esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Amino Acid Metabolism--Anmonia  
NH\({}_{3}\) can be generated from adenine nucleotides, but since no changes in XA, UA, and CRN were observed in the present study, the increase in plasma NH\({}_{3}\) in the N group may be indicative of increased amino acid degradation or reduced depletion of NH\({}_{3}\)[93; 104; 105]. In humans, radiolabeled NO\({}_{3}\)\({}^{-}\) has been found to be metabolized to radiolabeled urea [106]. Since urea is further catabolized to NH\({}_{3}\) by urease, it is possible that supplemented nitrate was metabolized to NH\({}_{3}\), albeit to a small degree [106; 107].  
NH\({}_{3}\) can also result as a product of amino acid metabolism. In the present study, an increase in plasma NH\({}_{3}\) concentration was observed during exercise, particularly in the late phase of training. An increase in NH\({}_{3}\) has also been observed in preliminary experiments and in another study with 30/30 intervals [63]. Furthermore, NH\({}_{3}\) accumulation during exercise has been found to be higher during training with 30/30 intervals than during sprint interval training or continuous exercise [63]. The authors suggest that glycogen depletion is higher in 30/30 intervals than in sprint interval training or continuous exercise. This is consistent with studies showing a greater increase in NH\({}_{3}\) accumulation with a lower glycogen concentration during exercise [108; 109]. Since higher concentrations of NO\({}_{3}\)\({}^{-}\) and NO\({}_{2}\)\({}^{-}\) in muscle and plasma may increase muscle glucose uptake [11; 13], there could be greater glycogen depletion during HIHVT and therefore increased amino acid deamination. On the other hand, in a muscle biopsy, [PERSON] et al. [26] found a tendency for a sparing of muscle glycogen and PCr after nitrate supplementation during prolonged moderate-intensity exercise [26].  
The total protein concentration in the venous plasma was not different between the N and P groups in the present study. This may indicate that there was no change in amino acid metabolism between the two groups during training. It is possible that protein synthesis is independent of NO\({}_{3}\)\({}^{-}\) or it cannot be observed during the short training phase.  
It is worth mentioning that the plasma concentration of the non-proteinogenic amino acid hArg increased after nitrate supplementation [38], strongly suggesting altered amino acid metabolism by the supplemented nitrate. Recent studies suggest that higher blood and urine hArg concentrations are associated with reduced renal and cardiovascular morbidity [110; 111; 112; 113; 114]. Whether nitrate supplementation may enhance exercise performance via hArg remains to be investigated. Furthermore, studies have found that dietary nitrate from vegetables may have a protective role in cardiovascular disease and muscle function [115; 116; 117; 118]. Since even a small chronic amount of supplemented nitrate can already increase stored NO\({}_{3}\)\({}^{-}\) in muscle and blood [48], daily consumption of a few handfuls of nitrate-rich vegetables may have health-promoting effects [115; 116].
## 2 Materials and Methods
### Data Sources
#### 2.1.1 Green Space 2018 Dataset  
Deep neural networks typically require a substantial number of paired images for training and testing. However, the currently available publicly accessible data are insufficient to achieve the requirements. To this end, this paper curated a dataset suitable for the training and accuracy verification of deep neural segment networks, named Green Space 2018 Dataset (GS2018). To provide better coverage of the distribution of UGS in various cities across China, GS2018 was created using Gaofen-2 remote sensing imagery, with a spatial resolution of nearly 1 m, from the main urban areas of six cities in the summer of 2018. These cities include Peking, Shanghai, Guangzhou, Shenzhen, Nanjing, and Wuhan. The global urban boundaries (GUBs) [32] were used to define the main urban areas of these cities. Among these, Peking, Shanghai, Guangzhou, and Shenzhen are the most economically developed cities in China, and studying these four cities helps analyze the impact of economic development on UGS. Nanjing and Wuhan are significant cities in the Yangtze River Basin and were chosen as representative subjects. The relative geographical locations of these six cities are shown in Figure 1.  
GS2018 is a high-precision UGSs semantic segmentation dataset with a unified standard. In GS2018, UGS are categorized based on their functionality into five types: park green spaces, protected green spaces, plaza green spaces, subsidiary green spaces, and ecological green spaces. GS2018 was created using the object-oriented method. The original remote sensing images and the annotation binary label images were uniformly divided into small patches with no overlapping areas. GS2018 comprises a total of 9536 pairs of remote sensing images and binary label images, all with a resolution of 256 \(\times\) 256 pixels. Among these, 7136 pairs were allocated for the training set, 2000 pairs for the test set, and 400 pairs for the validation set. Some examples of the GS2018 dataset are presented in Figure 2, where the highlighted whi  
Figure 1: Relative geographical location of the study area.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 2 Related Work
### Convolutional Neural Network
proposed a multiscale input strategy for multi-view deep learning and proved that the proposed method can improve prediction accuracy [43]. [PERSON] et al. investigated the classification performance of convolutional neural networks with different depths on SAT-4 and SAT-6 datasets [45]. [PERSON] et al. proposed a gradient boosting random convolutional network (GBRCN) framework which can combine many deep neural networks effectively for scene classification and achieved good result [1]. [PERSON] et al. proposed a deep random-scale stretched convolutional neural network (SRSCNN) for HSR remote sensing imagery and confirmed that the proposed method performs better than the traditional scene classification methods [46]. Based on the second idea, [PERSON] et al. fine-tuned CaffeNet and GoogleNet on the datasets of UC Merced and Brazilian Coffee Scenes to improve classification accuracy [5]. [PERSON] et al. used AlexNet, CaffeNet, VGGNet, and PlacesNet to extract the features of high-resolution remote sensing images, the results revealed that the features from pre-trained CNNs generalize well and are more expressive than the low-level and mid-level features [44]. [PERSON] et al. used pre-trained CNN models including AlexNet, VGGNet, and GoogleNet to extract features of remote sensing datasets, and used linear support vector machine (SVM) to classify the remote sensing scene categories [8]. [PERSON] et al. adopted OverFeat network to extract features and sent features into the CNN classifier for supervised learning, so as to solve the limited-data problem [47]. [PERSON] et al. performed experiments with six popular convolutional neural networks using three remote sensing datasets and obtained good  
Figure 3: Transfer learning. Figure 2: Full-trained network. results [48]. [PERSON] et al. fine-tuned VGG deep networks to classify high-resolution remote sensing images, which significantly reduced training times and computing burden as well [49]. The method of transfer learning, which is commonly used in most articles, will generally produce higher classification results compared with the full-trained network method. Since the method of building a new CNN model is driven by the target task, it can theoretically generate more accurate features and complete the classification task better. However, the number of high-resolution remote sensing images in the shared datasets is limited, so the classification accuracy is relatively low. At present, there are only a few works on the structural design of CNN based on shared remote sensing image datasets. However, in order to further develop the method of deep learning in high-resolution remote sensing scene classification, it is necessary to construct a new convolutional neural network based on the existing datasets.
## 5 Discussions  
The proposed PBFT method performs clustering based on node location information to minimize the latency in sending and receiving consensus messages. Additionally, each cluster divides its nodes into groups and conducts consensus based on a hierarchical structure. The proposed method also dynamically changes the hierarchical structure in response to changes in the blockchain network. Based on these mechanisms, the proposed PBFT method can improve the scalability issues of the PBFT-based consensus algorithm by reducing the consensus time. However, it is necessary to discuss the following factors.  
The Cluster Primary Node (CPN) is responsible for cluster management, which includes managing and supervising groups and hierarchies within the cluster. If the CPN is attacked, shut down, or compromised, the proposed PBFT scheme may not function properly. Our proposed scheme is intended for use in a relatively secure private blockchain network, where an administrator can preemptively block the participation of malicious nodes. However, the CPN may face operational issues due to network communication failures or physical factors. Therefore, when selecting CPNs, we can choose them based on their trustworthiness, considering factors like the availability of each node to mitigate these potential issues. Additionally, in the proposed PBFT scheme, although each cluster has a CPN, the number of CPNs is a configurable value. Therefore, the security issue of CPNs can be mitigated by using multiple CPNs to structure the blockchain network [25]. Another method involves using agents to periodically monitor key nodes, such as the Cluster Primary Node [26]. These agent nodes are responsible for surveilling and managing key nodes participating in the blockchain network, enabling them to take immediate action if a problem is detected during monitoring. Although utilizing agent nodes may introduce some overhead, this does not affect the consensus process since these agents do not participate in the consensus process and primarily focus on monitoring and managing the key nodes. Additionally, in a private blockchain, agent nodes can be regarded as administrator nodes similar to clients, thus reducing the likelihood of malicious behavior.  
The performance and effectiveness of the proposed PBFT scheme have only been verified in a simulation environment. This is an aspect that requires further improvement  
Figure 7: Results according to the ratio of intra to inter latency (Where, the total number of nodes (\(N_{total}\)) is 280). (**a**) Proposed PBFT. (**b**) DLM-PBFT.  
in our study. Consequently, it is necessary to analyze the performance and effectiveness of the proposed PBFT scheme in an actual blockchain network.
# Leaf-Level Spectral Fluorescence Measurements: Comparing Methodologies for Broadleaves and Needles
## Results
### Comparison of Measurement Protocols  
Despite purposeful adjustments to at-leaf surface PAR levels to similar levels across methods to minimize NPQ and to ensure that the resulting ChIP spectra would be readily normalized by PAR (Table 1), the resulting ChIP spectra still varied considerably among methods. For broadleaf (Figure 5a and Supplementary Material, Figure S6a) and single needle arrangements (i.e., 1N and 3N in Figure 6a-c), the OC delivered the highest average levels, whereas for pine mats the highest average levels were delivered by FW (Figure 6d-f). The IS method consistently yielded the smallest ChIP values across species, which was expected due to signal attenuation inside the sphere.  
We then normalized the ChIP spectra to the far-red peak at 740 nm, in order to examine differences in spectral shape (Figure 5b, Figure 6g-h and Supplementary material Figure S6b). The results indicated that the shape of ChIP spectra was quasi-identical when measured with the OC and FW methods, but presented a lower red peak and a broader far-red shoulder when measured in the IS, with red/far-red ratios significantly smaller for the IS compared to OC [p = 0.04 (lingonberry)] and FW [p = 0.02(lingonberry) and p = 0.01 (birch)]. Note that similarly to Figure 4e-f, the normalized spectra of the IS method also presented a substantial baseline beyond 800 nm.
## 2 Simplified DI-SMC Design of DC-AC Buck Converter  
The proposed control design approach includes the development of a control-oriented model, the derivation of an equivalent control law, and a controller gains selection method. A simplified DI-SMC method was reported in [31] and was applied to the classical dc-dcbuck and boost converters in continuous conduction mode (CCM). In this research, the simplified nonlinear control method is extended to the PWM dc-ac buck converter that works as a dynamic power supply for radio-frequency power amplifiers (RFPAs) [32]. This type of power converter steps the dc input voltage down to a sinusoidal output voltage by varying the duty cycle from 0.1 to 0.9 and tracking a sinusoidal reference signal. The block diagram of the simplified DI-SMC scheme of the dc-ac power converter is shown in Figure 1.
## 2 Structure and Principle of the Seat Suspension System
### Dynamic Model of the MRF Damper
#### 2.2.3 Inverse Dynamic Model of the MRF Damper  
During the semi-active control process, the control force is output through the variable damping force of an MRF damper. Therefore, the inverse dynamic model of an MRF damper had to be established to calculate the control current, according to the expected damping force. Since the forward dynamic model of an MRF damper is complex, it is difficult to directly calculate the inverse model from a forward model. A BP (back propagation) neural network is a feed-forward neural network with error back propagation that can solve nonlinear problems when the relationship between the input and output of a system is unknown. This characteristic is suitable for solving the problem of the control current for an MRF damper. Therefore, a BP neural network was used to establish the inverse model of the MRF damper in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(c_{0}\) & \(0.0305\)/2 + 0.1537/1 + 0.1112 (N\(\cdot\)s/mm) \\ \(\beta\) & 0.0070 \\ \(\gamma\) & 26.103 \\ \(n\) & 1 \\ \(k_{0}\) & 1.0036 (N/mm) \\ \(x_{0}\) & 203.86 (mm) \\ \(A\) & 56.325 \\ \(\alpha\) & \(-1.8611^{2}\) + 14.3\(l\) + 33.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bouc-Wen model parameters.  
Figure 5: Comparisons of simulation results with experimental results. (**a**) 10 mm-2 Hz displacement-force diagram; (**b**) 10 mm-2 Hz velocity–force characteristic diagram.  
The first step in using a neural network to establish an inverse model of an MRF damper is to select the training data. The neural network inverse model of an MRF damper consists of three inputs: damping force, relative displacement, and velocity; the output is control current. Combined with the structural parameters of the studied MRF damper, a random excitation signal with an amplitude of less than \(\pm 15\) mm was used as the displacement input training sample, and its derivative was used as the velocity input training sample. A random white noise signal in the range of 0-5 A was used as the training sample of the control current. These training samples were input to the Bouc-Wen MRF damper dynamic model, and the output damping force was obtained as the training sample data. The basic training process of the BP neural network is shown in Figure 6.  
From the prediction results and errors given in Figures 7 and 8, we can see that the fitting effect of the control current predicted by the BP neural network on the original current sample was good. The root mean square (RMS) value of the neural network's current prediction error was 0.167 A, which met the control requirements of the MRF damper. After establishing the dynamic model and the inverse dynamic model of the MRF damper, the control current could be calculated according to the expected control force that was obtained by the control algorithm in semi-active control.
## 3 Results and Discussion
### Correlations Between Damage Index and Impact Parameters
The probability of a central impact on the rods is lower than that on flat surfaces, which means that eccentric impacts can occur. The low RMSE for the prism for both variants suggests that the model predicts the damage very well but does not explain the variability in the data in this case (low R\({}^{2}\)). There is, therefore, no basis for rejecting the adopted hypothesis. The relationship between the potato tuber damage index, the peak acceleration, and change in velocity during an impact was justified. This is an existence hypothesis formulated for the first time by the authors. The values of the experimental coefficient have not yet been determined. Typically, the damage potential is interpreted based on the acceleration of the impact [7; 10]. This approach is justified if similar surfaces are present. Different velocity changes are generated if the impacts occur on different surfaces, which provide additional damage data. The determined values of the coefficient \(\lambda_{m}\) reflect the importance of the relationship between the peak acceleration and change in velocity during an impact on the tuber damage index. Due to the limited data range, the empirical model is only verifiable within a specific range of variables (i.e., for a given surface, type, and initial impact velocity). Using the model with a broader range may lead to a loss of model accuracy. Damage estimation may be unreliable for significant impacts with accelerations greater than 500 \(g\). The accuracy of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variety** & **Surface** & \begin{tabular}{c} **Coefficient** \\ \(\lambda_{m}\)**,\%-**s-**m-1** \\ \end{tabular} & \
\begin{tabular}{c} **Determination** \\ **Coefficient R\({}^{2}\)**, \%** \\ \end{tabular} &
\begin{tabular}{c} **The Root Mean** \\ **Squared Error** \\ **(RMSE), \%** \\ \end{tabular} \\ \hline \multirow{4}{*}{Satuma} & bulk potatoes & 0.0057 & 49.87 & 3.15 \\  & rod & 0.02821 & 77.22 & 13.23 \\  & concrete & 0.0132 & 87.03 & 12.58 \\  & steel & 0.0163 & 93.02 & 10.91 \\ \hline \multirow{4}{*}{Hermes} & bulk potatoes & 0.0145 & 55.31 & 7.21 \\  & rod & 0.0415 & 76.76 & 19.72 \\ \cline{1-1}  & concrete & 0.0158 & 87.57 & 15.33 \\ \cline{1-1}  & steel & 0.0205 & 92.91 & 13.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the values of the experimental coefficients \(\lambda_{m}\) for the regression models relating the damage index of the Saturna and Hermes potato tuber varieties with the parameters of the IRD 400 impact recording device \(DI=\lambda_{m}a\Delta\
u\) for different surfaces, along with a statistical evaluation. the empirical model may also be limited due to the measurement resolution of the device (10 points for a single impact).
# Reliability Analysis of Multi-Process Machining Based on Information Entropy
## 4 The Reliability Analysis of the Machining Process
### Vertex Entropy and Vertex Intrinsic Entropy  
Introducing information entropy into the reliability analysis of the machining process and using information entropy to measure the uncertainty of vertex machining quality distribution [28]. In actual mechanical manufacturing, each process has an ideal value for machining quality. The closer the machining quality of the process is distributed near the perfect value, the higher the process's reliability. This relationship maps to the information entropy: The lower the uncertainty of PQRV, the lower the information entropy of the vertex. The information entropy of the PQRV is defined as the vertex entropy \(H(\cdot)\). The vertex entropy measures the uncertainty of the vertex machining quality under the action of all disturbance and uncertainty factors.  
Because the machining errors of the preceding vertices will be transmitted to the current vertex, the entropy of the current vertex contains the uncertainty of the machining quality caused by the preceding vertices. Assuming that the machining quality of the previous vertices is ideal, there are no machining errors of the previous vertices transmitted to the current vertex. The vertex entropy of the current vertex under this assumption is defined as the vertex intrinsic entropy \(H(\cdot_{int})\). The vertex intrinsic entropy measures the uncertainty of the machining quality caused only by the inherent properties of the current vertex when the interference of other vertices is excluded.
###### Abstract  
Wireless passive temperature sensors are essential in environments where wired connections are impractical, such as rotating machinery and harsh conditions. A key advantage of these sensors is their ability to operate without a local power source. This study employs the antenna backscattering method, which relies on the wireless interaction between the interrogator antenna and the sensor antenna's resonant frequency, implemented in the far-field region to support long communication distances. To evaluate the impact of antenna shape on sensor performance, three microstrip patch antenna shapes--rectangular, circular, and equilateral triangular--were designed to operate in the fundamental mode at 2.4 GHz. These designs were simulated using HFSS in Ansys Electromagnetic Suite(r) 2023 R1 (Ansys Inc., Canonsburg, PA, USA), fabricated on alumina substrates, and assessed for performance metrics, including communication distance and sensitivity. Results indicated that the equilateral triangular patch outperformed the others, achieving a maximum communication distance of 16.5 cm, a sensitivity of 0.129 MHz/\({}^{\circ}\)C over a temperature range of 25 \({}^{\circ}\)C to 500 \({}^{\circ}\)C, and a simulated gain of 5.84 dBi. These findings underscore the importance of antenna shape selection and optimization for robust, wireless temperature sensing in demanding environments.  
wireless passive temperature sensor; microstrip patch antenna; antenna backscattering; equilateral triangular antenna; fundamental mode Article
# A Lightweight File System Design for Unikernel
###### Abstract  
Unikernels are specialized operating system (OS) kernels optimized for a single application or service, offering advantages such as rapid boot times, high performance, minimal memory usage, and enhanced security compared to general-purpose OS kernels. Unikernel applications must remain compatible with the runtime environment of general-purpose kernels, either through binary or source compatibility. As a result, many Unikernel projects have prioritized system call compatibility over performance enhancements. In this paper, we explore the design principles of Unikernel file systems and introduce a new file system tailored for Unikernels named ULPS (Ultra Lightweight File System). ULPS provides system call services akin to those of general-purpose OS kernels but achieves superior performance and security with significantly fewer system resources. Specifically, ULFS is developed as a lightweight file system embracing Unikernel design principles. It streamlines system calls, removes unnecessary locks, and omits permission checks for multiple users, utilizing a non-hypervisor architecture. This approach significantly reduces the memory footprint of the file system and enhances performance. Through measurement studies, we assess the performance and memory requirements of various file systems from major Unikernel projects. Our findings demonstrate that ULFS surpasses several existing Unikernel file systems, including Rumpvfs, Ramfs-u, Ramfs-q, 9 pfs, and Hcfs.  
Unikernel; file system; lightweight; non-hypervisor; ULFS; lock-free file system; ULV