esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Tasks of Handwriting Evaluation Method
#### 2.3.2 Drawing Tasks  
To evaluate handwriting more comprehensively, the participants are also assigned drawing tasks in addition to merely writing tasks. This drawing approach addresses the potential for muscle memory, developed from practice of familiar text, to obscure actual handwriting abilities. Moreover, the evaluation of writing tasks can be influenced by language proficiency, educational level, and cultural background. However, drawing is a universal form of expression that can avoid biases caused by these factors [35].  
Drawing tasks necessitate a wide range of motion, including varied strokes, lines, and shapes. These require enhanced hand control and coordination. Since motor coordination (MC) tasks are used to assess and train an individual's ability to coordinate precise and complex movements, integrating MC tasks into the design of drawing tasks is deemed highly effective [36]. Therefore, it has become crucial to determine the extent to which handwriting assistive devices restore the users' MC abilities. In this study, two types of simple MC tasks were employed to evaluate participants' MC abilities during specific drawing tasks on the tablet:  
1. **Drawing task with border.** This task was specifically adapted to evaluate handwriting assistive devices, based on the MC tasks from the Breey VMI-6 [37], as shown in Figure 2. The three graphics are evenly distributed across the tablet screen, each in a square with side length 400 pixels (38.38 mm). All border widths are 20 pixels (1.919 mm). The MC drawing task requires users to draw along a dotted line trajectory, ensuring as much as possible that the drawing stays within the border.
2. **Drawing task without border.** This task is based on an MC task from [PERSON]'s disease detection research [19]. An Archimedean spiral is placed at the center of the tablet screen, as shown in Figure 3, described by the formula: \[r(\theta)=\frac{385}{5\times 2\pi}\theta,\] (1) where \(r(\theta)\) represents the radial distance from the origin to a point on the spiral at a given angle \(\theta\). Here, \(\theta\) is the angular parameter measured in radians from the positive \(x\)-axis, increasing in the counterclockwise direction. The spiral's maximum radius reaches 385 pixels (36.95 mm) of the tablet, fitting the entire figure within a square with side lengths of 770 pixels (73.9 mm) of the tablet. The MC drawing task requires users to draw along the spiral line, ensuring they stay as close to the trajectory as possible.  
The key parameters have been selected to evaluate the quality of the two drawing tasks, including the task's trajectory, duration, pen pressure between the digital pen tip andtablet, and variations in digital pen angle. The high-precision specific sensors within the tablet and digital pen can record these data during drawing tasks for evaluation purposes.
# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 5 Experiment Analysis
### Ablation Experiment  
This study innovatively integrates a heterogeneous neural network model consisting of graph embedding representation learning (node2 vec), multi-task learning (MLT), gated recurrent unit network (GRU), and graph attention network (GAT) for OD traffic prediction features. The reliability of the proposed model was verified through ablation experiments on different components. Mainly consider the following situations:  
(1) Model_1: removes the node2 vec module and does not use graph embedding representation. Other settings of the model remain unchanged, and the input data are directly input into the proposed model structure. This model aims to verify the embedding representation effect of Node2 vec on input data.  
(2) Model_2: does not use multi-task learning. This model sets the main task weight to, without considering the constraints of subtasks, and other settings of the model remain unchanged. This model aims to validate the effectiveness of multi-task learning in predicting results.  
(3) Model_3: removes the data of radiation population during the feature extraction of export outflow, and verifies the performance of the model prediction results without considering the influence of radiation population factor.  
From Figure 14, it can be seen that the experimental performance of the proposed model is superior to other variant models, indicating that the graph embedding representation (node2 vec module), multi-task learning module, and radiation population data all contribute to the final prediction results of the model. Specifically, the performance evaluation of variant models without multi-task learning showed the most significant decline, with performance indicators RMSE, MAE, CPC decreasing by 17.84%, 14.30%, and 10.27%, respectively. This indicates that the OD traffic prediction task between highway stations should consider both the spatiotemporal correlation of the source station and the destination station. Secondly, the model that does not consider the impact of radiation population factors has the closest predictive performance to the optimal model, with performance indicators RMSE, MAE, CPC decreasing by 8.50%, 4.20%, and 5.07%, respectively. This variant model only affects the export outflow flow learning subtask module and contributes relatively less to the overall model.  
Figure 14: Performance evaluation of key components in the proposed model.
# Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning
## 3 Results  
### Experimental Setup and Details  
Experimental setup: We trained 80% of the image data for the federated network and 20% for the final network test, and followed the COVID-Net [28] scheme for the preprocessing of the data to perform a random transformation of the image with translation, rotation, and flip. The image size was adjusted to 224 \(\times\) 224 and normalized, and the pre-network initialization was performed using the network parameters input to the network pre-trained by ImageNet. Through experimental observations, we set the number of local clients as four, and used random sampling to equally divide the randomly disrupted image data into five subsets, of which one subset was assigned to the central server as labeled data and four subsets were assigned to the local clients as unlabeled data. The evaluation metrics consisted of AUC, sensitivity, specificity, accuracy, precision, and F1. The performance of the central server network was analyzed comprehensively, and the mean value of the best metrics from three independent trainings was used as the final result. Their computational relationships are as follows:  
\[\text{Sensitivity}=\text{TP}/\text{TP}+\text{FN} \tag{9}\]  
\[\text{Specificity}=\text{TN}/\text{TN}+\text{FP} \tag{10}\]  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{FN}+\text{TN}+\text{ FP}) \tag{11}\]  
\[\text{Precision}=\text{TP}/\text{TP}+\text{FP} \tag{12}\]  
\[\text{F1}=2(\text{Precision}\times\text{Sensitivity})/(\text{Precision}+\text{ Sensitivity}) \tag{13}\]  
Among them, sensitivity and specificity metrics are very important metrics in medical image classification tasks, but these two metrics are contradictory, so we chose to use Sensitivity metrics to measure the performance of the model in our experiments. Additionally, in our study, the AUC metric is very informative because it is not sensitive to whetherthe samples are balanced or not and is more suitable for dealing with unbalanced data distributions. In addition, the AUC is more sensitive to model changes and the larger the value of the AUC, the more likely the current classification algorithm will rank the positive samples, i.e., will be able to classify them better [29].  
Implementation details: To ensure fair consistency of the experiments we chose the network DenseNet121 consistent with FedIRM [12] and 30 cycles of warm-up; performed 200 cycle iterations, data batch size was set to 10; did random augment [30] data enhancement on the input images; and added Dropout layer to the network to prevent overfitting. Confidence level of network parameters threshold was 0.95, batch training was performed using Adam optimizer with momenta of 0.9 and 0.99, and the learning rate was set to \(2\times 10^{-4}\).
## 4 Conclusions  
This paper presents the design of an active vector synthesis phase shifter for phased array applications, implemented using a 0.18 \(\upmu\)m SiGe BiCMOS process. The proposed phase shifter utilizes a two-stage RC-CR quadrature signal generation circuit to extend the bandwidth and reduces gain fluctuation while improving phase accuracy by varying the VGA tail current. The designed phase shifter covers a 360\({}^{\circ}\) phase shifting range from 20 GHz to 30 GHz, with a phase step size of 5.625\({}^{\circ}\). The measured RMS phase accuracy remains below 3\({}^{\circ}\) across the temperature range of \(-\)40 \({}^{\circ}\)C to 85 \({}^{\circ}\)C, with gain fluctuation between \(-\)1.7 dB and 0.7 dB. The phase shifter demonstrates ultra-wideband, high-precision phase shifting with good inter-chip consistency, showing promising potential for future applications.  
Conceptualization, F.M. and H.J.; methodology, F.M. and H.J.; software, H.J.; validation, H.J.; writing--original draft preparation, H.J. and Z.Z.; writing--review and editing, F.M., Z.Z. and N.Z.; supervision, F.M.; project administration, H.J. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Key Research and Development Program Project of China under Project 2023 YFB4403200 and in part by the National Natural Science Foundation of China under Project U21A20459. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest.
# Self-Adaptive Differential Evolution with Gauss Distribution for Optimal Mechanism Design
## 5 Numerical Simulation
### Modified Differential Evolution for Real-World Problem
#### 5.4.1 Welded-Beam Design Problem  
The objective of this problem is to minimize the manufacturing cost by optimizing a welded beam under some constraint conditions [20]. Figure 23 shows the structure of a welded-beam. The target is to determine the four optimal design variables, such as \(x_{1}(h)\), \(x_{2}(l)\), \(x_{3}(t)\), and \(x_{4}(b)\) subjected to shear stress \(\tau\), bending stress \(\sigma\), and end deflection on beam \(\delta\) in the condition of bending force at the end of bar \(F\). The optimization problem is modeled by Equation (20).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multicolumn{2}{c}{**DE**} & \multicolumn{2}{c}{**ISADE**} & \multicolumn{2}{c}{**EOBLDE**} & \multicolumn{2}{c}{**G-ISADE**} \\ \cline{2-10}  & **SR** & **FE** & **SR** & **FE** & **SR** & **FE** & **SR** & **FE** & **IR** \\ \hline F1 & 1.00 & 35,688 & 1.00  
Minimize:  
\[f(x)\ =\ 1.10471x_{1}^{2}x_{2}+0.04811x_{3}x_{4}(14.0+x_{2}) \tag{20}\]  
Subject to  
\[g_{1}\ =\ \tau(x)-13,\!600\leq 0 \tag{21}\]  
With \(\tau(x)=\sqrt{\left(\tau\right)^{2}+\left(2\tau\cdot\!\tau_{\
u}\right)\frac{x _{2}+\left(\tau\cdot\!\tau\right)^{2}}{2K}}\); \(\tau=\frac{6,000}{\sqrt{2}x_{1}x_{2}}\); \(\tau\cdot\!\tau=\frac{MR}{J}\);  
\[M=6000\big{(}14.0+\frac{x_{2}}{2}\big{)};\,R=\sqrt{\frac{x_{2}^{2}}{4}+\left( \frac{x_{1}+x_{3}}{2}\right)^{2}};\,J=2\bigg{\{}x1x2\sqrt{2}\bigg{[}\frac{x_{ 2}^{2}}{40}+\left(\frac{x_{1}+x_{3}}{2}\right)^{2}\bigg{]}\bigg{\}}\]  
\[g_{2}\ =\ \sigma(x)-30,000\leq 0; \tag{22}\]  
With \(\sigma(x)=\frac{504,000}{\frac{x_{4}x_{3}^{2}}{4}}\)  
\[g_{3}\ =\ x_{1}-x_{4}\leq 0; \tag{23}\]  
\[g_{4}\ =\ 0.10471x_{1}^{2}+0.04811x_{3}x_{4}(14.0+x_{2})-5.0\leq 0; \tag{24}\]  
\[g_{5}\ =\ 0.125-x_{1}\leq 0; \tag{25}\]  
\[g_{6}\ =\ \delta(x)-0.25\leq 0; \tag{26}\]  
With \(\delta(x)=\frac{65,856,000}{(30\times 10^{6})x_{4}x_{3}^{3}}\)  
\[g_{7}\ =\ 6000-Pc(x)\leq 0; \tag{27}\]  
With \(Pc(x)=\frac{4.013(30\times 10^{6})\sqrt{\frac{x_{2}^{2}.6}{36.0}}}{196.0} \bigg{(}1-\frac{x_{3}\sqrt{\frac{30\times 10^{6}}{4(12\times 10^{6})}}}{28 0.0}\bigg{)}\)  
where \(0.1\leq x_{1},x_{4}\leq 2.0\), and \(0.1\leq x_{2},x_{3}\leq 10.0\).  
Our optimal solution is compared with several approaches, including the coevolutionary differential evolution method (CDE) [21], coevolutionary particle-swarm optimization (CPSO) [22], GA-based coevolution model (CGA) [23], and elite opposition-based learning differential evolution (EOBLDE) [9] as expressed in Tables 3 and 4. As can be seen, our optimal solution is better than the remaining methods; in particular, the standard deviation value is \"0\" that proved the exceptional convergence ability of our method while solving the welded-beam design problem.  
Figure 23: Welded-beam design problem.
## 4 Discussion
### The Applicability of 3D Clustering Algorithm  
Theoretically, 3D clustering algorithms may be subject to spatial constraints when applied to study areas with complex boundaries. In the Yellow River Basin, the lower reaches exhibit a narrow width, small area, and irregular shape due to the natural \"suspended river\" topography, which cannot be artificially adjusted. However, the drought events identified and analyzed in this study were primarily concentrated in the middle reaches, particularly the Loess Plateau where afforestation projects are focused. Although the lower reaches are included in the study area, they were not the spatial focus of this research. Therefore, the 3D clustering algorithm remains applicable to this study.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Covert Storage Patterns  
Since the focus of our analysis was on network covert channels within the Packet Data Convergence Protocol (PDCP) of the 5G-air interface, the network-specific pattern collection [4; 22] is used for the following analysis: First, the covert storage patterns that modify data in protocol-specific fields are analyzed.
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 3 Results
### Calculation of the Calcium Carbonate Precipitation Rates  
Figure 6 shows the rate of calcium carbonate precipitation for different reaction periods. It can be seen that the calcium carbonate precipitation rate increased rapidly in the initial reaction period (before 12 h), while the rate of increase continued to decline, and then the reaction gradually exhausted after 60 h. The increasing trend of the calcium carbonate precipitation rate was similar to that of the calcium carbonate content (Figure 5). T2 produced the highest final calcium carbonate precipitation rate, which was 10.27% higherthan that of T1. The final calcium carbonate precipitation rates of T1, T2, and T3 at 96 h were 73.45%, 81.43%, and 77.69%, respectively.  
Moreover, the mean of T1, T2, and T3 (Figure 6) shows the final calcium carbonate precipitation rate using the mixing method was 77.53% at 96 h. In comparison, the calcium carbonate precipitation rate at 60 h reached 76.10%, which reached 98.16% of that at 96 h.  
Figure 5: Calcium carbonate content in different reaction time periods.  
Figure 6: The rate of calcium carbonate precipitation in different reaction periods (T1: red square; T2: blue circle; T3: black triangle).
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 5 Modelling of DCSS Protocol
### Protocol Message Exchange
Msg3.UE\(\rightarrow\)SS: ({CachReq},{KUESS}) After receiving the cache token \(ChT\) from \(SPAAA\) in message 2, the \(UE\) sends a cache request message \(CachReq\) encrypted with preshared key \(K_{UESS}\) to the service server \(SS\) requesting authorization to cache the data in its possession. The message includes a \(ChT\) and a signed hash of the cache token \(hChT\) with \(SPAAA\) private key \(SK_{AAA}\). Msg4.SS\(\rightarrow\)UE: ({CachAck},{KUESS}) When the \(SS\) receives \(CachReq\) in message 3, it verifies the cache token \(ChT\), if it is valid then it acknowledges the \(UE\)'s request to cache the data by sending an acknowledgement \(Ack_{1}\) in \(CachAck\) message encrypted with \(K_{UESS}\), which also includes the hash of the Acknowledgement \(hAcK_{1}\) signed with the \(SS\) private key \(SK_{SS}\). **Sharing Authorization**  
Msg5:UE\(\rightarrow\)SPAAA: ({ShaTokenReq},{KUE3A}) Now that the \(UE\) is authorized to cache the data, it sends a share token request message \(ShaTokenReq\) to \(SPAAA\) to obtain sharing authorization of the cached data. The message includes access token \(AcT\) and cache acknowledgement \(Ack_{1}\), for validation and to notify the authorization server that it has permission to cache the data that it wishes to share. Msg6:SPAAA\(\rightarrow\)UE: ({ShaTokenResp},{KUE3A}) When \(SPAAA\) receives message 5, it verifies the access token, checks if the subscription policy includes sharing rights then sends a share response message \(ShaTokenResp\) to the \(UE\). The message includes a share token \(ShT\), hash of the share token \(hShT\) signed with \(SK_{AAA}\) and encrypted with the \(K_{UES3A}\) that authorizes the UE to share the content. Msg7:UE\(\rightarrow\)SS: ({ShaReq},{KUESS}) When the UE receives message 6, it sends a share request message \(ShaReq\) to \(SS\) encrypted with \(K_{UESS}\) that includes a share token \(ShT\) and its hash \(hShT\) that were sent to UE in message 6 by \(SPAAA\). Msg8:SS\(\rightarrow\)UE: ({ShaAck},{KUESS}) The \(SS\) verifies the received share token parameters in message 7 and sends an acknowledgement \(Ack_{2}\) with its hash \(hAck_{2}\) in a share acknowledgement message \(ShaAck\) to UE acknowledging the request to share the data.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications  
[PERSON]  
1 School of Electronics and Communication Engineering, Reva University, Bangalore 560064, India; [EMAIL_ADDRESS] (W.B.) [EMAIL_ADDRESS] (M.S.) 1  
[PERSON]  
1 School of Electronics and Communication Engineering, Reva University, Bangalore 560064, India; [EMAIL_ADDRESS] (W.B.) [EMAIL_ADDRESS] (M.S.) 1  
[PERSON]  
2 Department of Electronics and Communication, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India 2  
[PERSON]  
3 MMRFIC Technology Pvt Ltd., Bangalore 560106, India; [EMAIL_ADDRESS] 3  
[PERSON]  
4 Department of Information Technology, Sikikim Manipal Institute of Technology, Sikikim 737102, India; [EMAIL_ADDRESS] 4  
[PERSON]  
5 Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa 5  
[PERSON]  
6 Fractus Antennas, 08174 Barcelona, Spain; [EMAIL_ADDRESS] 6  
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effect of an SWB system over the narrowband system and vice-versa, an SWB antenna with a band-notch feature is desirable for coexisting wireless communications.  
A conventional approach for preventing intervention among the recently designed and present-day communication systems is to insert filter banks in the SWB system to filter out the interfering EM waves of different frequencies. However, introducing external band stop filters to accomplish the desired band rejection makes the system complex and enhances the dimension significantly.  
Another efficient way for suppressing interfering electromagnetic spectrums from the SWB system is to realize SWB antennas with frequency blocking ability. Antenna shape and structure orientation are two important aspects in designing this type of antenna structure. Several wideband antennas are reported in the literature with no notch, single notch, or dual notch functions. Nevertheless, there is a need to develop SWB antennas with a greater number of notch bands to establish multiple coexisting communications. However, it is very challenging to design an SWB antenna with multi notch bands due to the difficulty in bandwidth control of notch bands while maintaining space limitation and unwanted mutual coupling between the adjacent notched bands.
## 2 Materials and Methods
### Control Methods
#### 2.2.1 Conventional Controllers  
In the literature, proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) control methods were preferred for load-frequency control in power systems. These studies experienced some difficulties because of the sudden climatic changes and the load fluctuations [28].  
In this study, a PID control has been applied to the system and is simulated by MATLAB-Simulink. The output power of PV-SPP was modelled to take into account climatic conditions. The LFC was achieved when the power obtained from the PV-SPP changed. The expression of the control signal (\(u_{(t)}\)) required for the PID controller is given as follows [29]:  
\[u(t)=K_{p}\left[e(t)+\frac{1}{T_{i}}\int\limits_{0}^{t}e(t)dt+T_{d}\frac{e(t) }{dt}\right] \tag{25}\]  
The Ziegler-Nichols (ZN) method is used for calculating the optimum values of the parameters of the PID controller [30]. \"\(K_{u}\)\" is defined as the constant value of oscillation of the system and \"\(T_{u}\)\" is the oscillation frequency. Their formulas are given in Table 1.
## 6 Comparison Analysis and Research Challenges
### Research Challenges
#### 6.2.6 Sender Waiting Time  
While the receiver-initiated MAC protocol [45] is proposed to mitigate the multiple receiver problem, it introduces another problem, that of sender waiting time. In the sender waiting time, a sender node should keep its radio active until a receiver's beacon arrives. Thus, a longer sender waiting time may lead to energy waste. This issue may be tackled if the wakeup time of ADC is intelligently adjusted. PW-MAC is such an example wherein the receiver's wakeup time is predicted in order to find a shorter waiting time, based on the previous wakeup times. It is going to be interesting to study how to determine a good sender waiting time based on the wakeup times of ADC.