esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Discussion
Figure 6 displays the bio-thermal stress \(\sigma_{11}\) variation along the \(x\)-axis (\(y=0.5\)) for the proposed hybrid technique (Present), finite difference method (FDM) [35], and finite element method (FEM) [36]. It can be seen from this figure that the present hybrid technique results were in excellent agreement with the FDM and FEM results. Table 1 shows the processing times and numbers of iterations for the CA-Arnoldi, regularized, and GMSS iterative methods at each discretization level, where the number of equations is written inside the parentheses. It can be shown from this table that the GMSS was more efficient than the CA-Arnoldi and regu  
Figure 5: Distribution of \(\sigma_{11}\) along the \(x\)-axis for various bioheat models. Figure 6: Variation of the bio-thermal stress \(\sigma_{11}\) along the \(x\)-axis for different methods. Table 2 shows a comparison of the computer resource requirements for modeling the nonlinear fractional bio-thermomechanical interactions in FGA soft tissues for the finite difference method (FDM), finite element method (FEM), and HBEM (Present). This table demonstrates the efficiency of our proposed hybrid technique. The findings of this paper contribute to the development of mathematical models that can be applied in bio-thermal engineering applications, such as blood perfusion, temperature measurement during cryosurgery, analysis of microvascular heat transfer, thermal injury, cryotherapy, hyperthermia, and the influence of large blood vessels.
## 2 Study Sample and Methods
### Dietary Patterns (DPs)  
The frequency of the consumption was recorded using the lifestyle and nutrition knowledge assessment (KomPAN) [23; 24]. The KomPAN questionnaire consists of four sections, within which it provides the following information: (1) dietary habits, (2) frequency of food consumption, (3) views on food and nutrition, and (4) lifestyle and personal information [24]. It was validated internationally, including for the Polish population between 15 and 65 years old [24; 25; 26]. The frequency of the food intake is shown in Table S1. The responses were converted to daily consumption frequencies, following the KomPAN procedure [23]. Dietary indexes were calculated for the established products and product groups, and the results were interpreted using the tercile division recommended previously. Two diet quality scores, namely, the pro-healthy diet index (pHDI-10) and the non-healthy diet index (nHDI-11), were determined using the frequency of the food intake [23; 27; 28]. In addition, the \"sugar diet index\" (sDI-7), which represents the dietary sugar intake relating to seven food groups, has also been created. All the index components are shown in Table 1.  
The dietary patterns (DPs) were derived a posteriori using principal component analysis (PCA) with a varimax rotation. The input variables were the frequencies of the consumption of fruits, vegetables, fermented-milk drinks, cottage cheese, hard cheese, cured meat, buckwheat, oats, wholegrain pasta, legume-based foods, white bread and bakery products, butter, fried foods, sweetened beverages, and sweets. Other variables taken into account were sDI, nHDI, and pHDI. The data on the frequency of the food consumption were standardised. The sample size was sufficient to derive the DPs, as the ratio of the respondents to the input variables was 39:1 (624/16) [29; 30].  
Figure 1: The subjects’ flowchart through the study.  
Three PCA-derived dietary patterns were identified. The sweet-Western dietary pattern (SWDP) was heavily loaded by the frequent consumption of white bread and bakery products, sweetened beverages, candies, cured meat, butter, and fried foods and high sDI and nHDI values. A pro-healthy dietary pattern (PHDP) reflected mainly the consumption of vegetables, fruits, legume-based foods, buckwheat, oats, and wholegrain pasta and high pHDI values. The consumption of cottage cheese, fermented-milk drinks, and hard cheese contributed heavily to the third pattern, the dairy dietary pattern (DDP). All the patterns explained 57% of the total variance; the shares in the variance explanation equalled 25%, 22%, and 10%, respectively, for the first, second, and third patterns. For further analyses, tercile intervals were calculated for each PCA-derived DP.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 3 The Proposed Method  
Figure 1 shows the overview of the proposed method. It mainly consists of four components as follows:  
* Cluster construction: Vehicles form clusters autonomously based on their moving speeds and directions. Each cluster has a cluster head (CH) and a variable number of cluster members (CMs).
* Content fetching: On requesting content, each CM sends an interest packet to its associated CH. Each CH is responsible for forwarding interest and data packets and replies to an interest packet if the requested content is found in its cluster.
* Popularity management: Vehicles update the popularity of the content by monitoring requests from its cluster and the interest packets it forwards.  
* Cache management: Each CH manages cache buffers for itself and all its CMs. On receiving a data packet, it decides whether and where to store the content. If there is a shortage of cache buffers, the content with the lowest popularity is replaced by the new content.  
With these steps, the proposed method removes duplicated caches and enables each cluster to cache more content locally. On this basis, it reduces the number of packet transmissions and the response time accordingly.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps from Other Data Sources
#### 3.5.7 Pan-European Map of Forest Biomass Increment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Region** & **Base Year** & **Fixel Size** & **Forest Mask** & **Methodology** & **Accuracy/Uncertainty** & **Provider** \\ \hline \multirow{2}{*}{United States} & \multirow{2}{*}{2000} & \multirow{2}{*}{30 m} & \multirow{2}{*}{NLCD 2001} & Regression tree modelling (FIA & R = 0.7; \\  & & & data, Landsat, NLCD land cover, & RMSE = 139 Mg/hm2 & [PERSON] et al. [59] \\  & & & and SRTM) & & \\ \hline \multirow{2}{*}{United States} & \multirow{2}{*}{2001} & \multirow{2}{*}{250 m} & \multirow{2}{*}{NLCD92} & Classification and regression tree modelling (FIA data, MODIS & R = 0.31-0.92; \\  & & & data, and cover data, and ancillary information) & Relative Error = 0.51–0.92 & [PERSON] et al. [44] \\ \hline \multirow{2}{*}{Contreminous} & \multirow{2}{*}{2000–2009} & \multirow{2}{*}{250 m} & \multirow{2}{*}{NLCD tree canopy} & Phenological Gradient Nearest Neighbor (FIA data, MODIS data, and ancillary geospatial data) & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & and ancillary geospatial data) & & \\ \hline Alaska & \multirow{2}{*}{2009–2010} & \multirow{2}{*}{30 m} & \multirow{2}{*}{NLCD 2001} & regression analysis (field AGB & Mean absolute error = 21.8 Mg/ha; \\  & & & data and Landsat data) & Mean bias error = 5.2 Mg/ha & [PERSON] et al. [64] \\ \hline \multirow{2}{*}{Garcia River} & \multirow{2}{*}{2011} & \multirow{2}{*}{20–50 m} & \multirow{2}{*}{–} & \multirow{2}{*}{Several statistical models (field data and LiDAR data)} & \multirow{2}{*}{Represented by the 95\% confidence interval} & \multirow{2}{*}{[PERSON] et al. [78]} \\  & & & & data and LiDAR data) & & \\ \hline \multirow{2}{*}{Maine} & \multirow{2}{*}{2012} & \multirow{2}{*}{13 m} & \multirow{2}{*}{–} & Space-varying coefficients model (field inventory data, and LiDAR data) & RMSE = 17.52 Mg/ha (prediction) & [PERSON] et al. [79] \\  & & & & data) & \\ \hline Maryland & 2011 & 30 m & – & Random Forest regression model (field data, and LiDAR data) & Represented by the 95\% confidence interval & [PERSON] et al. [81] \\ \hline \multirow{2}{*}{Sonoma County} & \multirow{2}{*}{2013} & \multirow{2}{*}{30 m} & CALVEG land cover product & Random Forest regression model (field data, and LiDAR data) & Represented by the lower 10\({}^{\text{th}}\) percentile and upper 90\({}^{\text{th}}\) percentiles of confidence interval & \\  & & & & data) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 P-MAC Design Integrated with Routing
### Schedule Choosing Rule and Synchronization  
Suppose the time needed to finish the _GDSA_ process is within \(T_{g}\) time. The transmission duration of a GRADE message is denoted by _durGRADE_. Once a node receives a GRADE message and updates its grade to \(i\), it uses the following rules to choose an initial schedule:  
(1) if \(i\) % \(\tau=0\), the node will enter the _RECEIVE DATA_ period after (\(T_{g}-i\cdot\textit{durGRADE}\)) time.  
(2) if \(i\) % \(\tau=1\), the node will enter the _SEND DATA_ period after (\(T_{g}-i\cdot\textit{durGRADE}\)) time.  
(3) if \(i\) % \(\tau\geq 2\), the node will enter the _SLEEP_ period after (\(T_{g}-i\cdot\textit{durGRADE}\)) time. And its sleep duration should be:  
\[time=T_{SLEEP}-(i\%\tau-2)\cdot T_{s/R}=(\tau-i\%\tau)\cdot T_{s/R} \tag{4}\]  
Note that \(T_{g}\) should be long enough to enable each node in the network to get its grade and corresponding schedule after the _GDSA_ process.  
Similar to S-MAC [3] and RMAC [5], synchronization is also required among neighboring nodes in P-MAC to solve the problem of clock drift as time advances. However, the difference is that P-MAC doesn't use an extra period (e.g., _SYNC_) to achieve this goal. In P-MAC, each frame contains the  
Figure 3: An example illustration of _sleep_factor_, data transmission route: 3\(\rightarrow\)2\(\rightarrow\)1\(\rightarrow\)0.  
relative start time of the current period. When a sensor node receives a frame, it will adjust its schedule if the clock drift is too serious. If a node fails to receive correct data for a long enough time, it will stay active and listen to the channel for some time to revise its grade and/or schedule. This loose synchronization mechanism is used because in P-MAC, a node with pending data to send may have several adjacent lower grade nodes, any of which may become the relay node. We suppose this mechanism will guarantee reliability for data forwarding.
## 4 Control Methodology  
### Design of LADRC Controller  
The dynamic Equation (12) can be transformed into  
\[\left\{\begin{array}{l}\dot{\vartheta}_{1}=\vartheta_{2}\\ \dot{\vartheta}_{2}=q(t)+d(t)+Gu(x)\\ y=\vartheta_{1}\end{array}\right., \tag{14}\]  
where \(\vartheta=[\vartheta_{1}\ \vartheta_{2}]^{T}\) are measurable state variables; \(y\) is the output signal; \(f(t)\) is an unknown nonlinear function of the system; \(d(t)\) is the external disturbance. \(G\) a is partially known, and the known part is denoted as \(G_{0}\).  
All uncertainties in the system and all disturbances received by the system are regarded as the total disturbance, which is defined as  
\[F(\vartheta,u(\vartheta))=f(t)+[g(t)-b_{0}]u(\vartheta)+d(t). \tag{15}\]  
To observe the total disturbance, the extended state space is defined \(\zeta=[\zeta_{1}\ \zeta_{2}\ \zeta_{3}]\), where \(\zeta_{1}=\vartheta_{1}\), \(\zeta_{2}=\dot{\vartheta}_{1}\) and \(\zeta_{3}=F(\vartheta,u(\vartheta))\).  
The nonlinear system (12) can be transformed into  
\[\left\{\begin{array}{l}\dot{\zeta}=M\zeta+Nu+Th\\ y=U\zeta\end{array}\right., \tag{16}\]  
\[\text{where }M=\begin{bmatrix}0&1&0\\ 0&0&1\\ 0&0&0\end{bmatrix},\,N=\begin{bmatrix}0\\ b_{0}\\ 0\end{bmatrix},\,T=\begin{bmatrix}0\\ 0\\ 1\end{bmatrix},\,D=\begin{bmatrix}1\\ 0\\ 0\end{bmatrix}.\]  
According to (16), LESO is designed as follows:  
\[\left\{\begin{array}{l}\dot{z}=M\dot{z}+Nu+\gamma(y-\hat{y})\\ \hat{y}=U\dot{z}\end{array}\right., \tag{17}\]  
where \(z=\begin{bmatrix}z_{1}&z_{2}&z_{3}\end{bmatrix}\) is the state vector of the observer, which is used to observe the state of system \(\zeta=[\zeta_{1}\ \zeta_{2}\ \zeta_{3}]\); \(\gamma=\begin{bmatrix}\gamma_{1}\ \gamma_{2}\ \gamma_{3}\end{bmatrix}^{T}\) is the observation gain vector of LESO.  
The gain value of LESO is obtained from its characteristic equation:  
\[\lambda(s)=s^{3}+\omega_{1}s^{2}+\omega_{2}s+\omega_{3}=(s+\omega_{0})^{3}, \tag{18}\]  
where \(\omega_{0}\) is the bandwidth of LESO, denoted as \(\omega=[3\omega_{0}\ 3\omega_{0}^{2}\ \omega_{0}^{3}]^{T}\).  
**Remark 1**.: _The high bandwidth gain of LESO helps to improve the control accuracy of LADRC, but it will cause the output of the controller to increase, and the actuator will consume more energy, which is not conducive to the long-term flight of the quadrotor. This study introduces BP neural network to improve the overall performance of the controller to solve this problem._The extended state space observation value of LESO is defined as \(\hat{z}_{1}=\hat{x}_{1}\), \(\hat{z}_{2}=\hat{x}_{2}\), \(\hat{z}_{3}=\hat{F}(x,u(n))\). The control law can be written in the following form:  
\[u=(u_{p}-\hat{z}_{3})/b_{0}, \tag{19}\]  
where \(u_{p}=k_{p}(x_{d}-\hat{z}_{1})+k_{d}(\dot{x}_{d}-\hat{z}_{2})+\ddot{x}_{d}\) is the output of the PD controller.
## 2 Materials and Methods
### Data Sources and Searches  
The Cochrane, Sport Discus, PubMed, Scopus, and Web of Science (WOS) databases were systematically searched to identify the effectiveness of warm-ups that include lower extremity stretching on ROM and jump height. A comprehensive search of the literature was performed from database inception through to March 2023. A manual search was performed using a combination of the following terms: warm-up, stretching, performance, vertical jump, and range of motion. These concepts were applied using the search operator \"AND\" and \"OR\". The following keyword strategy was used: ((\"Warm-up\" OR \"Warm up\" OR \"Warm up\" OR \"Warmup\" OR \"Warming-up\" OR \"Warming up\") AND (\"Static\" OR \"Dynamic\" OR \"Passive\" OR \"Ballistic stretching\" OR \"PNF\" OR \"Flexibility\" OR \"Stretching\") AND (\"performance\" OR \"performance improvement\" OR \"improved performance\" OR \"jump\" OR \"vertical jump\" OR \"ROM\" OR \"range of motion\" OR \"joint flexibility\" OR \"passive range of motion\" OR \"acute effects\" OR \"prior exercise\" OR \"CJM\" OR \"strength\" OR \"1 RM\" OR \"1-RM\")).  
Authors A and B independently analysed the titles and abstracts, read the full articles, extracted the study properties, and assessed the methodological quality of the clinical trials. If there was no agreement between them, a third author (C) analysed the study to reach a consensus.
## 4 Experiments
### Experimental Setup
#### 4.1.2 Baselines  
The proposed SSA-GAN was compared to four baseline models to assess its performance: (1) Liner, a simple linear regressor; (2) S-LSTM [6], which features LSTMs with social pooling modules; (3) S-GAN [24], which utilizes social LSTMs under the adversarial nets framework; and (4) SoPhie [22], which incorporates social and physical attention into the S-GAN model.
## 5 Simulation Results and Analysis
### Gear Shift Performance Analysis
In addition, the torque of EM1 didn't increase from t=10.4 s to the maximum value as expected, mainly because the motor model established in this paper is a dynamic model that considers the first-order inertia link. Figure 23c shows that the output torque of the gearbox was positive at all times, and there was no power interruption during the shift process. According to Figure 23d, the maximum jerk was \(3.77\) m/s\({}^{3}\), and the slipping friction work, generated by the CL2, was \(821.3\) J during the entire shift process. Figure 21: Rotary velocities of active and driven ends of CL2. A further comparative analysis of the above two gear shift coordinated control strategies and their simulation results showed that, because EM2 had torque output during the whole shift process of 01-11, there was no shift power interruption phenomenon. In other words, the maximum jerk, obtained based on the above two control strategies, was relatively small. As for the other two evaluation indices of the shift effect (shift time and clutch slipping friction work), it can clearly be seen from Table 6 that, compared with the shift control strategy based on the PID control algorithm, the shift time and clutch slipping friction work of the shift control strategy based on the PID-bang-bang-fuzzy compound intelligent control algorithm proposed in this paper were reduced by 35.7% and 19.2%, respectively. Therefore, the gear shift coordinated control strategy proposed in this paper could significantly reduce the shift time and slipping friction work, and it could effectively improve the shift quality. Figure 22: Simulation results based on PID control algorithm. (**a**) Rotary velocity and pressure of CL2; (**b**) torque of power source and CL2; (**c**) output torque of gearbox output shaft; (**d**) jerk and slipping friction work.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 2 Star Pattern and Catalog
### Star Catalog Generation  
We select stars from the SAO J2000 [18] star catalog as navigation stars. Assuming the sensitivity magnitude of the star tracker is set to \(6M_{o}\), then unstable variable stars and difficult-to-distinguish binary stars are removed. Ultimately, 4960 stars are selected as navigation stars. During catalog generation, each of the 4960 stars are taken as the center of the FOV in the star tracker. Based on the size of the FOV, stars that have angular distances within the set FOV are calculated as neighboring stars. This results in \(n_{id}\) neighboring stars used to generate the pattern features of the navigation stars. \(n_{id}\) is determined by the reference star, which means different stars may have diverse values of \(n_{id}\). Generally, the value of \(n_{id}\) is greater than 0 and far less than 4960.  
Finally, the feature vectors \(v_{r}=\{r_{1},r_{2},\ldots,r_{n_{id}}\}\), \(v_{\theta}=\{\theta_{1},\theta_{2},\ldots,\theta_{n_{id}}\}\) of each navigation star are obtained and stored in a format shown in Tables 1 and 2. The first column contains a unique id for each navigation star, while the subsequent \(n_{id}\) columns represent the distance features and angular features corresponding to the \(n_{id}\) neighboring stars associated with the current navigation star.  
When calculating the distance ratios, the dynamic angles selected are not fixed every time. To enhance the robustness of the algorithm, real-time ratio calculations are employed. Starting from the initial matched angle, each neighboring star's distance is then computed  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Star ID** & \(\mathbf{r_{1}}\) & \(\mathbf{r_{2}}\) &... & \(\mathbf{r_{n_{id}}}\) \\ \hline
1 & 76.6 & 425.62 &... & 129.94 \\
2 & 127.34 & 407.67 &... & 435.60 \\... &... &... &... &... \\
4960 & 128.19 & 341.2 &... & 364.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Navigation star distance feature.  
as a ratio value to the previously matched neighboring star's distance. This method eliminates variations in star imaging caused by changes in the star tracker's focal length, and helps prevent issues with false or missing stars that affect identification. Since the distance ratio calculation is dynamic, ratios are computed during the algorithm's execution rather than being pre-calculated. The specific matching process will be detailed in the following sections.
###### Abstract  
Climate change and global warming are attributed to the increased levels of greenhouse Gases in the atmosphere. Miniature low-cost, lightweight instruments on-board low-cost nanosatellite platforms such as CubeSats could be used to provide precise measurements of greenhouse gases levels. CubeSats, which usually have a narrow field of view, cost a fraction of what more expensive satellites with wide swaths cost. MeznSat is a 3U CubeSat that will carry a shortwave infrared (SWIR) micro-spectrometer as its primary payload, with the aim of deriving greenhouse gas concentrations in the atmosphere by making observations in the 1000-1650 nm wavelength region. The satellite, which is planned for launch in March 2020, is the result of a collaborative project between Khalifa University of Science and Technology (KUST) and the American University of Ras Al-Khaimah (AURAK) with a fund from the United Arab Emirates Space Agency (UAE-SA). The primary payload, Argus 2000, is a miniature, low-cost, space-qualified spectrometer that operates in the shortwave infrared (SWIR) bands. Argus 2000 is a ruggedized unit with a mass of less than 230 g and power consumption of less than 1 W. Also, the Argus 2000 has 0.15 degrees viewing angle and 15 mm fore-optics. The secondary payload will consist of a high definition (HD) camera that will allow post-processing to achieve the high geolocation accuracy required for the SWIR spectrometer data. The RGB combination of visible and SWIR bands setup makes MeznSat a unique CubeSat mission that will generate an interesting dataset to explore atmospheric correction algorithms, which employ SWIR data to process visible channels. This paper describes the mission feasibility, mission analysis, design, and status of MeznSat.  
green-house gases; CubeSats; shortwave infrared (SWIR) spectrometer +
Footnote †: journal: Accepted: 27 September 2019; Published: 31 October 2019
## 3 Method
### JMI-Based Greedy Feature Selection Algorithm (JMI-Score)
Score = []
2. max\(J\)\(MI\) = 0
3. for i from 1 to \(D\)
4. \(S[\text{i}]=[f_{1}]\)
5. Temp = \(S[\text{i}]\)
6. for j from 1 to \(D\)
7. \(S[\text{i}]\). add (\(f_{j}\))
8. Score\([\text{i}]\) = model.fit(tmp, \(C\))
9. If Score\([\text{i}]\) \(>\) Score\([\text{i}-1]\)
10. \(S[\text{i}]=S[\text{i}]\). add(\(f_{j}\))
11. else
12. \(S[\text{i}]=\) Temp
13. End for
14. End for
15. Sort the Score, select the top ten largest, and record all subscript IDX
16. for i from 1 to 10
17. If MI \(<\) JMI\((S[\text{IDX}[\text{i}]],C)\) :
18. max\(J\)\(MI\) = JMI\((S[\text{IDX}[\text{i}]],C)\)
19. Ans = IDX\([\text{i}]\)
20. \(S=\)\(S[\text{Ans}]\)
21. Output: \(S\). ```  
**Algorithm 1** JMI-Score  
The algorithm first iterates through each feature, using a single feature as the starting set, and iterates through the features, other than the original features. Feeding the selected set of features into the pre-trained model, if the model score improves on this feature set, the newly traversed features are added to the feature set, otherwise they are not added. Therefore, all features are traversed, the set of features with the highest model score is selected, assuming that these feature combinations are most relevant to the labels and the features in each feature combination are complementary information. The ten features that make the highest model score are combined with the labels, and then to calculate the joint mutual information. Since JMI indicates the selection of the candidate selection features that maximize the cumulative sum of the joint mutual information, and the selected subset of features add them to the subset. The method performs well in terms of classification accuracy and stability. Therefore, the final optimal subset with the largest joint mutual information is selected and the algorithm ends. The feature types proposed in the previous section are selected to help reduce the features used for feature processing. The final size of manual features is 123 \(\times\) 528.