esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Evaluation Metrics  
Such as conventional machine interpretation, the produced expressive sentences for the correlative video can be estimated by looking at many reference sentences [46]. As of late, some regular measurements in machine interpretation likewise are utilized for assessing visual subtitling, i.e., Bi-Lingual Evaluation Understudy (BLEU) [37], Metric for Evaluation of Translation with Explicit Ordering (METEOR) [38], Consensus-Based Image Description Evaluation (CIDEr) [39], and Recall Oriented Understudy of Gisting Evaluation (ROUGE) [40].  
* BLEU [37]: It is one of the most notable measurements for assessing machine-produced sentences. It can be ready to figure out the correspondence between machine-created sentences and ground truth sentences. This measurement can give the best outcome regarding short sentences. The metrics BLEU (N = 1, 2, 3, 4) typically evaluate N-gram matches' precision in a sentence.
* METEOR [38]: It is one of the most utilized measurements for valuation machine-produced sentences. It has unigram-based accuracy and review rates. It has the highlights of equivalent coordinating, stemming, and definite word coordinating. The metrics are more robust than other metrics in terms of the human verdict.
* CIDEr [39]: This measurement is primarily utilized for image subtitling. Video is only a picture containing different ceaseless pictures. It can be ready for an agreement between machine-created sentences and human-explained sentences.
* ROUGE [40]: ROUGE is another measurement for valuation. It has highlights of n-gram events. It has four distinct kinds of measurements, where ROUGE-L is generally utilized for visual inscription assessment.
# Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions
## 4 Discussion
When the sensor node operates as a router, the power consumption is practically independent of the transmit power. The reduction in power consumption due to transmit power only occurs if the consumption of the control circuits of the directional antennas is less than that reduction. Electronically reconfigurable antennas are typically used to control the radiation pattern of directional antennas. The most common components employed in this control are  
Figure 14: Current consumption of the XBee S2C. Figure 15: Current consumption: (**a**) with application ACK; (**b**) without application ACK. Positive-Intrinsic-Negative (PIN) diodes, Radio-Frequency Micro-Electromechanical Systems (RF MEMS) switches, and Varactors [12]. Most works prefer PIN diodes because they provide fast switching, ranging from 1 to 100 ns [41; 42; 43]. The current consumption of PIN diodes is in the range of 3 mA to 20 mA [12; 44]. These values are higher than the increase in current consumption produced by omnidirectional antennas in the present study. RF MEMS represents another switching mechanism to control the radiation pattern. Their disadvantages are slow switching speed and high control voltage (20-100 V) [12; 44]. Although the power consumption of these components is low, a DC-DC converter may be required to generate this voltage from a lower-voltage source. These converters tend to be very inefficient for low currents. Therefore, the current consumption of the complete circuit can be several milliamperes.
###### Abstract  
For the detection of deep-sea natural gas hydrates, it is very important to accurately detect the \({}^{13}\)CO\({}_{2}\)/\({}^{12}\)CO\({}_{2}\) isotope ratio of dissolved gas in seawater. In this paper, a \({}^{13}\)CO\({}_{2}\)/\({}^{12}\)CO\({}_{2}\) isotope ratio sensor is investigated, which uses a tunable diode laser absorption spectroscopy (TDLAS) technique at 4.3 \(\upmu\)m. The proposed sensor consists of a mid-infrared interband cascade laser (ICL) operating in continuous wave mode, a long optical path multi-pass gas cell (MPGC) of 24 m, and a mid-infrared mercury cadmium telluride (MCT) detector. Aiming at the problem of the strong absorption intensity of the two absorption lines of \({}^{13}\)CO\({}_{2}\) and \({}^{12}\)CO\({}_{2}\) being affected by temperature, a high-precision temperature control system for the MPGC was fabricated. Five different concentrations of CO\({}_{2}\) gas were configured to calibrate the sensor, and the response linearity could reach 0.9992 for \({}^{12}\)CO\({}_{2}\) and 0.9996 for \({}^{13}\)CO\({}_{2}\). The data show that the carbon isotope measurement precision was assessed to be 0.0139\(\perm_{\infty}\) when the integration time was 92 s and the optical path length was 24 m. The sensor is combined with a gas-liquid separator to detect the \({}^{13}\)CO\({}_{2}\)/\({}^{12}\)CO\({}_{2}\) isotope ratio of CO\({}_{2}\) gas extracted from water. Results validate the reported sensor's potential application in deep-sea natural gas hydrate exploration.  
deep-sea natural gas hydrate exploration; \({}^{13}\)CO\({}_{2}\)/\({}^{12}\)CO\({}_{2}\) isotope ratio detection; TDLAS technique; mid-infrared ICL
###### Abstract  
Real-time prediction of remaining useful life (RUL) is one of the most essential works in prognostics and health management (PHM) of the micro-switches. In this paper, a linear degradation model based on an inverse Kalman filter to imitate the stochastic deterioration process is proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm are used to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solve the errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data, the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, the effectiveness of the proposed approach is validated on an experimental data relating to micro-switches for the rail vehicle. Additionally, it proposes another two methods for comparison to illustrate the effectiveness of the method with an inverse Kalman filter in this paper. In conclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors in RUL estimation of the micro-switches excellently.  
micro-switches; remaining useful life; linear degradation model; inverse Kalman filter +
Footnote †: journal: Applied Sciences
# Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks
## 3 Proposed Method  
### Overview  
Figure 3 shows the overall structure of the proposed method. In the first step, the data acquired from the CW radar were preprocessed to generate a spectrogram that represented the micro-Doppler signature caused by human movement. The preprocessing consisted of STFT, min-max normalization, and thresholding. In the second step, the generated spectrogram was input into the BNN to detect falls. During this process, features were extracted and classified through the convolution layer (CL) and fully connected layer (FCL).
## 2 Effect of Plasma on Seed Germination  
Seeds are the reproductive products of plants which have totipotency, i.e., the capacity to develop into whole plants. Seeds are necessary for plant survival, dispersal, and the maintenance of progeny. At the time of dispersal, seeds undergo a period of dormancy to avoid unfavorable environmental conditions. Many dormant seeds fail to germinate, even if favorable conditions exist. Therefore, it is necessary to break the dormancy stage to increase germination. Other than the environmental factors, the dormant phase and seed germination are influenced by the hard seed coat, the presence of inhibitors, the seed maturation period, the immature embryo, seed coat impermeability to oxygen and water, and hormone imbalances. The phytohormone abscisic acid (ABA) is responsible for maintaining dormancy, whereas gibberellic acid (GA) is responsible for breaking the dormancy phase. These phytohormones are synthesized in the seeds in response to physical factors, and they activate the signaling cascades and enzymes that promote the degradation of seed reserves and initiate germination. Germination occurs in different phases and involves various physiological, biochemical, and molecular events (Figure 1). Various seed treatment procedures have been applied to overcome dormancy. Popular seed treatment or priming methods, such as scarification, stratification, and chemical treatments, are used to induce germination in dormant seeds [7]. Cold (non-thermal) atmospheric or low pressure plasma is a new technology to enhance seed germination (Tables 1 and 2), and seed treatment involves (1) direct exposure to plasma (Table 1) or (2) indirect exposure via plasma-treated water and solutions (Table 2). Figure 1: Plausible events of seed germination initiated by plasma seed priming. Plasma treatment causes mechanical damage and facilitates a redox environment for the seed. This redox environment induces different pathways of seed germination. EMW: Electromagnetic wave, GA: Gibberellic acid, MAPK: Mitogen activated protein kinase, OxPPP: Oxidative pentose phosphate pathway, PCB: Protein carbonylation, TRX: Thioredoxin. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 3 Methodology
### Naive Bayes  
Naive Bayes (NB) is a simple, yet effective, machine learning algorithm and commonly used for classification tasks [42]. While decision trees are also widely applied in classification problems [43; 44; 45; 46], the NB model is rooted in classical probability theory [47; 48; 49; 50; 51],providing a strong mathematical foundation and consistent classification efficiency [52; 53]. One of the key advantages of the NB model is its ability to handle small-scale datasets efficiently, making it particularly useful for multi-class classification problems. It is also computationally efficient and easy to implement and requires fewer estimated parameters than other machine learning models. Additionally, NB is less sensitive to missing data, which enhances its robustness in real-world applications [54]. However, NB has limitations--its classification performance is highly dependent on the input data representation, and it does not always outperform other models, particularly on complex datasets. Furthermore, the model operates under the strong assumption of attribute independence, meaning that it assumes that all input features contribute independently to the classification outcome, which may not always hold true in real-world scenarios [55]. Despite this, the NB model remains a widely used and reliable classification method due to its simplicity and efficiency. The implementation of NB in landslide susceptibility assessments can be expressed as follows:  
\[y_{NB}=p(y_{i})\prod_{i=1}^{n}P(x_{i}|y_{i}) \tag{1}\]  
where \(p(y_{i})\) and \(P(x_{i}|y_{i})\) represent the prior probability and conditional probability, respectively. \(P(x_{i}|y_{i})\) can be expressed as follows:  
\[P(x_{i}|y_{i})=\frac{1}{\sqrt{2\pi\beta}}e^{\frac{-(x_{i}-x)^{2}}{2\beta^{2}}} \tag{2}\]  
where \(\alpha\) represents the mean, and \(\beta\) represents the standard deviation.
# A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network
## 2 Preform Design Methodology
### Convolution Neural Network Model Structure
Since the last layer is utilized to predict the binary values of the array for a predicted preform shape, the sigmoid function in Equation (5), whose range is \(0\leq f(x)\leq 1\), has been applied. The CNN algorithm is implemented in Python 3.7.4 (Python Software Foundation, Wilmington, USA) and Keras 2.3.1(Keras, Mountain View, USA) with the Tensorflow backend framework on an NVIDIA V100-SXM2 GPU and 180 GB RAM computer systems. The hyperparameters utilized in the training procedure were determined through the random search method consisting of 300 training steps, and are equal to \(\epsilon=10^{-8}\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\eta=5\cdot 10^{-5}\). \[\hat{y}_{pqr}=f\Bigg{(}\sum_{m\ i=1,j=1,k=1}^{c}x_{(p+i)(q+j)(r+k),m}\cdot w_{ ijk,m}+b\Bigg{)} \tag{1}\]  
Figure 3: Procedure in the first convolutional layer. \[E(y_{i},\hat{y}_{i})=-\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}y_{i}\log(\hat{y }_{i})+(1-y_{i})\log(1-\hat{y}_{i}) \tag{2}\]  
\[w_{i}=w_{i-1}-\eta\frac{\hat{n}_{1,j}}{\sqrt{\hat{m}_{2,j}+\epsilon}} \tag{3}\]  
where \(m_{n,i}=\beta_{n}m_{n,i-1}+(1-\beta_{n})\Big{(}\frac{dE}{dw_{i-1}}\Big{)}^{n}\) and \(\hat{m}_{n,i}=m_{n,i-1}/(1-\beta_{n}^{i})\), \(n=1,2\)  
\[f(x)=\left\{ \begin{array}{l}0\ for\ x\leq 0\\ x\ for\ x>0\end{array} \right. \tag{4}\]  
\[f(x)=\frac{1}{1+e^{-x}} \tag{5}\]  
Since the derived array of preform shapes had a range from 0 to 1, a threshold of 0.7 has been employed, meaning that if the output of the sigmoid activation function is greater or equal to 0.7 the relevant position is considered as part of the preform, whereas the position is neglected. The overall CNN-based preform design concept is summarized in Figure 4. To enhance the understanding of the proposed methodology, the explanation reported in Figure 4 is based on 2D images of the forging product and preform shapes.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Analysis of Statistical Distributions
#### 4.4.4 Validation and Selection Criteria  
Our review revealed inconsistencies in distribution validation approaches. Following best practices identified by [PERSON] and [PERSON] [53], we recommend the following statistical tests: the Kolmogorov-Smirnov test for continuous distributions, the Chi-square test for discrete distributions, and the Anderson-Darling test for tail-sensitive distributions. For visual analysis, we suggest Q-Q plots to assess distribution shape, P-P plots for cumulative probability comparison, and histogram overlays for direct distribution comparisons.
## 1 Introduction
Numerous studies have shown that (1) Fe\({}^{2+}\) can promote the synthesis of microbial denitrification-related enzymes and further activate the activity of microorganisms themselves [11], and (2) as the active center of NADH dehydrogenase, ubiquinone, cytochrome c, and cytochrome bc1, the interconversion between Fe\({}^{2+}\) and Fe\({}^{3+}\) plays a key role in the cellular electron transport chain [25]. In previous studies, sponge iron and polyurethane foam composite materials were co-packed into an aerobic sequencing batch reactor to construct a BSIS. The results showed that this system could achieve simultaneous nitrification and denitrification. High abundances of organic matter-degrading bacteria, aerobic denitriying bacteria, anoxic denitriying bacteria, and iron-related autotrophic denitriying bacteria were detected in the mixed-liquor sludge samples, which significantly enhanced the treatment efficiency of the reactor. In addition, the presence of anoxic and anaerobic zones within the reactor was detected using a dissolved oxygen probe [19]. Previous studies have also demonstrated that varying dosages of sponge iron exert different influences on the microbial community structure within intermittent reactors. [PERSON] [26] investigated the impact of adding different dosages (0, 45, 90, and 135 g/L) of sponge iron on the microbial community in an intermittent reactor. The results showed that the addition of sponge iron significantly promoted the growth of denitriying bacteria and iron bacteria, with the 90 g/L dosage having the most pronounced effect on the promotion of denittifying bacteria. Nevertheless, the dynamic changes in the microbial community and the fluctuations in the relative abundance of functional microorganisms throughout the operational phases of the BSIS system remain to be fully elucidated. Therefore, this study used 90 g/L of sponge iron (SFe) as a benchmark to conduct an in-depth analysis of the dynamic changes in the microbial community structure within the BSIS across different operational cycles using high-throughput sequencing technology. Furthermore, we explored the potential impact of various operational phases on denitrifying functional microorganisms. These analytical results elucidate the denitrification mechanism of the BSIS. This research provides a robust theoretical framework for the application of sponge iron in biological denitrification processes and lays a solid foundation for the practical implementation of this technology in the field of environmental engineering.
# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 4 Experiments and Results
### Comparison Experiments with Advanced Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Module**} & **Parameters** & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**P (\%)**} & \multirow{2}{*}{**R (\%)**} & \multirow{2}{*}{**[EMAIL_ADDRESS] (\%)**} & \multicolumn{2}{c}{**[EMAIL_ADDRESS]:0.95**} & \multirow{2}{*}{**FPS**} \\  & **(M)** & & & & & & \\ \hline YOLOv5s & 2.5 & 7.1 & 44.2 & 31.3 & 30.7 & 17.4 & 105.3 \\ YOLOv6s & 4.2 & 11.8 & 37.9 & 29.9 & 28.6 & 16.6 & 118.5 \\ YOLOv8n & 3.0 & 8.1 & 44.0 & 32.0 & 32.1 & 18.6 & 103.1 \\ YOLOv8s & 11.1 & 25.8 & 49.4 & 38.1 & 38.4 & 22.8 & 97.1 \\ YOLOv9c & 25.3 & 102.4 & 53.8 & 41.1 & 43.1 & 26.2 & 83.3 \\ FFCA-YOLO & 5.1 & 37.3 & 50.1 & 40.2 & 40.3 & 23.1 & 108.6 \\ Ours & 2.3 & 19.0 & 50.9 & 39.2 & 40.6 & 24.4 & 95.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison experiment results on the VisDrone2019 dataset. unmanned aerial vehicles is not as good as that of RSG-YOLO. In addition to YOLOv9c, the RSG-YOLO model presented in this paper also shows excellent detection performance. In the three scenarios, the least leakage and fewest misdetections are exhibited by this paper's model. Furthermore, for the same target, the highest confidence in detection is demonstrated by this paper's model, which fully proves that the model proposed in this paper has superiority in the task of detecting small targets from a UAV. In this paper, the improved model based on YOLOv8n not only achieves a lightweight status, but also significantly improves model performance. Even when compared with the YOLOv8s model, whose parameter count is 504% of the improved model, the detection accuracy indexes are still improved with lower computation. Specifically, P, R, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 are improved by 1.5%, 1.1%, 2.2%, and 1.6%, respectively, achieving comprehensive optimization and meeting the requirements for small target detection for UAVs. Figure 11: Changes in training indicators of each comparison model.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 4 Discussions and Conclusions
### Integrated Control Architecture and System Dynamics  
Our approach involved designing a comprehensive control architecture to manage the closed-loop regulation of pressure, thrust, and valve positions. The system's dynamics, which are characterized by nonlinearity and time variance, show that operational duration notably influences the dynamic response of pressure regulation. The RL-based control system adapts to these dynamic changes more effectively than traditional PID control, significantly improving response speed and reducing regulation time.
## 5 Conclusions  
This study demonstrated the effectiveness of combining multiple remote sensing data sources with machine learning algorithms for classifying tree species at different elevations in Shennongjia. The following conclusions were drawn: the diversity of the tree layer decreases with increasing elevation in the different elevation research areas. When classifying the three common tree species (_Pinus armandii_, _Larix chinensis_, and _Betula platyphyllla_) in the three elevation gradients, the classification accuracy of the same species varies at different elevations. Among them, the average producer accuracy and user accuracy of _Pinus armandii_ were 97.06%, 99.11%, and 100% at elevations of 1600 m, 1800 m, and 2000 m, respectively; the average accuracy rates of _Larix chinensis_ were 90.91%, 90.28%, and 83.77%; and the average accuracy rates of _Betula platyphyllla_ were 84.65%, 85.00%, and 91.90%. The results indicate that coniferous species perform better than broad-leaved species when using remote sensing data features to classify tree species at different elevations. Redundant features can be eliminated through feature selection, and combining the optimal features with four machine learning classifiers can achieve high accuracy in tree species classification. Among them, using the XGBoost classifier in combination with the optimal features yielded the highest overall classification accuracy for tree species at elevations of 1600 m, 1800 m, and 2000 m, which were 87.63%, 88.24%, and 84.03%, respectively. These research results can provide guidance for tree species classification applications.  
Conceptualization, Y.D.; data curation, X.L.; formal analysis, H.L.; funding acquisition, Y.D.; investigation, H.L., X.L., and H.C.; methodology, X.L. and Z.H.; project administration, Y.D.; resources, H.C. and Y.D.; software, H.L.; supervision, Y.D.; validation, X.L.; visualization, H.L.; writing--original draft, H.L.; writing--review and editing, H.L. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Research Fund of Key Laboratory of Digital Earth Science, Aerospace Information Research Institute Chinese Academy of Sciences, Chinese Academy of Sciences (No. 2022 LDE006).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Results and Discussion  
### Chip Photographs and Assemblies  
The photographs of the fabricated quasi-MMIC PAs are displayed in Figure 11a,b. These chips are adhered to a four-layer PCB using ABLEBOND 84-1 LMISR4 conductive  
Figure 10: The 3D EM views of the proposed continuous Class-J mode quasi-MMIC PAs: (**a**) split CoB wire-bond assembly design; (**b**) stack CoB wire-bond assembly design.  
Figure 9: The schematics of the proposed continuous Class-J mode quasi-MMIC PAs: (**a**) split CoB wire-bond assembly design; (**b**) stack CoB wire-bond assembly design.  
adhesive. Among them, the stack design GaN chip is mounted on top of the GaAs IPD. To prevent the conductive adhesive from overflowing onto the PCB pads, the bond wire in the stack design is slightly longer compared to the split design. In the split design, the GaN chip area, excluding the cutting street, was 2.87 mm\({}^{2}\) (1.75 \(\times\) 1.64 mm). The sizes of the GaAs IPD chips for input matching network (IMN) and OMN were 0.64 mm\({}^{2}\) and 2.19 mm\({}^{2}\) (0.8 \(\times\) 0.8 mm and 1.24 \(\times\) 1.77 mm), respectively. On the other hand, in the stack design, the GaN chip area, excluding the cutting street, was 2.1 mm\({}^{2}\) (1.75 \(\times\) 1.2 mm). As for the GaAs IPD chip in this configuration, the area was 12.11 mm\({}^{2}\) (4.64 \(\times\) 2.61 mm). Despite the fact that the stack design utilizes a larger area of GaAs IPD chips compared to the split design, it consumes less GaN chip area while providing greater potential for additional system integration. With the stack design, there are increased possibilities for integrating the remaining transceiver components directly on the GaAs chip using chip-on-chip (CoC) technology, ultimately enabling the integration of a system-in-package (SiP).  
As mentioned in Section 2.1, heat sinks are employed on the backside of the PCB to enhance heat dissipation from the output-stage GaN transistors. Additionally, off-chip bypass capacitors are soldered onto the PCB to improve the stability of the low-frequency band. These measures ensure that the power amplifiers operate within safe temperature limits and maintain reliable operation while minimizing potential stability issues.  
The measurement signal input and output were performed using RF wafer probing, while the bias currents were supplied by bond wires through PCB traces. It is noted that the current-carrying capacity of a 1.0 mil diameter gold bond wire is 0.5 A. Given that the simulated drain current of the output-stage transistor is 0.57 A, double bonds are employed from the drain DC pad to the PCB pad to ensure sufficient current-handling capacity.