esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Test Results
### Synthetic vs. Real-World Signature Impact Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Maximum** \\ **Horizontal Position** \\ **Error (m)** \\ \end{tabular} & \begin{tabular}{c} **Maximum Vertical** \\ **Position Error (m)** \\ \end{tabular} & \begin{tabular}{c} **Position Fix** \\ **Availability** \\ \end{tabular} & \
\begin{tabular}{c} **J/SPVT_Jost (dB)** \\ \end{tabular} &
\begin{tabular}{c} **J/SPVT_reobtained** \\ **(dB)** \\ \end{tabular} \\ \cline{2-10}  & S & R & S & R & S & R & S & R & S & R \\ \hline MM01 & 0.44 & 6.14 & 1.3 & 8.3 & 43.16\% & 65.07\% & 45 & 50 & 10 & 35 \\ \hline MM02 & 3.12 & 5.11 & 3.2 & 4.5 & 50.98\% & 67.96\% & 50 & 50 & 25 & 45 \\ \hline MM03 & 0.81 & 40.3 & 2 & 32.28 & 41.47\% & 60.44\% & 50 & 45 & 10 & 35 \\ \hline MM04 & 1 & 5.68 & 2.1 & 3.7 & 39.91\% & 61.47\% & 55 & 50 & 0 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test results for MM RUT when with synthetic and recorded interference signals are used.
# Energy Saving Potential of PCMs in Buildings under Future Climate Conditions
## 4 Results
### Effect of PCM on Energy Consumption  
The simulation results for overall annual electricity and gas consumption at the present, as well as in 2020, 2050, and 2080 for the building with PCM and building without PCM are shown in Tables 9 and 10. The percentage of energy savings (electricity and gas) was calculated using Formula (7). In all zones, the percentages of electricity and gas savings were higher at higher temperature-fluctuation rates. Incorporating PCMs into the building envelope resulted in electricity and gas savings during both the winter and summer. Similar to the trend for temperature fluctuation values, the PCM with the highest melting point worked best during the simulation years in Seoul, Tokyo, and Hong Kong. Table 9 and Figure 16 show that BioPCM (29 \({}^{\circ}\)C) and RUBITHERMPCM (25 \({}^{\circ}\)C) achieved the highest electricity savings in Tokyo, followed by Seoul and Hong Kong. DuPonEnergainPCM (21.6 \({}^{\circ}\)C) (Table 10 and Figure 17) achieved the highest gas savings in Hong Kong, followed by Tokyo and Seoul. In Seoul, the thermostat heating and cooling set points are 20 \({}^{\circ}\)C and 26 \({}^{\circ}\)C, respectively. When the zone temperature was above 29 \({}^{\circ}\)C (BioPCM), 25 \({}^{\circ}\)C (RUBITHERMPCM), and 21.6 \({}^{\circ}\)C (DuPonEnergainPCM), the PCMs were in their liquid phase. The thermostat set point for cooling indicates that energy was required to cool the zone \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **PCM** & **Present (kWh)** & **2020 (kWh)** & **2050 (kWh)** & **2080 (kWh)** \\ \hline \multirow{3}{*}{Seoul, Korea} & Without PCM & 26,101 & 29,576 & 30,343 & 32,097 \\ \cline{2-6}  & BioPCM & 25,206 (3.43\%) & 28,693 (2.99\%) & 29,511 (2.74\%) & 31,342 (2.35\%) \\  & DuPonEnergainPCM & 25,017 (4.15\%) & 28,539 (3.51\%) & 29,356 (3.25\%) & 31,195 (2.81\%) \\  & RUBITHERMPCM & 25,035 (4.08\%) & 28,518 (3.58\%) & 29,350 (3.27\%) & 31,188 (2.83\%) \\ \hline \multirow{3}{*}{Tokyo, Japan} & Without PCM & 28,445 & 34,487 & 36,030 & 41,617 \\ \cline{2-6}  & BioPCM & 27,347 (3.86\%) & 33,481 (2.92\%) & 35,091 (2.61\%) & 41,327 (0.70\%) \\ \cline{1-1}  & DuPonEnergainPCM & 27,178 (4.45\%) & 33,468 (2.95\%) & 35,127 (2.51\%) & 41,311 (0.73\%) \\ \cline{1-1}  & RUBITHERMPCM & 27,202 (4.37\%) & 33,391 (3.18\%) & 35,033 (2.77\%) & 41,173 (1.07\%) \\ \hline \multirow{3}{*}{Hong Kong, China} & Without PCM & 34,586 & 38,246 & 39,717 & 41,617 \\ \cline{2-6}  & BioPCM & 34,098 (1.41\%) & 37,738 (1.33\%) & 39,273 (1.12\%) & 41,327 (0.70\%) \\ \cline{1-1}  & DuPonEnergainPCM & 34,149 (1.26\%) & 37,815 (1.13\%) & 39,332 (0.97\%) & 41,311 (0.73\%) \\ \cline{1-1}  & RUBITHERMPCM & 33,994 (1.71\%) & 37,634 (1.60\%) & 39,170 (1.38\%) & 41,173 (1.07\%) \\ \hline \hline \end{tabular} \({}^{1}\) Percent energy saving with PCM.  
\end{table} 
Table 8: Annual HVAC system energy loads (operation) during the cooling period.
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 4 Upstream DFT-Spread Hybrid OFDM-DFMA PON Performance
### Performance Tolerance to Limited DAC/ADC Quantization Bits  
In this subsection, simulations are carried out to demonstrate the performance tolerance of the proposed technique to limited DAC/ADC quantization bits, and to determine the minimum number of required DAC/ADC quantization bits to achieve BERs at or below the FEC limit. The DAC/ADC quantization bits vary from 4 to 8, and the ROP is fixed at \(-7\) dBm. The results are presented in Figure 4 for the 25 km SSMF IMDD PON.  
Figure 4 reveals that, while adopting the optimal CRs of 11 dB or 12 dB as determined in Section 3.3, the case including DFT-spread can reach BERs at the FEC limit when the DAC/ADC resolution is as low as 6 bits. On the other hand, for the case excluding DFT-spread, the minimum number of required quantization bits to achieve similar performance extends from 6-bit to 7-bit DAC/ADC resolution. This numerical result confirms that the application of DFT-spread in the proposed PON can improve the upstream performance tolerance against quantization noise induced by the limited quantization bits. Most importantly, from a practical PON operation point of view, such an improvement is highly desirable for PON designs, as lower DAC/ADC hardware achieves both lower cost and lower power consumption.  
From the above discussion, it is easy to understand that the proposed DFT-spread spectrally overlapped hybrid OFDM-DFAM PON confirms the practicability of utilizing the DFT-spread technique in the PONs reported in [6], and that the proposed PON has the ability to reduce the minimum required DAC/ADC bit resolution and, consequently,  
Figure 4: BER versus quantization bits over a 25 km SSMF IMDD PON.  
minimize the transceiver DSP complexity, thus making the proposed PON a promising solution for implementation in future cost-sensitive 5G networks, and beyond.
## 3 Class Imbalance Problem
### Dealing with Class Imbalance  
As the imbalanced dataset is a critical issue in data classification, therefore, a lot of techniques have been developed to effectively address this problem. These techniques can broadly be classified into three groups [14]:  
1. _Algorithm level approaches:_ These approaches adapt the existing classifier to bias their learning towards the minority class. The knowledge about the application domain and the corresponding classifier is required by these methods to comprehend the reasons for classifier failure in the presence of uneven class distribution.
2. _Data level approaches:_ These approaches proceed by re-balancing the class distribution by re-sampling the data space. Due to their versatility, these methods can be applied with any classifier. The class imbalance effect is decreased with a data preprocessing step.
3. _Cost-sensitive learning:_ These frameworks add the misclassification cost to the instances and bias the classifier towards the class having the higher misclassification costs that is usually the minority class. The overall goal is to minimize the total cost errors of both classes. However, as the misclassification costs are not explicitly available in the datasets, these techniques cannot be tuned in a practical manner.
4. _Ensemble Approaches:_ The ensemble based techniques that combine multiple classifiers for classification are appearing as another category to address the class imbalance problem [29]. The ensemble based methods are usually applied in combination with any of the approaches mentioned earlier.  
In the current work we have applied the data pre-processing approach with GCCL based ensembles to generate a new hybrid method to address the class imbalance problem for image classification in CBIR. In the present work, the class imbalance problem in image classification is considered as an optimization problem and our target is to maximize the relevant results against the image queries.
###### Abstract  
Various models have been used to demonstrate the pronounced effects of the microwave frequency range electromagnetic fields, as well as weak and very weak combined collinear magnetic fields (CMF) with static and variable components in the order of micro- and nano-tesla. One of such models, previously shown to be sensitive to variations in the parameters of applied magnetic fields, is the neutrophil respiratory burst. Using luminol-enhanced cell chemiluminescence assay, we studied the effects of the CMF exerted directly on neutrophil suspensions and, indirectly, through aqueous solutions. To experimentally create a uniform standard weak CMF with 60 uT static and 100 nT alternating magnetic field components, we engineered a shielded magnetic field induction device. CMF applied directly to neutrophils enhanced reactive oxygen species (ROS) production by more than 36%. The pronounced stimulating effect was observed only when using the signals that included the frequency of 12.6 Hz that corresponds to the ion cyclotron resonance (ICR) frequency of the hydrated hydronium ion. Similarly, to direct exposure, CMF pre-treatment of a water sample subsequently added to the neutrophil suspension increased ROS production by 66%. The effect of CMF pre-treatment was retained after a series of dilutions and mechanical treatment but disappeared in \"magnetic vacuum\" or without mechanical influence. Therefore, weak and super weak magnetic fields may indirectly, via water, activate ROS production by neutrophils, provided that modulation of super weak component of collinear field corresponds to the ICR frequency of the hydrated hydronium ion.  
combined magnetic fields; weak magnetic fields; respiratory burst; NADPH-oxidase; mechanical water treatment +
Footnote †: journal: Applied Sciences
## 4 Results
### Rotational Stiffness of Connections
The derived rotational stiffness model shows the need for the analysis of the rotational stiffness of pocket-type connections in different rotation directions other than downward rotation, which is the rotation direction that is generally considered in the existing literature pertaining to the rotational stiffness of connections in temporary equipment. Using the downward rotational stiffness of pocket-type connections as a reference, the initial rotational stiffness of upward rotation (\(k_{1}\)) was 1.66 times that of downward rotation, and the second rotational stiffness (\(k_{2}\)) was 1.70 times that of the downward rotation. In the case of lateral rotation, the initial rotational stiffness was 0.11 times that of the downward rotation, and the second rotational stiffness was 0.4 times that of the downward rotation. As such, it was found that there was a large decrease in the rotational stiffness. Therefore, the rotational stiffness of pocket-type connections is proposed as a bilinear model in which the initial stiffness and the second stiffness vary according to the rotation direction (downward rotation, upward rotation, or lateral rotation). Figure 15: Bilinear rotational stiffness model of pocket-type connection divided according to the rotation direction.
# Encoding Text Information with Graph Convolutional Networks for Personality Recognition
## 3 Background: Graph Convolutional Networks  
In this section, this paper gives a brief overview of GCN. Extracting the spatial features of the topological graph is the essential purpose of GCN, and the theoretical basis of GCN is the spectral domain. The idea is to use graph theory to implement convolution operations on topological graphs [35]. Throughout the research process, first, scholars who studied graph signal processing (GSP) defined Fourier transformation on graphs, then defined convolution on graphs, and finally, proposed a graph convolutional network in combination with deep learning.  
Graph convolutional networks are multi-layer neural networks that can be operated directly on the graph and derive the node's embeddings vector based on the properties of its neighborhood [36]. Usually, consider a graph \(G=(V,E)\), where \(V(|V|=n)\) and \(E\) are sets of nodes and edges, respectively. Assume that each node is connected to itself, that is any \(v(v,v)\in E\). Let \(X\in R^{n\times m}\) be a matrix containing all \(n\) nodes with their features, where \(m\) is the dimension of the feature vectors, and each \(x_{v}\in R^{m}\) is the feature vector for \(v\). \(A\) and \(D\) are the adjacency matrix and degree matrix of \(G\), respectively. The formula for \(D\) is as follows:  
\[D_{ii}=\sum_{j}A_{ij} \tag{1}\]  
Due to the self-loops, the diagonal element of \(A\) is set to one1. One layer of GCN can only capture information about direct neighbors. When multiple GCN layers are stacked, information about larger neighborhoods will be integrated. One layer of GCN can be expressed as follows:  
\[H^{(1)}=\sigma\left(\tilde{A}H^{(0)}W_{0}\right) \tag{2}\]  
\[\tilde{A}=D^{-\frac{1}{2}}AD^{-\frac{1}{2}} \tag{3}\]  
where \(\tilde{A}\) is the normalized symmetric adjacency matrix and \(W_{0}\in R^{m\times k}\) is a weight matrix. \(\sigma\) is an activation function. \(H^{(0)}\) is the matrix of hidden states in the lth layer and \(H^{(0)}=X\). As mentioned previously, high-order neighborhood information can be merged by stacking multiple GCN layers. One layer of GCN can be expressed as follows:  
\[H^{(j+1)}=\sigma\left(\tilde{A}H^{(j)}W_{j}\right) \tag{4}\]  
where \(j\) is the number of layers and \(W_{j}\) is the layer-specific trainable weight matrix.  
Multiple GCN layers can be stacked to capture high-order relations in the graph. We considered using three layers of GCN for semi-supervised node classification in this paper [34]. Our forward model takes the form:  
\[V=\tilde{A}\operatorname{ReLU}\left(\tilde{A}\operatorname{ReLU}\left( \tilde{A}XW_{0}\right)W_{1}\right)W_{2} \tag{5}\]  
where \(X\) is the input matrix with one-hot representation and \(V\) is the representation matrix for all nodes in the graph.
## 2 Materials and Methods
### Methods
#### 2.2.2 Calculation of the Spatiotemporal Variation in VPD and LAI  
The temporal variations in the annual mean VPD and LAI during the growing season in the Yellow River Basin were estimated from linear trends. Spatial variation in VPD was characterized through ArcGIS interpolation of the VPD calculation results during the annual growth season at each meteorological station. The spatial resolution was consistent with the LAI product (8 km). The spatial distribution changes in VPD were determined at five time nodes (1982, 1990, 2000, 2010, and 2019). In addition, the spatial variation in LAI was analyzed through a combined Theil-Sen estimator and Mann-Kendall test.  
The calculation principle is as follows:  
\[\beta=median[(LAI_{j}-LAI_{i})/(j-i)], \tag{4}\]  
where \(LAI_{i}\) and \(LAI_{j}\) are data samples, \(\beta\) is the median of the slopes of the calculated n (n \(-\) 1)/2 data combinations, and \(\beta\) > 0 indicates that the time series shows an increasing trend; otherwise, it indicates a weakening trend.  
The Mann-Kendall test was performed as follows:  
\[Z_{C}=\begin{cases}(S-1)/\sqrt{Var(S)},S>0\\ 0,S=0\\ (S+1)/\sqrt{Var(S)},S<0\end{cases}, \tag{5}\]  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sign(LAI_{j}-LAI_{i}), \tag{6}\]  
Figure 1: Overview of the Yellow River Basin ((**a**) topography and meteorological station; (**b**) land use).  
\[Var(S)=\left[n(n-1)(2n+5)\right]/18, \tag{7}\]  
\[sign\left(LAI_{j}-LAI_{i}\right)=\left\{\begin{array}{l}1,LAI_{j}-LAI_{i}>0\\ 0,LAI_{j}-LAI_{i}=0\\ -1,LAI_{j}-LAI_{i}<0\end{array}\right., \tag{8}\]  
The \(Z_{C}\) value at the \(\alpha=0.05\) confidence level (\(1Z_{C}\)! = 1.96) of the two-tailed test divides the changes into four categories: Significant Increase (Sign-I, \(Z\geq 1.96\)), Slight Increase (Slig-I, \(1.96>Z>0\)), Significant Decrease (Sign-D, \(-1.96\geq Z\)), and Slight Decrease (Slig-D, \(0>Z>-1.96\)).
## 1 Introduction
Considering the requirements of this task, more attention should be paid to the steady-state accuracy, overshoot, and anti-interference of AUHs. In view of the excellent anti-interference performance of ADRC, the authors plan to use the linear active disturbance rejection control with tracking differentiator (LADRC-TD) algorithm in this paper, aiming at realizing AUH motion control at a low speed. According to practical experience, the pitch angle does not show a dramatic change at low speeds. As exhibited in Figure 2, the pitch angle varies within \(\pm 5^{\circ}\) when the AUH is moving at a speed of 0.2 m/s. Moreover, the simulation and pool experiments were conducted to verify the practicability and anti-interference performance of the LADRC-TD algorithm. This work's main contribution is the application of the LADRC-TD to the heading and depth control of the AUH--a new disk-type AUV--and demonstrating its superiority over LADRC and PID through simulations and pool experiments. Figure 1: Work pattern of AUH. Section 2 introduces the basic physical parameters and components of the Z-AUH, the software and hardware models of the control system, and the AUH's dynamics and kinematics models. In Section 3, the algorithm principle and the controller design of the LADRC-TD are described. In Section 4, the effects of the PID, LADRC, and LADRC-TD algorithms are compared by using simulation experiments, and disturbing force and torque are added to the depth control and heading control, respectively. In Section 5, the excellent performance of the LADRC-TD is highlighted by the fixed-point pool experiment and dynamic experiment and is mirrored by the simulation results. Furthermore, due to the fixed-point experiment, the steady-state accuracy and overshoot of the PID, LADRC, and LADRC-TD in the depth control and heading control are also obtained. Finally, the conclusions are presented in Section 6.
## 3 Sliding Mode Controller Design
### Sliding Mode Controller Design  
The following linear sliding mode surface is constructed in this study, which can be described as  
\[s=\zeta+\beta_{1}\theta+\beta_{2}\varphi \tag{8}\]  
where \(\beta_{i(i=1,2)}>0\) denote the tuned parameters.  
Taking the derivate of (8), one can obtain  
\[\begin{array}{l}\dot{s}=\dot{\xi}+\beta_{1}\dot{\theta}+\beta_{2}\dot{ \varphi}\\ =v\text{sin}\theta+\frac{\beta_{1}vu}{L_{1}}-\frac{\beta_{2}v\text{sin} \theta}{L_{2}}-\beta_{2}v\Big{(}\frac{L_{3}}{L_{1}L_{2}}\text{cos}\varphi+ \frac{1}{L_{1}}\Big{)}u\\ =-\frac{\beta_{2}v\text{sin}\theta}{L_{2}}+v\text{sin}\theta+v\Big{(}\frac{ \beta_{1}L_{2}-\beta_{2}L_{3}+\beta_{2}L_{3}}{L_{1}L_{2}}\Big{)}u\end{array} \tag{9}\]The following controller is created utilizing the conventional constant velocity reaching law to make the sliding mode variable converge to the designed sliding mode surface (8):  
\[u=\frac{L_{1}\Big{(}\beta_{2}v\text{sin}\varphi-L_{2}v\text{asin}\theta-L_{2}K \text{sign}(s)\Big{)}}{v(\beta_{1}L_{2}-\beta_{2}L_{3}+\beta_{2}L_{2})} \tag{10}\]  
Further, through the inverse trigonometric function, the front wheel angle is calculated as  
\[\delta=\arctan\left(\frac{L_{1}\Big{(}\beta_{2}v\text{sin}\varphi-L_{2}v\text {asin}\theta-L_{2}K\text{sign}(s)\Big{)}}{v(\beta_{1}L_{2}-\beta_{2}L_{3}+ \beta_{2}L_{2})}\right) \tag{11}\]  
where \(K\) denotes the controller gain.  
A Lyapunov function (\(V_{1}=\frac{1}{2}s^{2}\)) is selected to verify the stability of the closed system, and by taking the derivate of the Lyapunov function one can obtain  
\[\dot{V}_{1} = s\dot{s}=s(-K\text{sign}(s)) \tag{12}\] \[= -K|s|\leq 0\]  
In accordance with the Lyapunov stability theory, the system is stable with the controller designed as (10). However, it is important to note that the chattering is inevitable, as the front wheel steering angle (11) contains discontinuous terms. Additionally, in (10), the approaching stage of the sliding mode variable adopts the constant reaching law, which results in a slow online speed. For the purpose of addressing the abovementioned issues, a sliding mode controller is applied based on the power-reaching law. Fuzzy rules are utilized to implement controller parameter adjustment, ensuring the quick convergence of the system and reducing the chattering. Figure 2 illustrates the proposed FSMC control frame.  
**Remark 2**.: _The main difference between FSMC and SMC is the power-reaching term, which can significantly reduce the chattering problem and improve the disturbance rejection ability. In addition, the fuzzy control can realize the control parameter tuning._
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Key Space  
Currently, a key space must contain at least \(2^{218}\) elements [47] to ensure that an encryption algorithm can withstand brute force attacks with a certain level of security. Our approach offers two options: a single key or two independent keys.
## 3 Methodology
### Evaluation Metric  
The selection of evaluation metrics, namely Root Mean Squared Error (RMSE) for the second research question and accuracy for the third and fourth research questions, is based on the specific nature of the target variables and research aims.  
* In the case of numerical 'Goals and Assists', RMSE is an ideal choice since it estimates the average size of forecast errors. It gives a comprehensive assessment of how well the ML model's predictions match the actual numerical outcomes. Because RMSE prioritizes avoiding both overestimation and underestimation, it is appropriate for evaluating the prediction accuracy of models attempting to estimate continuous variables such as goals and assists.
* Accuracy is a suitable choice for categorical 'Injured' and 'Not Injured' outcomes since it represents the proportion of accurately predicated cases. This metric is critical when it comes to appropriately classifying the occurrence or non-occurrence of an event. Given the significance of accurately detecting injuries, accuracy clearly indicates the model's ability to categorize these occurrences.