esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods
### Sensor Performance
#### 2.4.1. Particulate Sensors  
The Sharp DN particulate matter sensor response was evaluated with salt, a common laboratory test aerosol [37]. Salt particles were generated with a vibrating nebulizer (Aerogen, Aeroneb Solo System, Galway, Ireland) and a 0.9% salt solution (Fisher Scientific, \(w/v\), #7210, Hampton, NH, USA). The aerosol was diluted with clean air from two HEPA filters (0.25 m\({}^{3}\)/min) positioned at the entrance of the sampling zone. Salt size distribution and median diameter were not measured for this experiment. Measurements of the particle mass concentrations were compared to a personal DataRAM 1500 Aerosol Monitor (Thermo Fisher Scientific., pDR, Shoreview, MN, USA). Prior to our experiment, the pDR was sent to the manufacturer for calibration, and was operated with an inlet cyclone (cut-off diameter of 10 mm). [PERSON] et al. [39] evaluated the pDR compared to filter-corrected, high-cost reference instruments for the same salt solution used and found that the pDR measurements were linear and had a correlation of 0.99, a slope of 1.1, and a bias of just 6% compared to the reference instruments. Clean air (pDR concentration = 0.01 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)) was measured inside the chamber for five minutes before generating salt aerosols. The concentration range for the salt aerosol inside the chamber varied from 0 to 300 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\), concentration levels consistent with earlier measurements in the factory environment. The mean temperature and relative humidity inside the chamber was 24 \(\mathrm{\SIUnitSymbolC}\) (standard deviation (SD) = 0.4 \(\mathrm{\SIUnitSymbolC}\)) and 21% (SD = 1.7%), respectively. We therefore expect that temperature and relative humidity effects were negligible. Steady-state concentrations were maintained at six levels for five minutes in order to eliminate response time differences between the Sharp DN sensors and the pDR. The Sharp DN sensors recorded a measurement every two seconds; the pDR recorded a measurement every second. Five-minute averages were calculated for every sensor at each steady state level, comparing slopes, intercepts, and R-values. [PERSON] et al. [37] evaluated the Sharp DN sensors with different aerosol types and found that each sensor needed to be calibrated once purchased from the manufacturer. Also, the Sharp DN sensor correlation was similar with a linear response for different aerosol types; however, the magnitude (in voltage) was higher for reflective aerosols, such as salt, and lower for darker absorbing aerosols, such as welding and diesel fumes. Therefore, the authors concluded that the Sharp DN sensors should be calibrated on-site with a reference instrument.
###### Abstract  
Cardiac computed tomography angiography (C-CTA) is crucial in assessing the right ventricular outflow tract (RVOT) prior to a transcatheter pulmonary valve replacement (TPVR), as an incorrect evaluation can make the procedure more challenging and can lead to device-related complications. This study aimed to evaluate the feasibility and accuracy of 4D straightened segmentation for a landing zone analysis over anatomical segmentation. Seven pre-operative CTAs and seven post-operative CTAs were used to measure the cross-sectional area, circumference, and diameters at five selected planes as the landing zone for TPVR and compared these to the 4D straightened model with the anatomical model. Furthermore, the right ventricular volume, stent volume, and 4D ellipticity index were calculated from the 4D straightened model. The 4D straightened segmentation had comparable accuracy and efficacy for the measurements at the landing zone. The cross-sectional area and the circumference varied greatly at the RVOT and the basal plane of the pulmonary valve compared with the other three planes of the 4D straightened models from the pre-operative CTAs; however, only the values at the RVOT were found to vary greatly from the post-operative CTAs. The 4D straightened model can provide accurate measurements and is thus a useful method for the periprocedural evaluation of TPVR.  
Keywords:four-dimensional computed tomography; transcatheter pulmonary valve replacement; cardiac catheterization; 4D dynamic segmentation; landing zone +
Footnote †: journal: Applied Sciences
## 4 Experiment
### Objective Evaluation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PI\(\downarrow\)** & **RMSE\(\downarrow\)** & **NIQE\(\downarrow\)** & **BRISQUE\(\downarrow\)** \\ \hline SRIE (2016) & 18.5994 & 0.6479 & 4.6264 & 32.2827 & 8.2338 & 37.0769 \\ \hline BIMEF (2017) & 19.3802 & 0.8839 & 3.2687 & 31.201 & 4.7516 & 19.2054 \\ \hline EFF (2017) & 21.07 & 0.7090 & 4.0820 & 24.1850 & 7.7022 & 32.7328 \\ \hline RetinexNet (2018) & 16.3971 & 0.7724 & 3.5378 & 35.603 & 5.8523 & 32.9832 \\ \hline JED (2018) & 18.9021 & 0.8199 & 3.8976 & 32.1988 & 5.6357 & 35.8391 \\ \hline FFM (2019) & 17.7743 & 0.8517 & 3.3305 & 37.0954 & 4.5366 & 19.9998 \\ \hline KinD (2019) & 19.6922 & 0.8606 & 3.091 & 25.571 & 5.1806 & 24.4180 \\ \hline EnGAN (2020) & 19.3250 & 0.8789 & 3.05 & 27.9136 & 4.3873 & 20.9074 \\ \hline DLN (2020) & 17.9633 & 0.8643 & 3.2012 & 29.6263 & 4.3616 & 20.3596 \\ \hline Zero-DCE (2020) & 19.1880 & 0.8837 & 3.2012 & 29.6263 & 4.8345 & 23.5798 \\ \hline SDD (2020) & 18.4178 & 0.8263 & 3.6015 & 33.7773 & 4.7409 & 28.4139 \\ \hline DRBN (2020) & 18.5074 & 0.8549 & 3.4016 & 30.4341 & 4.3457 & 24.9967 \\ \hline Zero-DCE++ (2021) & 16.9810 & 0.5293 & 4.1063 & 30.197 & 4.4001 & 19.8254 \\ \hline RetinexDIP (2021) & 15.1113 & 0.7614 & 3.3943 & 34.3869 & 4.7571 & 23.0474 \\ \hline Ours & 23.6867 & 0.8197 & 2.9344 & 17.0415 & 4.1834 & 18.0237 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Objective evaluation results of the six metrics PSNR, SSIM, PI, RMSE, NIQE, and BRISQUE for the MIT [32] dataset.
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 5 Simulation and Analysis
### Simulation Settings  
We chose the Walker delta satellite constellations with an orbital altitude of 1000 km and an orbital inclination of 54\({}^{\circ}\). The GSs are randomly located in the domain from 90\({}^{\circ}\) E to 120\({}^{\circ}\) E and from 20\({}^{\circ}\) N to 45\({}^{\circ}\) N. The minimum elevation angle of the GS antennas is 5\({}^{\circ}\). The number of satellites, the number of GSs, and the weights of the utility function are set as variables. The parameters of sigmoid function \(\alpha\) and \(\beta\) are determined based on prior knowledge. Other detailed parameters of the simulation are presented in Table 1.  
To verify the effectiveness and advantages of the proposed scheme, we selected the Maximum Service Time Handover Strategy (MSTS) [22] and the Maximum GSL (ground-to-satellite) Capacity Handover Strategy (MGCS) as performance comparison benchmarks. The GSL capacity is calculated based on Equation (7).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of satellites & 100\(\sim\)1500 \\ Orbital altitude & 1000 km \\ Orbital inclination & 54\({}^{\circ}\) \\ Number of GSs & 5\(\sim\)20 \\ Locations of GSs & 90\({}^{\circ}\) E\(\sim\)120\({}^{\circ}\) E, 20\({}^{\circ}\) N\(\sim\)45\({}^{\circ}\) N \\ Minimum elevation of antennas & 5\({}^{\circ}\) \\ Capacity of ISLs/USLs & 5 Gbps/2 Gbps \\ Carrier frequency & 20 GHz \\ Bandwidth of feeder links & 2 GHz \\ Other parameters in Equation (7) & \(\chi=-2\), Ri = 1, \(P_{l}g_{l}g_{s}=90\) dB \\ Parameters of sigmoid function & \(\alpha=4\), \(\beta=0.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Flow chart of the feeder link handover procedure.
## 6 Thematic Taxonomy of UWSNs
### Underwater Acoustic Communications
#### 6.2.1 Sound Velocity
the inverse-square law, acoustic wave strength decreases when a wave with spherical symmetry geometrically deviates from the primary source point. Nevertheless, in the SOFAR channel, the action is relatively different. The rays do not bend spherically but spread from a line source in the form of a cylinder symmetry. The inverse-square law concludes that geometric distribution can reduce the strength of acoustic waves in parallel with decreasing distance. There is a significant difference in the acoustic absorption rate between seawater and pure water within the frequency range of 5-50 kHz. The difference in frequency occurs due to a mechanism associated with viscosity under 100 kHz, where the element of magnesium sulfate is loose. As highlighted previously, water temperature is one of the main factors affecting sound velocity. At a depth of 1 km in the ocean, temperature and the sound speed increase when sound spread over distances with 4.6 m/s per degree centigrade. In conclusion, both frequency-dependent absorption and noise and low and variable propagation speed have a significant impact on the design of underwater communication networks. Different scenario characteristics and even current different time spots lead to different channel responses. As a consequence of the long propagation delays of underwater transmission, the networks also be affected of space uncertainty, and spatial inequity [54]. As the packet reception time depends on the distance to the transmitter, the channel becomes free first at the transmitter and later on at the receiver.
# Improved Adaptive Successive Cancellation List Decoding of Polar Codes
## 4 Experiment and Analysis
Compared with the SCAD-SCL and AD-SCL algorithms, SAD-SCL makes full use of previous decoding results and digs out the available information, which reduces redundant calculations. When the code length is 1024 and the code rate is 0.5 respectively, the average number of calculations of value _PM_ for AD-SCL, SCAD-SCL, SAD-SCL with \(L_{max}=16\) is shown in Figure 9. It is observed that the complexity of the three adaptive algorithms declines with the increase of SNR. Figure 8: The number of _PM_ calculations for CRC, two-segmentation CRC and merge sort of N/2-bits under different code lengths. Figure 9: The average number of calculations of value _PM_ for AD-SCL, SAD-SCL, SCAD-SCL with \(L_{max}=16\). The reason is that there are more error frames caused by inter-symbol error propagation in low SNR and the adaptive algorithm needs a large value of \(L\) to complete the decoding. With the increase of SNR, the frame number of inter-symbol error propagation decreases gradually, and less values of \(L\) are required for the adaptive algorithm, so the complexity in high SNR is lower than that in low SNR. In addition, the computation complexity of the SAD-SCL algorithm proposed in this paper is lower than AD-SCL and SCAD-SCL. But with the increase of SNR, the adaptive algorithm tends to complete decoding once. At this time, the complexity of three kinds of algorithms is almost the same. In conclusion, the gap of complexity of three kinds of algorithms will narrow with the increase of SNR. Figure 10 shows the comparison curves of the average number of calculations of _PM_ value for SAD-SCL algorithm under different code rates and \(L_{max}\). It can be seen that the smaller the code rate and \(L\) are, the less the average _PM_ update times of SAD-SCL are. Figures 11 and 12 show the BER and FER performance curves of the SAD-SCL (\(m=140\)), SCAD-SCL, AD-SCL algorithms when \(L_{max}=16\) under AWGN channel and BPSK modulation. It is observed that there is little performance loss from the SAD-SCL algorithm compared to the other two adaptive algorithms.
## 4 Experimental Setup
### Baselines  
We compare our model with multiple state-of-the-art baseline systems below.  
**HiGRU**[49]: A hierarchical GRU structure that jointly trains individual word and utterance-level information and long-range contextual information.  
**DialogueRNN**[30]: A recurrent neural network-based approach that uses three GRUs to model the speaker, the context of the preceding discourse, and the emotion of the preceding discourse.  
**COSMIC**[50]: A common sense-based framework for conversational emotion recognition, using a transformer pre-training model to extract contextual features. It fuses an external knowledge base and learns different common sense elements to study the emotional interactions between interlocutors, and discriminates the emotion categories of conversational discourse.  
**DAG-ERC**[47]: A model that combines the advantages of graph-based neural networks and recurrence-based networks. A directed acyclic graph (DAG) is designed to encode discourse.  
**ERMC-DisGCN**[37]: A discourse-aware graph neural network for multi-party conversational sentiment recognition, designed with a relational convolution to exploit the self-speaker dependence of interlocutors to propagate contextual information.  
**DialogueGCN**[31]: A model that uses directed acyclic graphs to encode the structure of conversations, combining graph neural networks and recurrent neural networks to model distant conversation context and nearby contextual information, and the approach also takes into account speaker relevance.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Conversations**} & \multicolumn{3}{c}{**Utterances**} \\ \cline{2-7}  & **Train** & **Val** & **Test** & **Train** & **Val** & **Test** \\ \hline IEMOCAP & & 120 & 31 & & 5810 & 1623 \\ MELD & 1038 & 114 & 280 & 9989 & 1109 & 2610 \\ DailyDialog & 11,118 & 1000 & 1000 & 87,170 & 8069 & 7740 \\ EmoryNLP & 713 & 99 & 85 & 9934 & 1344 & 1328 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of four datasets.  
**DialogueXL**[35]: An integrated XLNet model that modifies the recursive mechanism of XLNet from the segment level to the sentence level and introduces a multi-attention mechanism to compose dialogue-aware self-attention to handle the emotional impact of multi-party multi-turn chat interactions.  
**DialogueCRN**[51]: A new contextual reasoning network that simulates human cognitive emotions by mimicking human cognitive thinking, and designs a multi-turn reasoning module to extract and integrate emotional cues.  
**CoG-BART**[52]: Uses the pre-trained model BART as the backbone model, and supervised contrast learning is introduced to distinguish similar emotions.
## 4 Experimental Environment and Results
### Field Study Results
#### 4.3.3 Comprehensive Analysis Results  
Collectively, both research phases yielded three major findings.  
1. The intergenerational interaction effects were significant. The statistical analysis showed that the elderly-children co-learning mode (SUS 92.12) significantly outperformed that of the elderly-only group (SUS 80.70), with statistical significance (\(p<0.001\)) and a large effect size ([PERSON]'s d = 1.24). This finding validates the effectiveness of our intergenerational interaction design.
2. The usage context significantly influenced the system's effectiveness. As shown in Table 3, group usage contexts (SUS 85.32) outperformed individual usage contexts (SUS 77.85). Similar trends appeared, in terms of learnability (92.12 vs. 89.12) and usability (83.63 vs. 75.03), validating the system's design goals in promoting social interaction.
3. The system optimization demonstrably enhanced its usage effectiveness. The second-phase implementation results showed significant improvements across all metrics, particularly in usability (9.90% improvement). These results validate both the design framework's practicality and the system optimization's effectiveness.
# Controller-Pilot Data Link Communication Security
## 5 Potential Countermeasures
### Host Identity Protocol (HIP)
Concerning the HIP overhead, according to [24] ESP reduces the data rate by 32% in the wireless local area network (WLAN) access point (AP) network. More to the point, a Lightweight HIP (LHIP) [24] version has been developed to counter and reduce the impact of the increased overhead cost. By replacing the public-key cryptography, used for host authentication, in HIP with Interactive Hash Chain (IHC) authentication, the LHIP reduces the computational cost of HIP to less than 2.5% of the cost of a base exchange of normal HIP with 1024-bit RSA. Removing the use of an asymmetrical key system results in a lower level of security, which is resolved by signing distinct kinds of messages (update, close, and upgraded messages), with IHC signatures to reinforce the protection against man-in-the-middle attacks, as important control messages cannot be forged. Namespace security is an important part to prevent impersonation attacks and namespace conflicts. The LHIP is considered compatible as it does not change the way HIP works in general and has the ability to interact with HIP implementations. In addition, Diet HIP [26] is a protocol version that was specifically designed to function on resource-constrained devices and links, such as CPDLC.
## 4 Numerical and Experimental Investigation of MR Semi-Active Control  
To make the test proceed smoothly, the numerical simulation of the control system was carried out before the test. The numerical simulation and experimental results of the two control strategies controlled by the two algorithms respectively will be analyzed in the following section.  
The control force equation of the MR damper is as follows: \(F=F_{\eta}+F_{\tau}\). While, \(F_{\eta}\) is a viscous damping force, which is only related to the viscous coefficient of magnetorheological fluids, and \(F_{\tau}\) is a coulomb damping force, which is related to the voltage of magnetorheological fluid. \(F_{\eta}/F_{\tau}\) is the output adjustable multiple of the MR damper.  
The relationship between the input current and the output force of MR damper is shown in Equation (6), in which the parameters are calculated by the least square method from the experimental data, so the Equation (6) should always have the solutions within 0-4 Ampere. According to the required damping force and velocity response, the current required for MR damper can be obtained.  
\[F=C_{0}v+F(I)=10629v+1500+10891I+2291I^{2}-1468I^{3}. \tag{6}\]  
Based on MATLAB, a program for solving the above equation has been developed. For the Equation (6), when the solutions of this equation fell within 0-4 Ampere, we took the smallest value as the current input. In Equation (6) force F, being the actual force applied to the structure, is represented by force u which is calculated by the control algorithms in terms of input value. Therefore, the single current value corresponding to each time step can be determined by the above approach.  
In order to measure the actual working condition of the MR semi-active isolation control system, the shaking table test of the controlled structure model under two control rules has been carried out at the seismic research center of Guangzhou University for this research. Specific test results and analysis are described in the following.
## 2 Results
### The Self-Assembly Approach
#### 2.1.5 Evaluation of the Imprinting Effect  
The rebinding of **1** was initially evaluated by batch adsorption experiments, using 0.8 mM solution of **1** in CH\({}_{3}\)CN or CHCl\({}_{3}\) with 30 min exposure to polymers of various masses (5, 10, 20 and 30 mg). As expected, the rebinding capacity was a function of the polymer mass used, typically plateauing at 20-30 mg polymer loading and increasing with increasing proportion of FM for both MIPs and NIPs. Our data suggest that the rebinding of **1** was not affected by the nature of the crosslinker; instead, it was porogen and FM dependent. In all instances, higher rebinding was observed with the CH\({}_{3}\)CN MIPs, but the imprinting effect was enhanced in CHCl\({}_{3}\) MIPs, as a consequence of lower NIP rebinding of **1**.  
This initial rebinding assays allowed us to screen MIPs and select those with an imprinting efficacy or imprinting factor IF (MIP binding/NIP binding) of \(\geq\)2 for all mass loadings for further binding characterisation. This imprinting efficacy criterion was obtained with MIPs E71-MIPCHC13, E72-MIPCHC13, T71-MIPCHC13 and T72-MIPCHC13, with CHCl\({}_{3}\) as binding solvent; and E71-CH3 CN, bound in CH\({}_{3}\)CN (Figure 4). Template uptake by these polymers varied from 17 to 75% and from 3 to 26% across the MIPs and their corresponding NIPs, respectively.
## 3 Results
### Result of a Squall Line Case from a CINRAD S-Band Radar  
Squall lines are a type of convective system that can cause damaging winds, heavy rain and hail. Strong updrafts and downdrafts are present in the convective area of a squall line. This squall line involving severe thunderstorms grew in the Guangxi Province of China between 29 March and 31 March 2014, with wind speeds greater than 14 m/s observed by 34 automatic meteorological stations. Hail damage was reported in 13 cities. Two people died from this hazardous weather event and the economic and property losses were appraised to be $9.32 million.  
When strong wind shear appears in radar radial velocity observations, such as that often existing in tornadoes or squall lines, most of the current algorithms can easily fail because of the difficulty in distinguishing the non-meteorological shear caused by the aliased data from the real shear. Squall line systems have the characteristics of rapid development, fast movement and short lifetimes. Data voids often appear at low elevation angles of radar observations of squall lines ([PERSON], 2000). An effective dealiasing algorithm should have the ability to address these difficulties. Figure 4 shows the radial velocities before and after dealiasing the squall line case observations from the Guangxi S-band radar at 20:28 (UTC), UTC 30 Mar 2014, at several elevation angles. Figure 4a is the raw radial velocity at the 4.3\({}^{\circ}\) elevation angle. Figure 4b shows the result of the flag field after module 3 in the ADDA algorithm. Module 3 was able to successfully protect the real convection regions with flag 3 throughout the whole case period. The yellow line represents the position of the first reference radial where the algorithm started for this particular volume. Figure 4c is the dealiasing result after the first two steps of module 4. Because of the existence of a discontinuous echo area in the southwest of the radar observation, between the 50 and 100 km ranges, an incorrect dealiasing area appears in the white ellipse in Figure 4c. The last two steps in module 4 helped correct the errors in this area but resulted in incorrectly dealiased data (blue shaded area in the white ellipse in Figure 4d) due to the strong velocity difference near the wind shear line. Figure 4e shows the final dealiased result after the local error check of module 5. All the aliased data are correctly dealiased at this elevation angle. This example showed the benefit of the two added dealiasing passes in module 5 in ADDA.