esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analyses
### Ablation Study
#### 4.5.5 Influence of Out-of-Vocabulary Characters  
To verify the recognition performance of the NER model under the condition of out-of-vocabulary characters, we divided the test set of each dataset into two parts: one containing out-of-vocabulary characters and the other containing no out-of-vocabulary characters. The experimental results are shown in Table 11. It can be found that the results of the model on the subset without out-of-vocabulary characters are better than those on the subset with out-of-vocabulary characters, indicating that it is still challenging for the NER model to recognize entities correctly in the presence of out-of-vocabulary characters. In addition, our CGR-NER outperforms BERT-BiLSTM-CRF, regardless of whether the subsets contain out-of-vocabulary characters. For the subset containing out-of-vocabulary characters, the F1 score of our model surpasses that of BERT-BiLSTM-CRF by 3.32% and 3.9% on Weibo and OntoNotes 4, respectively. One possible reason is that CGR-NER can infer the approximate  
Figure 5: Performance of the model under different training set sizes. The training set size represents the percentage of training samples used in the original training set.  
semantics of out-of-vocabulary characters by extracting the semantic information contained in the Chinese character glyph structure, so that the model can better understand the text semantics and improve the recognition performance.
# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 2 Overview of Healthcare Applications
### eHealth
A more specialized branch of IoT is HIoT (healthcare IoT), or IoMT (Internet of Medical Things), where the objective is to build medical applications that can monitor a patient's health, prevent health issues, or predict disease.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 6 Results
### Research Gaps and Challenges
#### 6.1.4 IoT Datasets  
The construction of IoT datasets from commercial products faces immense challenges due to the restrictions on obtaining attacks of diversity or privileged access. Mirai malware and its variants are believed to be known attacks, but only for particular types of IoT devices, such as IP camera surveillance devices. Therefore, some studies resorted to constructing synthetic datasets through simulation tools [99] or carrying out experiments using limited sets of smart home devices and generating datasets that way. It is worth mentioning that some IoT datasets available for the public have been used by many researchers, such as N-BaIoT [50], IoT-IoT [66] and the recently published IoT23 [100] and MedIoT [101]. On the other hand, and regarding the selected studies, the used datasets do not include sufficient different suspicious activities related to bot networks, as the datasets were either not derived from IoT devices and thus does not reflect the IoT ecosystems, or do not represent the data of Internet of Things. Furthermore, they were extracted from IoT devices and are limited to specific purposes or to specific environments, such as the home system. As a consequence, a number of proposed solutions may face difficulties if they are applied to other ecosystems or if different other devices are present. More IoT botnet datasets are needed, and researchers should investigate building more datasets and finding solutions for extracting real datasets.
# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 2 Materials and Methods
### Performance Evaluation  
The performance metrics used to evaluate the image classification models include true positive (TP), true negative (TN), false positive (FP), and false negative (FN). In the context of the models developed, a true positive (TP) is considered when an image of a leaf belonging to group 1 is correctly classified by the model in that group. Similarly, a true negative (TN) is obtained when an image of a leaf belonging to group 2 is correctly classified in that group. These results indicate that the model's diagnosis is aligned with the actual condition of the leaves [35].  
On the other hand, a false positive (FP) occurs when the image of a leaf in group 2 is incorrectly classified as belonging to group 1. Conversely, a false negative (FN) occurs when an image of a leaf in group 1 is incorrectly classified as belonging to group 2 [35]. Thus, in terms of TP, TN, FN, and FP, Equations (6), (7), (8), and (9) refer, respectively, to the sensitivity, specificity, accuracy, and balanced accuracy of the models evaluated.  
\[\text{Sensitivity}=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{6}\]  
\[\text{Specificity}=\frac{\text{TN}}{(\text{TN}+\text{FP})} \tag{7}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{(\text{TP}+\text{TN}+\text{FP}+ \text{FN})} \tag{8}\]  
\[\text{Balanced accuracy}=\frac{\text{Sensitivity}+\text{Specificity}}{2} \tag{9}\]  
Thus, sensitivity refers to the chance of correctly classifying leaves belonging to group 1, specificity refers to the chance of correctly classifying leaves belonging to group 2, and accuracy refers to the model's overall success rate. Balanced accuracy is the arithmetic mean of sensitivity and specificity and is a more appropriate indicator than accuracy when the data have unbalanced groups [36]. These indicators were calculated in the R environment using the caret package [37].  
Finally, the receiver operating characteristic (ROC) curve was generated for each model, and the area under the ROC curve (AUC) was calculated. In total, eight ROC curves were generated. For LDA and QDA models, the ROC curve is obtained by varying the  
Figure 3: General methodology flowchart for group division and data analysis.  
posterior probability threshold required to classify an individual into one of the groups and calculating the resulting true positive and false positive rates.  
The result is a curve in which the vertical axis expresses sensitivity and the horizontal axis relates to specificity. Therefore, the AUC value is a summary measure of the model's performance across all thresholds, and ranges from 0 to 1. A perfect classifier has AUC = 1, while a random classifier has AUC = 0.5 [31]. The \(pROC\) package (version 1.18.5) was used to generate the ROC curves and calculate the AUC [38].
# Image Magnification Based on Bicubic Approximation with Edge as Constraint
## 5 Experimental Results
### Runtime  
We compare the runtime of the 8 methods, and the comparison results are shown in Table 3, where the time unit is seconds. In Table 3, the runtime of the new method is basically the same as the bicubic method. The time in Table 3 of the new method is the time used to magnify the image, but the new method takes 0.207 s to construct the approximation surface. The approximation surface only needs to be constructed once. After the surface is constructed, the image can be magnified by any multiple, and the time taken is basically the same as that of the bicubic method.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 6 Future Directions
### Privacy and Security-Related Challenges  
As discussed in Section 4.2, in recent times various studies have highlighted data security and privacy-related issues in the distributed learning frameworks. Many distributed frameworks allow wireless users to participate in the training process, which can also invite the various adversarial attacks and malicious entities. Distributed frameworks such as FL can limit the data privacy leakage by allowing the individual nodes to perform the training operations and limiting the device-server communication for model parameters. However, sensitive data still can be compromised through various data poisoning attacks. Such security and privacy-related issues pose several challenges to the upcoming 6G networks with heterogeneous users, devices, services, and applications. Technologies such as blockchain can be explored more to tackle these issues.
## 4 Multi-Branch SEC Receiver
### SECps Receiver  
The PDF of the instantaneous SNR, \(\gamma_{SECps}\), of the SECps can be written as [4]:  
\[f_{\gamma_{SECps}}(\gamma)=\left\{\begin{array}{ll}L[F_{\gamma}(\gamma)]^{L- 1}f_{\gamma}(\gamma),&\gamma\leq\gamma_{\tau};\\ P_{2}f_{\gamma}(\gamma),&\gamma>\gamma_{\tau},\end{array}\right. \tag{50}\]where \(f_{\gamma}(\gamma)\) and \(F_{\gamma}(\gamma)\) can be obtained by using (1) and (2), respectively. By integrating the expression of \(f_{\gamma_{SEC\!ps}}(\gamma)\) in (50) with respect to \(\gamma\), the corresponding CDF of \(\gamma_{SEC\!ps}\) can be written as:  
\[F_{\gamma_{SEC\!ps}}(\gamma)=\begin{cases}\left[F_{\gamma}(\gamma)\right]^{L},& \gamma\leq\gamma_{\tau};\\ P_{2}F_{\gamma}(\gamma),&\gamma>\gamma_{\tau}.\end{cases} \tag{51}\]  
Similar to (41), the \(q\)th moments of \(\gamma_{SEC\!ps}\) can be given as:  
\[\mu_{\gamma_{SEC\!ps}}(q)=\underbrace{\int_{0}^{\gamma_{\tau}}\gamma^{q}L[F_{ \gamma}(\gamma)]^{L-1}f_{\gamma}(\gamma)d\gamma}_{l_{3}}+\underbrace{P_{2} \int_{\gamma_{\tau}}^{\infty}\gamma^{q}f_{\gamma}(\gamma)d\gamma}_{l_{4}}. \tag{52}\]  
In order to calculate the integral \(I_{3}\) in (52), we first express (1) and (2) by using the univariate Fox's H-function with the aid of ([47], Equation (1.126)) and ([47], Equation (1.132)) as follows:  
\[f_{\gamma}(\gamma)=\frac{\Lambda^{m}\gamma^{m-1}}{\Gamma(m)\Gamma(n)}H_{1,1}^ {1,1}[\Lambda\gamma]_{(0,1)}^{(1-m-n,1)}\,, \tag{53}\]  
\[F_{\gamma}(\gamma)=\frac{(\Lambda\gamma)^{m}}{\Gamma(m)\Gamma(n)}H_{2,2}^{1,2 }[\Lambda\gamma]_{(0,1),(-m,1)}^{(1-m-n,1),(1-m,1)}\,, \tag{54}\]  
Then, by utilizing the Mellin-Barnes-type integral of the univariate H-function defined in ([47], Equation (1.2)) and substituting (53) and (54) in the integral term \(I_{3}\), \(I_{3}\) can be expressed as:  
\[I_{3}=\frac{\Lambda^{Lm}}{\Gamma^{L}(m)\Gamma^{L}(n)}\bigg{(}\frac{1}{2\pi j} \bigg{)}^{L}\int_{L_{1}}\cdots\int_{L_{L}}\left(\Delta_{1}\Delta_{2}\right) \Lambda^{-\sum_{l=1}^{L}t_{l}}dt_{1}\cdots dt_{L}, \tag{55}\]  
where \(\Delta_{1}=\int_{0}^{\gamma_{\tau}}\gamma^{Lm-\sum_{l=1}^{L}t_{l}+q-1}d\gamma _{\tau},\Delta_{2}=L(m-t_{L})\prod\limits_{l=1}^{L}\frac{\Gamma(t_{l})\Gamma (m+n-t_{l})\Gamma(m-t_{l})}{\Gamma(1+m-t_{l})}\,,j=\sqrt{-1}\), and \(L_{l}\) denotes the \(l\)th appropriate contour, which starts at the point \(\tau_{l}-j\infty\) and goes to the point \(\tau_{l}-j\infty\) with \(\tau_{l}\) as a constant value, \(l\in\{1,2,\ldots,L\}\).  
To this effect, substituting the solution of \(\Delta_{1}\) into (55) and employing the multiple Mellin-Barnes-type contour integral of the multivariate Fox's H-function defined in ([47], Equation (A.1)), the compact form of \(I_{3}\) can be re-expressed as:  
\[I_{3}=\frac{L\Lambda^{Lm}\gamma_{\tau}^{Lm+q}}{\Gamma^{L}(m)\Gamma^{L}(n)}H_{1,1;[2;2]_{l=1:L-1:L-1};1,1}^{0,1;[1,2]_{l=1:L-1};1,1}\big{(}\underbrace{ \gamma_{\tau}\Lambda,\cdots,\gamma_{\tau}}_{(-Lm-q;\{1\}_{l=L})}\big{|}_{(0,1 ),(-m,1)}^{(1-m-n,1),(1-m,1)}\big{|}_{l
## 2 Materials and Methods
### Experimental Setup  
#### 2.9.1 Hardware Specifications  
The visual content was displayed to the participants using an HTC Vive Pro Eye (HTC Corporation, Taoyuan, Taiwan) virtual reality headset running on a Windows 10 PC with a NVIDIA GeForce GTX 1070 graphics card (NVIDIA Corporation, Santa Clara, CA, USA). The field of view of the headset and the refresh rate reported by the manufacturer are \(110^{\circ}\) and \(90\,\mathrm{Hz}\), respectively. The participants operated within the environment via the Valve Index controller held in the right hand. The headset's and controller's positions and rotations were tracked via four HTC base stations 2.0. To facilitate the interaction with the VR environment, their gaze was used for the selection of the respective option, to continue or to check the model's decision. Once the intended button was gazed on, the participant confirmed their choice by pressing the trigger on the controller. In the case of checking the model's decision, the participant could scroll through the objects using the joystick on the controller and select the intended object by pressing the trigger. The eye-tracking data were collected using the built-in eye tracker at a frequency of 90 Hz. During the experiment, the participant was in a sitting position and could freely rotate their head within the working space. The experimental setup is schematically shown in Figure 3.  
Figure 2: The generated texture pattern for the cube objects. The upper row corresponds to the objects designed for the scenarios of low accuracy, whereas the lower row regards the scenarios of high accuracy. The color was an uninformative feature for the task and was used to diversify the objects.
## 4 Conclusions  
In this study, the effects of using fly ash and fine lightweight aggregates on the properties of foamed concrete with a density lower than 500 kg/m\({}^{3}\) were investigated. Based on the experimental results, the following conclusions can be drawn:  
1. The incorporation of fly ash slightly increases the dry density of foamed concrete, while fine lightweight aggregates have a varied influence because of their different characteristics.
2. Dry density, as well as mixture composition, influences the compressive strength of foamed concrete. As the dry density increases, so too does the compressive strength.
3. Fine perlite is more effective in improving the compressive strength of foamed concrete than fine Liaver.
4. The thermal conductivity of foamed concrete depends mainly on concrete density, with the mix composition having a minimal influence.
5. Fly ash reduces the drying shrinkage of foamed concrete.
6. Fine Liaver significantly reduces the shrinkage of foamed concrete to about 30%, with fine perlite increasing the drying shrinkage only marginally.
7. Because of the slow hydration rate of fly ash, it has a negative influence on the compressive strength of foamed concrete.  
Conceptualization, M.A.E., M.E.E.M., and S.-Y.C.; methodology, M.A.E. and S.-Y.C.; validation, M.A.E., S.-Y.C., and M.E.E.M.; investigation, M.A.E., M.E.M., and P.S.; resources, D.S.; writing--original draft preparation, M.A.E and M.E.E.M.; writing--review and editing, M.A.E, S.-Y.C., and D.S.; visualization, M.A.E. and P.S.; supervision, D.S.; funding acquisition, D.S. and M.E.E.M.  
The project is supported by the German Federal Ministry of Education and Research (BMBF, Project number: 13 XP5010B and 01 DR16007) and by the Polish National Academy for Academic Exchange within project No. PPN/BEK/1/00078.  
The authors would like to express our appreciation to Heidelberg Cement and Sika companies for providing materials and devices. We would like also to thank [PERSON] from TU Berlin for supporting this research with SEM measurements.  
The authors declare no conflict of interest.
## 2 Review of Low Frequency Ultrasonic Communication-based Audio Markers
### Audio Marker Receiver  
In the audio marker receiver, the input signal recorded by the COTS microphone, \(y_{i}(n)\), is the superposition of \(s_{i}^{T_{b}}(n)\) in (3) and the additive environmental noise, \(z_{i}(n)\), so that:  
\[y_{i}(n)=h(n)*s_{i}^{T_{b}}(n)+z_{i}(n) \tag{4}\]  
where \(h(n)\) is the impulse response of the propagation channel from the LFU transmitter to the receiver. As shown in the lower part of Figure 2, the carrier frequency bandpass filter (BPF), \(g(n)\), is then applied to \(y_{i}(n)\) to estimate \(s_{i}^{T_{b}}(n+qT_{b})\), so that:  
\[\hat{s}_{i}(n+qT_{b})=y_{i}(n+qT_{b})*g(n+qT_{b}). \tag{5}\]  
By assuming that \(z_{i}(n)\) rarely overlaps with \(s_{i}^{T_{b}}(n)\) at the carrier frequency band [20], \(z_{i}(n)\) contained in \(y_{i}(n)\) (see Equation (4)) is filtered out. Similar to Equation (3), the filtered receiver signal corresponding to the \(q\)-th bit, \(\hat{s}_{i}(n+qT_{b})\), is normalized by its gain to compensate for energy loss resulting from the frequency-selective characteristics of \(h(n)\), so that:  
\[\hat{c}_{i}(n+qT_{b})=\frac{G\hat{s}_{i}(n+qT_{b})}{\frac{1}{T_{b}}\sum_{n=1}^ {T_{b}}\abs{\hat{s}_{i}(n+qT_{b})}} \tag{6}\]  
where \(\hat{c}_{i}(n+qT_{b})\) is an estimate of \(\overline{\hat{b}_{i}^{q}}\) at the \(i\)-th symbol, and also \(G=2^{14}\).  
Finally, DPSK demodulation is applied using non-coherent detection [20], so that:  
\[\overline{\hat{b}_{i}^{q}}=\left\{\begin{array}{ll}1,&if\sum\limits_{n=1}^ {T_{b}}\hat{c}_{i}(n+qT_{b})\hat{c}_{i-1}(n+qT_{b})>0\\ 0,&otherwise\end{array}\right. \tag{7}\]  
and the received audio marker for the \(p\)-th binary bit at the \(i\)-th symbol, \(\hat{b}_{i}^{p}\), is obtained by applying the FEC decoding with \(G_{23}\) on \(\overline{\hat{b}_{i}^{q}}\)[19].  
Proposed User-Aware Audio Marker System Using Low Frequency Ultrasonic-based Object Detection and Communication  
The proposed user-aware audio marker system comprises LFU-based object detection and LFU communication, as shown in Figure 3. The proposed system consistently senses a user's existence around the marker's location. Consequently, when a user is detected by an LFU-based object detection algorithm, the proposed system sends the marker's information to the detected user using LFU communication. The following subsections describe the details of the LFU-based object detection and LFU communication for the proposed system.
# Design and Experimentation of Tensegrity Jumping Robots
## 4 Experiment Verification
### Experimental Results  
This experiment aims to evaluate the performance of the jumping robot in a real environment and compare it with the simulation results. The objective is to verify the accuracy of the theoretical model and provide practical data support for further optimization of the robot. By analyzing the role of stiffness, it provides data support for design optimization and, by comparing the simulation results under different stiffness, it provides a comprehensive understanding of the role of stiffness in jumping robots and guides the actual design and control optimization.  
We tested the jumping performance of the prototype for stiffnesses ranging from 0 to 24 N/mm and tilt angles ranging from 0 to 18\({}^{\circ}\), as shown in Figures 12 and 13. We summarized the experimental data we obtained, as shown in Figure 14. When the robots were released at the same time, we experimentally measured a maximum jump height of 229.32 mm and a maximum jump distance of 21.42 mm. When the step-ups were released, we experimentally measured a maximum jump height of 270.59 mm and a maximum jump distance of 23.86 mm. We choose the position \(\Delta t_{3}\) above to release the energy of the second module. Moreover, it can be seen from the experimental results (Figures 12 and 13) that the robot can achieve a stable jumping process on the two occasions, which benefits from the stable tensegrity structure.  
Figure 11: Environment of the experiment.  
Figure 10: Prototype of the robot with two modules.  
For the experiments with simultaneous jumping, the maximum error between our measured data and the simulated data is 9.1%, while the maximum difference between the experimental data and the simulated data for step-up jumping is 13.2%. The overall experimental results match the results of the simulation. A better jumping effect can be obtained by increasing the stiffness and a larger lateral travel distance can be obtained by tilting the fuselage. At maximum, the jumping performance of step-up jumping was approximately 24% higher than the jumping height of simultaneous jumping. A higher  
Figure 14: Data from experiments. (**a**) Simultaneous jumping. (**b**) Step-up jumping.  
Figure 12: Experiments with simultaneous jumping.  
Figure 13: Experiments with step-up jumping.  
energy utilization efficiency can be obtained by step-up jumping and a higher jumping distance can be obtained under the same conditions.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 3 Experimental Area and Data Source
### Overview of the Experimental Area  
The Changsha Meteorological Radar Calibration Center is the only national radar calibration center in China and is based at the Changsha National Climatological Observatory (Figure 4). It is responsible for national research on meteorological radar calibration technology and related operational tasks. Different models of S-, C- and X-band radars have been built around the experimental site. The S-band radar is located about 2.6 km north of the experimental site on Mount Lianhua, and the X-band radar is in the calibration area to the east of the experimental site. In Figure 5, which shows the Digital Elevation Model (DEM) of the experimental site, all the radars are located at elevated positions in mountainous terrain, with relatively little impact from topographical obstacles.
## 3 Results and Discussion
### Stability of Periodic Orbits  
Figure 2 shows the variation of the spectral radius of the matrix given in Equation (30). According to Equation (29) and with the parameters \(\gamma\in(0.3,0.5)\) and \(T=0.18\), the system presents \(1T\)-periodic orbits.  
Figure 3 shows a \(1T\)-periodic orbit in the state space. The green line corresponds to the evolution of the orbit for the \(1N\) configuration (first switching). The blue line corresponds to the evolution of the orbit for the configuration that considers the switch OFF. Finally, the red line corresponds to the last configuration with the switch ON.  
Figure 4 represents the evolution of the current (\(x_{1}\)) and the voltage (\(x_{2}\)) in a commutation period. Similar to the previous figure, each color represents the orbit evolution for each configuration: green is for \(1N\), blue is for OFF, and red is for ON. In a possible experimental test, seeking the steady-state operation, it is expected that the evolution of the system will tend to the limit cycle and to a point close to the desired reference value for the controlled signal \(x_{2}\). When the controller stops working correctly, the behavior is no longer a limit cycle, but subharmonics of higher order periods appear, which lead to lower frequency signals and, finally, chaos can occur. It should be noted that in an experimental test, an ideal limit cycle is impossible because of the electronic noise that is always present in the voltage and current signals [9; 28].  
Figure 2: Variation of the spectral radius as a function of \(\gamma\).  
When implementing a boost converter, it is necessary to keep in mind the evolution of the voltage \(x_{2}\), as this generally must have low ripple and a regulation error of less than 3%. The ZAD technique is used to control the output signal \(x_{2}\), and this signal gets close to the reference signal defined by the user, as shown in Figure 4.