esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Comparative Analysis of a Ground-Level Trellising Tool for Greenhouse Tomato Cultivation
## 3 Results
### Results of Field Experiments
#### 3.3.3 Analysis of Risk Reduction for Farmers with the Designed Tool  
For the application of the INSST method [29], the following information was collected during the period of observing and interviewing workers:  
* Workplace: In both greenhouses, the support was laid along 1.5 m wide lines, so the worker could comfortably place bulky tools such as ladders and scaffolding. The sandy ground made the ladder unstable.
* Tools used: Farmers often used ladders, stilts, or scaffolding to carry out their work. During the observation, one of the farmers also used the newly designed tool for comparison.
* The materials used were hangers and raffia, so they should not have increased the risk.
* The working height was approximately 3 m. Work could not be carried out from the ground without the assistance of a tool for this purpose.
* The ladders and stilts did not have safety measures against falls from height. The scaffolding had railings on some parts of its perimeter. The ladder and manual scaffolding involved repeated climbs up and down throughout the work being carried out.
* The new tool allowed work to be carried out at the ground level.  
The probability of damage and the consequences were determined from the information collected. From these, the risk level for each trellising method was obtained (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Probability of Falling to** & **Consequence of Falling to** & **Risk Level** \\  & **Different Levels** & **a Different Level** & **Risk Level** \\ \hline Ladder & High & Harmful & Important \\ Manual scaffolding & High & Harmful & Important \\ Motorized scaffolding & Average & Harmful & Moderate \\ Stilts & High & Extremely harmful & Intolerable \\ Newly designed tool & None & Not applicable & No risk \\ \hline \hline \end{tabular}
\end{table}
Table 4: Risk level for each trellising method.  
Figure 16: Cost analysis.  
The methods commonly used for the trellising of tomato crops pose a risk to workers. The highest level of risk (intolerable) is presented when trellising is performed with the help of stilts. This is followed by a'significant' risk when ladders or wheeled scaffolding are used to reach the required height. The third most harmful level of risk according to the method also appears in the case of a worker on a motorized scaffold. The newly designed tool does not present a risk of falling from a height (Table 4).
## 5 Results and Discussion
### Localization Accuracy in Ambiguous Field  
With reference to the previous section, the results for localization error in an unambiguous environment does not require the sequence of touches or further exploration in the environment to localize itself. Furthermore, similar stimuli are present at multiple locations in an ambiguous environment, which confuses the agent to choose a single one in multiple predictions. Therefore the agent must integrate its current path with several previous forecasts until the agent predicts only one location.  
The proposed model has outperformed the RatSLAM [17], as depicted in Figure 26, where there were compared in two ambiguous environments. In the first ambiguous environment, on the first sensory input, the model has recalled two different places. That is why the model is confused in the very first decision, and it has chosen randomly one from the two. It results in a poor success rate of around 0.5 in the first decision. However, as it missed the reward due to the first wrong decision, the model has resolved the confusion, and one true place is activated. Thereafter, the only active correct place neuron will activate the correct neuron on path integration so the agent will only make the right decision. On the other hand, the second environment, which is highly ambiguous despite the fact that the model has resolved the confusion using the trip sequence learner (it is an episodic memory). It can  
Figure 25: Effect of grid neuron population on localization.  
store the grid code of the places along with the event activities, which helps in recalling the decisions. Even in the second environment, the first decision is filled with ambiguity, but later the agent resolves the ambiguity using the location sequence learner so that further decisions are all correct.
# Capacitor-Less Low-Power Neuron Circuit with Multi-Gate Feedback Field Effect Transistor
## 4 Conclusions  
The existing von Neumann-based computing structure has great limitations in terms of power consumption and speed in terms of high-level information processing, and in order  
Figure 10: The neuron circuit with multi-gate FBFET simulation result. (**a**) Simulation results of single and two gates that applied voltage electron accumulation (**b**) Steep drain current graph of proposed neuron circuits with no leakage current during the Vin pulses. (Control Gate V = 1.1 V).  
Figure 9: Proposed neuron circuit diagram with Multi-gate FBFET.  
to implement deep learning that operates in real-time, many processors have problems related to area and power consumption. Neurons, known as nerve cells in the brain, recognize information through body sensory organs, send information to the brain, process it, and deliver commands to motor organs, and the human brain performs recognition, judgment, and learning at as low power as 20 W. To process a large amount of data, a neuromorphic architecture is essential, and a neuron circuit is required to implement a neuromorphic structure. Most of the neurons of the current neuromorphic chip are composed of analog and mixed-signal technology, and in the neural network, neurons generate spike signals when receiving stimulus signals, and each neuron exchanges signals and learns. However, analog neurons began to show limitations in terms of area and energy using operational amplifiers and capacitors. These problems are caused by the membrane capacitor, so we proposed a neuron circuit structure without the membrane capacitor. Additionally, according to the simulation results, a multi-gate FBFET can effectively receive multiple inputs and reduce the number of devices by replacing a membrane capacitor. Additionally, the neuron circuit using the device can solve the power consumption problem by suppressing the short-circuit current. We demonstrate through SILVACO TCAD simulations that a neuromorphic structure can be implemented using multi-gate FBFET. In other words, we suggest a new direction for neuromorphic architecture.  
Conceptualization: M.-W.K.; validation: [PERSON] and M.C.; investigation: [PERSON] and M.C.; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON] and M.-W.K.; visualization: [PERSON] and M.C.; funding acquisition: M.-W.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2021R1G1A1093786) and this research was supported by the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2022M3J7A1078936).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Model Derivation
### Transfer Matrix of the Entire System  
The transfer matrix, which is derived in Section 2.3, allows computation of the fluid dynamics. The transfer matrix, which is derived in Section 2.4, allows computation of the pipe dynamics (in the axial direction) but without fluid inside. Now, the last step is to convert these two matrices into one transfer matrix that describes the pipe + fluid system. Therefore, the following boundary conditions at the ends of the pipe are used: fluid velocity equals flange velocity, and force, which acts on the flange, equals the force that acts on the fluid.  
\[\frac{\partial u}{\partial t} = \frac{Q}{S}, \tag{42}\] \[F = p\cdot S. \tag{43}\]  
After the Laplace transform:  
\[\bar{u} = \frac{Q}{s\cdot S}, \tag{44}\] \[\bar{F} = \bar{p}\cdot S. \tag{45}\]Now, Equations (29), (31), (44) and (45) give:  
\[\left(\begin{array}{c}0\\ 0\\ 0\\ 0\\ 0\end{array}\right)=\left(\begin{array}{ccc}\mathbf{P_{f}}&-1&0\\ 0&-1\\ \mathbf{P_{m}}\cdot\mathbf{P_{p}}\cdot\mathbf{P_{m}}\cdot\mathbf{\Lambda}&- \frac{1}{s\cdot S}&0\\ 0&-S\end{array}\right)\cdot\left(\begin{array}{c}\tilde{\mathcal{Q}}_{0}\\ \tilde{\mathcal{Q}}_{0}\\ \tilde{\mathcal{Q}}_{3}\\ \tilde{p}_{3}\end{array}\right), \tag{46}\]  
where the matrix \(\mathbf{\Lambda}\) originates from Equations (44) and (45):  
\[\mathbf{\Lambda}=\left(\begin{array}{cc}\frac{1}{s\cdot S}&0\\ 0&S\end{array}\right). \tag{47}\]  
When the eigenvalue from the experiment is substituted into the matrix in Equation (46), it is possible to find fluid properties (speed of sound and second viscosity) from the zero determinant of this matrix.
# Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data
## 2 Learning Algorithms and Methodologies
### Least Absolute Shrinkage and Selection Operator (LASSO)  
The objective of the linear regression model is to find a relationship between two variables by fitting a linear equation to observed data points. The most common way to find a fitted line is to use the least-squares method, in which the model finds a fitted line by minimizing the sum of squared residuals; however, shrinking or setting some coefficients to 0 can increase the accuracy of the mentioned model. In the LASSO model, an \(L_{1}\) regularization term is added to the cost function to address the above-mentioned issue. \(L_{1}\) regularization is a technique that modifies the objective function of a model by adding a penalty based on the absolute values of the coefficients, leading to simpler and sparser models [29]. The penalty term, \(\lambda\), controls the amount of regularization. LASSO is a good method to eliminate irrelevant variables and only consider related variables to compute the output of the model. The cost function, \(J\), of the LASSO method is presented in (1). Here, \(m\) represents the size of the dataset, while \(g\) denotes the model.  
\[J_{\text{LASSO}}(\theta)=\frac{1}{2}\sum_{i=1}^{m}(g_{\text{LASSO}}(x_{i})-y_ {i})^{2}+\lambda\|\theta\|_{1} \tag{1}\]  
In this paper, the LASSO method is utilized to predict the DAM price, the RTM price, and the price gap between the DAM and RTM directly. LASSO can set some coefficients to zero, so it can perform variable selection. On the other hand, LASSO has difficulties handling correlated features. One of the correlated features will have a high coefficient, while the rest will be nearly zero. However, this one feature is selected randomly. In addition, the LASSO algorithm can only learn linear mappings; thus, due to the nature of non-linearity in the existing dataset, it may not be the best family of functions in the hypothesis space.
## 1 Introduction
The authors found that both the share of the reinforcing phase and the quantitative relationship between SiC and fly ash have the greatest impact on the wear rate and the microhardness of the composites. They also found that the uniformity of the SiC/fly ash reinforcement dispersion is the most important factor in improving the hardness and wear resistance of the composites. Fly ash was also utilized by [PERSON] et al. [46] to produce composites by means of the high-temperature vacuum stir casting technique. Metal matrix composites made of AA7075 aluminum alloy with 0 wt%, 6 wt%, and 12 wt% fly ash reinforcement were analyzed. The authors found that an increase in the fly ash content leads to a rise in the hardness of the composite. This hardness in the case of the composite containing 12 wt% fly ash was up to 28% higher than the hardness of the material not reinforced with fly ash. Also in the case of the tribological test, it was found that the composite containing the highest amount of fly ash had the best tribological properties. The mechanical and wear properties of AA7075-T6 reinforced with SiC and aloe vera ash, fabricated using friction stir processing, were analyzed by [PERSON] et al. [47]. To more precisely examine the behavior of the samples during wear tests, the authors used different loads, i.e., 20 N, 30 N, and 40 N. The tests revealed that the lowest resistance was observed in the non-composite sample, while the wear of the composite samples grew with the increment in the applied load. The authors noted the highest wear resistance of the samples in the case of the load of 20 N, which they explained by the formation of an oxide tribolayer that acted as a solid lubricant. The authors also found a lower coefficient of friction for the composite samples than for the aluminum alloy samples. The examples discussed above demonstrate that waste materials have enormous potential, which, unfortunately, is not fully exploited. The work attempts to use ashes from the combustion of fossil fuels and biomass as a reinforcing phase in metal matrix composites. The main emphasis in the research was placed on assessing the size and nature of changes in the microstructure and their impact on selected properties of the studied material, i.e., the hardness and wear resistance. The properties of the composite are a consequence not only of the properties of the components constituting the composite and their volume fractions, but also of changes occurring directly in the microstructure of the material.
## 3 Mathematical Formulation/Performance Metrics
### Latency Model  
The average latency of the NoC is shown in Equation (5):  
\[Lt_{av}=\frac{1}{N}\sum_{i=1}^{N}\frac{1}{N_{i}}\sum_{j=1}^{N_{i}}Lt_{i,j} \tag{5}\]  
where \(Lt_{i,j}\) is the latency of packet \(j\) from one tile to another, \(N\) represents the number of processors in the mesh, and \(N_{i}\) reflects the quantity of packets received by a processor after the warm-up period.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 4 Challenges and Development Trends in Reflector Antenna-Based SAR Technology
### Challenges for Reflector Antenna-Based SAR  
Despite the potential of reflector antennas in spaceborne SAR applications, several challenges remain in practical implementation:  
(1) Limited Beam Steering Accuracy: Compared to phased array antennas, reflector antennas face greater difficulty in achieving precise beam steering. Due to their superior pointing accuracy, phased array antennas remain the preferred choice for many modern SAR systems.  
To address this, integrating two-dimensional phased array feeds (PAFs) with reflector antennas is proposed. This configuration combines the high-gain benefits of reflectors with the beamforming flexibility of DBF. By employing digital beamforming in the range direction and multi-channel azimuth processing, the system can achieve dynamic beam synthesis and improved real-time steering accuracy. The SweepSAR technique can further enhance coverage while reducing real-time pointing demands.  
(2) Ground Testing Limitations: Simulating a zero-gravity environment on the ground is highly challenging, making it difficult to accurately test the structural deformations and mechanical stress of spaceborne reflector antennas. These uncertainties introduce errors in antenna surface accuracy and structural precision, affecting pointing accuracy and stability.  
Future research is directed toward high-accuracy ground simulation techniques and active surface control mechanisms. By incorporating precision shape error modeling, structural dynamics simulations, and closed-loop calibration techniques, the uncertainties in beam pointing and surface accuracy can be reduced. Moreover, active reflector surface control can help compensate for in-orbit deformations.  
(3) Structural Complexity and High Precision Requirements: Deployable reflector antennas involve intricate structures that are susceptible to shape errors due to uneven cable tension distribution. Additionally, these antennas often exhibit lower interference resistance, high assembly and calibration complexity, and strict precision requirements for deployment and stowing mechanisms.  
To address this, future work should emphasize the development of novel lightweight truss configurations, high-strength reflective materials, and more robust deployment mechanisms. Additionally, modular or segmented reflector architectures, combined with real-time shape monitoring and in-orbit adjustment capabilities, can enhance the reliability and scalability of large deployable antennas.
# Volatile Metabolome and Aroma Differences of Six Cultivars of _Prunus mume_ Blossoms
## 2 Results and Discussion
### Analysis of Volatile Metabolite Differences in Six Cultivars of P. mume Blossoms
The contents of benzaldehyde, benzyl alcohol, and benzyl acetate were higher in YD and lower in DFZS. These results suggested that there may also be differences in this metabolic branch among _P. mume_ cultivars. Benzyl alcohol acetyltransferase genes in _P. mume_ (PmBEAT) have been identified, and overexpression or inhibition of this gene has been demonstrated to promote or inhibit the synthesis of benzyl acetate [16]. Differences  
Figure 3: HCA analyses of fifteen differential volatile compounds of six varieties of _P. mume_ blossom samples. in the contents of benzaldehyde, benzyl alcohol, and phenylmethyl acetate in _P. mume_ cultivars may reveal differences in the expression of the PmBEAT gene and other related genes in the six cultivars of _P. mume_ blossoms. Eugenol and 4-allyphenol also showed consistency of variation across cultivars, with eugenol and 4-allyphenol being higher in YD and DFZS, and methyleugenol being higher in YD and GLH. In the majority of plants, these compounds belong to the same p-coumaric acid branch of the shikimic acid pathway [17]. p-Coumaric acid is gradually reduced to p-coumaryl alcohol, which is then catalyzed by 4-Coumaryl/Coniferyl alcohol acetyl transferase (CAAT) and Eugenol synthase (EGS) to produce 4-allyphenol. Methylation of 4-allyphenol by clavicol O-methyl transferase (CVOMT) can also produce 4-allyplanisole, which was also detected in all the samples. As for the synthesis of eugenol, p-Coumaric acid is first converted to Caffeoyl CoA, which is catalyzed by Caffeoyl-CoAO-methyl transferase (CoAOMT), Cinnamoyl-CoA/coniferyl-CoA reductase (CCR), Coumaryl/coniferyl alcohol dehydrogenase (CAD), and coniferyl alcohol acyltransferase (CFAT) in turn to produce Coniferyl acetate, and is then catalyzed by EGS or Isoeugenol synthase (IGS) to produce eugenol or isoeugenol. The latter can also be methylated to obtain the corresponding methyleugenol and methyl isoeugenol. The differences in the contents of eugenol, 4-allyphenol, and methyleugenol in various cultivars of _P. mume_ may imply differences in the regulation of expression of enzymes catalyzing the relevant synthetic reactions, which need to be further investigated.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 4 Experiments
### Dataset and Metrics  
We use the DanceTrack [13] and CrowdHuman [31] to train the network, and then evaluate it on the DanceTrack dataset.  
DanceTrack is a large-scale dataset targeting the association problem in multi-human tracking under dancing scenes. The objects in this dataset have uniform appearance and a wide range of motions, making it difficult to associate objects across frames.  
CrowdHuman is a dataset containing static images of crowded scenes. It features objects with severe occlusion. Random augmentations are applied to generate pseudo videos from these images to improve the detection performance under occlusion situations.  
To evaluate the performance of FusionTrack, we mainly use the higher-order tracking accuracy (HOTA) [33] metric. We also report the detection accuracy (DetA), association accuracy (AssA), and multi-object tracking accuracy (MOTA) [34] and IDF1 [35] metrics for comparison.
## 4 Wireless Enabling Technologies
### 5g  
The roadmap of vehicular communications is marked by the evolution toward 5G, which is typically addressed as the future technology that will provide three main requirements: peak data rates faster than 10 Gbps, device density higher than 1 million per km\({}^{2}\) and latency lower than 1 ms. A new radio technology, called NR, is being developed to this aim.  
The three major players for 5G NR standardization are the International Telecommunication Union (ITU), 3 GPP and IEEE. Candidate technologies are being evaluated by ITU Radiocommunication Standardization Sector (ITU-R) through its 5D Working Party (WP) [41].  
3 GPP's 5G standards are expected to be finalized with Release 15 (Phase 1) and Release 16 (Phase 2) by September 2018 and late 2019, respectively, and it is the Release 16 that includes the draft standard for 5G V2X [42].  
5G works, as LTE, in frequency bands up to 6 GHz, but new frequency are also considered at 28 GHz and 39 GHz. 3 GPP is also identifying regulatory requirements of direct communications between vehicles in the spectrum beyond 6 GHz in different regions: 63-64 GHz and 76-81 GHz. Hence, 3 GPP is working to manage the allocation of mmWave spectrum for 5G cellular applications and it is expected to be fixed by 2018.  
As an evolution of LTE, in its first phase, 5G will be based on OFDM with subframe duration fixed to 1 ms and the frame length to 10 ms. Subcarrier spacing is 15 kHz * 2n, hence each symbol length (including cyclic prefix) of 15 kHz equals the sum of the corresponding 2n symbols of the subcarrier spacing. A slot is 7 or 14 OFDM symbols (for subcarrier spacing up to 60 kHz) and 14 OFDM symbols (for subcarrier spacing higher than 60 kHz). Future development of 5G will also include a viable option to halve the subframe duration (so far, LTE-V2X supports only a fixed subframe of 1 ms) and reduce the latency. In addition, non orthogonal multiple access (NOMA) is also being studied.  
The wide channel bandwidth and multiple subcarrier spacing options enable the 5G NR system to efficiently and flexibly operate from sub-6 GHz band to mmWave band with appropriate handling of multi-path delay spread and phase noise depending on the carrier frequency.  
As anticipated in Section 4.4, IEEE has recently undertaken a 5G track to enhance existing standards and to evolve new technologies for the mmWave unlicensed bands. These efforts include IEEE 802.11 ad/aj/ay, IEEE 802.15.3c, ECMA-387, among the others. The standardization is expected to be completed by late 2019, ahead of the much-anticipated 2020 timeline for commercial deployment [41].