esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Ecosystem Assessment Methods
#### 2.2.2 Ecosystem Quality Indicator  
In accordance with China's Ministry of Ecology and Environment [19], the ecosystem quality index (EQI) was constructed using the relative density for three ecological parameters (the annual average LAI, the annual maximum FVC, and the annual cumulative GPP).  
\begin{table}
\begin{tabular}{l l l c c} \hline \hline \multicolumn{1}{c}{**Index**} & \multicolumn{1}{c}{**Calculation**} & \multicolumn{1}{c}{**Interpretation**} & \multicolumn{1}{c}{**Units**} & \multicolumn{1}{c}{**Valid Range**} \\ \hline FCI & The percentage of forests. & \begin{tabular}{l} Measure how many forests are \\ distributed in a certain area. \\ \end{tabular} & - & \begin{tabular}{l} [0\%, 100\%] \\ \end{tabular} \\ \hline Annual average LAI & \begin{tabular}{l} The average value of LAI \\ for a whole year. \\ \end{tabular} & \begin{tabular}{l} Illustrate the average annual growth of \\ the forest. \\ \end{tabular} & \begin{tabular}{l} m\({}^{2}\)/m\({}^{2}\) \\ \end{tabular} & [0, 8] \\ \hline Annual maximum FVC & \begin{tabular}{l} The maximum value of \\ FVC during the year. \\ \end{tabular} & Display the peak growth over the year. & - & [0\%, 100\%] \\ \hline Annual cumulative GPP & \
\begin{tabular}{l} The cumulative value of \\ GPP for a whole year. \\ \end{tabular} &
\begin{tabular}{l} Demonstrate the carbon sequestration \\ potential of vegetation. \\ \end{tabular} & gC/m\({}^{2}\) & [0, 5000] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the forest coverage index (FCI), annual average leaf area index (LAI), annual maximum fraction of green vegetation cover (FVC), and annual cumulative gross primary productivity (GPP) indicating the vegetation growth status for forest ecosystem.  
Figure 2: Technical route for forest health assessment based on remote sensing products.  
The relative density is determined by considering the maximum and minimum values of these ecological parameters for different forest types within each Koppen-Geiger climate classification. To achieve this, each of these parameters was normalized to a range of 0-1 using the following equation:  
\[RVI_{i,j,k}=\frac{F_{i,j,k}-F_{\min i,j,k}}{F_{\max i,j,k}-F_{\min i,j,k}} \tag{1}\]  
where \(RVI_{i,j,k}\) is the relative density of the ecological parameter \(k\) in year \(i\) for climate type \(j\); \(F_{i,j,k}\) is the value of ecological parameter \(k\) in year \(i\) for climate type \(j\); and \(F_{\max i,j,k}\) and \(F_{\min i,j,k}\) are the maximum and minimum values of the ecological parameter \(k\) in year \(i\) for climate type \(j\), respectively.  
We calculated the EQI as follows [19; 20]:  
\[EQI_{i}=\frac{LAI_{i}+FVC_{i}+GPP_{i}}{3}\times 100 \tag{2}\]  
where \(EQI_{i}\) is the ecosystem quality for the \(i\)th year; \(LAI_{i}\) is the relative density of LAI in year \(i\); \(FVC_{i}\) is the relative density of FVC in year \(i\); and \(GPP_{i}\) is the relative density of GPP in year \(i\).  
Based on the ecosystem quality assessment standard of China's Ministry of Ecology and Environment [19], the value of each EQI classification is shown in Table 2.
## 6 HVDC Converters
### Operational Advantages of VSC  
According to [45], VSCs have multiple advantages over both CSC and LCC, some related to their efficient operation and functioning principles, others associated with the compatibility of today's power electronics industry [49]. Some of these include:  
1. Minimal environmental impact.
2. Ability to connect to weak AC networks and dead networks.
3. Rapid development in power electronics technology.
4. Ongoing development in the availability of various types of semiconductor switches.
5. Integration of renewable energy resources and storage systems in electric power grids.  
Several PWM, and more advanced space-vector techniques suitable for VSCs, are described in detail in [27], which lead to a smooth operation capable of delivering allow-harmonic sinusoidal output to the AC-side of the system. This capability is more notorious in modern Modular Multi-Level VSC configurations, which are an emerging configuration in [27, 50, 51] with notorious technical and economic advantages [52].  
Furthermore, the increasing use of VSC HDVC in smart-grids indicates that the role of power electronics in power systems is growing. Several power electronics experts have predicted that VSC-HVDC systems will probably replace thyristor-based systems, much like thyristors replaced mercury-arc systems [53].  
It is important to mention that VSC can also modulate their valves to control reactive power, obtaining any desired power factor [45, 54]. Therefore, VSC-HVDC links lend excellent voltage and frequency control for the AC grids connected to them.
# PET/CT Radiomics in Lung Cancer: An Overview
## 2 Methodology
### Acquisition  
This is the procedure whereby the scans are obtained, and includes both the examination itself and the patient preparation protocol. The output will be a three-dimensional matrix of intensity values (voxel model), which in the remainder we shall refer to as the _raw_ data. A wide range of parameters intervene in the acquisition process, among them tube current and voltage (for CT); spatial resolution (voxel size), reconstruction algorithm and related settings both for CT and PET. All these variables may have a significant impact on the radiomics features computed [6; 18], with certain features being affected more than others [19].
## 3 Results
### Metamodel Selection
This demonstrates that incorporating the trend component improves LSTM model predictions, indicating the trend component's influence on LSTM prediction outcomes. From the RMSE metric, the Seasonal LSTM outperforms the Original LSTM in all groups and exceeds the Trend LSTM in residential groups two and five and office group one, slightly behind the performance of the proposed ensemble model. From the MAE metric, the Seasonal LSTM also surpasses the Original LSTM in all groups, and in residential group six, it even outperforms the proposed ensemble model. This shows that the introduction of seasonal components also impacts LSTM predictions, further reducing the prediction error for the LSTM model. The proposed ST-LSTM-RR ensemble model exhibits an excellent performance in most groups, particularly in residential groups two, three, four, and five and office groups one and two, achieving the best results. This indicates that the proposed ensemble model can integrate the strengths of each model to achieve more stable predictive outcomes. In summary, introducing trend or seasonal components can enhance the predictive performance of LSTM models, and the proposed ensemble model offers better predictive outcomes and more stable results. From Table 6, it is clear that each basic model has its strengths in different groups. To visually verify the diversity of the basic models, line graphs of the predictive results of the three basic models in residential group four and office group one were plotted, as seen in Figures 10 and 11. Figure 10 shows that in the first five days, the predictive results of  
Figure 9: The MAE of the model on each group. the three basic models are quite close and perform very well, but in predicting the turning points on the sixth and seventh days, OLSTM's predictions differ from the actual values by more than 100, indicating a significant error, whereas TLSTM and SLSTM continue to predict the turning points accurately. The data's pattern of change in residential group four is complex. Observing Figure 11, the three basic models demonstrate their strengths at different time points, making better predictions. This illustrates the diversity of the three models, capable of making accurate predictions at different times, laying a solid foundation for the success of the ensemble learning model.
## 3 Results and Discussion
### Verification and Validation of Computational Modeling of Buckling
#### 3.1.3 Validation of Elastoplastic Buckling Model Under Combined Loading on Stiffened Plate  
The validation was performed by comparing the \(P_{u}\) obtained by the present study with the \(P_{u}\) obtained experimentally by [PERSON] et al. [26]. The authors applied uniaxial compressive loading and lateral pressure \(P_{z}=0.06\) MPa to a stiffened panel, illustrated in Figure 12, having \(a=1160\) mm, \(b=960\) mm, \(t_{p}=4\) mm, \(E=180\) GPa, \(\
u=0.3\), and \(\sigma_{yp}=218\) MPa. The panel has four longitudinal stiffeners located with \(a_{1}=100\) mm from the sides and spaced \(c_{2}=320\) mm apart, as well as four transverse stiffeners spaced by \(b_{1}=60\) mm from the ends and \(c_{1}=280\) mm between them. The stiffeners have \(t_{s}=5\) mm, \(h_{s}=50\) mm, \(E=180\) GPa, \(\
u=0.3\), and \(\sigma_{ys}=300\) MPa.  
For the proposed model, the \(\sigma_{u}\) was obtained with the mesh convergence test. The obtained values of \(\sigma_{u}\) are presented in Table 7.  
This model results in \(\sigma_{u}=122.90\) MPa, with an error of 0.39% compared to the experimental result of \(\sigma_{u}=123.38\) MPa obtained by [PERSON] et al. [26]. The displacements and von Mises stress distributions are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element Length** & **Number of** & \(\sigma_{u}\) \\
**(mm)** & **Elements** & **(MPa)** \\ \hline
75 & 382 & 140.32 \\
50 & 894 & 123.55 \\
35 & 1472 & 123.25 \\
20 & 3936 & 122.90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mesh convergence test for validation of \(\sigma_{u}\) under combined loading.  
Figure 12: Stiffened plate considered in validation: (**a**) dimensions and (**b**) loading and boundary conditions, with the blue symbols representing the displacement constraints according to the axis direction, the red arrows representing the biaxial compressive loading, and the red lines on the plate representing the applied lateral pressure.  
The \(\sigma_{u}\) obtained for the stiffened panel under combined loading aligns well with the experimental result of [PERSON] et al. [26]. The transverse stiffeners presented less stress concentration than longitudinal, meaning that the stiffeners in the loading direction are responsible for supporting majority stress, corroborating with [PERSON] et al.'s [13] conclusions about that. Therefore, both quantitatively and qualitatively, the computational model was duly validated.
## 2 Results
### Identification and Analysis of Differentially Expressed Genes (DEGs)  
To identify differentially expressed genes under MeJA treatment, reads were mapped to the unigenes and reads per kb per million reads (RPKM) was used to measure the transcriptional levels. The Pearson correlation analysis indicated that there were high correlations between three biological replicates of both CK and MeJA treatment (Supplementary Figure S5). Then using a two-fold change of RPKM with False Discovery Rate (FDR) <0.05 as the cut-off criteria, 4430 differentially expressed genes (DEGs) were identified between CK and MeJA-treated samples in _M. canadensis_. Among them, 2383 DEGs were up-regulated and 2047 DEGs were down-regulated under MeJA treatment (Figure 1). To further explore the possible roles of the DEGs, GO and KEGG enrichment were conducted. For GO enrichment, 88, 27 and 6 GO terms were significantly enriched in \"biological process\", \"molecular function\" and \"cellular component\" categories, respectively (Supplementary Table S1). \"Terpene synthase activity\" and \"oxidoreductase activity\" were the most significantly enriched terms in \"molecular function\". In \"biological process\" category, biosynthetic processes involved in lipid, small molecule, dicarboxylic acid, phospholipid, terpene and sesquiterpene were the most significantly enriched terms. For KEGG pathway enrichment, 25 pathways were significantly enriched under MeJA treatment (Figure 2 and Supplementary Table S2). Of these, pathways involved in secondary metabolism accounted for a large part, including biosynthetic pathways of phenylpropanol, terpenoid, alkaloid, flavonoid, glucosinolate and so on.  
Figure 1: Statistics of DEGs induced by MeJA in _M. canadensis_ transcriptomes. (**A**) Volcano plots of the unigenes in the comparisons of MeJA-treated and CK samples; (**B**) DEG numbers in the comparisons of MeJA-treated and CK samples. RPKM: Reads per Kb per Million Reads, FDR: False Discovery Rate, DEG: Differentially Expressed Gene.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Annotation Database** & **Number of Unigenes** & **Percentage (\%)** \\ \hline Nr & 52,700 & 64.39 \\ Swissprot & 34,565 & 42.23 \\ KOG & 29,536 & 36.09 \\ KEGG & 19,013 & 23.23 \\ Annotated in at least one database & 52,826 & 64.55 \\ Total unigenes & 81,843 & 100 \\ \hline \hline \end{tabular}  
* Nr: National Center for Biotechnology Information (NCBI) non-redundant protein database; KOG: eukaryotic Orthologous Group; KEGG: Kyoto Encyclopedia of Genes and Genomes.  
\end{table}
Table 3: Functional annotations of _M. canadensis_ unigenes.
## 3 Parametric Study
### Soil Parameters  
The results in Figure 4 provide insights into the variation of the dimensionless bearing capacity \(Q=\frac{q_{u}}{\gamma B}\), see Equation (2), induced by the soil features, under different values of the horizontal seismic acceleration coefficient \(k_{h}\). Figure 4a shows the outcome of the analysis for a broad range of values of the undrained shear strength. The graph confirms that for a low shear strength, that is for \(c_{0}/\gamma B<1\), the model can be unstable (especially regarding the lower bound state): void collapse occurs without any loading and the value of \(Q\) gets close to zero, though it cannot be computed due to the instability of the model. As far as the effect of \(kB/c_{0}\) on the seismic and static bearing capacity of the shallow footing is concerned, Figure 4a also shows that the bearing capacity raises slightly under seismic conditions, however, it then becomes independent of \(kB/c_{0}\).  
Results in Figure 4b instead show that by increasing the angle of internal friction of the soil, the bearing capacity of the strip footing increases as well: for \(\varphi\leq 30^{\circ}\), the bearing capacity increases at a low rate, but for larger values, it keeps increasing sharply.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 3 Applications of Smart Drug Delivery Devices
### Implantable Smart Drug Delivery Devices  
IoT (internet of things)-based implantable drug delivery devices are a growing trend in the healthcare industry, offering new and innovative solutions for drug delivery. These devices leverage IoT technology to provide real-time monitoring and control of drug delivery, improving the accuracy and efficiency of the delivery process.
## 2 Materials and Methods
### Global Optimization Algorithms  
#### 2.1.1 Basin-Hopping (BH)  
Basin-hopping is the popular name [30] given to a \"Monte Carlo-minimization\" method initially proposed ten years earlier [29]. The basic version of this algorithm, usually called _monotonic_ basin-hopping [36] is presented in Algorithm 1. Starting from a given initial configuration \(q\), this algorithm iterates the following steps until one of the conditions in the function Stopping Criteria is satisfied (see Section 2.2): (line 3) a relatively large-amplitude perturbation is applied to the current conformation \(q\), aiming to escape from local basins; (line 4) a new conformation \(q_{\text{new}}\) is generated by local energy minimization from the perturbed conformation \(q^{\prime}\); (line 5) the transition to the new local minimum is accepted or rejected based on the usual MetropolisTest[38]. Following this stochastic acceptance test, the new conformation \(q_{\text{new}}\) is accepted if its energy is lower than or equal to the one of the previous conformation \(q\). Otherwise, it is accepted with a probability that decreases exponentially with the positive energy difference between the two configurations. The implementation of BH used for the comparative analysis in this work follows a _multi-start_ procedure, as presented in Algorithm 2. Aiming to cover the space more globally, it performs several rounds of the monotonic-BH algorithm, starting from randomly sampled conformations generated by the function SampleRoot. Note that we successfully applied this implementation of BH in previous work [39].  
```
input :Conformational space variables \(\mathcal{C}\)  Algorithm parameters and energy function \(P\)  Initial conformation \(q\) output :Set of local minima \(\mathcal{T}\leftarrow\emptyset\) whilenotStoppingCriteria(\(P,\mathcal{T}\))do
1\(q^{\prime}\leftarrow\)LargeAmplitudePerturbation(\(\mathcal{C},P,q\))
2\(q_{\text{new}}\leftarrow\)LocalMinimization(\(\mathcal{C},P,q^{\prime}\)) ifMetropolisTest(\(P,q,q_{\text{new}}\))then
3\(\mathcal{T}\leftarrow\)AddMinimum(\(\mathcal{T},q_{\text{new}}\))
4\(q\leftarrow q_{\text{new}}\) return\(\mathcal{T}\)
```  
**Algorithm 1**Monotonic-Basin-Hopping  
```
input :Conformational space variables \(\mathcal{C}\)  Algorithm parameters and energy function \(P\)  Initial conformation \(q\) output :Set of local minima \(\mathcal{T}\) \(\leftarrow\) \(\emptyset\) whilenotStoppingCriteria(\(P,\mathcal{T}\))do
2\(q^{\prime}\leftarrow\)LargeAmplitudePerturbation(\(\mathcal{C},P,q\))
3\(q_{\text{new}}\leftarrow\)LocalMinimization(\(\mathcal{C},P,q^{\prime}\))
4 ifMetropolisTest(\(P,q,q_{\text{new}}\))then
5\(\mathcal{T}\leftarrow\)AddMinimum(\(\mathcal{T},q_{\text{new}}\))
6\(q\leftarrow q_{\text{new}}\) return\(\mathcal{T}\)
```  
**Algorithm 2**Monotonic-Basin-Hopping
## 4 Test Results and Discussion  
### Failure Modes of FHSB T-Stub Connection  
The specimen failures observed in the tests are displayed in Figure 7. It can be seen that the specimens mainly suffered from shear failure, as one bolt was cut off in most cases. During the tests, each specimen gave off a sound when the static friction was overcome. The sound was followed by a string of clear sounds. Finally, an ultra-loud sound was heard when the bolt was cut off. With the increase in the shear-compression ratio, the sounds became less loud, the hole wall underwent greater deformation, and the worn area at the middle of contact surfaces and the peripheral of bolt hole continued to shrink and roughen.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 3 Methodologies  
### Pso  
PSO is a group-search-optimization algorithm. The motion of each particle is determined by the value of the fitness function, and the \"direction\" and \"target\" of its motion are determined by the \"velocity\" of each particle. Then, the particles iterate in the solution space according to the direction of the best particle.  
In PSO, \(x\) represents the position of the particles, \(v\) represents the velocity of the particles, and \(Pbest\) represents the best position of the particles. The PSO initializes a group of random particles and finds the best solution through iteration. In each iteration, the particle updates its position by tracking two best values. One best value is the best solution that the particle can find. This solution is called particle best. The other best value is the best solution found by the whole population at present, which is called the global best. Suppose that a population composed of \(K\) particles is searched in the \(D\)-dimensional solution space, where the position of the \(i\)-th particle is expressed as a \(D\)-dimensional vector:  
\[X_{i}=(x_{i1},x_{i2},\ldots,x_{iD}),i=1,2,\ldots,K \tag{9}\]  
The motion velocity of the \(i\)-th particle is also a vector of the \(D\)-dimension:  
\[V_{i}=(v_{i1},v_{i2},\ldots,v_{iD}),i=1,2,\ldots,K \tag{10}\]  
The best position searched by the \(i\)-th particle, namely, the particle best, is expressed as:  
\[Pbest_{i}=(p_{i1},p_{i2},\ldots,p_{iD}),i=1,2,\ldots,K \tag{11}\]  
The best position searched by the whole population, namely, the global best, is expressed as:  
\[Gbest=(g_{1},g_{2},\ldots,g_{D}) \tag{12}\]  
The updated formula of velocity and position is as follows:  
\[v_{id}^{t+1}=\omega*v_{id}^{t}+c_{1}r_{1}\big{(}p_{id}^{t}-x_{id}^{t}\big{)}+ c_{2}r_{2}\big{(}g_{d}^{t}-x_{id}^{t}\big{)},\ \ \ d=1,2,\ldots,D \tag{13}\]  
\[x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t},\ \ \ d=1,2,\ldots,D \tag{14}\]  
where \(c_{1}\) and \(c_{2}\) are the acceleration constant, \(r_{1}\) and \(r_{2}\) are a uniform random number, and \(\omega\) is the inertia constant.  
According to the above description, PSO is applicable to the continuous-function calculation, and the update of velocity and position adopts a continuous-vector calculation. Based on the discrete data characteristics of the DSTT, combined with the previous research, we carry out a targeted operation of the DPSO.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 3 Materials and Methods
### Model Training  
Data preprocessing: For training, time-sequential frames and data labeled with information on motion due to respiration are needed to estimate the degree of motion. In this study, we generated a dataset for respiration estimation, as shown in Figure 6, using video and respiration signals collected with a webcam and contact sensor. First, two time-adjacent frames of a captured video were used as input images, and the difference between the normalized respiration signals corresponding to the two images was obtained. The difference between the respiration signals was a value between \(-1\) and \(1\), and the stronger the inhalation, the closer to \(1\), while the stronger the exhalation, the closer to \(-1\). By multiplying the predefined body mask for the image with the movement value due to respiration, the torso area pixel values are assigned a movement value due to respiration, and the pixel values of areas other than the mask are assigned a value of \(0\) to create a respiration signal mask. The proposed model is trained to infer the torso mask for movement changes due to breathing by taking two images as the input.  
Implementation details: The L\({}_{1}\) loss, as given in Equation (1), was used as the loss function, the Adam optimizer was used, and a total of 1,000,000 iterations with a batch size of 10. The learning rate started at 0.0001, and a step scheduler was applied to reduce the learning rate by half every 200,000 iterations.  
\[\mathrm{L}_{1}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-f(x_{i})\rvert \tag{1}\]  
In the model inference, the size of the input image was fixed at 200 \(\times\) 200 pixels, but we allowed the actual size and aspect ratio of the RoI to differ from the size of the input image. In this case, the characteristics of the image may be modulated by resizing to match the RoI image with a fixed image size. Therefore, we selected a bounding box of random location, size, and aspect ratio to consider these factors, and resized the bounding box to 200 \(\times\) 200 pixels for use as input data. The size and aspect ratio of the bounding box were randomly selected from 0.5 to 1.5 times based on 200 \(\times\) 200 at each learning time. Inaddition, color augmentation was performed by scaling and shifting randomly for each RGB channel of the image to include information on the texture changes of diverse colors. Furthermore, the proposed method needs 759,760,000(0.7 G) floating point operations (FLOPs) to respiration signal estimation.