esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Evaluation of Antenna Pointing Control Error  
### Conditions  
A simulation of the antenna pointing control system was performed by using MATLAB and Simulink. Table 2 shows the parameters for the UAV and the onboard antenna.  
Figure 17: Format after conversion.  
Figure 18: Control frequency of antenna driving against antenna pointing accuracy.  
Assuming that the UAV weighs 30-100 kg, we have adopted the UAV with a mass of 65 kg as an example.  
Table 3 shows the flight conditions and the positional relation between the UAV and the ground station as conditions of the simulation. In addition, the wind disturbance was set to 10 m/s with reference to the wind speed in the stratosphere [3].  
Errors of attitude angles and accuracy of RF sensor are expected to greatly deteriorate antenna pointing accuracy. So, the pointing accuracy is evaluated by computer simulation assuming that all sensors except RF sensor are normal. The standard deviations of RF sensor are selected as a parameter.  
Noises of sensors were considered to be random variables with a zero mean and a specified variance \(\sigma^{2}\). The standard deviations \(\sigma\) for sensors used are summarized in Table 4. As for the RF sensor, its standard deviations are selected as 0.1, 0.05, and 0.01 degrees because the noise deviation should be less than the required antenna pointing accuracy of 0.13 degrees, mentioned in Section 4.  
In this simulation, the attitude angle sensor noises become the error of the target command of the antenna driving angle. Therefore, the sensor output of the attitude angle is passed through a low-pass filter with a time constant of 0.3 s and used for the target command Equations (7), (13), and (14).
# Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis
## 3 Neighborhood Construction Based on MD
### Neighborhood Construction Based on MD  
In this part, we improve the LLE algorithm in neighborhood construction. In order to solve the problems which bearing failure data has non-stationary, non-linear and high correlation characteristics, M-LLE algorithm uses MD to replace Euclidean distance to construct the neighborhood map.  
An improved neighborhood algorithm based on MD is shown as following:  
1. Initialize the relevant parameters: neighborhood size \(k\), and the reduced dimension of the intrinsic dimension \(d\). Define a starting point \(x_{i}\), which chooses arbitrarily in high-dimensional space, and the neighborhood of this point is set to \(U(U=\varnothing)\).
2. Calculate the covariance matrix \(\sum\) by (5), so we can determine the relationship between the center point \(x_{i}\) of the neighborhood and the rest of the sample points \(x_{j}\left(j=1,\ 2,\ 3,\ldots,n-1\right)\). \[\sum=\frac{1}{n-1}\sum_{k=1}^{n}\left(x_{i}^{k}-\overline{x_{i}}\right)\left( x_{j}^{k}-\overline{x_{j}}\right)^{T}\] (5)
3. Using the obtained covariance matrix of sample points and the center point, we can obtain the MD between the center point \(x_{i}\) and the rest of the sample point \(x_{j}(j=1,\ 2,\ 3,\ldots,n-1)\): \[D_{M}\big{(}x_{i},\ x_{j}\big{)}=\sqrt{\big{(}x_{i}-\ x_{j}\big{)}^{T}\sum^{-1 }\big{(}x_{i}-\ x_{j}\big{)}}\] (6)
4. Get a descending order of \(D_{M}\big{(}x_{i},\ x_{j}\big{)}\), and select \(k\) nearest points as the neighborhood of the center point \(x_{i}\). Set this neighborhood as \(U_{i}\), and update the center point set \(U=U\cup\{x_{i}\}\).
5. To determine the next center point, we select any one points of \(U_{i}\) as the next center point of \(x_{i}\), and this point does not belong to the center point set \(U\).
6. When the new center point is determined, the algorithm turns back to step 2 to repeat all above operations.
7. Until the number of elements in \(U\) is \(n\), it means that all the sample points have been completed traversal. Finally, output all sample points \(\{x_{1},\ x_{2},\ x_{3},\ldots,\ x_{n}\}\) and their neighborhoods \(\{U_{1},\ U_{2},\ U_{3},\ldots,\ U_{n}\}\).  
The flow chart of improved neighborhood construction algorithm which based on MD is shown in Figure 1.  
When using MD to replace the Euclidean distance in the original LLE algorithm, it is guaranteed that the local linear relationship in the neighborhood is invariant to construct low-dimensional projection. After we adopt the improved neighborhood construction algorithm, the correlation between the data can be eliminated. It will play a very important role in the subsequent realization of obtaining the intrinsic manifold in high-dimensional space.
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### RNA m\({}^{6}\)A Modification in GBM
ADAM3H, a disintegration and metallopeptidase domain 19: ADAR1,adenosine dismutase RAN 3: ENOGLE, 1: A FORBC3, a glycoprotein 19: ENskin editing enzyme catalytic subunit 30: BLK, BC-12: BLEU, 1: BLNI, bridging integrator 1: \(\Uplambda\), 1: F-ELP, similar FULC (RAD2-like 1: E-1)-converting enzyme-mhibhibitor repetitive: C-Myc, avian myodycentous viral oncogene homolog: CNO2, cyclin 102: CDCLB, cells division cycle 14: DEBE, CEBA, CCAT (enhunder binding protein alpha), CNO2, mitochondrial cytochrome oxidase subunit 37: EER, epidermal growth factorizer, EMPI, epithelial-to-meosomal transition; FOMAI, forkhead RM; IOKM-1, as GRhaled RM; M1-antisense RNA, GM, glioblastoma-GSC, glioma stem-like cells HE1A, hypoxia-inducible factor 1 alpha; HE1A-AS2, hypoxia-inducible factor 1 alpha-antisense RNA 2: HMCB4, mI2, highly group AT-hook 1/2: HSP0, host shock protein 70: FQZ, insulin-like growth factor 2: LOG, low-grade glioma, MAPK, mitogen-activated protein kinases, MMG, marker of proliferation k4:67, MMG-2, matrix metalloproteinase 2: MRG, miRNA evolution element; NOK2, nuclear  
Actually, m\({}^{6}\)A methylomes of brain tissues are highly specific [35], but the status of m\({}^{6}\)A may be different in GBM. A bioinformatic analysis in The Chinese Glioma Genome Atlas (CGGA) microarray and RNA sequencing databases [58] show that m\({}^{6}\)A writers, including METTL3, METTL14, WTAP, RBM15 and RBM15B, as well as m\({}^{6}\)A readers including YTHDC2, YTHDF1, YTHDF2, YTHDF3, hnRNPA2B1 and hnRNPC are elevated, while m\({}^{6}\)A writer ZC3H13 and m\({}^{6}\)A eraser FTO are decreased in gliomas. In GBMs, m\({}^{6}\)A writers WTAP and RBM15, m\({}^{6}\)A eraser ALKBH5, and m\({}^{6}\)A reader YTHDF2 are significantly elevated, while m\({}^{6}\)A writers METTL3, VIRMA and ZC3H13, m\({}^{6}\)A eraser FTO, m\({}^{6}\)A readers YTHDC2 and hnRNPC are significantly decreased, compared with low-grade gliomas (LGGs). In addition, four lncRNAs including MIR9-3 HG, LINC00900, MIR155 HG, and LINC00515 seem to be related with m\({}^{6}\)A. Another bioinformatic analysis [92] revealed that the expressions of WTAP, RBM15, YTHDF and ALBKH5 are positively, while FTO expression is negatively associated with WHO grades of glioma. Additionally, expression levels of ALKBH5, YTHDF2, RBM15, METTL3, METTL14, FTO and YTHDC1 in LGGs with or without mutant isocitrate dehydrogenase (IDH) are significantly different. Moreover, FTO, YTHDC1 and METTL3 are also differentially expressed between GBM with and without IDH mutation. Importantly, the expressions of gliomas malignant clinicopathological features-related m\({}^{6}\)A RNA methylation regulators are tightly correlated with pro-oncogenic genes in gliomas. These evidences imply that m\({}^{6}\)A modifications are tightly correlated with GBM progression. According to present studies in GBM, m\({}^{6}\)A modification seems to play pivotal roles since both m\({}^{6}\)A writers and erasers contribute to the tumorigenesis of GBM (Figure 1), especially glioma stem-like cells (GSCs).
# Estimation of Driving Direction of Traffic Accident Vehicles for Improving Traffic Safety
## 4 Experimental Results
### Dataset  
In this paper, we conducted a study to estimate the driving direction of traffic accident vehicles in Chungcheongnam-do. Despite various prevention activities such as traffic control, publicity campaigns, and facility improvement conducted by the police and related transportation safety agencies to reduce traffic fatalities, Chungcheongnam-do is one of the areas with the highest traffic accidents in South Korea [30]. Chungcheongnam-do is composed of a total of 15 \"si\" and \"gun\" areas. Table 2 shows the statistics of traffic accidents reported to the police as of 2021. When comparing the number of traffic accidents, Cheonan-si and Asan-si had the highest traffic accidents. In addition, Cheonan-si and Asan-si showed the highest Traffic Accident Rate per unit area (TAR). TAR is metric that represents the number of traffic accidents per unit area, is typically expressed as \"the number of traffic accidents/dimension\" and is usually measured as the number of traffic accidents per 1 km2. Despite the narrow dimension and a low number of reports, Gyveryong-si had a high TAR, indicating that it cannot be considered a traffic accident safety area.  
This paper collected 5181 traffic accident data items in Chungcheongnam-do from 2016 to 2018. First, we removed 418 duplicate, missing, and outlier data items in the data preprocessing stage. Second, in the NER process, we removed 802 data items in which the destination was not output. Finally, we excluded 368 data items that were not returned as destination point coordinates through geocoding from the experiment. As a result, we performed the analysis on a total of 3593 data items.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
###### Abstract  
Video surveillance is needed to control many activities performed in underwater environments. The use of wired media can be a problem since the material specially designed for underwater environments is very expensive. In order to transmit the images and videos wirelessly under water, three main technologies can be used: acoustic waves, which do not provide high bandwidth, optical signals, although the effect of light dispersion in water severely penalizes the transmitted signals and therefore, despite offering high transfer rates, the maximum distance is very small, and electromagnetic (EM) waves, which can provide enough bandwidth for video delivery. In the cases where the distance between transmitter and receiver is short, the use of EM waves would be an interesting option since they provide high enough data transfer rates to transmit videos with high resolution. This paper presents a practical study of the behavior of EM waves at 2.4 GHz in freshwater underwater environments. First, we discuss the minimum requirements of a network to allow video delivery. From these results, we measure the maximum distance between nodes and the round trip time (RTT) value depending on several parameters such as data transfer rate, signal modulations, working frequency, and water temperature. The results are statistically analyzed to determine their relation. Finally, the EM waves' behavior is modeled by a set of equations. The results show that there are some combinations of working frequency, modulation, transfer rate and temperature that offer better results than others. Our work shows that short communication distances with high data transfer rates is feasible.  
video transmission; underwater video surveillance; underwater communications; underwater wireless sensor network (UWSN); 2.4 GHz; freshwater; electromagnetic waves; modulations 2016
# Intelligent Decision Forest Models for Customer Churn Prediction
## 6 Conclusions and Future Works  
In this research work, DF models and their enhanced ensemble variants were developed for customer churn prediction. Specifically, LMT, FT, RF, Weighted Soft Voting Ensemble Decision Forest Method (WSVEDFM), and Weighted Stacking Ensemble Decision Forest Method (WSEDFM) were developed and tested on original (imbalanced) and balanced (SMOTE) telecommunication customer churn datasets. Experiments were conducted to examine the efficacy and applicability of the suggested DF models. Empirical results showed that the DF models outperformed base-line ML classifiers such as NB, kNN, and DS on the imbalanced and balanced Kaggle and UCI telecommunication customer churn datasets. This discovery validates the applicability of DF models for CCP. Furthermore, the enhanced ensemble versions of the DF models (WSDFM and WSVEDFM) beat Bagged and Boosted models on imbalanced and balanced datasets, indicating their usefulness in CCP. Furthermore, the suggested DF models (LMT, FT, RF, WSVEDFM, and WSDFM) outperformed the best models in the literature on Kaggle and UCI telecommunication customer churn datasets. As a result, this research recommends deploying suggested DF models for CCP.  
As a continuation of this research work, we intend to investigate the spotting and removal of outliers and extreme values, which could contribute to improved outcomes (CCP models). Also, the characteristics of projected customer churns were not explored in this research work, though they may be relevant to corporations deciding whether to retain certain churn customers. As a result, good churn clients may have a higher lifetime value. Nonetheless, we hope to address these critical concerns in future research work.  
**Author Contributions:** Conceptualization, F.E.U.-H., A.O.B. and L.F.C.; Data curation, A.O.B., S.A.S., R.T.A. and N.K.S.; Formal analysis, F.E.U.-H., A.O.B., S.A.S. and A.G.A.; Funding acquisition, L.F.C. and S.B.; Investigation, F.E.U.-H., A.O.B., H.A.M., A.G.A., S.M., R.T.A. and N.K.S.; Methodology, F.E.U.-H., A.O.B., S.B. and S.M.; Project administration, L.F.C., S.M. and S.B.; Resources, H.A.M., S.B., S.A.S., S.M. and A.G.A.; Software, A.O.B., H.A.M., S.M. and S.B.; Supervision, L.F.C., S.M. and S.B.; Validation, L.F.C., S.M. and S.B.; Visualization, F.E.U.-H., A.O.B., S.A.S., R.T.A. and N.K.S.; Writing--original draft, F.E.U.-H., A.O.B. and H.A.M.; Writing--review & editing, L.F.C., S.M. and S.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Available on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
## 3 Experimental Results and Analysis  
### Vibration Mode and Frequency Analysis for the Field Loading Test of Pingnan Xiangszhou Bridge  
The dynamic characteristics of Pingnan Xiangszhou Bridge were investigated according to the ambient vibration tests method introduced above. According to the finite element calculation results, the first four vertical bending vibration modes and the first transverse bending vibration mode of the main bridge of Pingnan Xiangszhou Bridge are shown in Figure 7, and the experimental results are compared with the three-dimensional finite element analysis results, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Eccentric loading of the maximum negative \\ moment (section E) of No. 3 span \\ \end{tabular} & \(40\),\(677.5\) & \(40\),\(040.8\) & \(0.984\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Field loading test arrangement and load efficiency in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vehicle** & **Wheelbase 1** & **Wheelbase 2** & **Mass of Front Axle** & **Mass of Rear Axle** & **Total Weight of Vehicle** \\
**No.** & **(m)** & **(m)** & **(kN)** & **(kN)** & **(kN)** \\ \hline \(1\#\)–\(24\#\) & \(3.80\) & \(1.35\) & \(70\) & \(300\) & \(370\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical parameters of vehicles in the field loading test.  
less than 0.01 Hz. In addition, it can be seen that the experimental vibration frequencies are slightly larger than the analytical numerical results of the finite element model, which may have been caused by systematic errors. Meanwhile, during the entire non-destructive field loading test for Pingnan Xiangsizhou Bridge, the vibration mode shape of vertical bending is easier to identify than the vibration mode shape of transverse bending.
## 3 Maritime Cognitive Radio Network and Channel Modeling
### Channel Model  
Unusual challenges arise for maritime wireless networks, due to variable channel statistics. The sea surface works as a reflector for the radio propagation, and as a result, the signal degrades completely along the path. In a terrestrial environment, there are obstacles of different sizes, which result in reflection, refraction and scattering of the signal in the communication channel. The path loss in the terrestrial environment is higher than in free space, and it was defined by [PERSON] [21] as:  
\[L_{T}(d)=L_{s}(d_{o})+10\;\alpha\;log(d/d_{o})+X_{f} \tag{1}\]  
where \(d_{o}\) is the distance of a reference location from the transmitter with measured path loss \(L_{s}(d_{o})\), \(d\) is the physical distance between the transmitter and receiver and \(\alpha\) is the path loss exponent for the radio environment and the Gaussian random contributor, \(X_{f}\), with zero mean and standard deviation, \(\sigma\), which represents fast fading effects. The accurate estimation of the path loss exponent is the major characterization of the communication channel. Usually, values of the path loss exponent range from one to four, depending on the physical terrain features.  
The path loss in the maritime environment increases with the sea state, and it increases rapidly in sea state 5 and above. According to [PERSON] _et al_. [22], the path loss in the maritime communication channel during shadowing is as follows:  
\[PL(h,f)=PL(d_{o})+10\times[(0.498 log_{10}(f)+0.793)\times h+2]\times log_{10}( d/d_{o})+X_{f} \tag{2}\]  
where \(f\) is the frequency in GHz, the observable sea height is \(h\) in meters, \(d\) is the physical distance between the transmitter and receiver in meters and \(PL(d_{o})\) is the path loss simulated at 1 m and the random variable, \(X_{f}\), with zero mean and standard deviation, \(\sigma_{f}\), which is also represented as a function of wave height:  
\[\sigma_{f}=[0.157f+0.405]*h. \tag{3}\]
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing  
The application of the DDPM in image processing has gained significant attention due to its ability to generate high-quality images, denoise noisy data, and serve as a powerful tool in various domains.
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 4 Multi-Frequency RTK Positioning Accuracy Analysis
### Ratio-Test  
The ratio-factor ratio is defined as the ratio of the weight sum of the squared residuals by the second-best solution to one by the best. Generally, the larger the ratio value, the higher the reliability of the ambiguity fixing solution. Therefore, the magnitude of the ratio value is also an important way to evaluate the positioning accuracy. Taking baselines TGCU-TGBU (30 km), TGCU-TGDA (96 km), and TGCU-TGD2 (128 km) on DOY218 as examples, the variation in ratio values for the five frequency sets is shown in Figures 9-11. At the short baseline RTK positioning mode, the ratio values are larger. BDS-3 has fewer visible satellites and poorer geometry distribution, resulting in a smaller ratio value for BDS-3 (B1C/B2a), BDS-3 (B1I/B3I) and BDS-3 (B1C/B2a/B3I) than for BDS-2+BDS-3 (B1I/B3I) and GPS (L1L2). When the baseline length increases, the increase in the atmospheric residual error reduces the accuracy of the floating-point solution, and the ratio value decreases. For the period where the ratio values are too small, it may be that there are several satellites with low elevations during that period. It is also found that the fact that the ionosphere is most active around 2:00 p.m. at the local time leads to low ratio values for the five frequencies.  
Figure 10: Ratio value variation of TGCU-TGDA (96 km).  
Figure 9: Ratio value variation of TGCU–TGBU (30 km).
## 4 Secure Cooperation  
The cooperative algorithms of the previous section critically relay on the trustworthiness of the transmitted information. This is achieved by means of two services, the Secure Dispatching Service (SDS) and the Key Management Service (KMS). The SDS is responsible for protecting confidentiality and authenticity of messages by encrypting and decrypting them as well as generating and verifying proofs of their authenticity. The KMS is responsible for revoking the current key and distributing a new one either periodically or upon a vehicle leaving.  
Implementing these services in a underwater acoustic network is challenging due to the severe limitations of the networking environment in terms of very high message propagation delay, very low bandwidth, and high energy consumption for communication. Limitation in the message size is hence of paramount importance in order to reduce battery consumption in autonomous nodes.
## 2 Systems Overview
### Characteristics of the Implantable Devices  
There are several characteristics that are shared by most biomedical implantable devices as follows:  
1. _Low Power Consumption_: low power consumption is the main requirement for medical implant devices where the large dissipation in power increases the possibility to damage the soft tissues in the human body. Changing or charging batteries can be inconvenient, difficult, costly and even risky for the patient; all implantable medical devices need to consume as little energy as possible.
2. _High Reliability:_ A failure of an implantable medical device can result on inconvenience, pain, damage or even death for the patient. Maintenance is also costly and risky. Thus, reliability for the implanted devices must be very high.
3. _Low Voltage Signals_: Most of the natural signals are inside the human bodies as well as the output of the transducers are in the \(V\) or _mV_ range, which requires special care in sensing and amplifying.
4. _Low Frequencies:_ The natural frequency span of biological signals varies from a fraction of a hertz to several kilohertz, However, most of the implantable devices are powered by low-frequency (\(<\)1 MHz signal) magnetically coupled coils (often modulated to include the data telemetry). Nowadays, the designers design the bio-implanted devices to operate in the medical implant communications service (MICS) band.
5. _Small Size:_ Implantable devices need to be as small as possible, so as to be less invasive to the human body. This does not always means that the silicon area should be as small as possible, because increasing the silicon area to minimize external components can reduce overall size. In addition the use of some methods such as auto-zeroing techniques, FPGA methods, and artificial intelligence methods can help minimize the overall size.  
Figure 2: Communication technologies for wireless telemetry.