esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Covalent Modification Cycle and Brink Controller (BC)
#### 3.1.2 An Application of the Covalent Modification Cycle--Brink Controller (BC)  
A controller BC based on the covalent modification cycle is constructed in the following work, which can realize an ultrasensitive switch-like input-output response. Furthermore,  
Figure 3: Graphical representation of Equation (4).  
it can be described by the following abstract chemical reactions of Equation (7). The schematic of the BC is shown in Figure 4. Obviously, \(R\) and \(Y\) are the inputs of the BC, while \(U\) denotes the output of the controller. The parameters \(k_{c}\), \(\theta_{c}\) and \(\alpha_{c}\) denote the catalysis rate, \(\gamma_{c}\) and \(\beta_{c}\) represent the binding rate, and \(\phi_{c}\) indicates the degradation rate. In Equation (7), species \(R_{r}\) and \(R_{y}\) can combine to form complex \(R_{r}\cdot R_{y}\), which does not interact with any other substances. More importantly, \(R\) can produce \(R_{r}\), which in turn reacts with \(U^{*}\) to form \(U\), while \(Y\) can produce \(R_{y}\), which in turn reacts with \(U\) to form \(U^{*}\).  
For the BC, the corresponding CRNs can be expressed as  
\[\begin{split} R\xrightarrow{k_{c}}R+R_{r}\\ Y\xrightarrow{\theta_{c}}Y+R_{y}\\ R_{r}+R_{y}\xrightarrow{\gamma_{c}}R_{r}\cdot R_{y}\\ R_{r}\xrightarrow{\phi_{c}}\varnothing\\ R_{y}\xrightarrow{\phi_{c}}\varnothing\\ R_{r}+U^{*}\xrightarrow{\alpha_{c}}U+R_{r}\cdot R_{y}\\ R_{y}+U\xrightarrow{\beta_{c}}U^{*}\end{split} \tag{7}\]  
Using the mass action kinetics, the ODEs of Equation (7) are given by  
\[\begin{split}\frac{d[R_{r}]_{t}}{dt}&=k_{c}[R]_{t}- \gamma_{c}[R_{r}]_{t}\big{[}R_{y}\big{]}_{t}-\phi_{c}[R_{r}]_{t}-\alpha_{c}[R_{ r}]_{t}[U^{*}]_{t}\\ \frac{d[R_{y}]_{t}}{dt}&=\theta_{c}[Y]_{t}-\gamma_{c }[R_{r}]_{t}\big{[}R_{y}\big{]}_{t}-\phi_{c}\big{[}R_{y}\big{]}_{t}-\beta_{c} \big{[}R_{y}\big{]}_{t}[U]_{t}\\ \frac{d[U^{*}]_{t}}{dt}&=-\alpha_{c}[R_{r}]_{t}[U^{* }]_{t}+\beta_{c}\big{[}R_{y}\big{]}_{t}[U]_{t}\\ \frac{d[U]_{t}}{dt}&=\alpha_{c}[R_{r}]_{t}[U^{*}]_{t }-\beta_{c}\big{[}R_{y}\big{]}_{t}[U]_{t}\end{split} \tag{8}\]  
Equation (9) can be obtained due to Equation (8).  
\[\frac{d[U^{*}]_{t}}{dt}+\frac{d[U]_{t}}{dt}=0 \tag{9}\]  
Note that the result \(\big{(}d[U^{*}]_{t}/dt)+\big{(}d[U]_{t}/dt)=0\) which implies that the total mass \(U^{*}+U\) is conserved, i.e., \(U_{\text{Total}}=U^{*}+U\), can be obtained based on Equation (9).  
Figure 4: Schematic diagram of the BC scheme for a process.
## 3 Design of Proposed 3-DOF Spherical Motor  
This section introduces the basic structure of the proposed 3-DOF spherical motor in this study. Its basic structure is shown in Figure 5a. When comparing Figures 1 and 5a, the proposed 3-DOF spherical motor has the same structure except for 8 symmetrical coils wound around the outer yoke A. As shown in Figure 5a, 8 symmetrical coils are wound around the outer yoke A. Among the 8 coils, 4 coils are used for rotating around the \(X\)-axis, and other coils are used for rotating around the \(Y\)-axis.  
Figure 5b shows the Y-Z cross-section of the proposed 3-DOF spherical motor when the rotor is rotating around the \(X\)-axis at 25\({}^{\circ}\). Here, the 4 coils are called C\({}_{1}\), C\({}_{2}\), C\({}_{3}\), and C\({}_{4}\), respectively. As shown, when the rotor is rotating around the \(X\)-axis from 0\({}^{\circ}\) to 25\({}^{\circ}\) in a counter-clockwise direction, the magnetic flux through the coils C\({}_{2}\) and C\({}_{4}\) is too weak, so the coils C\({}_{2}\) and C\({}_{4}\) are turned off in the proposed design. Due to the proposed electromagnetic structure with a particular controlling strategy, the proposed 3-DOF spherical motor has higher torque output in the large rotation angle when compared to the original 3-DOF spherical motor [25]. The flowchart designed to actuate the proposed 3-DOF spherical motor for rotating around the \(X\)-axis is illustrated in Figure 6. In this paper, we discuss the actuator rotating around the \(X\)-axis between 0\({}^{\circ}\) and 25\({}^{\circ}\), so we only need  
Figure 4: Y-Z cross-section of original 3-DOF spherical motor when the rotor is rotating around the \(X\)-axis at (**a**) 0\({}^{\circ}\) and (**b**) 25\({}^{\circ}\), respectively.  
Figure 5: (**a**) Basic structure of proposed 3-DOF spherical motor; (**b**) Y-Z cross-section of proposed 3-DOF spherical motor when the rotor is rotating around the \(X\)-axis at 25\({}^{\circ}\).  
to power on the coil C\({}_{1}\) and C\({}_{3}\). For another scenario, if we want to let the actuator rotate around the \(X\)-axis between \(0^{\circ}\) and \(-25^{\circ}\), we only need to power on the coil C\({}_{2}\) and C\({}_{4}\). In the next section, the improvement in the static torque of the proposed 3-DOF spherical motor is verified by using the 3D FEM.
## 3 The Sensor Network Calculus
### Calculation of Network-Internal Traffic Flow  
When incorporating the required network calculus components into the system model, we added output arrival curves of servers. Next, we present their computation in a sink tree network. The traffic a sensor node \(i\) forwards to its parent sensor node is constrained by arrival curve:  
\[\alpha_{i}^{*}=\bar{\alpha}_{i}\dot{\odot}\beta_{i}. \tag{3}\]  
Equation (3) can be expanded to a recursive computation by considering the output of child nodes as given in Equation (2). However, making use of the sink tree structure of the network allows for the iterative computation of all \(\alpha_{i}^{*}\) given by Algorithm 1. Note that this procedure requires perfect knowledge about the sink tree structure to correctly handle aggregation of flows in Step 3.  
```
1. Assume a sink tree network consisting of \(n\) sensor nodes; all arrival curves \(\alpha_{i}\) are given at the locations sensed input enters the network and service curves \(\beta_{i}\) are known for all sensor nodes, \(i=1,\ldots,n\).
2. The output arrival curve computation for leaf nodes strictly follows Theorem 2: 1. \(\alpha_{i}^{*}=\alpha_{i}\dot{\odot}\beta_{i}\) where sensor \(i\) is a leaf node. 2. Mark every sensor \(i\) as \"calculated\".
3. Continue with a non-leaf node \(i\) whose child nodes are all marked \"calculated\", but is not marked itself. We apply Equation (3): 1. \(\alpha_{i}^{*}=\left(\alpha_{i}+\sum_{j=1}^{n_{i}}\alpha_{child(i,j)}^{*} \right)\dot{\odot}\beta_{i}\) where \(child\left(i,j\right)\) are the \(n_{i}\) child nodes of non-leaf sensor \(i\) that were marked \"calculated\". 2. Mark non-leaf sensor \(i\) as \"calculated\".
4. Check if all sensor nodes just below the sink are marked \"calculated\". 1. Yes: the algorithm terminates. 2. No: repeat Step 3.
```  
Figure 2: TDMA service curve.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
###### Abstract  
The use of the millimeter wave (mmWave) spectrum and further exploration of sub-mmWave has led to a new era in wireless communication, as the need for higher data rates grows. High frequencies, on the other hand, incur a higher path loss, requiring an increase in antenna gain requirements. Metasurfaces, which emerge as a promising technology for mitigating path loss effects by utilizing two dimensional (2D) arrays of engineered meta-atoms resembling metamaterials that control the surface's electromagnetic response have been introduced. Currently, metasurfaces are primarily considered as passive reflecting devices in wireless communications, assisting conventional transceivers in shaping propagation environments. This paper presents an alternative application of metasurfaces for wireless communications as active reconfigurable antennas for next generation transceivers. A framework that demonstrates the design process of a metasurface antenna structure was introduced and further used to design a 4 \(\times\) 4 array and its reconfigurable counterpart. In contrast to conventional phased array antennas, a reconfigurable metasurface (RMS) antenna does not require phase-shifters and amplifiers, which leads to reduced cost. Instead, each individual element achieves reconfigurability by shifting the resonating frequency using semiconductor devices such as PIN diodes. The proposed metasurface antenna is designed to operate at a frequency of 28 GHz and 40 GHz. In addition, an increase in gain and directivity was observed when diodes were added to the metasurface antenna array. However, due to PIN diodes being connected to metallic strips in the metasurface antenna array, loss can occur due to power dissipation, which results in a decrease in radiation efficiency.  
massive MIMO; reconfigurable metasurface antenna; antenna design; split ring resonators Article
# Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi
## 1 Introduction
* The EMS performance was also assessed experimentally by connecting it to a network of wireless sensors according to the IoT paradigm. The goal of the investigation was twofold: (1) evaluating the theoretical and measured computational efficiency, showing the real-world feasibility of the implemented EMS; and (2) measuring the effectiveness of the EMS for both the utility and the users during a 30-day period. The paper is organized as follows. The fundamentals of the chosen EMS are highlighted in Section 2, and the case study is described in Section 3. Section 4 is focused on the embedded implementation of the EMS. After describing the chosen embedded platform and the test bench, Section 5 presents and discusses the experimental results. Finally, some conclusions are drawn.
# Time Series Visualization and Forecasting from Australian Building and Construction Statistics
## 4 Experiments
### Model Settings  
Since the SARIMA model is one of the most widely used model for time series forecasting among all the models, we choose it as the baseline model. It is formed by adding seasonal terms in the ARIMA models and is written as  
\[SARIMA(p,d,q)(P,D,Q,m)\]  
where \((p,d,q)\) and \((P,D,Q,m)\) are the non-seasonal and seasonal part of the model, respectively. The parameter \(m\) is the number of periods per season. To evaluate the SARIMA model, Akaike information criterion (AIC) is used:  
\[AIC(p)=nln(RSS/n)+2K\]  
Where \(n\) is the number of data points and RSS is the residual sums of squares, \(K\) is the number of estimated parameters in the model. AIC is an estimator of out-of-sample prediction error, and a lower AIC score indicates a more predictive model. A lower AIC score means that the prediction data points have a lower distance with the ground truth data, compared with other parameter combination. Each parameter for both the non-seasonal and seasonal parts can be either zero or one. Therefore, there are in total 32 combinations; they are all tested out, and only the one with the lowest AIC score will be  
Figure 5: Main navigation and saved icon.  
kept as the setup for the SARIMA model. The model with such a parameter setup will be used for the result comparison later. In our dataset, \(SARIMA(1,1,1)(0,1,1,4)\) shows the lowest AIC value 7022.6870.  
The LSTM model is also well-adopted in the time series community. We used Keras implementation for the LSTM-based model. To keep track of the model quality during training, we used the MAE (mean absolute error) as the loss function. MAE is calculated as the average of the absolute difference between the actual and predicted values. The optimizer is an Adam optimizer, which adjusts the individual learning weights for each parameter. For both models, the dataset is split for training and test; 90% of the dataset is used for training and 10% of it is used for testing. There are also variable settings for the LSTM model, which will be talked about in Section 4.2.
# Emerging Trends in Pharmacotoxicological and Forensic Sample Treatment Procedures
## 2 Biological Fluids for Forensic Analyses
### Alternative Biological Fluids: Bile and Oral Fluids  
In addition to the primary biological fluids of forensic interest, researchers have tried to understand what information could be obtained from other types of samples, such as bile and oral fluids, and whether this information correlated with conventional matrices. The subject of many studies was bile that can be considered an excellent matrix for identifying all substances with a biliary excretion. The main problem is that bile is a very complex fluid subjected to several changes after death. Some works in the literature testify to the use and evaluation of bile as a matrix for forensic use, and [PERSON] et al. proposed an interesting review that compared the concentration of several substances in bile and blood, emphasizing that in some cases, the concentration in bile was higher than in blood [8].  
[PERSON] et al. quantified the concentration of morphine in bile compared with blood and liver using GC-MS. However, the complexity of this matrix requires careful sample preparation that needs more steps. Briefly, the sample was first hydrolyzed with glucuronidase overnight at 50 \({}^{\circ}\)C, for basic analytes were carried out an extraction using a column-type ISOLUTE HCX conditioned with methanol, deionized water, and phosphate buffer (pH 6). After adding phosphate buffer (pH 6), the sample was loaded into the column, and the interferences were washed first with water and hydrochloric acid and then with methanol. The analytes were eluted with dichloromethane:isopropanol (80:20, _v.v._). Before the analysis, the eluate was dried, reconstituted in methylene chloride, and derivatized with 20 \(\mu\)L of N,O-bis(trimethylsilyl)trifluoroacetamide with 1% of trimethylsilyl (TMS). The authors conclude that the results obtained do not show a precise correlation between the bile and the other matrices, probably caused by the complex mechanisms that are the basis of biliary excretion, and for this reason suggest the use of this matrix only as a screening test in cases where the blood are not available [52]. In addition, oral cavity fluids can be collected from corpses to obtain critical information about the presence of illicit substances. In most cases, heroin metabolites that have a short window of detection could be detected more readily in oral fluids than in other conventional matrices. [PERSON] et al. investigated a new method for the evaluation of substances in postmortem oral cavity fluids using an innovative device for simple and fast sampling. This device consists of a cellulose pad that is placed in the mouth of the corpse for 15 min and allows it to absorb fluids, then is placed into a buffer protected by a filter, and the obtained mixture (cavity fluid released/buffer) was transferred into a vial with methanol and internal standard before the LC-MS/MS analysis [53].
# The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
## 3 Deep Neural Networks (DNNs)
### Test Results  
Finally, the test set was tested using the network model with the lowest loss and highest accuracies among the 50 epochs. The accuracy of various models was evaluated using confusion matrices, and the corresponding results are shown in Figure 7. Categories 0 and 1 represent the unqualified and qualified displacement time curves, respectively. The diagonal elements of the matrix represent the number of samples with the correct predicted class, and the other pieces represent the number of samples with the incorrectly predicted class. The third column of the third row of the matrix is the ratio of correctly classified samples to the total number of samples, which is the accuracy rate.  
As shown in Figure 7, the test results remained consistent with those in Table 2, and the InceptionResNetV2-unfrozen model performed the best with an overall accuracy of 96.9%. The five models that performed better had relatively high classification accuracies for qualified displacement curve images, with a maximum of 99.2%. For all the models, misclassifying unqualified displacement curve pictures as qualified was an essential factor affecting the overall accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**DNN**} & **VGG19-** & **VGG19-** & **InceptionV3-** & **InceptionV3-** & **ResNet50-** & **ResNet50-** & **InceptionResNetV2-** & **InceptionResNetV2-** \\  & **Frozen** & **Unfrozen** & **Frozen** & **Unfrozen** & **Frozen** & **Unfrozen** & **Frozen** & **Unfrozen** \\ \hline \(acc_{train}\) & 0.979 & 0.998 & 0.770 & 0.999 & 0.768 & 0.999 & 0.670 & 0.999 \\ \(acc_{valid}\) & 0.980 & 0.997 & 0.801 & 0.997 & 0.801 & 0.997 & 0.682 & 0.998 \\ \(loss_{train}\) & 0.061 & 0.008 & 4.804 & 0.004 & 42.364 & 0.004 & 212.492 & 0.003 \\ \(loss_{valid}\) & 0.052 & 0.010 & 3.356 & 0.011 & 30.580 & 0.009 & 214.860 & 0.009 \\ \(Perf\) & 3.479 & 23.04 & 0.043 & 37.40 & 0.004 & 36.07 & 0.000 & 40.43 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of deep transfer neural networks.  
Figure 7: Confusion matrix of test set, where green and yellow represent the number of correctly classified samples with labels of 0 and 1, respectively. (**a**) VGG19-frozen; (**b**) ResNet50–frozen; (**c**) InceptionV3–frozen; (**d**) InceptionResNetV2–frozen; (**e**) VGG19-unfrozen; (**f**) ResNet50–unfrozen; (**g**) InceptionV3-unfrozen; (**h**) InceptionResNetV2–unfrozen.  
In summary, among all the network models that only train the fully connected layer, the VGG19 network model has the best performance in the validation and testing process. Models with all the network layers trained can achieve good classification results. Therefore, in the following work of this paper, the VGG19-frozen model and the four models that train all the layers were chosen to identify whether the displacement curve was qualified.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.4 Calculating the Average Buffer Occupancy  
Each UAV calculates its average buffer occupancy (\(\varphi\)) and shares it with neighboring UAVs through hello messages. Before sending a hello message, a UAV calculates its current buffer occupancy (\(\varphi_{c}\)) as follows:  
\[\varphi_{c}=\frac{B_{current}}{B_{total}}, \tag{9}\]  
where \(B_{current}\) and \(B_{total}\) are the currently occupied buffer size and total buffer size, respectively. Then, it calculates the \(\varphi\) as follows:  
\[\varphi=\omega\times\varphi_{c}+(1-\omega)\times\varphi_{c}, \tag{10}\]  
where \(\omega\) is a constant with a value of 0.6.
## 3 Results
### Transfer Factors of Heavy Metals in B. ischaemtum and Their Driving Factors  
Transfer factors of Pb and Zn were significantly higher than that of Cd and Cr for _B. ischaemtum_ (Figure 7). There was no significant correlation between microbial gene copy numbers and transfer factors of heavy metals, but the ratio of rhizosphere bacteria and fungi was significantly positively correlated with transfer factor of Pb (TF-Pb) (Table 3). TF-Pb was significantly negatively correlated with phosphatase, and transfer factor of Zn (TF-Zn) was significantly negative correlated with urease (Table 4). For the rhizosphere microorganisms, TF-Pb was negatively related to the diversity of rhizosphere fungi, but there was no significant correlation between bacterial diversities and other transfer factors. For non-rhizosphere, the transfer factor of Cu (TF-Cu) was significantly correlated with bacterial diversity, and there was no significant correlation between different transfer factors and fungal diversity (Table S3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Log Number of Microbial _rRNA_**} & \multirow{2}{*}{**TF-Cd**} & \multirow{2}{*}{**TF-Cr**} & \multirow{2}{*}{**TF-Cu**} & \multirow{2}{*}{**TF-Pb**} & \multirow{2}{*}{**TF-Zn**} \\ \multicolumn{1}{c}{**Gene Copies \(\mathbf{g^{-1}}\) Dry Soil**} & & & & & \\ \hline Non-rhizosphere & Bacteria & \(-0.130\) & \(-0.069\) & \(-0.013\) & \(-0.718\) & \(-0.155\) \\ Fungi & \(0.057\) & \(0.062\) & \(0.002\) & \(-0.632\) & \(-0.411\) \\  & B/F & \(-0.341\) & \(-0.262\) & \(-0.048\) & \(0.292\) & \(0.673\) \\ Rhizosphere & Bacteria & \(-0.783\) & \(-0.818\) & \(-0.670\) & \(0.741\) & \(0.215\) \\ Fungi & \(-0.130\) & \(-0.069\) & \(-0.013\) & \(-0.718\) & \(-0.155\) \\  & B/F & \(-0.423\) & \(-0.517\) & \(-0.515\) & \(0.895\) * & \(-0.004\) \\ \hline \hline \end{tabular}  
* * Correlation is significant at the 0.05 level (2-tailed).  
\end{table}
Table 3: The Pearson correlations among microbial gene abundances and transfer factors of different heavy metals of soil.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Microbial Diversity**} & \multirow{2}{*}{**Catalase**} & \multirow{2}{*}{**Urease**} & \multirow{2}{*}{**Sucarse**} & \multirow{2}{*}{**Phosphatase**} \\ \hline Fungi & \(H^{\prime}\) & \(0.736\) & \(0.442\) & \(-0.886\) * & \(0.758\) \\  & \(S\) & \(0.708\) & \(0.384\) & \(-0.884\) * & \(0.739\) \\  & \(d_{Mex}\) & \(0.716\) & \(0.398\) & \(-0.887\) * & \(0.746\) \\  & \(En\) & \(-0.790\) & \(-0.662\) & \(0.667\) & -0.448 \\  & \(D\) & \(0.760\) & \(0.502\) & \(-0.881\) * & \(0.772\) \\ \hline \hline \end{tabular}  
* Note: Abbreviations represent Shannon-Wiener index (\(H^{\prime}\)), Species richness (\(S\)), Margalef (\(d_{Mex}\)), and Evenness (\(E_{n}\)), Simpson index (\(D\)).
* Correlation is significant at the 0.05 level (2-tailed).  
\end{table}
Table 2: _Cont._  
Figure 7: Transfer factors of different heavy metals in _B. ischaemtum_. The different case letters indicate that the means are significantly different among reclaimed scenario (\(P<0.05\)) with Duncan test (a \(>\) ab \(>\) b).
## 5 Results
### Baseline Versus Automatic Hierarchy Method: Results  
#### 5.1.1 Usability  
Questions 1 through 2 asked the participants to rate their usability by exploring the records of each method. We will refer to these questions as Q1 through Q2 in the following sections. The participants rated the automatic hierarchy method higher than the baseline for Q2 (Figure 5a). The results for each question are described separately below. For Q1, participants showed no statistically significant difference between the two methods (\(Z=-1.395\), \(p=0.16\)), with baseline (\(M=4.50\), \(SD=1.45\)) and automatic hierarchy (\(M=5.42\), \(SD=1.50\)) being relatively similar. However, for Q2, participants showed a statistically significant difference (\(Z=-2.88\), \(p<0.01\)), with automatic hierarchy (\(M=5.92\), \(SD=0.79\)) being rated higher than the baseline (\(M=3.00\), \(SD=1.60\)).