esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Energy CT Applications in Urological Diseases
## 3. DECT Benefits
### Virtual Non-Contrast Enhanced Image  
Attenuation measurements obtained at the second energy level allow the decomposition of a mixture of two or three materials into its constituent materials and represent the base of virtual non-contrast enhanced (VNC) and iodine images.  
VNC images can be generated by subtracting the iodine from the contrast-enhanced DECT images, using three-material decomposition [18]. VNC images facilitate the differentiation of calcifications or high-attenuation materials from iodine-enhanced tissues. The reconstruction of VNC images instead of the acquisition of real unenhanced images allows radiation exposure reduction when unenhanced CT scans are needed (Figure 2) [19].
# A NavCom Signal Authentication Scheme Based on Twice Two-Way Satellite Time Transfer
###### Abstract  
Low Earth Orbit (LEO) satellite communication systems typically achieve identity authentication through the encryption and decryption of two-way information, which requires complex key management systems. In contrast, the integration of navigation and communication (NavCom) signals provides novel opportunities for physical observation and authentication solutions due to its measurement functions. This paper introduces a novel signal authentication scheme based on twice two-way satellite time transfer (TWSTT) for LEO satellite systems. It leverages the non-mutated nature of the clock difference to ascertain the legitimacy of the signal by measuring the clock difference of signals at different instances. Unlike traditional authentication methods, this approach directly exploits the temporal and spatial characteristics of the signal, negating the necessity for intricate authorization key systems. Additionally, it adeptly tackles the challenges posed by spoofing interference. The performance analysis indicates that this scheme can achieve a high detection probability for the repeater spoofing signal in the low carrier-to-noise ratio conditions.  
low Earth orbit; integration of communication and navigation; secure authentication +
Footnote †: journal: remote sensing  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 3 Channel Effects Furthermore, RF-Impairments
### DC Offset  
The DC offset is also caused by the imperfection of the LO in direct-conversion receivers. where it is induced due to the leakage self-mixing of LO and transistor mismatch in the RF components [29]. DC offset will result in a shift on the symbols used in the constellation diagram (I/Q plane), and as shown in Figure 10d,e, this shift might occur on the I-component (\(\gamma_{I}\sqrt{E_{s}}\)), the Q-component \(\gamma_{Q}\sqrt{E_{s}}\), or both of them [51]. Where \(E_{s}\) is energy per symbol.  
The comparison of the impact of the DC-offset on the BER performance of OTFS and OFDM is shown in Figure 11. In general, the BER performance of the OTFS waveform is superior to the OFDM waveform under the effect of the DC offset. Furthermore, it demonstrates that the BER performance degrades as the value of the DC-offset increases, and this will happen with nearly the same amount of effect for both OTFS and OFDM waveforms. Specifically, if the amount of the DC-offset is raised by around \(20\%\sqrt{E_{s}}\), then the system will need approximately \(SNR\approx 2\) dB more in order to maintain the same BER\(=10^{-3}\). As demonstrated, the effect of the DC-offset has a smaller impact on the system when the SNR is low, but it becomes more noticeable as the SNR rises. This is because the DC-offset expresses in the form of interference in the center of the transmission frequency spectrum, and as the SNR rises, the interference's impact on the system's overall performance accumulates.  
The impact that varying values of the DC-offset have on the performance of the OTFS waveform is seen in Figure 12. According to the results, the degradation in system performance may be attributed to an increase in the DC-offset values. Additionally, it was shown that whether the in-phase DC-offset or the quadrature DC offset was used, the system had the same influence on the performance of the system.  
Figure 11: Comparison of the BER performance between OTFS waveform with \(M=32,N=32\) and OFDM waveform with \(N=1024\) considering different DC-offset in both I-component (\(\gamma_{I}\)) and Q-component (\(\gamma_{Q}\)) at 2.4 GHz carrier frequency.
# Microstructural, Electrochemical and In Vitro Analysis of Mg-0.5 Ca-xGd Biodegradable Alloys
## 3 Results and Discussions
### Electrochemical Corrosion Resistance Analysis
This little \"backwards\" shift of the cathode branch may be explained by the modification of the specimens' surface following corrosion (as a consequence of corrosion, the metal surface increases, making the current density decrease). The very small area of the cyclic curve loop reflects an insignificant increase of zonal corrosion (pitting type). Figure 6 shows SEM images of the surface of experimental Mg-0.5 Ca-xGd after the electro-corrosion resistance test with different image magnification powers. A generalized corrosion surface is observed, covered with reaction compounds that passed from the electrolyte solution (SBF) to the surface. In addition to the layer of magnesium oxide formed on the surface of the alloy, there were several different formations of compounds that could be oxides of other elements, carbonates or salts (sodium or potassium chlorides). The compounds that covered the alloy formed a layer that insulated the alloy from the electrolyte medium. Compounds formed on the surface had numerous cracks and fissures that confirm the instability of these compounds on the surface. Figure 6a shows the typical morphology of magnesium oxide. The compounds on the surface have different morphologies, which indicate the possibility of forming compounds with different chemical compositions. Figure 6b shows the identification of the chemical elements that appear on the corroded surface of the Mg-0.5 Ca-0.5 Gd alloy. Figure 5: Tafel diagrams of Mg-0.5 Ca–xY-based experimental alloys. Surface oxidation in all cases was confirmed by the chemical composition determinations (Table 6) using the EDS detector (values are average of five determinations from 1-mm\({}^{2}\) areas). Sample Mg-Ca-2 Gd presents higher stability of the oxides on the surface and, together with samples Mg-Ca-1.5 Gd and Mg-Ca-1 Gd, also has traces of chloride and sodium as salts or other compounds. Figure 6: SEMs of the samples surfaces after electrochemical tests: (**a**) Mg–0.5 Ca; (**b**) Mg–0.5 Ca–0.5 Gd; (**c**) Mg–0.5 Ca–1 Gd; (**d**) Mg–0.5 Ca–1.5 Gd; (**e**) Mg–0.5 Ca–2 Gd; (**f**) Mg–0.5 Ca–3 Gd. The main elements identified (with percentages higher than 0.01%) are: oxygen, magnesium, calcium and gadolinium. The elements calcium, gadolinium and chlorine appear, which implies the participation of these elements in two or more bonds characteristic of different compounds. The increase in Gd content at values greater than 2% is a success in terms of corrosion resistance, confirmed by the Tafel linear results. In the case of the 3% Gd alloy, the presence of O, Na, C and Cl elements was identified on the surface by the interaction of the experimental alloy with the SBF electrolyte solution. The presence of chlorine-based salts is also noted.
## 2 Related Work
In addition, in the above studies, a single channel model is used to characterize the channel. In fact, in a complex urban environment, due to the different heights of scatterers such as buildings and trees and changes in their distribution positions, channel characteristics at different heights are different. The single channel model has insufficient communication reliability in this case. Correspondingly, some scholars also proposed a hierarchical adaptive channel model. [PERSON] et al. proposed an adaptive channel based on the improved LOO model and the Rice model [13], in which the LOS component obeyed the lognormal distribution and the multipath component obeyed the Rayleigh distribution. Directional antennas were used for communication by the improved Rice model. This article successfully expanded the degrees of freedom of the two models. However, communication reliability has not been fully discussed. In addition, directional antennas have poor performance in high mobility environments. The omnidirectional antenna has the advantages of large coverage, low price, and better performance in high mobility scenarios. Therefore, in the urban emergency management environment of UAVs, considering the use of omnidirectional antennas will have better performance. Based on the discussion of the above research, an adaptive SUUL-SULA channel model is proposed in this paper. The channel characteristics are described more accurately through this model. UAVs could select better channels according to different flight altitudes to improve communication reliability to a greater extent. It shows better performance in urban emergency management. The specific roadmap of the research is shown in Figure 2.  
Main contributions of this paper are as follows:  
1. A height-based adaptive air-to-ground channel is proposed based on the channel characteristics of different heights in the urban environment to improve channel reliability;
2. In the ultra-low altitude area, BPSK method is adopted to model the ultra-low altitude communication channel of UAV with reference to LOO model. In the low altitude region, the separation and compensation of Doppler frequency shift are emphasized by using beamforming technology and multipath characteristics;
3. The simulation experiment proves that this model can effectively reduce the bit error rate of communication and improve the reliability of communication. It provides effective physical level support for the application of UAV in smart city emergency management.
## 4 Results and Discussion
### Measurements for Virtualizing an Existing Event Source
#### 4.1.2 Time to Subscribe the Gateway to an Existing Event Source  
Although it is not directly related to the virtualization itself, another interesting measurement is the required one to subscribe the gateway to the event source in the WSN. The process is quite simple: once the gateway is aware of the existence of an event source, it tries to subscribe to it. Figure 6 shows the results after 230 tests. As for the results, the maximum time for subscription was 2104 milliseconds and the minimum was 578. The average mean was 948.63 and the standard deviation was 167.54 milliseconds.  
Taking a look at Figure 6 shows how there are a few times where the required time is almost the double of the usual. This is due to communication issues.
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 5 Evaluation
### Experiment  
#### 5.3.1 Quantitative Results  
Tables 2 and 3 are the comparison of experimental results under ADIAC and ADBAC evaluation metrics, respectively. We compare the four methods on the four attributes of the above six datasets. Attribute 1 of VisPCNet is the keywords, attribute 2 is the journal, attribute 3 is the year, and attribute 4 is the number of citations; Attribute 1 of RCNet1, RCNet2, RCNet3, and RCNet4 is interests, attribute 2 is the number of papers, attribute 3 is the number of citations, and attribute 4 is the H index. Attribute 1 of OMNet1 is the working years, attribute 2 is the message type, attribute 3 is the message number, and attribute 4 is the device type.  
It can be seen from Table 2 that under each attribute of each dataset, the ADIAC value of the method in this paper is much smaller than the ADIAC value of the other three methods. Compared with other methods, ADIAC in this paper is the lowest, about 0.1. This shows that our method has good layout stability, can obviously gather nodes with the same attributes together, and ensure the differentiation between nodes. It can be seen from Table 3 that under each attribute of each dataset, the ADBAC value of this method is much larger than that of other methods. The results of the comparison of evaluation metrics show that compared with other methods, the method in this paper can obtain clearer and more distinguishable results of attribute community layout.  
Figure 6: The result of attribute community layout under the attribute ’Year’. (**a**) FR layout; (**b**) LinLog layout; (**c**) ForceAtlas2 layout; (**d**) layout of our method.  
Figure 7: The result of attribute community layout under the attribute ’Number of Citations’. (**a**) FR layout; (**b**) LinLog layout; (**c**) ForceAtlas2 layout; (**d**) layout of our method.
## 3 Data Sources and Research Methodology
### Research Methodology
#### 3.2.3 Significance Test  
The Theil-Sen Median method can be used to calculate the trend in the time series. However, it cannot present the significance level of the trend. Therefore, the Mann-Kendalltest [26; 27] was used to judge the significance level of the trend in the time series. For the time series {x} (i = 1, 2,..., n), the statistic of the trend in change can be defined as Z.  
\[\mathbf{V}=\left\{\begin{array}{ll}\frac{\mathbf{S-1}}{\mathbf{\rho}_{s}}& \mathbf{S>0}\\ \mathbf{0}&\mathbf{S=0}\\ \frac{\mathbf{S+1}}{\mathbf{\rho}_{s}}&\mathbf{S<0}\end{array}\right. \tag{3}\]  
\[\mathbf{S=\sum_{k=1}^{n-1}\sum_{j=k+1}^{n}Sgn\left(X_{j}-X_{k}\right)} \tag{4}\]  
\[\mathbf{Sgn\left(X_{j}-X_{k}\right)}=\left\{\begin{array}{ll}+1&\left(X_{j}- X_{k}\right)>0\\ \mathbf{0}&\left(X_{j}-X_{k}\right)=0\\ -1&\left(X_{j}-X_{k}\right)<0\end{array}\right. \tag{5}\]  
\[\mathbf{\rho}_{s}=\sqrt{\left(n(n-1)(2n+5)-\sum_{i=1}^{m}t_{i}(t_{i}-1)(2t_{i}+ 5)\right)/18} \tag{6}\]  
where n is the number of elements in the time series, m is the number of repetitive data in the time series, and ti is the number of repetitive data in the ith group. At a given significance level \(\alpha\) (99%, 95%, 90%), if \(|\)V\(|\)\(\geq\) V\(1-\alpha/2\), this means that the trend is significant; otherwise, the change trend is not significant.
## 1 Introduction  
Attention-deficit hyperactivity disorder (ADHD) is the most common neurobehavioral disorder in schoolchildren; its prevalence in the United States ranges from 2% to 18% in this age group [1]. If not treated, ADHD may not only affect the patients' functionality in childhood but also cause social and educational problems in later periods of their lives. Therefore, ADHD diagnosis and treatment are crucial in its early stages [2]. ADHD treatment may consist of drug therapy, behavioral therapy, or their combination. According to neurochemical evidence for ADHD, drug therapy is considered more effective than behavioral therapy [3]. Although stimulants are widely used for ADHD drug therapy, approximately 20% of children do not respond to the treatment [4]. There are several methods for evaluating the therapeutic effects of ADHD medication, including the Swanson, Nolan, and Pelham(SNAP) questionnaire [5], the Vanderbilt ADHD Diagnostic Rating Scale [6], and visual analog scales [7]. However, these rating scales are scored by parents or teachers of the patients, and this can lead to subjective bias during evaluation. Therefore, an objective method to measure these therapeutic effects is essential for monitoring the treatment effects of ADHD.  
Currently, sensor technology has developed, to the point where tiny wireless sensors, as small as a watch, can record data for long periods. Devices with such sensors have been used in the research of many medical conditions, such as osteoarthritis, cerebral palsy, Parkinson disease, and stroke [8; 9; 10; 11]. Increased activity is characteristic of patients with ADHD compared to those without ADHD [12; 13]. Furthermore, two studies have used an accelerometer as an objective tool for ADHD diagnosis [14; 15]. In addition, in two studies, a gyroscope combined with an accelerometer was used to distinguish the results of patients with ADHD from those without ADHD for various activities during a visit to a psychiatric consultancy [16] and when performing nine motor tasks [17]. However, the use of accelerometer measurements to evaluate therapeutic effects of ADHD medication is lacking. Moreover, our unpublished data demonstrated that the variance values of accelerometers in patients with ADHD were significantly higher than those in individuals without ADHD. In this study, we evaluated the therapeutic effect of the ADHD medication, methylphenidate, by using a smart watch equipped with an accelerometer and then analyzing the movements of patients with ADHD.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.8 Time Domain Reflectometer (TDR)  
TFRs [13] are used to diagnose faults in RF transmission lines. By analyzing the time delay of reflected signals, these tools help locate disruptions in signal pathways that could be contributing to interference.
# RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis
## 7 Conclusions  
This research study presents the novel RoBERTa-GRU model, which combines the state-of-the-art deep learning techniques in NLP, RoBERTa and GRU models. The RoBERTa, which is part of the Transformer family, utilizes dynamic attention masking to produce meaningful text embeddings, thus enabling the model to generalize better. The GRU model, on the other hand, enables the capture of long-term dependencies in text sequences, and its gating mechanism helps to overcome the vanishing gradient problem that is commonly encountered in Recurrent Neural Networks (RNNs). In addition, the proposed model incorporates data augmentation through the use of the GloVe word embedding technique on the imbalanced Twitter US Airline dataset to improve the minority class representation.  
The results of the experiments carried out on the IMDb, Sentiment140, and Twitter US Airline datasets indicate that the RoBERTa-GRU model outperforms all other comparison methods, with accuracy scores of 94.63%, 89.59%, and 91.52%, respectively. The combination of the RoBERTa and GRU results in a powerful and efficient model for sentiment analysis, making it a promising solution for a variety of NLP tasks.  
Conceptualization, K.L.T. and C.P.L.; methodology, K.L.T., C.P.L. and K.M.L.; software, K.L.T. and C.P.L.; validation, K.L.T. and C.P.L.; formal analysis, K.L.T.; investigation, K.L.T.; resources, K.L.T.; data curation, K.L.T. and C.P.L.; writing--original draft preparation, K.L.T.; writing--review and editing, C.P.L. and K.M.L.; visualization, K.L.T. and C.P.L.; supervision, C.P.L. and K.M.L.; project administration, C.P.L.; funding acquisition, C.P.L. All authors have read and agreed to the published version of the manuscript.  
The research in this work was supported by the Fundamental Research Grant Scheme of the Ministry of Higher Education under award number FRGS/1/2021/ICT02/MMU/02/4 and Multimedia University Internal Research Grant with award number MMUI/220021.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Naive Bayes [8] & 69.5 & 79 & 44 & 45 \\ Logistic Regression [23] & 80.5 & 78 & 69 & 72 \\ Decision Tree [24] & 71.14 & 62 & 56 & 58 \\ KNN [23] & 68.41 & 60 & 60 & 60 \\ AdaBoost [24] & 74.59 & 67 & 63 & 65 \\ GRU [25] & 78.55 & 73 & 71 & 72 \\ LSTM [25] & 77.56 & 71 & 69 & 69 \\ BiLSTM [26] & 77.46 & 71 & 69 & 70 \\ CNN-LSTM [12] & 76.02 & 68 & 69 & 69 \\ CNN-BiLSTM [12] & 77.32 & 70 & 65 & 67 \\ RoBERTa-LSTM & 85.69 & 86 & 86 & 86 \\
**RoBERTa-GRU** & **91.52** & **91** & **91** & **91** \\ \hline \hline \end{tabular}
\end{table}
Table 9: The comparison results on the Twitter US Airline Sentiment.
## 4 Results and Discussion
### Case Study: Pitch-Motor Driver Fault  
Ten alarm lists belong to the pitch-motor drive fault. Five alarm lists are used to extract the feature vector. The obtained feature vector is \(C_{1}=[c_{1,1},c_{1,2},\ldots,c_{1,1309},c_{1,1724},\ldots c_{1,368}]=[0,0, \ldots,1,1,\ldots 0]\). The binary values of two alarms in the feature vector are one. These alarms are for the blade driver fault and the fault of the pitch-driver speed. The codes of the alarms are T309 and T724, respectively. The occurrence numbers of T309 and T724, referred to when a fault occurs, are shown in Table 4.  
The weight of an alarm for a pitch--motor driver fault is expressed as \(w_{1,i}\), where \(i=1,2,\ldots,368\). The weights of alarms T309 and T724 are \(w_{1,1309}\) and \(w_{1,1724}\), respectively. The calculation processes of \(w_{1,1309}\) and \(w_{1,1724}\) are provided as examples:  
1. T309 is a fault alarm, thus \(\lambda_{\rm T309}=1\);
2. \(\alpha_{1,1309}=\frac{1}{5}(4*\delta(1-1)+\delta(0-1))=0.80\);
3. \(\beta_{\rm Cl309}=\frac{1}{5}(\beta_{2,{\rm X}09}+\beta_{3,{\rm X}09}+\beta_{4,{ \rm X}09}+\beta_{5,{\rm X}09}+\beta_{6,{\rm X}09})=\frac{1}{5}(\frac{1}{4}+0+0+ \frac{2}{23}+0)=0.07\);
4. \(w_{1,1309}=\lambda_{1,1309}(2\iota_{1,1309}-1)(1-\beta_{7,1309})=0.56\).
5. The same process of steps 1-4 is repeated to achieve: \(\lambda_{\rm T724}=1\); \(\alpha_{1,1724}=1.0\); \(\beta_{\rm t,T309}=0.13\); \(w_{1,1724}=\lambda_{1,1724}(2\iota_{1,1724}-1)(1-\beta_{7,1724})=0.87\).  
The other weights for pitch-motor driver fault are calculated in the same way. After we obtain the feature vector of pitch-motor driver fault and the weights of each alarm for the fault, the weighted distance can be calculated according to Formula (24) and Formula (25).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Faults** &
\begin{tabular}{c} **The Total Number of Alarm Lists** \\ **(Feature Vector Extraction/Test)** \\ \end{tabular} \\ \hline
1 & Pitch–motor driver fault & 10(5/5) \\
2 & Pitch–system communication fault & 7(4/3) \\
3 & Hub speed encoder fault & 8(4/4) \\
4 & High temperature of generator stator & 10(5/5) \\
5 & Wind vane fault & 46(23/23) \\
6 & Vibration sensor fault & 9(5/4) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Faults and the number of their alarm lists.