esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parametric Analysis
### Effect of \(S_{\text{wtl}}\)  
To determine the effect of \(S_{\text{wtl}}\), parameter analysis was performed by changing \(S_{\text{wtl}}\) (using 3 m, 4 m, 5.5 m, and 7 m widths) while keeping the other parameters constant, Figure 7 shows the lateral displacement and internal force diagram calculated under different \(S_{\text{wtl}}\) values. As shown in Figure 7a, it is noticeable that, when the buried depth is less than 10 m, the lateral displacement values of piles at different \(S_{\text{wtl}}\) values are quite close. This suggests that the impact of local scour on the lateral displacement of a single pile is limited to a specific depth so that the effect of \(S_{\text{wtl}}\) below that depth can be ignored. The top area of the upstream scour hole increases from 24 m\({}^{2}\) to 56 m\({}^{2}\) while keeping the other parameters constant, and the lateral displacement of the single pile will increase. It can be seen from Figure 7b that the change in \(S_{\text{wtl}}\) has the most significant influence on the bending moment of the pile body in the range of 5-12.5 m. When \(S_{\text{wtl}}\) increases by 1.3, 1.8, and 2.3 times, respectively, while the other parameters remain constant, the lateral displacement of a single pile at the groundline increases by 0.90%, 1.67%, and 4.27%, respectively. Overall, the existence of an asymmetric scour hole shape leads to a difference  
Figure 6: Comparison of the proposed method with the results of references [7; 28].  
in pile deformation, which means that the upstream and downstream of the pile are eroded to varying degrees. This phenomenon leads to the difference in soil pressure between the upstream and downstream of the pile, resulting in a large horizontal stress difference, so the pile will undergo greater bending displacement. Therefore, the geometric shape of the asymmetric scour hole shows a greater influence on the deformation response of the horizontal load pile. Because, in many design standards, the scour hole formed around the pile is usually idealized as a symmetrical shape, obviously, the assumption of a symmetrical scour hole is relatively unsafe. To ensure the safe operation of offshore wind turbines and bridge structures, it is crucial to account for the asymmetric geometry of a scour hole in pile foundation design.
## 1 Introduction
In this work, we propose a combination of an artificial neural network (ANN) and a particle filter (PF) to estimate wind speeds based on the reflected signal information of the BeiDou G4 satellite. The study area focuses on the ocean surface of the South China Sea near the cost of the Yangjiang province. In addition, particle swarm optimization (PSO) was applied to find the optimal weight and bias of the ANN because of its high efficiency and easy implementation; it only requires the adjustment of a few  
Figure 1: Map of typhoon Utor’s track. The minimum and maximum wind speeds were approximately 18 and 54 m/s, respectively. parameters [25; 26; 27]. Moreover, the particle filter was implemented as an adaptive algorithm that is independent of missing factors and more robust in nature [28; 29; 30; 31]. The remainder of this paper is organized as follows: Section 2 describes the study area and the specific details of the data collection; Sections 3 and 4 present the algorithm for wind speed estimation, which includes the ANN, PSO, and PF techniques; and Section 5 discusses the error comparison and experimental results. Finally, the conclusions are presented in Section 6.
## 5 Conclusions  
In [PERSON]'s disease detection research, prior studies focused on a single specific anomaly or early detection using various modalities. Our work stands out by addressing multiple anomalies across body parts, including tremors, dyskinesia and bradykinesia, achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). Utilizing data captured with the on-board IMUs of smartwatches attached on the most affected upper limb, our approach introduces a novel two-stage pipeline for movement disorder detection and classification (severity quantification). Unlike methods relying on vision or speech, our model provides a comprehensive solution. This study presents optimal pre-processing configurations, highlights key features for machine learning, and presents a deep neural network-based pipeline that is suitable for real-world deployment, even on low-end devices, with real time performance. This marks a significant advancement in [PERSON]'s disease research, offering a holistic approach to movement disorder detection and severity quantification. Our current research emphasizes movement disorder detection and classification, excluding the prediction of severity for individual anomalies due to limitations in the dataset.  
Future endeavors could involve addressing the skewed distribution by creating a more balanced dataset and incorporating individual anomaly severity quantification into our pipeline. Our future work aims to go beyond detection and quantification by incorporating anomaly forecasting, making our approach more proactive. Moreover, the dataset used in this study has 28 subjects, which may impact the generalization. This constraint should be considered when interpreting the results. To improve generalization, we plan to work with larger and more diverse datasets. Additionally, we aim to explore lower-limb and waist data to better capture full-body movement, including gait. These expansions will enhance [PERSON]'s disease monitoring by providing a more comprehensive understanding and early anticipation of movement anomalies.  
Conceptualization, U.K., Q.R.; methodology, U.K., Q.R., M.H., M.Z. and B.K.; software, U.K., Q.R. and M.H.; validation, U.K., M.Z. and B.K.; investigation, U.K., Q.R., M.H., M.Z. and B.K.; writing--original draft preparation, U.K., Q.R., M.H., M.Z. and B.K.; writing--review and editing, U.K., M.Z. and B.K.; visualization, U.K., M.H. and M.Z.; supervision, Q.R.; project administration, Q.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 2 Related Work
### Terrain Recognition  
Terrain recognition is crucial for gait planning, speed control and surrounding environment observation of outdoor mobile robots. Before the emergence of CNN models, traditional terrain recognition solutions usually performed classification by extracting basic visual features such as the color and texture of terrain images. [PERSON] et al. [21] proposed an extreme learning method with filters and clustering algorithms to classify terrain images, and achieved remarkable results. [PERSON] et al. [22] extracted color features from road digital images collected by cameras installed on cars, and then used multi-layer perceptron to classify the color features, thereby achieving the classification of four types of soil, grass, stone, and asphalt. For the purpose of terrain classification, [PERSON] et al. [23] designed a complex terrain sample segmentation scheme, using a combination of graph segmentation and watershed segmentation to identify terrain images.  
The traditional vision-based terrain recognition method has many shortcomings. The recognition effect is not good under the influence of complex terrain environment, lighting and other external environments, and it cannot meet the real-time needs. With the continuous development of CNN, deep learning methods have gradually become mainstream, which can effectively solve the limitations of traditional terrain recognition methods and improve recognition accuracy and reliability [24]. [PERSON] et al. [25] first introduced the deep learning method into scene terrain recognition, and proposed a terrain classification method based on a deep sparse filtering network, which combines the spatial information between image pixels with the input data, and uses the deep learning network to automatically learn features from the input data to classify the terrain. [PERSON] et al. [26] proposed a lightweight and efficient deep neural network for pixel-level terrain recognition in complex environments to achieve the global perception of outdoor environments. The deep learning network can not only extract the spatial geometric features of terrain images, but also extract the color texture details of terrain images, achieving end-to-end learning [27; 28; 29].
###### Abstract  
Object detection is a crucial research topic in the fields of computer vision and artificial intelligence, involving the identification and classification of objects within images. Recent advancements in deep learning technologies, such as YOLO (You Only Look Once), Faster-R-CNN, and SSDs (Single Shot Detectors), have demonstrated high performance in object detection. This study utilizes the YOLOv8 model for real-time object detection in environments requiring fast inference speeds, specifically in CCTV and automotive dashcam scenarios. Experiments were conducted using the 'Multi-Image Identical Situation and Object Identification Data' provided by AI Hub, consisting of multi-image datasets captured in identical situations using CCTV, dashcams, and smartphones. Object detection experiments were performed on three types of multi-image datasets captured in identical situations. Despite the utility of YOLO, there is a need for performance improvement in the AI Hub dataset. Grounding DINO, a zero-shot object detector with a high mAP performance, is employed. While efficient auto-labeling is possible with Grounding DINO, its processing speed is slower than YOLO, making it unsuitable for real-time object detection scenarios. This study conducts object detection experiments using publicly available labels and utilizes Grounding DINO as a teacher model for auto-labeling. The generated labels are then used to train YOLO as a student model, and performance is compared and analyzed. Experimental results demonstrate that using auto-generated labels for object detection does not lead to degradation in performance. The combination of auto-labeling and manual labeling significantly enhances performance. Additionally, an analysis of datasets containing data from various devices, including CCTV, dashcams, and smartphones, reveals the impact of different device types on the recognition accuracy for distinct devices. Through Grounding DINO, this study proves the efficacy of auto-labeling technology in contributing to efficiency and performance enhancement in the field of object detection, presenting practical applicability.  
deep learning; computer vision; object detection; auto-labeling +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this study, two tree-based machine learning models (the XGBoost and RF models) were used to model the water carbon fluxes (ET, NEE, and FCH\({}_{4}\)) of seven rice paddies, and the importance of these water carbon flux input factors were analyzed.  
By analyzing the statistical indicator results, it was found that the XGBoost and RF model was available for predicting rice paddy ET, NEE, and FCH\({}_{4}\), and the XGBoost model outperformed the RF model at most sites. Thus, utilizing the XGBoost model for predicting water carbon fluxes can achieve highly accurate results while reducing resource consumption, such as the installation and calibration of measuring instruments. This is particularly significant for sustainable development in the fields of rice paddy.  
Similarly, understanding the causes of water carbon fluxes also contributes to sustainable development by allowing us to focus on the most important factors and save time and resources. When predicting ET, SW was the most important at all sites. NETRAD and NDVI were more sensitive in predicting NEE.TA, LW, and SWC were more sensitive at individual sites. FCH\({}_{4}\) exhibited varying sensitivity to input factors across different sites, with SWC, TS, RECO, and NDVI emerging as the most critical factors influencing its emissions.  
For future research, the current study suggests further investigation of XGBoost's predictive water carbon fluxes performance in different wetland types. Additionally, investigating the combination of XGBoost with other optimization models, such as binary particle swarm optimization, to iteratively search for the optimal parameter combination of the XGBoost model and study the optimal input combination for water carbon fluxes.  
Software, L.W.; Writing--original draft, X.G.; Writing--review & editing, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Science and the Natural Science Foundation of Jiangxi Province of China (20192 ACBL20041 and 20212 BDH80016), and the Key Project of Water Resources Department of Jiangxi Province of China (202124 ZDKT14).  
Not applicable.  
Data is available for download at [[https://fluxnet.org/data/fluxnet-ch4-community-product/](https://fluxnet.org/data/fluxnet-ch4-community-product/)]([https://fluxnet.org/data/fluxnet-ch4-community-product/](https://fluxnet.org/data/fluxnet-ch4-community-product/)).  
The authors declare no conflict of interest.
# Reaction Forces and Apparent Thrust in Dual Oscillating Control Moment Gyroscopes
## 2 Materials and Methods
### Impulse of Reaction Forces
#### 2.8.3 The Most General Case  
The above choice of Hermite polynomials ensures an angular velocity \(\omega(t)\) which exhibits _even_ symmetry with respect to the polar angle \(\theta\), i.e., it satisfies Equation (34). However, the aforementioned condition is not absolutely necessary, because any other piecewise continuous function leads to zero impulse of the reaction force due to the inertial forces.  
Actually, regarding the gyroscopic term, since \(\omega(t)=\frac{4\theta}{dt}\), the corresponding impulse within a period \(T\) is written as follows:  
\[I_{\text{gyro}} = \int_{0}^{T}\frac{2L_{\text{rotor}}}{l_{1}+l_{2}}\omega\sin \theta dt \tag{37}\] \[= \frac{2L_{\text{rotor}}}{l_{1}+l_{2}}\Big{(}\int_{\theta_{\text {min}}}^{\theta_{\text{max}}}\sin\theta d\theta+\int_{\theta_{\text{max}}}^{ \theta_{\text{min}}}\sin\theta d\theta\Big{)}\] \[\equiv 0\,.\]  
Therefore, the foregoing elementary analysis indicates that no net impulse is generated by the gyroscopic inertial forces. From a physical point of view, this is obvious, considering the couple of equal and opposite reaction forces which withstands \(\tau_{\text{int}}\).  
Let us now see what happens with the Dean drive type of inertial forces, due to the rotating concentrated mass \(2m\). To answer this question in a mathematical manner, the corresponding parts in Equations (24) and (25) are written as follows:  
\[F_{\text{front}}^{\text{Dean}}=\frac{l_{2}}{l_{1}+l_{2}}2 mr\Big{(}\dot{\omega }\cos\theta-\omega^{2}\sin\theta\Big{)}=\frac{l_{2}}{l_{1}+l_{2}}2 mr\big{(} \dot{\theta}\cos\theta\big{)}\,, \tag{38}\]  
and  
\[F_{\text{near}}^{\text{Dean}}=\frac{l_{1}}{l_{1}+l_{2}}2 mr\Big{(}\dot{\omega }\cos\theta-\omega^{2}\sin\theta\Big{)}=\frac{l_{1}}{l_{1}+l_{2}}2 mr\big{(} \dot{\theta}\cos\theta\big{)}\,. \tag{39}\]  
Therefore, when integrating Equations (38) and (39) with respect to time \(t\), the integral is canceled by the time derivative, and thus the total impulse becomes proportional to \(\big{(}\dot{\theta}\cos\theta\big{)}\):\[I_{\rm front}^{\rm Dean}=\frac{2 mrl_{2}}{l_{1}+l_{2}}\int_{0}^{T}(\dot{\theta} \cos\theta)dt=\frac{2 mrl_{2}}{l_{1}+l_{2}}\left[\dot{\theta}\cos\theta\right]_{ \theta_{\rm min}}^{\theta_{\rm max}}. \tag{40}\]  
Since the initial and final values are characterized by the condition \(\dot{\theta}=0\), it is obvious that Equation (40) dictates that the total impulse of the Dean-drive term at the front support will vanish. A similar conclusion can be derived for the rear support as well (i.e., \(I_{\rm rear}^{\rm Dean}=0\)).  
**Remark 1**: _Equation (40) shows that the impulse of the Dean-drive-type inertial forces vanishes between any two states of a vanishing angular velocity (\(\dot{\theta}=0\)). This case is met between \(t=0\) and \(t=T_{\rm rise}\), between \(t=T_{\rm rise}\) and \(T=T_{\rm rise}+T_{\rm reset}\), and so on._
## 2 Materials and Methods
### Oxidative Stress Parameters  
#### 2.7.1 Determination of Protein Carbonyl Content (PCOs)  
Protein carbonyl content was measured using the modified method of Reznick and Packer [14]. An aliquot (1 mL) of 10 mM DNPH in 2 M HCl was added to the reaction mixture (2 mg tissue protein). The sample was incubated for 1 h in the dark at room temperature and vortexed at intervals of 15 min. Then, 1 mL of cold trichloroacetic acid (TCA) (10%, _w/v_) was added to each reaction mixture which was centrifuged at 3000\(\times\)_g_ for 10 min. The protein pellet was washed three times with 2 mL of ethanol/ethyl acetate (1:1, _v/v_) and dissolved in 1.5 mL of guanidine hydrochloride (6 M), incubated for 10 min at 37 \({}^{\circ}\)C. The absorbance was measured at 595 nm. The carbonyl content was calculated in nmol of carbonyl groups per mg of protein based on the molar extinction of DNPH (e = 2.1 \(\times\) 104 mL\({}^{-1}\)-cm\({}^{-1}\)).
## 2 Mathematical Model and Analysis
### Model of Hepatitis B Virus (HBV) Drug Therapy
\begin{split}&\frac{dT}{dt}=rT\left(1-\frac{T+I+R}{K}\right)-(1-b_ {1}\eta)\beta TV+qR\\ &\frac{dI}{dt}=rI\left(1-\frac{T+I+R}{K}\right)+(1-b_{1}\eta) \beta TV-((1+a_{1}\epsilon)\mu+\rho)IE\\ &\frac{dV}{dt}=\pi(1-fa_{2}\epsilon-(1-f)b_{2}\eta)I-cV\\ &\frac{dE}{dt}=s+\alpha I(t-\tau)E(t-\tau)-d(1-a_{3}\epsilon)E\\ &\frac{dR}{dt}=\rho IE+rR\left(1-\frac{T+I+R}{K}\right)-qR-vRE\\ \end{split}  \tag{1}\]  
As this is a system of delay differential equations with fixed delay \(\tau\), the initial conditions for the system are defined on the interval \([t_{d}-\tau,t_{d}]\) by functions:  
\[T(t)>0,\quad V(t)\geq 0,\quad R(t)\geq 0,\quad I(t)\geq 0\text{ and}\quad E(t)\geq 0 \tag{2}\]  
In many cases, these will be assumed to be constant and equal  
\[J(S_{0})= \begin{bmatrix}-r&-r&-\beta_{1}K&0&q-r\\ 0&-(\rho+\mu_{1})s/d_{1}&\beta_{1}K&0&0\\ 0&\pi_{1}&-c&0&0\\ 0&\alpha s/d_{1}&0&-d_{1}&0\\ 0&\rho s/d_{1}&0&0&-q-vs/d_{1}\end{bmatrix} \]  
and the characteristic equation:  
\[(r+\lambda)(\lambda^{4}+A_{1}\lambda^{3}+A_{2}\lambda^{2}+A_{3}\lambda+A_{4} )=0\]  
where  
\[ \begin{split} A_{1}&=c+d_{1}+q+(\rho+\mu_{1}+\eta)\frac{s}{d_ {1}}\\ A_{2}&=\frac{s}{d_{1}}\left(\
u(c+d_{1})+(\mu_{1}+\rho)(d_{1}+q+\eta \frac{s}{d_{1}})\right)+qd_{1}+cd_{1}+qc+\left(c(\mu_{1}+\rho)\frac{s}{d_{1}}- \pi_{1}\beta_{1}K\right)\\ A_{3}&=\frac{s}{d_{1}}(bd_{1}q+cd_{1}\
u+\rho d_{1}q+vs(\rho+\mu_{1}))+ cd_{1}q+\left(d_{1}+q+\
u\frac{s}{d_{1}}\right)\left(c(\mu_{1}+\rho)\frac{s}{d_ {1}}-\pi_{1}\beta_{1}K\right)\\ A_{4}&=d_{1}(q+\
u\frac{s}{d_{1}})\left(c(\mu_{1}+\rho)\frac{s}{d_{1}}- \pi_{1}\beta_{1}K\right)\end{split}  \tag{3}\]  
By the Routh-Hurwitz criteria, the characteristic equation has roots with negative real parts if and only if \(A_{i}>0\), \(A_{1}A_{2}-A_{3}>0\) and \(A_{1}A_{2}A_{3}-A_{3}^{2}-A_{1}^{2}A_{4}>0\). We can show that these conditions are satisfied when \(A_{4}>0\). Therefore, when \(\tau=0\), \(S_{0}\) is locally asymptotically stable when:  
\[\frac{\pi_{1}\beta_{1}Kd_{1}}{c(\mu_{1}+\rho)s}=\frac{(1-b_{1}\eta)(1-fa_{2} \epsilon-(1-f)b_{2}\eta)(1-a_{3}\epsilon)\pi\beta Kd}{(a_{1}\epsilon\mu+\mu+ \rho)cs}<1 \tag{4}\]  
and is unstable otherwise. This condition gives us a minimal drug efficacy for viral clearance in the absence of delay in immune activation. When \(\tau>0\), the characteristic equation at state \(S_{0}\) is:  
\[\det(B+e^{-\lambda\tau}C-\lambda I_{5})=0 \tag{5}\]  
where:  
\[B= \begin{bmatrix}-r&-r&-\beta_{1}K&0&q-r\\ 0&-(\rho+\mu_{1})s/d_{1}&\beta_{1}K&0&0\\ 0&\pi_{1}&-c&0&0\\ 0&0&0&-d_{1}&0\\ 0&\rho s/d_{1}&0&0&-q-vs/d_{1}\end{bmatrix} \]  
and:  
\[C= \begin{bmatrix}0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&\alpha s/d_{1}&0&0&0\\ 0&0&0&0&0\end{bmatrix} \]  
We see that the transcendental Equation (5) reduces to polynomial:  
\[(r+\lambda)(\lambda^{4}+A_{1}\lambda^{3}+A_{2}\lambda^{2}+A_{3}\lambda+A_{4})=0\]as in the \(\tau=0\) case. Therefore, the infection-free equilibrium \(S_{0}\) is locally asymptotically stable for all \(\tau\) when (4) holds.
## 2 Literature Review
### EVs Scheduling Problem Solving Methods  
The EV scheduling problem is similar to the traveling salesperson problem, which is also an NP-hard problem [23]. The computational effort of generating accurate algorithms grows exponentially with the size of the problem, especially since CER requires the processing of more complex scheduling information. Therefore, heuristic algorithms arecommonly used for solutions; these include genetic algorithms [24], differential evolutionary algorithms [25], and simulated annealing algorithms [26]. Researchers often enhance these basic heuristic algorithms to adapt them to problems with specific characteristics and their established mathematical models. [PERSON] et al. [27] proposed an improved particle swarm algorithm to solve the task coordination model by abstracting the CER task into a multipoint-to-multipoint transportation delivery problem within a multimodal traffic network. [PERSON] et al. [28] constructed a Markov Decision Process (MDP) model to dynamically determine the type of rescue vehicles to dispatch based on resource availability. They designed a greedy threshold heuristic algorithm to solve the model and found through experiments that it closely matches the optimal strategy. Table 1 lists several studies related to EV scheduling, summarizing the types of response, the models, the optimization objectives, the solution algorithms, and whether or not the competition between multi-disaster sites was considered in each study.  
In summary, this paper explores the challenges of concurrent emergency response based on existing research. Scholars have noted the competing needs between disaster sites and utilized equitable supply distribution to maintain balance. However, the arrival time of rescue forces, a critical factor affecting injury mortality rates and causing panic, has rarely been considered [29]. Simultaneously, by the 'no free lunch' theorem [30], we argue that it is imperative to tailor the algorithms for the models presented in this paper to enhance the quality of the solutions. To fill these research gaps, we developed a joint scheduling model for the CER situation and designed a tailored solution algorithm. These efforts will enhance the efficiency of emergency response and aid in decision-making for management units.
## 5 Performance Evaluation
### Performance Metrics  
The performance metrics used in this paper for performance evaluation purposes are the following [29]:  
* Packet delivery ratio (PDR): This is defined as the number of data packets delivered to multicast destinations over the number of data packets supposed to be delivered to multicast destinations. This ratio represents the effectiveness of the routing strategy.
* Control overhead: This refers to the average number of control packets sent to nodes during the route creation process per session per node per multicast data delivered.
* Delay: This is defined as the average delay to establish a multicast route from source to multicast destinations per one session.
* The average number of cluster head changing: This refers to the number of cluster heads changing per cluster per session on average.
* Packet loss ratio: This is defined by the proportion of data packets that are lost during transmission from the sender in a multicast group. A lower packet loss ratio indicates better performance and reliability of the secure multicast routing protocol.