN, the adapting fully-residual convolutional auto-encoder, and general GAN, is the base for fast and precise CS-MRI reconstruction. A chained network enhances the reconstruction quality [117]. Traditional CS-MRI is affected by slow iterations and noise-induced artifacts during the high acceleration factor. The RSCA-GAN uses spatial and channel-wise attention with long skip connections to improve the quality at each stage, accelerating the reconstruction process and removing the artifacts brought by fast-paced under-sampling [118]. Parallel imaging integrated with the GAN model (PI-GAN) and transfer learning accelerates MRI imaging with under-sampling in the k-space. The transfer learning removes the artifacts and yields smoother brain edges [119]. Reforming multi-contrast brain MR images from down-sampled data points can save scanning time [120].
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Informal Analysis
#### 7.2.8 Anonymity and Untraceability  
An attacker (\(A\)) can intercept all messages exchanged during authentication and attempt to reveal and track the user's identity. However, the user's ID is masked in \(HID_{M}=h(ID_{M}\parallel HPW_{M}\parallel r_{M})\) using a cryptographic one-way hash function, along with the masked password and a random number. Furthermore, during authentication, the GCS updates \(HID_{M}\) to \(HID_{M}^{new}\) by hashing it with a random number (\(r_{3}\)). Therefore, the proposed protocol provides anonymity and untraceability.
## 4 Experimental Results and Discussion  
### Environment  
An application was implemented to examine the effectiveness of the proposed method. The implemented application was designed to interact with virtual objects using gestures such as grab or pinch, and the experimental environment is shown in Figure 8.  
In Figure 8, the red double line is the measured arm length of the subjects, and the green dashed line is the calculated length based on the arm length to limit the virtual object generation space. In Figure 8, the virtual object generation space, expressed with the black dash-dotted line, is defined as the maximum space in which the virtual object can appear, and was set based on the calculated length. The environment based on arm length was constructed to minimize the effect of differences of each subject's body on the experiment.  
Figure 6: Example of deriving consecutive joint angles.  
Figure 7: Interaction prediction procedure of the proposed method.  
If the virtual object appears only in a specific position, the subject can easily adapt to interaction, and accordingly, the experimental results can be biased. Therefore, the virtual objects were set to appear randomly at the positions represented by the spheres in Figure 8 (blue, red, and green). The order and the position of the virtual object were set to be counterbalanced across the conditions. In the application, an additional effect (e.g., color darkening or a sound effect) was added when the interaction was completed so that the subject could check whether the interaction was completed. The application for the experiment was executed at 30 fps on Microsoft's Hololens2 [20].  
When using the application, the sharpness of the virtual object felt by the subjects may differ. This can be caused by the relative position of the light and the average indoor illuminance. Therefore, the experiment was conducted at the same position in the room under the same posture (sitting). In addition, artificial lighting was applied to the experimental environment to maintain a constant average illuminance (140 lux).
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
###### Abstract  
Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below \(-0.4\) for BTD (11-12 \(\upmu\)m) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The PoFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.  
dust detection; FY-4A; BTD; NDDI; IDDI; DST +
Footnote †: journal: Applied Sciences
###### Abstract  
The fraction of absorbed photosynthetically active radiation (FPAR) is widely used in remote sensing-based production models to estimate gross or net primary production. The forest canopy is composed primarily of photosynthetically active vegetation (PAV, green leaves) and non-photosynthetic vegetation (NPV e.g., branches), which absorb PAR but only the PAR absorbed by PAV is used for photosynthesis. Green FPAR (the fraction of PAR absorbed by PAV) is essential for the accurate estimation of GPP. In this study, the scattering by arbitrary inclined leaves (SAIL) model was reconfigured to partition the PAR absorbed by forest canopies. The characteristics of green FPAR and its relationships with spectral vegetation indices (NDVI, EVI, EVIZ, and SAVI) were analyzed. The results showed that green FPAR varied with the canopy structure. In the forests with high coverage, the green FPAR was close to the total FPAR, while in the open forests, the green FPAR was far smaller than the total FPAR. Plant area index had more important impacts on the green FPAR than the proportion of PAV and optical properties of PAV. The significant relationships were found between spectral vegetation indices and the green FPAR, but EVI was more suitable to describe the variation of canopy green FPAR.  
green FPAR; SAIL; forest; vegetation index +
Footnote †: journal: plants
# Simulation and Design of Three 5G Antennas
## 4 Metal Frame Mobile Terminal Dual-Element \"Loop-Groove\" MIMO Antenna Design
### The Dual-Element \"Loop-Groove\" MIMO Antenna Design Process
#### 4.2.3 Implementation of a Dual-Element \"Ring Slot\" MIMO Antenna  
Once the ring antenna and slot antenna have been individually designed, the next step is to arrange these two antenna elements in a manner that ensures high isolation and achieves antenna miniaturization.  
Initially, the placement of the ring antenna and slot antenna is considered to be horizontally aligned along the longer edge, as depicted in Figure 32.  
In this traditional placement method, in order to avoid mutual coupling between the antenna elements and prevent a decrease in isolation, the ring antenna and slot antenna are spaced apart by a certain distance. Consequently, this conventional arrangement increases the horizontal length of the antenna, resulting in a larger overall size for the designed MIMO antenna array. This can be limiting for mobile terminal devices with limited space, reducing the number of antenna elements that can be accommodated and thus hindering the achievement of true miniaturization in antenna design. Therefore, a new placement approach needs to be considered.  
As discussed in Section 4.2, the rectangular slots on the ground for the designed ring antenna and slot antenna have dimensions that are very close in both length and width. Hence, it is feasible to explore a novel placement method where the ring antenna and slot antenna are designed to overlap within a shared rectangular slot, as illustrated in Figure 33, which represents a schematic structure for the co-located placement of the ring antenna and slot antenna.  
To achieve impedance matching for the ring antenna and slot antenna, it is necessary to connect an inductor in series with the \"ring-slot\" structure. The design of the inductor connected between the metal frame and the midpoint of the rectangular slot in the ground can optimize the overall antenna performance. Figure 34 is an illustrative diagram of the dual-element \"ring-slot\" antenna structure.  
Figure 33: Co-located placement of the ring antenna and slot antenna.  
Figure 32: Horizontal placement of the ring antenna and slot antenna.