esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Upper Limb Modeling
### Dh Parameters of Upper Limb Model  
To describe the forward kinematics of the upper limb model, the DH parameters have to be derived. A quadruple of such parameters represent the relation among two sequential coordinate frames of the kinematic chain model. In Table 3, the DH parameters are presented, describing the model configuration that is depicted in Figure 1. The variables \(l_{c},l_{ua},l_{fa}\) and \(l_{h}\) stand for the length from the chest to the shoulder joint, the upper arm length and the forearm and hand lengths, respectively. The estimation of these lengths is based on biomechanics literature [35] and correspond to appropriate fractions of the human height \(H\). Hence, if these parameters cannot be measured, the estimation of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Motion** & **DoF Range** \\ \hline Shoulder extension-flexion & \(-60^{\circ}\)–\(180^{\circ}\) \\ Shoulder adduction-abduction & \(0^{\circ}\)–\(180^{\circ}\) \\ Shoulder internal-external rotation & \(-90^{\circ}\)–\(20^{\circ}\) \\ Elbow extension-flexion & \(0^{\circ}\)–\(130^{\circ}\) \\ Elbow supination-pronation & \(-90^{\circ}\)–\(90^{\circ}\) \\ Wrist extension-flexion & \(-70^{\circ}\)–\(70^{\circ}\) \\ Wrist radial-ulnar deviation & \(-10^{\circ}\)–\(25^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Upper limb DoFs ranges of motion.  
upper limb segments' lengths are given by: \(l_{c}=0.129*H\), \(l_{ua}=0.186*H\), \(l_{fa}=0.146*H\) and \(l_{h}=0.108*H\).  
The \(A^{i}_{i-1}\) of Equation (1) corresponds to the transformation matrix between the two sequential coordinate frames \(i-1\) and \(i\):  
\[A^{i}_{i-1}=\begin{bmatrix}c\theta_{i}&-s\theta_{i}c\alpha_{i}&s\theta_{i}s \alpha_{i}&a_{i}c\theta_{i}\\ s\theta_{i}&c\theta_{i}c\alpha_{i}&-c\theta_{i}s\alpha_{i}&a_{i}c\theta_{i}\\ 0&s\alpha_{i}&c\alpha_{i}&d_{i}\\ 0&0&0&1\end{bmatrix} \tag{1}\]  
where \(s\) and \(c\) represent sine and cosine of the corresponding angle and the four quantities \(\theta_{i}\), \(\alpha_{i}\), \(d_{i}\), and \(a_{i}\) are parameters associated with link \(i\) and joint \(i\). These four parameters of Equation (1) are generally given the names joint angle, link twist, link offset and link length, respectively, and represent specific aspects of the geometric relationship between two coordinate frames [38]. Since the matrix \(A^{i}_{i-1}\) is a function of a single variable, as shown in Table 3, it concludes that the three out of the four parameters for a link have constant values and the only variable is \(\theta_{i}\) or \(d_{i}\) in case of a revolute or a prismatic joint, accordingly.  
The transformation matrix from the chest position, where the origin frame is assumed, to the end-effector \(T^{7}_{0}\) is given by Equation (2).  
\[T^{7}_{0}=\prod_{i=0}^{7}A^{i}_{i-1} \tag{2}\]
## 3 Results and Discussion  
### Effects on Engineering Performance  
#### 3.1.1 Raw Material Analysis  
Figure 1 shows the particle size distribution of OPC and GGBS used in the study. Specifically, OPC exhibited a mean size of 19.46 \(\upmu\)m and a fineness modulus of 1.18, and GGBS exhibited a mean size of 22.47 \(\upmu\)m and a fineness modulus of 1.08. The mean size of OPC was lower than that of GGBS although the fineness modulus of OPC exceeded that of GGBS. To observe the detailed grain shapes of OPC and GGBS, SEM analysis was performed via Genesis-2020 (Emcrafts, Gwangju-si, Korea), as shown in Figure 2. The SEM analysis results indicated that OPC and GGBS appeared similar and corresponded to ellipsoidal polyhedrons for particles exceeding 10 \(\upmu\)m and atypical crystals for particles lower than 10 \(\upmu\)m. Additionally, sieve analysis (ASTM C136) was conducted to analyze the grading of the mixed fine aggregate and crushed coarse aggregate used in the study. The results satisfied all the passing criteria for fine and coarse aggregates based on the sieve opening size as shown in Figure 3, and the fineness modulus was analyzed as 2.84 and 6.94, respectively. Figure 4 shows the results of analyzing the X-ray diffraction (XRD, X'pert3 Powder PW 3050, Malvern Panalytical, Seongnam-si, Korea) patterns to identify the types of crystalline materials in the OPC and GGBS particles. The results indicated that OPC was mainly composed of C3S(3 CaO-SiO2), C2S(2 CaO-SiO2), C3A(3 CaO-Al2O3), and C4 AF(4 CaO-Al2O3-Fe2O3) minerals while GGBS was composed of gehlenite, akermanite, and a very high X-ray amorphous content.  
Figure 1: Particle size distribution of ordinary Portland cement (OPC) and ground granulated blast-furnace slag (GGBS).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Series** & **Evaluation Item** & **Test Method** \\ \hline \multirow{4}{*}{I. Raw material analysis} & Particle size distribution (\%) & ASTM C204 \\  & Scanning electron microscope & ASTM C1723 \\  & X-ray fluorescence & ASTM C114 \\  & X-ray diffraction & ASTM C457 \\  & Grading distribution (\%) & ASTM C136 \\ \hline \multirow{2}{*}{II. Engineering performance analysis} & Slump (mm) & ASTM C143 \\  & Air content (\%) & ASTM C231 \\ \cline{2-3}  & Compressive strength (MPa) & ASTM C873 \\ \hline \multirow{2}{*}{III. Durability performance analysis} & Carbonation depth (mm) & KS F 2584 \\  & Chloride penetration depth (mm) & NT Build 492 \\ \cline{1-1}  & Relative dynamic elastic modulus (\%) & ASTM C666 \\ \cline{1-1}  & Weight reducing ratio (\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test methods by evaluation items.  
Figure 4: X-ray diffraction patterns of OPC and GGBS.  
Figure 3: Gradation sieve analysis curves of aggregates used: (**a**) coarse aggregates and (**b**) fine aggregates.  
Figure 2: Scanning electron microscopy images of (**a**) OPC and (**b**) GGBS.
## 3 Experiments and Results
The only exception is the confidence bound for the regression intercept, which is lower for the GA regression model. The non-parametric distribution is negatively skewed since the mean values for arithmetic crossover GA models are always lower than the median values. When tight bounds are desired, it is possible to use the GA regression model first to understand the underlying properties of the non-parametric posterior distribution and then select the appropriate data likelihood and prior distributions in Bayesian regression. This way, Bayesian regression modelers can make an informed decision and improve confidence in the results of their investigations. Table 3 illustrates the parameter values for the best-fitness population member found in all GA generations. Both models provide somewhat similar results in terms of RMS values, which in turn are identical to the RMS results obtained through OLS regression. Given that there are three values for a regression parameter (mean, median, and best-fitness population member genes), the question is, which value should be used as the final set of regression parameters? A decision maker should use the best member fitness parameter values from Table 3 if those values fall within the 95% confidence bounds provided in Table 2. For arithmetic crossover, the value for \(\beta_{1}=1.616\) does not belong to its 95% confidence bounds of [0.568, 1.604] from Table 2. Thus, it should be rejected. The reason for this rejection is that the value of \(\beta_{1}=1.616\) may not be a natural outcome of GA population evolution but was retained due to the initial seeding of the GA population with OLS regression parameters. Once a value for the best member fitness is rejected, the median values should be used as the final set of regression parameters. Ideally, the best fitness values should be chosen if these values fall within their respective 95% confidence bounds. Otherwise, median values represent the next best solution. In the case of single-point crossover, the best member fitness values fall within the 95% confidence bounds provided in Table 2, which are the final set of regression parameters. It may be possible to use an ensemble value, the average of all three values (median, mean, and best member fitness genes) as the final value for the regression model. The merits of different approaches in deciding the final set of regression function parameters are considered to be out of scope for the current study. However, multiple values offer different selection possibilities, where each possibility may have advantages and disadvantages.
# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 2 Materials and Methods
### Clustering by K-Means  
The principle of the K-Means clustering algorithm is simple. Given a data set \(X_{i}(i=1,2,3\ldots n)\). \(K\) value and the cluster centers are specified. Sum of the squared errors (SSE) is the objective function of the K-Means clustering algorithm  
\[SSE=\sum_{i=1}^{k}\sum_{x\in C_{i}}\left|x-\overline{x}_{i}\right|^{2} \tag{2}\]  
\(C\) represents one class of the cluster result, \(k\) represents the number of categories, and \(\overline{x}_{i}\) is the average of one class. When the objective function obtains the minimum value, the clustering effect is optimal.  
The step of K-Means clustering algorithm is divided into the following three steps:  
Step 1: Assign samples to their nearest center vector and reduce the value of the objective function  
\[\sum_{i=1}^{n}\underset{j\in\{i=1,2,3\ldots k\}}{min}\left|x_{i}-p_{j}\right| \right|^{2} \tag{3}\]  
Distance formula between points and points adopts Euclidean distance:  
\[d\left(x_{i},x_{j}\right)=\sqrt{\sum_{k=1}^{d}\left(x_{ik}-x_{jk}\right)^{2}} \tag{4}\]  
\(p\) represents \(k\) cluster centers. \(d\) represents the attribute of \(x\).  
Step 2: Update cluster average  
\[\overline{x}_{i}=\frac{1}{\left|C_{i}\right|}\sum_{x\in C_{i}}X \tag{5}\]  
Step 3: Calculate the objective function  
When the value of the objective function is the lowest, the cluster effect is optimal.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 3 Proposed Method
### The Structure of RFPNet  
The RFPNet is based on the residual block and fusion strategy of pooling layer feature maps. The overall structure is shown in Figure 5. The RFPNet consists of an input layer, 21 convolutional layers, 6 pooling layers, 7 fully connected layers, and 1 output layer. The basic principle of RFPNet construction is that the lower layers of the model use large convolutional kernels to extract shallow features, and the higher layers of the model use small convolutional kernels to extract deep features. The input of RFPNet is \(256\times 256\times 3\) pixels. Next, a \(7\times 7\) convolutional kernel is used to extract large features. A maximum pooling layer is used to reduce dimension. A \(5\times 5\) convolutional kernel is then used to extract features that are relatively smaller than the features extracted by the \(7\times 7\) convolutional kernel. After that, a residual block (each residual block contains three convolutional layers) is used. Next, a maximum pooling layer is used to reduce feature dimensions. Through a number of convolutional layers, residual blocks, and pooling layers, the features are feed into the fully connected layer named FC layer7. In order to solve the problem of information loss caused by the pooling operation, each maximum pooling layer is transformed into a one-dimensional feature vector by using a fully connected layer, and then the vectors are cascaded and served as the input of a fully connected layer named FC layer7, thus ensuring the integrity of information to a certain extent. Finally, a Softmax classifier is used in the output layer of the model to complete the classification task. The output size depends on the number of remote sensing scene classification categories. Subsequently, the residual block and fusion strategy of pooling layer feature maps are introduced in detail respectively.
###### Abstract  
Despite the importance of tidal ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these wetlands remain poorly understood. This limited understanding results from the challenges associated with in situ flux studies and their correlation with satellite imagery which can be affected by periodic tidal flooding. Carbon dioxide eddy covariance (EC) towers are installed in only a few wetlands worldwide, and the longest eddy-covariance record from Georgia (GA) wetlands contains only two continuous years of observations. The goals of the present study were to evaluate the performance of existing MODIS Gross Primary Production (GPP) products (MOD17A2) against EC derived GPP and develop a tide-robust Normalized Difference Moisture Index (NDMI) based model to predict GPP within a _Spartina alterniflora_ salt marsh on Sapelo Island, GA. These EC tower-based observations represent a basis to associate CO\({}_{2}\) fluxes with canopy reflectance and thus provide the means to use satellite-based reflectance data for broader scale investigations. We demonstrate that Light Use Efficiency (LUE)-based MOD17A2 does not accurately reflect tidal wetland GPP compared to a simple empirical vegetation index-based model where tidal influence was accounted for. The NDMI-based GPP model was capable of predicting changes in wetland CO\({}_{2}\) fluxes and explained 46% of the variation in flux-estimated GPP within the training data, and a root mean square error of 6.96 g C m\({}^{-2}\) in the validation data. Our investigation is the first to create a MODIS-based wetland GPP estimation procedure that demonstrates the importance of filtering tidal observations from satellite surface reflectance data.  
MODIS GPP Calibration; MOD17A2; Normalized Distribution Moisture Index; Tide Adjusted Wetland Index; flux GPP; salt marsh; tidal wetlands +
Footnote †: journal: remote sensing
## 10 Conclusions  
Carrier aggregation (CA) was originally proposed as a way to increase data rates in mobile communication systems by increasing the amount of spectrum available to users through the aggregation of different spectrum bands. This work has shown that CA can also lead to increased data rates without requiring additional spectrum when used as a diversity technique. By dividing a block of existing spectrum into sub-blocks and treating each of them as a component carrier (CC) via regular CA, the channel frequency diversity can be exploited, which results in a higher spectral efficiency, and therefore, in a higher user data rate with the same amount of available spectrum. In this context, a set of mathematical models and analytical expressions have been proposed to characterise the performance of CA as a diversity technique in terms of both the ergodic and secrecy capacities. The definitions of these capacities are independent of the type of information being transmitted, and therefore, the obtained results are representative for any type of data service. It has been shown that the proposed mathematical modelling approach can correctly predict the performance of CA as a diversity technique as well as the impact of various relevant configuration parameters. The obtained numerical results are in line with previous simulation studies and demonstrate that CA can be effectively exploited as a diversity technique to improve the performance of mobile communication systems. However, it has also been shown that both the ergodic and secrecy capacities should be taken into account in order to provide a robust system design. In this context, the mathematical models and expressions presented in this work constitute a useful tool to achieve this end.  
Conceptualization, P.U.A. and M.L.-B.; methodology, P.U.A.; formal analysis, P.U.A.; writing--original draft preparation, P.U.A.; writing--review and editing, M.L.-B.; supervision, M.L.-B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Dynamic and Active THz Graphene Metamaterial Devices
## 3 Applications of Graphene Metamaterial
### THz Phase Modulation
It demonstrated that p-polarized light experiences zero reflection when incident at the Brewster angle, and the reflected light undergoes a 180\({}^{\circ}\) phase shift when the incident angle crosses the Brewster angle. Due to the complex conductivity of graphene, the reflection amplitude at Brewster's angle is not zero, and the phase changes gradually as the incidence angle approaches Brewster's angle, which is slightly different from the regular Brewster's law. Moreover, they also experimentally applied voltage to the graphene monolayer, demonstrating that the Brewster angle of the metasurface could be tuned from 65 to 71\({}^{\circ}\) by varying the conductivity of graphene. When the incident angle is fixed, the maximum phase modulation of approximately 140 \({}^{\circ}\) can be achieved in 0.5-1.6 THz by using the phase jump characteristics of Brewster angle.
## 2 Materials and Methods
### Compositional Grammar  
The SNOMED CT Compositional Grammar specification [24] defines the syntax of SNOMED CT expressions, used both for the formal definition of precoordinated concepts as well as for PCEs. The overall structure is shown in Figure 2.  
Figure 1: An overview of the architecture conceptualized for the envisioned application, including a mockup of the user interface.  
Figure 2: To formulate a correct postcoordinated expression, SNOMED CT’s Compositional Grammar and Concept Model needs to be applied: (**a**) The panel above shows an exemplary PCE for “Biopsy of distal left femur using computed tomography guidance” using 71388002 |Procedure| as Focus concept. Different components of the expression are colored according to the explanation of the Compositional Grammar in the main text. (**b**) The panel below pictures schematically the Concept Model constraints for domain and range of the Attribute 260686004 |Method|. “\(\omega\)” indicates that a subset is defined including the respective concept and all of its descendants. 129314006 |Biopsy - action| is a subconcept of 129264002 |Action|; thus, the Attribute relationship of the expression above is valid.  
The basis for each PCE is the Focus concept (green), which can then be refined (purple) by adding Attribute relationships (orange). Each relationship consists of the Attribute itself (blue) and an Attribute value (red), both being SNOMED CT concepts as well. By surrounding them with curly braces, Attribute relationships can be grouped into Role groups (light blue) to imply a composite meaning.  
The Compositional Grammar is formally defined in Augmented Backus-Naur Form and is needed within the project to develop a suitable user interface to transform the user's input into the correct syntax and to parse an existing PCE for further editing.
## 3 Results
### Influence of Plasma Gas Temperature on Bactericidal Effect  
Figure 3 shows the relationship between the gas temperature and bactericidal effects of the carbon dioxide and nitrogen plasmas. At a gas temperature of 30 \({}^{\circ}\)C, the carbon dioxide plasma showed a bactericidal effect of more than a 4-log reduction in the number of surviving bacteria in 60 s. At a gas temperature of 45 \({}^{\circ}\)C, the carbon dioxide plasma demonstrated a bactericidal effect of more than a 6-log reduction in the number of surviving bacteria in 60 s. At gas temperatures of 60 and 75 \({}^{\circ}\)C, the carbon dioxide plasma showed a bactericidal effect of more than a 6-log reduction in the number of surviving bacteria in 15 s. At a gas temperature of 90 \({}^{\circ}\)C, the carbon dioxide plasma showed a bactericidal effect of more than a 6-log reduction in the number of surviving bacteria in 10 s. The increase in the gas temperature of the plasma significantly increased its bactericidal effect. In the case of the carbon dioxide gas treatment at 90 \({}^{\circ}\)C, the number of surviving bacteria did not decrease by 1-log even after 60 s of treatment. The maximum bactericidal effect  
Figure 2: Influence of gas species and gas temperature of plasma on the generation of reactive species.  
of the nitrogen plasma was only approximately a 1-log reduction after 60 s of treatment, showing a significant difference from the bactericidal effect of the carbon dioxide plasma. No difference in its bactericidal effect was observed when the plasma gas temperature was varied from 30 to 75 \({}^{\circ}\)C. In the case of the nitrogen gas treatment at 90 \({}^{\circ}\)C, the number of surviving bacteria did not decrease by 1-log even after 60 s of treatment.  
The \(BA\) values that were calculated for the carbon dioxide plasma showed a significant bactericidal effect. The results are shown in Table 1. The \(BA\) value in the carbon dioxide plasma treatment increased with an increase in the plasma gas temperature. In the case of the plasma treatment at 90 \({}^{\circ}\)C, the \(BA\) value increased by approximately seven times, compared to that at 30 \({}^{\circ}\)C.
# An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks
## 3 Models and Assumptions
### Assumptions  
We make the following assumptions in this part.  
1. All sensor nodes are homogeneous but deployed randomly around the sink node on the sea floor. These nodes rarely move after deployment. The data packets are considered to be transmitted successfully when they reach the sink node. The sink node is equipped with more battery power or can be recharged though energy harvesting. So the sink node can keep on working until all sensor nodes are dead.
2. Each sensor node is equipped with an adaptive power control module. That is to say, all sensor nodes can adjust the transmit power according to the expected transmission range. The maximum transmit power and the maximum transmission range of sensor nodes are identical. The sink node is within the transmission range of the farthest sensor node. The signal processing power is negligible when compared with data transmission power.
3. The sensor nodes may not be aware of their own location, but they can obtain the relative distance to their neighborhood nodes by measuring the Received Signal Strength Indicator (RSSI) of the received signal. The technique is widely used in underwater sensor networks and this assumption is justified by the fact that acoustic directional antennae are of much smaller size than RF directional antennae due to the extremely small wavelength of sound. Moreover, underwater sensor nodes are usually larger than land-based sensors, and they have room for such devices [13].
4. Proper medium access control methods (e.g., CDMA-based or slotted contention window based technologies [30]) can be used to achieve multiple simultaneous wireless transmissions.  
These assumptions are reasonable due to the development of underwater modems and other acoustic communication hardware.
## 5 Experimental Results and Discussion
### Metrics
#### 5.1.1 Pixel-Wise Precision, Recall and F-Measure  
We first used the pixel-wise precision, recall and f-measure metrics. They are computed for each page in the test set and averaged over all pages. They can be calculated as:  
\[Precision=\frac{TruePositive}{TruePositive+FalsePositive} \tag{1}\]  
\[Recall=\frac{TruePositive}{TruePositive+FalseNegative} \tag{2}\]  
\[F_{measure}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{3}\]