esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 2 Tunnel Lighting Controls: Strategies and Technologies
### Intelligent Control Strategy  
The intelligent control strategy uses artificial intelligence algorithms (e.g., fuzzy control algorithm, neural network) to match luminance to drivers' needs under different environment stages and traffic levels while minimizing energy consumption. The lamps' lighting intensity in this control strategy is dimmed taken into account actual current demand, such as weather conditions, traffic volume, vehicle speed, and vehicle presence/absence. This control scheme is shown in Figure 2.  
Vehicle detectors employ motion-sensing technology to identify the presence of a vehicle and vehicle speed in a given range of space. The technology of sensors can be of different types and costs. Infrared, microwave, ultrasonic, and other types are currently in use and each has its pros and cons. Despite the difference in the technology used to detect the vehicles, the tunnel lighting system's operation's underlying algorithm is very similar.  
The above brief discussions indicate that all control strategies require several sensors except for the manual control strategy. Savings and consumption reports can vary based on which type of control strategy has been implemented. In the next section, data of tunnel lighting for eleven tunnels in China are used to compare the hardware cost and energy  
Figure 2: Time scheduling, daylight adaptation, and intelligent control schemes.  
savings produced by four common control strategies. The tunnel lighting for all 11 tunnels is based on a SCADA system.
## 4 Experiments
### Datasets and Experimental Settings  
In order to make a comprehensive comparison with other baseline models and the most advanced models, we conducted experiments on five datasets. Among them, Twitter is composed of Twitter posts collected by [PERSON] et al. [2], the other four (Lap14, Rest14, Rest15, Rest16) are, respectively, from SemEval 2014 task 4 [35], SemEval 2015 task 12 [36], SemEval 2016 Task 5 [37]. Among them, SemEval 2014 task 4 contains two datasets, Lap14 and Rest14. The detailed statistical results of each dataset are shown in Table 1.  
In the experiment, we used two different input methods. In AEGCN-GloVe, we use a 300-dimensional pre-trained GloVe vector as a static embedding, and the vector dimension of the hidden state is also set to 300. In AEGCN-BERT, we use pre-trained BERT as the embedding layer and fine-tune it on our task. Both the embedding dimension and the hidden state dimension are 768. All weight parameters in the model (except BERT) are initialized with uniform distribution. We use Adam optimizer [38] in our model, using different learning rates for Glove static embedding and BERT embedding: 1 \(\times\) 10\({}^{-3}\) and 3 \(\times\) 10\({}^{-5}\). The coefficient of the \(L_{2}\) regularization term is 1 \(\times\) 10\({}^{-5}\). The dropout rate and batch size are 0.1 and 64, respectively. In addition, according to the optimal experimental results of the model, the number of AEGCN layers is set to 2. We use Accuracy and Macro-F1 indicators as the criteria for evaluating model performance. The experimental results are the average of three random initialization runs.
## 4 Discussion
### Quantitative Analysis of Heat Transfer  
The high-temperature area can reflect the maximum thermal invasion of the ablation antenna in the tissue. To explore the location and temperature of the high-temperature region in biological tissue near the ablation antenna, multiple thermometric points were obtained at d = 1.2 mm parallel to ablation antenna. So, the highest temperature of the ablation antenna is found. Then, at the thermometric point of the highest temperature, new multiple thermometric points were taken from the center to the boundary of biological tissue. Temperature gradient changes were analyzed (Figure 5). The specific operation is to take the selected highest temperature point position along the radius of ablation antenna (d = 1.2 mm) from z = 10.0 mm to z = 20.0 mm. They are separated by a 0.5 mm interval, and the recording time is from 0 s to 120 s. When heated for 0-60 s, the muscle temperature spreads faster. The curve of 10 mm has a significant increase, while fat is relatively flat and thermal diffusion performance is poor.  
After 60 s, the heating is stopped. The closer muscle is to the ablation antenna, the more significant the cooling. Positions far away from ablation antenna (d \(\geq\) 7.0 mm) can still receive transferred heat and heat up. The highest temperature of fat is much lower than that of muscle. After the heating is stopped, thermometric points larger than d = 4.0 mm have a noticeable temperature drop hysteresis. Bone is special; the same heating method for fat and muscle produces a high-temperature area higher than 300 \({}^{\circ}\)C in bone. To observe the heat transfer of bone, a temperature cloud map is extracted during heating for 4 s, and the highest temperature has reached 100 \({}^{\circ}\)C. Due to the high temperature and fast heating, MWA in bone requires more caution. When recording is stopped, the lowest temperature in all positions is above 75 \({}^{\circ}\)C. After the temperature exceeds 50 \({}^{\circ}\)C, irreversible damage may occur to the bone, and some areas that have not reached necrotic temperature will be further heated after heating is stopped. Therefore, large areas of bone necrosis will still occur after heating is stopped.  
Figure 4: Temperature cloud map near ablation antenna.
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
## 3 Conclusions  
This study aimed to establish a pressure variation model (PVM) for the state estimation of the AUV based on dynamic pressure data obtained through numerical analysis. The study yielded the following results.  
1. The dynamic pressure characteristics were analyzed by numerical simulations for straight, turning, and gliding motions under speed, angular velocity, and angle of attack conditions.
2. The coefficients for the PVM were derived by performing regression analysis on the dynamic pressure obtained from numerical simulations, considering the coefficient of determination and the MAE for evaluation.  
Figure 12: Pressure on the array of sensors during spiral motion.  
3. The state estimation algorithm was presented by combining the regression equations of the pressure sensors array to form an inverse matrix. Furthermore, it was validated for single and multiple motions, confirming the targeted prediction accuracy within 15%.  
In this research process, it was confirmed that as the velocity of the AUV increases, the pressure difference becomes more pronounced, enhancing the accuracy of predicting both the speed and drift angle. Therefore, it is anticipated that the accuracy of estimating the state of the AUV will be higher when the size and speed of AUVs are larger than the present target. However, due to the concern about nonlinearity, such as the correlation between angular velocity and drift angle, potentially leading to a decrease in prediction accuracy, plans are in progress to further improve the PVM to address this issue. Additionally, in future research, we plan to validate the PVM based not only on pressure data obtained through the numerical analysis but also using pressure data acquired through experiments.  
Conceptualization, methodology and writing, J.-H.K. and H.K.Y.; CFD calculation, T.L.M.; data analysis, A.C. and N.H.; resources, J.-Y.P. and S.-H.B. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the Endowment Project of \"Development of smart sensor technology for underwater environment monitoring\", funded by the Korea Research Institute of Ships and Ocean Engineering (PES4400) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C1093055).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 2 Related Knowledge  
### Acoustic Communication  
Due to the underwater characteristics, both RF and optical signal are not suitable for UWSNs. Fortunately, the acoustic signal is suitable for estimating the position information in a marine environment. Usually, in UWSNs, the absorption of acoustic signal frequency is less than three orders of magnitude, which is acceptable [14]. Acoustic communication with omnidirectional transmission and a distribution channel seem good to use in UWSNs providing the low cost, ad hoc nature, and densely deployed application. However, acoustic communication has many challenges. In [15], the authors conclude these characteristics of the challenges:  
* High propagation delay: The RF wave speed in the air is faster than that of acoustic waves underwater, which leads to the severe propagation delays [16].
* High energy consumption: The transmission power of UWSNs is superior to that of the terrestrial. In addition, the continuous movement of the underwater node may increase the energy.
* High noise and interference: The transportation and the movement of water currents can cause the noise during the acoustic communication. Meanwhile, various factors such as the animals, reflections resulting from the surface or bottom can bring interference.
* High dynamic topology: Because of the water current, the sensor nodes are not static, which means that the network topology is not fixed and changes with time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Taxonomy Method** & **Reference Number** & **The Newest Reference** & **Standard of Comparison** & **The Related Knowledge** \\ \hline \multirow{4}{*}{Ours} & Computation algorithm, & & & & \\  & Spatial coverage, & & & & \\  & Range measurement, & & & & \\  & The state of the nodes, & & & & \\  & Communication between nodes & & & & \\ \hline \multirow{2}{*}{[4]} & Centralized techniques, & & & & \\  & Distributed techniques & & & & \\ \hline \multirow{2}{*}{[10]} & Centralized localization techniques, & & & & \\  & Distributed localization techniques & & & & \\ \hline \multirow{2}{*}{[11]} & Centralized localization techniques, & & & & \\  & Distributed localization techniques & & & & \\ \hline \multirow{2}{*}{[12]} & Ranging algorithms in UASNs, & & & & \\  & UASN architecture & & & & \\  & and localization schemes & & & & \\ \hline \multirow{2}{*}{[13]} & Stationary localization algorithms, & & & & \\  & Mobile localization algorithms, & & & & \\  & Hybrid localization algorithms & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of survey papers for the localization algorithms.
## 5 Methodology
### Design of Privacy-Preserving, Patient-Centric Health Record Access Control
#### 5.4.1 Definitions  
**Definition 1** (create Patient health record).: _A patient health record, PR, can be created by the hospital, HS, to ensure the integrity and correctness of the data and the eligibility for treatment._  
**Definition 2** (update Patient health record).: _A patient health record PR can be updated by the hospital HS to update the data or discover any errors in the registration._  
**Definition 3** (choose Patient appointment).: _The patient will choose the appointment AP, with the desired doctor \(D\) at the specified date, DA, at time \(T\)._  
**Definition 4** (set access policy).: _The patient will permit the specified doctor \(D\) to access the health record during the pre-set time \(T\) and for a specified number of access times \(N\)._  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Definition** & **Symbol** \\ \hline Patient health record & PR \\ \hline Hospital & HS \\ \hline Appointment & AP \\ \hline Doctor & D \\ \hline Date & DA \\ \hline Time & T \\ \hline Blockchain & B \\ \hline Number of access times & N \\ \hline Healthcare personnel & HP \\ \hline Family member & FM \\ \hline Emergency doctor & ED \\ \hline \hline \end{tabular}
\end{table}
Table 5: Definitions and symbols used.  
**Definition 5** (Blockchain access).: _The blockchain admin \(B\) will store the health record in the clinic of doctor \(D\) in time T after the permission of patient P._  
**Definition 6** (Delegate access).: _The doctor \(D\) can delegate access to other healthcare personnel \(HP\) if tests or other requests are needed._  
**Definition 7** (Emergency case).: _A family member \(FM\) will permit emergency doctor [PERSON] in time T for a specified number of access N if the patient is in an emergency or has an accident. If all family members suffer from the same accident, permission will be given directly to the emergency doctor ED by the blockchain admin \(B\) at the specified time T to save the patient's life._
# Reaction Forces and Apparent Thrust in Dual Oscillating Control Moment Gyroscopes
## 1 Introduction
Beyond space applications, experimental terrestrial set-ups have also been developed, such as those aimed at stabilizing walking robots [48]. Early research explored the possibility that gyroscopes could generate net thrust [49], but later efforts focused on utilizingthe angular momentum of flywheel gyroscopes for purposes such as vibration isolation in vehicles [50] and stabilization in two-wheel self-balancing robots [51]. Other terrestrial applications, including rover mobility, are discussed in [52; 53; 54; 55; 56]. It is important to note that the mechanical analyses in the abovementioned literature are primarily limited to internal torque effects, with little or no discussion of the externally induced inertial forces. In contrast, the core focus of the present paper lies in the analysis of the net force generated by such systems. As stated earlier, the objective of this paper is to disprove the theory that control moment gyroscopes (CMGs) can generate propulsive forces and offer a consistent explanation--grounded in Newtonian mechanics--for the observed reinforcement of one of the support reactions (specifically the front support, as mentioned in [20; 21; 22; 23; 24; 25]) of a cart-mounted CMG. While this asymmetric support reaction has been experimentally observed, a similar effect involving the entire cart (i.e., both the front and rear ends) has not been reported. If such a result were demonstrated, then it could dispel any remaining doubts regarding a CMG's potential to induce inertial propulsion and generate motion of the system's center of gravity. However, since such an experiment has not yet been conducted, the present paper provides a theoretical demonstration that no net overall thrust can be justified within the framework of [PERSON]'s laws.
## 4 Discussion  
### Effect of UV Radiation on Plant Morphology and Pigment Content  
It is known that basil varieties with purple leaves are more sensitive to UV-A radiation, which is reflected in the decrease of leaf area and plant height [43]. However, in our experiments, UV-A irradiation had a stimulating effect: on the 60 th day, the height, the leaf surface area and the fresh and dry mass of the \"Red Ruby\" variety basil plants significantly increased. This can be explained by the fact that at low PPFD, UV-A can be used as an additional source of light energy to intensify the photosynthesis [47; 48]. There are differences in the UV-A susceptibility of different plant species. UV-A had a positive effect on the fresh mass and the number of lettuce shoots and had practically no effect on the dry mass [49]. The increase in height and plant mass may be associated with the higher photosynthetic activity of UV-A radiation compared with UV-B and UV-C. It has been found that soybean plant height increases when UV-A irradiation is screened in a glass greenhouse [50], while UV-A does not affect the leaf area and plant height of peppers and eggplants and decreased the length of internodes [51]. The combined illumination with UV-A and UV-B also positively influenced the height, leaf area and fresh and dry sweet basil plant mass at the first stage of cultivation (30 days). It is known that small doses of UV-B irradiation (2-4 kJ m\({}^{-2}\) day\({}^{-1}\)) lead to an increase in leaf surface area, fresh and dry mass and the total content of phenolic compounds of sweet basil [52]. It was previously established that a seven-day illumination of basil with UV-B increases plant height, photosynthetic leaf area and fresh and dry biomass [43]. In general, our data are consistent with previous studies. Unlike UV-A and UV-B radiation, the UV-C strongly suppresses plant growth [49], which is also confirmed by our experiment (Table 2).
## 4 Discussion
### Effect of Sensor Distance from the Source  
It is widely conceived that the more sensors installed, the more information acquired, hence this leads to the higher accuracy in source localization. This is only valid when any of the acquired information is not false. By contrast, even though many sensors are installed, if one of them provides unclear information, it would interrupt data interpretation. Therefore, not only the information quantity but also the information quality is important. In real-time AE monitoring, filtering unclear information is as important as gathering more information from sensors.  
Meanwhile, AE signals attenuate fairly fast with increasing propagation distance due to the high-frequency characteristics. As an AE signal propagates through the model PRF, the signal becomes smoothed and its frequency range broadened evidently due to the various factors such as the frequency-dependent nature of scattering, geometric spreading, internal friction, mode conversion, and intrinsic attenuation in high-frequency content [8]. Additionally, the amplitude of a signal decreases rapidly, which results in a decrease in the signal-to-noise ratio (SNR). When the source location is distant from the AE sensor, the captured AE signal often shows low SNR, which prevents accurate determination of onset time. Expectedly, the source localization results for the PM cases in the corner zones show the high average EDE, compared to the central zones or the RT and RB cases, despite the use of the two-stage AIC method (Table 1). As an example, Figure 11 shows the onset times determined in all the accelerometers for the PM cases in Zone 2. In the two closest accelerometers ACC1 and ACC5, there was no difference in the onset times determined by single- and two-stage AIC pickers due to the large SNR. In the other accelerometers, there was the onset time difference in association with low SNR, owing to the long travel distance. There were some deviations between actual and estimated onset times in ACC3 and ACC4, which were the farthest accelerometers from the AE source. Therefore, the blind use of such inaccurate information can degrade the source localization.  
We examined if the exclusion of the onset times captured at the farthest two accelerometers would improve the accuracy in source localization. Herein, we computed the accuracy values while reducing the number of sensors from six to four. The accelerometers excluded changed, taking into account the source-to-sensor distance. For example, ACC 3 and ACC 4 were excluded for Zones 2 and 7. ACC 4 and ACC 5 were excluded for Zone 14. As a result, the accuracy increased when one or two farthest sensors were excluded for PM corner cases, as shown in Figure 12. However, the accuracy decreased when the sensors were excluded in most cases of RT and RB, which showed higher SNR when compared to the PM cases. This exercise confirms that filtering unclear information, particularly the signals captured at a far distance and with low SNR, can enhance the source localization accuracy.
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
The data collected with the IRX3 aberrometer were exported to an Excel database (Microsoft(r) Office Excel 2021, Washington, DC, USA). The aberrometric data were obtained on the corneal plane and at the maximum pupil diameter. Since the number of aberrations depends on the pupil diameter, a rescaling process [28; 29] up to the 6 th order of aberration for a 4.00 mm diameter pupil was performed via a programming code written in MATLAB version R2022b (MathWorks Inc., Natick, MA, USA). Once the Zernike coefficients were rescaled for a 4.00 mm pupil, the corresponding objective refraction was calculated using the spherical equivalent (M), J0, and J45 of each subject under each lighting condition [30]. Finally, all aberrometric measurements were treated as left eyes (OS), transforming the measurements from right eyes (OD) to OS using the ISO 24157:2008 standard [31].  
The variables under study were analyzed via the Statistical Package for the Social Sciences (SPSS 26.0, SPSS Inc., New York, NY, USA). First, a descriptive statistical analysis of the sample was conducted for each of the three computerized tests and the aberrometric measurements under each ambient lighting condition. This included calculating the mean, standard deviation, and minimum and maximum values for each variable. The sample variables did not follow a normal distribution on the basis of the Kolmogorov-Smirnov test, likely due to the small sample size and data skewness. Therefore, the different variables from the designed tests and the IRX3 aberrometer across different lighting conditions were compared pairwise via the nonparametric Wilcoxon signed-rank test for related samples. A \(p\) value \(\leq\) 0.05 was considered statistically significant in all of the cases.
# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
## 5 Conclusions  
In the current study, we report the performance of a data-driven ROM based on a deep convolutional neural network for real-time prediction of the spatial-temporal drug trajectory and concentration field in blood vessels. The model is trained and validated using the drug delivery datasets at different injection locations and the performance of the neural network model is further tested with cases that have never been seen by the network model. The average prediction accuracy in straight and bifurcated blood vessels reaches 97.1% and 99.4%, respectively. Applying the two convolutional paths to encode the features of vessel geometry and injection location, for the most complex problem, the hepatic artery system, the accuracy reaches 99.9%.  
Although the proposed model requires about 35 min to train, the prediction time for each concentration field is less than 0.07 s, which is more than 4 orders of magnitude faster than that of numerical (CFD) simulations. The current model is hoped to provide a useful clinical tool to facilitate transarterial drug injection treatment of liver cancer patients, in real time, while providing accurate prediction of the drug trajectory and concentration field. This tool should assist physicians to direct the drugs to specific tumor-bearing segments, thus, improving the efficacy of treatment. In addition, the proposed method shows great potential for use in clinical interventional treatment of other diseases.  
Conceptualization, Z.-F.Z. and J.-Z.P.; methodology, X.-Y.Y.; formal analysis, Y.H.; writing--original draft preparation, X.-Y.Y. and Y.H.; writing--review and editing, Y.H., N.A., M.Z. and J.F.A. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Jiangsu Province grant number BK20201302 and NIH/NHLBI grant number R01 HL089456. The authors declare no conflict of interest.