esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time HIFU Treatment Monitoring Using Pulse Inversion Ultrasonic Imaging
## 4 Discussion
If a target moving distance of 10 \(\upmu\)m is acceptable for construction of ultrasound monitoring images, the proposed method can be used to monitor HIFU surgery for the organs of which maximum moving velocity is up to 7.7 cm/s (i.e., 10/\(T_{PRI}\)) when HIFU has a center frequency of 1.1 MHz and \(T_{PRI}\) is 130 \(\upmu\)s.  
In addition to the target motion, the cavitation induced by HIFU may make it possible that the difference between the HIFU interference signals obtained in the successive transmission and reception events becomes large and thus the elimination efficiency may be considerably degraded, although this was not observed in this study (see Figure 5 and video clips in the Supplementary Materials). The cavitation may lead to an increase in magnitude of the fundamental and harmonic frequencies of the HIFU interference remaining after the inference elimination by the proposed method. In this case, a few notch filters can be used to further remove the remaining HIFU interference [14] because its fundamental and harmonic frequencies are known and their spectral bandwidths are typically narrow. Figure 10: Ultrasound B-mode images of the tissue-mimicking phantom in the cases of (**a**) no HIFU exposure, (**b**) HIFU exposure but no target motion, (**c,d**) HIFU exposure after moving the target by (**c**) 10 \(\upmu\)m and (**d**) 50 \(\upmu\)m.  
One requirement for the proposed method is simultaneous transmission of HIFU for therapy and ultrasound for monitoring. For this reason, \(T_{PRI}\) determines the maximum pulse length of HIFU as well as the frame rate of monitoring imaging; the duty cycle defined as the ratio of the HIFU pulse length over \(T_{RPI}\) serves as one of the HIFU operation parameters. Therefore, the frame rate inevitably decreases if HIFU pulse is elongated to increase acoustic energy. Furthermore, the proposed method cannot be directly used for continuous-wave HIFU treatment because the essential requirement cannot be met. As a remedy, HIFU with a high duty cycle such as 99% can be used instead of continuous-wave HIFU without reducing the frame rate [17]. This long HIFU wave causes a portion of the HIFU interference generated by the current HIFU transmission to be received at the next transmission and reception event. This phenomenon can also occur due to the reverberation of HIFU. The previously proposed method [16] in which two HIFU pulses with phases \(0^{\circ}\) and \(180^{\circ}\) are transmitted had the same problem. To overcome this problem, a new pulse sequence in which three HIFU pulses with phases of \(0^{\circ}\), \(180^{\circ}\), and \(0^{\circ}\) were subsequently transmitted and received for one scanline has been proposed, and we have shown that the pulse sequence can efficiently remove HIFU interference even at a high duty cycle such as 90% [17]. This pulse sequence can be also employed for the method proposed here to solve the same problem.
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 4 Experimental Results
### Experimental Metrics  
We employ four metrics to evaluate the model's predictions of the number of accurate and inaccurate outcomes. These metrics are as follows: (1) True Positives (TPs), which represent the number of correctly classified benign samples; (2) False Positives (FPs), which represent the number of attack samples that are incorrectly predicted to be benign; (3) True Negatives (TNs), which represent the correct number of classified attack samples; and (4) False Negatives (FNs), indicating the number of benign samples that are incorrectly predicted as attacks. These four metrics produce four evaluation metrics: accuracy, precision, recall, and F1-Score. Accuracy measures the proportion of correctly classified samples. Precision measures the accuracy of predicting benign samples, while recall measures the accuracy of identifying benign samples. The F1-Score is an indicator of the classification model's performance and is the harmonic mean of precision and recall. The formulas for these metrics are summarized below:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\[Precision=\frac{TP}{TP+FP} \tag{15}\]  
\[Recall=\frac{TP}{TP+FN} \tag{16}\]  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{17}\]
## 2 Methods
### Case Studies  
Once the characteristics of the reference building were defined, the simulation model and the recycled materials were selected, a series of case studies are set in which some traditional materials are replaced by recycled materials. The objective is to evaluate the potential of recycled materials to improve the energy efficiency of buildings. The overall energy behavior of the building will largely depend on the thermal properties of the materials of its envelope. Table 5 summarizes the recycled materials that will be incorporated  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \
\begin{tabular}{c} **Year,** \\ **Reference** \\ \end{tabular} & **Waste** & **Binder** & **Application** &
\begin{tabular}{c} **Thermal Conductivity** \\ **[w/mK]** \\ \end{tabular} \\ \hline
2018, [50] & \
\begin{tabular}{c} Sugarcane bagasse ash (SBA) \\ Rice husk ash (RHA) \\ \end{tabular} & Clay & Bricks &
\begin{tabular}{c} 0.35 (15\% SHA) \\ 0.37 (15\% RHA) \\ \end{tabular} \\
2019, [54] &
\begin{tabular}{c} Expanded polystyrene (EPS) \\ Extruded Polystyrene (XPS) \\ Ceramics (CER) \\ Expanded polystyrene (EPS) \\ \end{tabular} & Gypsum & Plasterboard & 0.15 (2\% EPS + 3\% XPS) \\
2020, [58] &
\begin{tabular}{c} Creamics (CER) \\ Expanded polystyrene (EPS) \\ \end{tabular} & Gypsum & Blocks & 0.28 (75\% CER + 2/3 EPS) \\
2021, [59] & \begin{tabular}{c} Granular c c \\ Granular c \\ \end{tabular} & Cement & \begin{tabular}{c} Non-load carrying \\ elements and \\ insulation elements \\ \end{tabular} & \
\begin{tabular}{c} 0.38 (100\% waste, m\({}^{3}\)) \\ 0.15 (300\% waste, m\({}^{3}\) + 3\% \\ slag) \\ \end{tabular} \\ Own testing & Extruded Polystyrene (XPS) &
\begin{tabular}{c} Cement and \\ expanded clay (EC) \\ \end{tabular} & Slope mortar & 0.04 (50\% EC + 50\% XPS) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recycled materials selected for the simulations phase.  
into the facade and roof of the building, as well as the traditional material for which it is substituted. In addition, the thermal resistance values used in the simulation models are included. The thicknesses used are the same as those described in Tables 2 and 4.  
In total, eight simulations were carried out. The four simulations carried out in each of the mentioned locations (Seville and Soria) correspond to the following scenarios:  
* CASE 1 (reference case). Roof and facade with traditional materials (Tables 2 and 3);
* CASE 2. Roof with traditional materials (Table 3) and facade incorporating recycled materials (Table 5);
* CASE 3. Roof that incorporates recycled materials (Table 5) and facade with traditional materials (Table 2);
* CASE 4. Roof and facade incorporating recycled materials (Table 5).
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 3 Counter-UAS Sensors Modeling
### Active Radar  
Two different types of active radars have been modeled, quasi-monostatic radars and MIMO radars.
## 2 Beidou-Binocular Cooperative Navigation Model  
In order to address the issues of interference and signal blockage in the Beidou signal within the complex environment of substations, this paper proposes a Beidou-Binocular Cooperative Navigation Model (BBCN). The model fully utilizes the high-precision global positioning information provided by the Beidou satellite system, as well as the local environmental perception capability offered by the binocular vision system. Through multi-sensor data fusion and path optimization algorithms, a stable and robust autonomous navigation and obstacle avoidance method is established. Specifically, the BBCN model consists of three core modules: the Beidou navigation positioning module, the binocular vision recognition and perception module, and the intelligent collaborative fusion and path planning module.
## 2 Materials and Methods
### Photocatalytic Experiments  
The methylene blue (MB) photodegradation was captured under UV light based on the absorption spectroscopic technique. For this analysis, 0.1 mmole of the MB dye wasdispersed in 500 mL of the aqueous solution with the addition of 15 mg of HfO\({}_{2}\)-graphene composite. Before illumination, the prepared solution was homogenized under stirring for 30 min in a dark room to achieve adsorption-desorption equilibrium between the catalyst and dye. We used a 4 W UV lamp (Philips, TUV4 W/G4 T5) with 365 nm wavelength for the irradiation experiment. Later, we collected 3 mL of the sample with a particular time interval of UV illumination. Then, the sample was centrifuged at 3000 rpm for 5 min. The final sample absorption spectra were measured using a UV-Vis spectrophotometer.  
For the tetracycline experiment, a 250 mL glass reactor was filled with 20 ppm aqueous tetracycline solution mixed with 15 mg of HfO\({}_{2}\)-graphene (50 wt%) as a photocatalyst. Subsequently, the reaction mixture was stirred for 2 h in the dark to reach a complete equilibrium between adsorption and desorption. The solutions were then exposed to UV light irradiation while being stirred continuously over 180 min. At 30-min intervals, 3 mL of the suspension was taken to measure the degradation of tetracycline. Before examining UV-visible absorption, the suspension of the tetracycline solution was centrifuged to remove the catalyst. A control experiment without the photocatalyst was also conducted.
# Experimental and Numerical Predictions of Cryogenic Leakages in Welded Steel Plates
## 3 Numerical Preparations
### Thermal Analyses  
The whole heat transfer process including welding, cooling by the air, and chilling by LN\({}_{2}\), based on [PERSON]'s law for transient heat transfer analysis, is illustrated as follows [33]:  
\[\left\{\frac{\partial}{\partial x}\bigg{(}K(T)\frac{\partial T}{\partial x} \bigg{)}+\frac{\partial}{\partial y}\bigg{(}K(T)\frac{\partial T}{\partial y} \bigg{)}+\frac{\partial}{\partial z}\bigg{(}K(T)\frac{\partial T}{\partial z} \bigg{)}\right\}+Q_{v}\ (t,T)=\rho(T)c(T)\frac{\partial T}{\partial t} \tag{5}\]  
where \(x\), \(y\), and \(z\) denote the Cartesian coordinates. \(K(T)\) is thermal conductivity; \(c(T)\) is the specific heat. \(T\) is the current temperature. \(\rho(T)\) is the density of the based metal. In addition, the internal heat generation rate is \(Q_{v}\). The simulated model shows the transient heat transfer. An initial condition is prescribed at time \(t=10^{-5}\) s, which is set as the first ambient temperature. A specified initial temperature, which is also the ambient temperature on the entire welding plate, is given by  
\[T\Big{(}x,y,z,10^{-5}\Big{)}=T_{\infty} \tag{6}\]  
where \(T_{\infty}\) is the temperature of ambient, and it is set to 25 \({}^{\circ}\)C in this study. Owing to the influence of phase transformation on welding deformation, the welding residual stress is inconsequential in low-carbon steel. The transformation of the solid-state phase is neglected in the welding simulation progress [34]. During the welding process, the heat losses to the ambient on all surfaces of the specimen are attributed to convection and radiation. Heat losses due to convection are considered for the whole surface based on [PERSON]'s law:  
\[Q_{conv}=h_{conv}(T-T_{\infty}) \tag{7}\]  
where \(h_{conv}\) is the convection heat transfer coefficient, which was approximated to be 25 W/(m\({}^{2}\)\({}^{\circ}\)C\({}^{4}\)). Radiation losses are accounted for on all the surfaces using the Stefan-Boltzmann law:  
\[Q_{rad}=\varepsilon\sigma\Big{(}T^{4}-T_{\infty}^{4}\Big{)} \tag{8}\]  
where \(\varepsilon\) is the emissivity of the steel plate, which was set to 0.9. The Stefan-Boltzmann constant \(\sigma\) is approximately \(5.675\times 10^{-8}\) W/(m\({}^{2}\)\({}^{\circ}\)C\({}^{4}\)). The absolute zero temperature is \(-273.5\)\({}^{\circ}\)C. For the chilling process, the convective heat transfer between the surface of the steel in the trough and LN\({}_{2}\) is presented by  
\[Q_{conv}=h(T_{\infty}-T_{n}) \tag{9}\]  
where \(h\) is the convective heat transfer coefficient of LN\({}_{2}\). \(T_{n}=-196\)\({}^{\circ}\)C expresses the temperature of LN\({}_{2}\) as a coolant.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 6 Simulation Results
### Conventional WLS State Estimator  
The voltage estimation results of the conventional WLS state estimator are shown in Figure 5. Based on the simulation results, this method initially gave an accurate estimation of the voltage on bus 5 in Figure 5e, bus 6 in Figure 5f, and bus 8 in Figure 5h, as those buses were the voltage measurement point used as the inputs of the conventional WLS state estimator. However, because of the low number of measurements close to the load buses, even with the presence of power injection as a pseudo-measurement, the accuracy gradually became worse as the distance between the estimated bus and the voltage measurement point increased. This is especially obvious in the estimation of bus 1 in Figure 5a, bus 2 in Figure 5b, and bus 3 in Figure 5c, as they were the furthest buses from the measurement points. In those three buses, although the estimation fitted well at the start of the data, from hour 9 onwards the DC voltage estimation deviated in the range of 0.15-0.75 V from the true simulated voltage value. This phenomenon did not discernibly occur on the voltage measurement point buses (Figure 5e,f,h). The addition of pseudo-measurement of those buses did not increase the accuracy of the estimation, as pseudo-measurements have high error variance. The estimation of the remainder of the buses also did not follow the true simulated voltage value pattern.  
Figure 5: Cont.
# 5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
## 4 Simulation Results
### BER Performance Analysis for maxlogBCJR
Figure 6: Mean percentage BER improvement when the velocity (traffic model) changes for maxlog-BCJR and for 128-, 256- and 256-bit frames. (**a**) BER improvement for velocity change from 50 to 100 Km/h having NLOSv channel state. (**b**) BER improvement for velocity change from 50 to 100 Km/h having LOS channel state. Figure 7: Mean percentage BER improvement when the channel state changes for maxlogBCJR and for 128-, 256- and 256-bit frames. (**a**) BER improvement for channel state change from to NLOSv to LOS in a highway traffic model. (**b**) BER improvement for channel state change from to NLOSv to LOS in an urban traffic model. Of course, the impact of changing the channel state on BER efficiency is greater on the highway traffic model (Figure 7a), due to the presence of a strong LOS component. As a result, BER in the highway model is 4- to 10-times better than in the urban traffic model. Figure 7b,c show that NLOS and NLOSv have similar BER behaviour, since BER improves similarly, on average, when the channel state changes to LOS in urban areas. Another point to note is that the mean BER improvement is rapid when the channel state changes from NLOSv to LOS in the highway case (Figure 7a), and this acceleration largely depends on the frame length. Contrariwise, in the urban case (Figure 7b,c), the improvement is large but does not depend greatly on the frame length, due to the existence of the error floor. This error floor is more intense in the urban than in the traffic model affecting the obstacles that affect the communication quality and the BER performance more.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 7: Sea State from SAR and Instrumentation Located Onboard the Vessel
The idea is to use the radar imaging systems mounted on the micro-satellites constellation, which are to be integrated with the GNSS and the instrumentation on board, in order to provide an integrated ship monitoring system, which can be considered as a mobile laboratory capable of carrying out in situ measurements for the estimation of ship motions and, subsequently, to derive the characteristics of the incident wave (e.g., [270; 279; 281; 282]).
## 2 Materials and Methods
### Implementation  
The system was implemented in the TI solar microinverter (Figure 17), which has an active clamp flyback converter, a dual-buck inverter, an F28035 Piccolo controlCARD development board, and conditioning circuits for signal reading. Figure 18 shows the sensed signals and the PWM output necessary to control the microinverter.  
The F28035 Piccolo card uses the TMS320F28035 microcontroller from the C2000 line of TI. This card is optimized for processing, sensing, and actuation, thus improving  
Figure 17: TI solar microinverter.  
Figure 18: Input and output diagram of the F28035 Piccolo controlCARD used to control the DC-DC and DC-AC stages of the microinverter.  
the performance of the control loops for real-time applications, such as industrial motor controllers, solar inverters, electric vehicles, and signal processing. The programming of this card is divided into tasks. In addition, it makes use of parallel processing with a central processing unit (CPU) and the secondary core control law accelerator (CLA), which is a floating-point mathematical accelerator of 32 bits. Figure 19 shows the program initialization process and the creation of tasks that are executed in the CPU and CLA.  
One of the essential implementation tasks is protection (Figure 20). This task turns off the microinverter when an overvoltage or overcurrent is presented in any of its sections. This task runs at a frequency of 1 kHz on the main CPU.  
When controlling active and reactive power, it is necessary to supply the reference values from an external device, and for this, the UART communication module is used. Figure 21 shows a flow diagram of the communication task.  
Figure 19: Processes of the program and tasks in the CPU (main core) and CLA (secondary core).  
Figure 20: Overvoltage and overcurrent protection task.  
As current-based MPPT is implemented, it is necessary to run the MPPT algorithm at a lower frequency than that of the 2P2Z compensator to achieve good tracking of the current reference given by the MPPT algorithm. For this case, the MPPT runs at a frequency of 100 Hz, changing the reference of the compensator every 10 ms. Figure 22 shows a flow diagram of the MPPT task.  
Finally, the flow diagram of the inverter task is shown in Figure 23; in runs with a 50 kHz frequency to achieve a better control action and synchronize with the grid.  
Figure 21: Serial communication task.  
Figure 22: Current-based incremental conductance MPPT task.
## 4 Results
### Evaluation of Performance of Key Features
#### 4.1.1 Quantitative Evaluation (Ablation Study)  
Table 1 outlines the segmentation performances for the ablation study. The LCS model generated better segmentation than the LD model (1.07% better Dice score). It implies that the model with a selection gate can detect the target lateral bony features better than the LD model. The LDI model further improves the performance by introducing the multi-scale path with a 1.53% higher Dice score than the LCS model. Finally, both the selection gate and multi-scale paths were included in the LDS U-Net model, and it generates a Dice score of 3.26% higher than that of the LDI model. In terms of Jaccard index, F1 score, and accuracy, the proposed LDS U-Net performed the best. Hence, the LDS U-Net model was chosen as the proposed model. This model also obtained the smallest standard deviations in the four evaluation indices.
## 3 System Model
### Optimal Relay Selection
#### 3.2.3 Hop Tradeoff  
In the next-hop selection, if we only pursue large throughput and small delay for one hop, this will result in an increase in the hop count of the entire T2T link. Therefore, we design a \"hop tradeoff\" indicator to optimize the number of hops on the entire link. The initial train \(T_{n}\) and the destination train \(T^{\prime}{}_{n}\) need to transmit information, and the distance of the whole T2T link is \(d_{T_{n},T^{\prime}{}_{n}}\). During the transmission process, the distance between node \(a\) and node \(b\) for one-hop distance is \(S_{a,b}\). We calculate the number of hops \(k_{a,b}\) required to complete the entire T2T link for the one-hop distance \(S_{a,b}\), which can be represented as  
\[k_{a,b}=\frac{d_{T_{n},T^{\prime}{}_{n}}}{S_{a,b}}. \tag{22}\]
## 4 Conclusions  
In this paper, the theory of Optomechanical coupling vibration active control is proposed. Through the first coupling between active control parameters and structural response and the second coupling between structural response and beam pointing, the active control parameters are introduced into the beam transmission matrix of the Optomechanical coupling system, and the point-to-point link between active control parameters and beam pointing is realized. An active control scheme with different excitation is implemented to solve the problem of output light angle misalignment caused by the deviation of the amplitude response of the spatial filter confocal lens under different microvibration excitation. When the microvibration phase of the confocal lens is opposite, the output light angle of the spatial filter transmission system oscillates back and forth with \(-\frac{1}{m}\theta_{1}\) as the center angle. Under the active control, the maximum amplitude of the output light angle oscillation deviation is reduced by 98.13%, and the attenuation is close to two orders of magnitude. To a certain extent, the convergence speed of the beam pointing is improved, and the dual control of the peak value of the output light angle response and the convergence speed is realized. The results show that the proposed method can effectively control the peak value of the output light angle response of the full frequency domain spatial filter and make it fall within the pointing threshold. The control analysis results are consistent with the expectation. The research results can guide cascaded multi spatial filters and other similar systems.  
Conceptualization, W.C. and J.Z. ([PERSON]); methodology, W.C.; software, W.C.; validation, Z.L., Z.J., Q.F. and J.Z. ([PERSON]).; formal analysis, W.C.; investigation, W.C.; resources, J.Z. ([PERSON]); data curation, W.C.; writing--original draft preparation, W.C.; writing--review and editing, W.C.; visualization, W.C.; supervision, W.L. and Z.D.; project administration, W.C.; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the International Partnership Program of Chinese Academy of Sciences (No.181231 KYSB20170022), and the Strategic Priority Research Program of Chinese Academy of Sciences (No.XDA25020304).  
Not applicable.  
Data Availability Statement: Data are available from the corresponding author upon reasonable request.  
The authors declare no conflict 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.