esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrodegradation of Selected Water Contaminants: Efficacy and Transformation Products
## 2 Materials and Methods
### Analytical Methods
#### 2.3.3 Methods for Triton X-100, Alkylphenols, and Their TPs  
The HPLC-DAD method was used only for t-OPPE. The mobile phase was composed of acetonitrile (A) and MQ with 0.1% HCOOH (B), with the flow rate set at 0.65 mL/min, while the injection volume was 15.0 \(\mu\)L. The gradient profile started at 15% A and was held for 1 min, then increased to 95% A at 4.5 min, held for 10.5 min, and finally reduced to 15% A, where it was held for 1 min to equilibrate the system. Detection was at 227 and 275 nm. LC-MS analyses were performed under the same conditions, except for flow rate, which was 0.5 mL/min. ESI ionisation in positive mode was used, while MS spectra were recorded in total ion current (TIC) mode at \(m/z\) range 80-800.  
Alkylphenols and short-chain APPE were analysed by gas chromatography-tandem mass spectrometry (GC-MS/MS) in the form of trimethylsilyl derivatives. Derivatization was performed on a 20 \(\mu\)L standard solution or sample extract in a vial, which was mixed with 10 \(\mu\)L of internal standard (2,4,6-triiodophenol, TIP) 10 mg/L solution and 70 \(\mu\)L of BSTFA + 1% TMCS reagent, left at room temperature for 60 min, and injected into a GC-MS/MS instrument (Thermo Scientific, Waltham, MA, USA). An HP-5 MS (30 m \(\times\) 0.250 mm, 0.25 \(\mu\)m; Agilent Technologies, Santa Clara, CA, USA) column was used with the following temperature programme: 0-2 min at 65 \({}^{\circ}\)C, increase to 150 \({}^{\circ}\)C at 7.7 min, then to 230 \({}^{\circ}\)C at 16.2 min, hold for 0.5 min, and finally increase to 300 \({}^{\circ}\)C at 22.8 min and hold for 2 min. Helium was a carrier gas at 1.0 mL/min. Splitless injection (1 \(\mu\)L) at 280 \({}^{\circ}\)C was used. The MS transfer line temperature was 280 \({}^{\circ}\)C and the ion source temperature was 250 \({}^{\circ}\)C. Electron ionisation at 70 eV was applied. Two selected reaction monitoring (SRM) transitions were monitored for each alkylphenol: t-OP \(m/z\) 207.1 \(\rightarrow\) 45.0, 73.1; n-OP \(m/z\) 278.2 \(\rightarrow\) 179.1 and 179.1\(\rightarrow\)73.1; n-NP \(m/z\) 292.2 \(\rightarrow\) 179.1 and 179.1 \(\rightarrow\) 73.1; and internal standard TIP \(m/z\) 543.8 \(\rightarrow\) 528.8 and 528.8 \(\rightarrow\) 401.7. Mono- and diethoxplates were also determined on the same GC column, but with a faster temperature gradient: 0-1.5 min at 80 \({}^{\circ}\)C, increase to 300 \({}^{\circ}\)C at 45.5 min, and hold for 5 min. Also, the flow rate was 1.5 mL/min and the injector temperature was 290 \({}^{\circ}\)C. Selected ion monitoring (SIM) was used for the quantitation: \(m/z\) 251.1 for t-OPME, \(m/z\) 295.2 for NPDE, and \(m/z\) 528.8 for the internal standard TIP.
# Design of Adaptive Kalman Consensus Filters (a-KCF)
## 6 Conclusions  
In this paper, the problem of \(n\) distributed agents working cooperatively through a communication network to estimate the true state of a linear dynamic system has been considered for both the non-adaptive DKF algorithm proposed by [PERSON] [17] and our proposed adaptive Kalman consensus filtering (a-KCF) algorithm. Stability and performance analyses are demonstrated for the a-KCF algorithm, showing that our adaptive extension of [PERSON]'s work in [17] has better convergence performance for the collective dynamics of the estimation errors. The simulation results in Section 5 match our theoretical analysis. Moreover, the a-KCF algorithm turns out to be a suboptimal one which neglects the coupling effect among the agents' cooperation. For simplification, we only consider the no-noise scenario. However, we recognize the benefit of the a-KCF algorithm in that it lowers the communication complexity from \(O(n^{2})\) to \(O(n)\) and has better scalability when more agents join the network.  
**Author Contributions:** [PERSON] was responsible for the conceptualization of the research study, carried out the formal analysis, and contributed to the original draft of the manuscript. He also conducted the simulation experiments and took the lead in the final writing of the paper. [PERSON] guided the direction of research through the development of the methodology and was responsible for the validation of the  
Figure 4: Adaptive gains evolution vs. time.  
Figure 3: Performance comparison examining state estimation error and disagreement term evolution vs. time.  
analysis and simulation results. Additionally, [PERSON] secured the funding for the project and oversaw its administration. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China OF FUNDER grant number U20B2054.  
All data generated or analyzed during this study are included in this published article.  
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
# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 3 Experimental Results
### Ablation Studies  
Ablation experiments were conducted to validate the necessity of each module in the RACF model. According to the experimental results presented in Table 5, the removal of the contrastive learning module led to a significant drop in model accuracy, highlighting the critical role of contrastive learning in cross-modal semantic alignment. Without this module, the model was unable to effectively constrain feature consistency between different modalities, which consequently impaired classification performance. After the removal of the residual connection, the accuracy decreased by 19.5%, and the recall rate dropped from 0.895 to 0.735, emphasizing the importance of this module in mitigating the gradient vanishing problem. The absence of the residual connection made the training process more challenging, as gradient updates were constrained, resulting in a substantial performance decline.  
Furthermore, when the feature fusion module was replaced with simple feature concatenation, the model's performance nearly collapsed, with the F1 score dropping to only 0.641. This indicates the necessity of the attention mechanism and residual design for heterogeneous feature fusion. Simple feature concatenation was insufficient to adequately handle data from different modalities, which hindered the ability of the model to capture cross-modal information effectively, severely affecting the final classification results.  
In contrast, the complete RACF model demonstrated excellent performance across all evaluation metrics. These results highlight the strong performance of the model in multimodal fusion tasks. Particularly in handling cross-modal features from SNP and MRI data, RACF was able to fully exploit the correlations between the two, providing precise support for PD diagnosis.
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
## 3 Results
### Accuracy Analysis of Electron Density Profile
#### 3.1.1 Comparison with Ionosonde Data
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Station**} & \multicolumn{2}{c}{**MAE (10\({}^{\text{12}}\) el/m\({}^{\text{3}}\))**} & \multirow{2}{*}{**PI**} & \multicolumn{2}{c}{**RMSE (10\({}^{\text{12}}\) el/m\({}^{\text{3}}\))**} & \multirow{2}{*}{**PI**} \\ \cline{3-3} \cline{5-8}  & & **IRI-2016** & **Upda-IRI-2016** & & & **IRI-2016** & **Upda-IRI-2016** & \\ \hline \multirow{3}{*}{2015} & Beijing & 0.1030 & 0.0709 & 31.2\% & 0.1436 & 0.0986 & 31.4\% \\  & Wuhan & 0.1672 & 0.1128 & 32.5\% & 0.2302 & 0.1551 & 32.6\% \\ \cline{1-1}  & Beijing & 0.0324 & 0.0270 & 16.5\% & 0.0455 & 0.0382 & 16.0\% \\ \cline{1-1}  & Wuhan & 0.0633 & 0.0354 & 44.1\% & 0.0805 & 0.0466 & 42.1\% \\ \hline \end{tabular}
\end{table} 
Table 1: MAE, RMSE, and its precision improvement (PI) percentage for the difference between the bottomside electron density and ionosonde data for IRI-2016 and upda-IRI-2016 in January 2015 and January 2019. Figure 4: Comparison of ionosonde electron density profiles at Beijing station from 00:00 to 07:30 UTC on day 8, 2019, with those output from IRI-2016 and upda-IRI-2016 driven by IG12 and IG-up, respectively. Figure 3: Comparison of ionosonde electron density profiles at Wuhan station from 00:00 to 07:30 UTC on day 8, 2019, with those output from IRI-2016 and upda-IRI-2016 driven by IG12 and IG-up, respectively. accuracy. According to Table 1, it can be found that both in the low solar activity year and high solar activity year, with ionosonde data as a reference, the MAE and RMSE of the bottomside electron density of upda-IRI-2016 are smaller than those of IRI-2016 at both stations, and the precision of the bottomside electron density at both the Beijing and Wuhan stations has been significantly improved. In 2015, the MAE of the bottomside electron density output by IRI-2016 was 0.103 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) and 0.1672 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) at the Beijing and Wuhan stations, respectively, and correspondingly, the MAE of upda-IRI-2016 was 0.0709 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) and 0.1128 \(\times\) 10\({}^{12}\) el/m\({}^{3}\). Compared with the IRI-2016, the precision improvement of MAE for bottomside electron density output by upda-IRI-2016 is 31.2% and 32.5% at Beijing and Wuhan stations, respectively. In 2019, the MAE of the bottomside electron density output by IRI-2016 is 0.0324 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) and 0.0633 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) at the Beijing and Wuhan stations, respectively, and correspondingly, the MAE of upda-IRI-2016 is 0.027 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) and 0.0354 \(\times\) 10\({}^{12}\) el/m\({}^{3}\). Compared with to IRI-2016, the accuracy improvement of MAE for bottomside electron density output by upda-IRI-2016 is 16.5% and 44.1% at Beijing and Wuhan stations, respectively.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 5 Proposed Scheme EIA  
In this section, we delineate the EIA scheme, which leverages certificateless cryptographic techniques for anonymous authentication and key agreement. The proposed protocol is structured into four distinct phases: (1) the initialization phase to generate foundational parameters, (2) registration to enroll OBUs into the network, (3) mutual authentication between OBUs to ensure secure and verified V2V communication with a session key agreement, and (4) a pseudonym update for periodic anonymity renewal. The protocol employs a set of specific symbols, each with a defined purpose and function, as detailed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notion** & **Description** \\ \hline \(\lambda\) & The security parameter for the system. \\ \(p\), \(q\) & The large primes. \\ \(l\) & The length of session keys. \\ G & The additive group over the elliptic curve cryptography. \\ P & The generator of G. \\ s & The master key of system. \\ \(P_{pub}\) & The public key of system. \\ \(h_{i}\) & The hash functions (i = \(1,\ldots,A\)). \\ \(a\), \(b\), \(d\) & The random number. \\ \(T_{a}\), \(T_{b}\) & The timestamps chosen in the communication. \\ \(RID_{i}\), \(RID_{j}\) & The real identity of \(Vehicle_{i/j}\). \\ \(ID_{i}\), \(ID_{j}\) & The pseudoidentity of \(Vehicle_{i/j}\). \\ \((x_{i/j},y_{i/j})\) & The static private key pair of \(Vehicle_{i/j}\). \\ \((X_{i/j},Y_{i/j})\) & The static public key of \(Vehicle_{i/j}\). \\ \((SK_{i-j},SK_{i-j})\) & The session keys generated in the proposed method. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation and description of EIA.
## 5 Machine-Learning-Based Transfer Point Optimisation of Journey Profiles (Online)  
Once a journey profile Pareto set has been generated for a commuter's query, the task is to define precise paths and transfer points for each journey profile while taking into account real-time transport network traffic flows and disruptions, i.e., using real-time transport network graphs and GTFS data. Real-time transport network data capture traffic conditions by specifying how long it takes to traverse each transport network arc at any given time of the day; therefore, the path calculations outlined in this section account for varying traffic conditions. Figure 3 outlines the proposed procedure for this, which is applied to each non-dominated journey profile in turn. The approach is to cycle through the individual legs of a journey profile one at a time. Each journey leg-iteration builds a many-to-many shortest-path tree from the previous transfer zone (_origin(s)_) to the nodes in the current transfer zone (_reachedNodes_/_Stops_) of the journey profile's transfer-zone sequence. This tree is built within the transport network defined by the journey profile's mode sequence. If the current mode corresponds to an open transport network, the relevant many-to-many shortest-path algorithm is [PERSON]'s algorithm with numerous speed-up techniques (see Section 5.1), which calculates and stores the shortest paths and arrival times at each node in the current transfer zone. Conversely, if the current mode is public transport, the relevant many-to-many shortest-path algorithm is a specially adapted version of RAPTOR, which calculates and stores shortest time public transport journeys from the previous transfer zone to all of the stops in the current transfer zone (see Section 5.2). Once a many-to-many shortest-path tree has been built to the current transfer-zone, machine-learning travel time predictions for the next leg of the journey profile are used to select one of the _origins_ of the current many-to-many shortest-path tree as a transfer point. At which point, the shortest path leading to that transfer point is locked into the journey solution. If the current transfer zone is the last one, machine-learning predictions are not required since we just commit to the transfer point corresponding to the origin of the shortest path to the actual destination node. Section 5.3 describes the input features and training procedure of the neural-network approach for predicting travel times between any pair of locations in each open transport network. Section 5.4 details the machine-learning-based transfer point selection procedure.
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 2 System Model
### Hardware Architecture of the WPCN-Based Underwater Acoustic Terminal Node  
As shown in Figure 2, the WPCN-based underwater acoustic terminal node consists of the antenna module, receiving module, power module, processing module, transmitting module, and data ports. Specifically, the antenna module consists of a hydrophone and a loudspeaker. The receiving module includes the energy harvesting (EH) unit and the information decoding (ID) unit. The EH unit consists of a matching network and a rectifier, which is an essential block for the implementation of WPT. The EH unit converts the output voltage energy of the hydrophone into direct current (DC) electricity for energy scavenging and then inputs the harvested energy to the power module for battery charging. The power module consists of a power management unit and an energy storage unit, where the energy storage unit is usually a rechargeable battery plus a supercapacitor. The processing module consists of a small center processing unit (CPU) and a data storage unit, where the data storage unit is usually a memory card. The transmission module consists of transmitter circuitry and a digital-to-analog converter. The data ports, usually USB or RS232 ports, are used for stored data download from or programming the device and provide the interface for the sensors.  
For more details, an example of the representation of hardware devices, along with their specifications used in the design, is given as follows. A duty circuit, including a voltage comparator, an electric relay, and a microcontroller unit, determines whether the received signal is an energy signal or an information signal, and based on this, sends the signal to the ID or EH circuit. The receiver circuitry of the ID unit includes an automatic gain amplifier circuit based on chip VCA810 and instrumentation amplifier INA828, a filter based on the RS722 XK operational amplifier, and a voltage follower based on the RS721 XM operational amplifier, which is used to decode the information signal. The rectifier circuit of the EH unit adopts a voltage-doubling circuit with the BAT54S diode as the core, which converts the input alternating current (AC) signal into DC and outputs it to the power module. The power management unit is based on the BQ25504 chip, which increases the low voltage output of the rectifier circuit to a high voltage and outputs it to the energy storage unit. The CPU adopts a STM32F103 ZET6 processor. The transmitter circuit consists of a signal generator AD9850 chip and a power amplifier IRS2092S chip, where the initial data can be converted into an electrical signal by the signal generator and amplified by the power amplifier.
# Polar Code BP Decoding Optimization for Green 6G Satellite Communication: A Geometry Perspective
###### Abstract  
The rapid evolution of mega-constellation networks and 6G satellite communication systems has ushered in an era of ubiquitous connectivity, yet their sustainability is threatened by the energy-computation dilemma inherent in high-throughput data transmission. Polar codes, as a coding scheme capable of achieving Shannon's limit, have emerged as one of the key candidate coding technologies for 6G networks. Despite the high parallelism and excellent performance of their Belief Propagation (BP) decoding algorithm, its drawbacks of numerous iterations and slow convergence can lead to higher energy consumption, impacting system energy efficiency and sustainability. Therefore, research on efficient early termination algorithms has become an important direction in polar code research. In this paper, based on information geometry theory, we propose a novel geometric framework for BP decoding of polar codes and design two early termination algorithms under this framework: an early termination algorithm based on Riemannian distance and an early termination algorithm based on divergence. These algorithms improve convergence speed by geometrically analyzing the changes in soft information during the BP decoding process. Simulation results indicate that, when \(E_{b}/N_{0}\) is between 1.5 dB and 2.5 dB, compared to three classical early termination algorithms, the two early termination algorithms proposed in this paper reduce the number of iterations by 4.7-11% and 8.8-15.9%, respectively. Crucially, while this work is motivated by the unique demands of satellite networks, the geometric characterization of polar code BP decoding transcends specific applications. The proposed framework is inherently adaptable to any communication system requiring energy-efficient channel coding, including 6G terrestrial networks, Internet of Things (IoT) edge devices, and unmanned aerial vehicle (UAV) swarms, thereby bridging theoretical coding advances with real-world scalability challenges.  
s +
Footnote †: journal: _arXiv:1807.00044v1_  
_Article_
## 4 Empirical Study and Result
### Analysis of Merging Behavior in Different Work Zones Layouts
#### 4.3.2 Merging Gap  
Although the gap distribution of Sites A3, B4, and A4 look similar, as shown in Figure 9, the Kolmogorov-Smirnov test of every pair of the three indicates that they are not following the same distribution. The relationship between the gap and start speed at Site A3 is identical to Site B4, as shown in Figure 10. However, the gap and start speed distribution of Site A4 is uniform on both axes. That may be because of the low traffic volume and small sample size.  
The gap distribution in Figure 9 shows that while Sites A3, B4, and A4 exhibit similar trends, the Kolmogorov-Smirnov test confirms that each site follows a distinct distribution. Site A4, in particular, shows a more uniform gap distribution, which may be attributed to its smaller sample size and lower traffic volume. In contrast, Sites A3 and B4 display more defined peaks in their gap distributions, indicating that vehicles tend to choose gaps within specific ranges. The scatter plots in Figure 10 further illustrate the relationship between start speed and accepted gap, demonstrating that at Site A4, drivers tend to accept larger gaps regardless of their starting speed, whereas at Sites A3 and B4, higher speeds are generally associated with larger gaps. These findings suggest that drivers in lower-density scenarios (like Site A4) have more flexibility in selecting gaps, while in denser scenarios, speed becomes a more critical factor in gap acceptance.
# Numerical Simulation Research on Aerodynamic Characteristics during Take-Off Phase in Ski Jumping
###### Abstract  
In view of the inability to directly and accurately obtain an athlete's aerodynamic force during the take-off phase through the wind tunnel test, the athlete's aerodynamic force and surrounding flow field form under different take-off postures are obtained through numerical simulation research, and the effects of different take-off modes on the aerodynamic characteristics during take-off in ski jumping are discussed. The multi-body system composed of the athlete and skis was selected as the research object. By using a partially averaged Navier-Stokes (PANS) turbulence model and a 3D numerical simulation of computational fluid dynamics (CFD), the aerodynamic characteristics of the athlete under different take-off postures were predicted. The take-off modes include the knee-push-hip (KPH) mode and hip-drive-knee (HDK) mode, and the hip joint angle of the HDK mode is significantly greater than that of the KPH mode. First, the aerodynamic force ratio of the athlete's torso and legs is obviously large. Although the aerodynamic forces of arms themselves are not obvious, they have a great impact on the overall aerodynamic characteristics of the athlete, so the posture of the arms cannot be ignored. The total drag and moment of the HDK mode are significantly higher than that of the KPH mode, and the lift-to-drag ratio of the HDK mode is significantly lower than that of the KPH mode. At first, the total lift of the HDK mode is higher than that of the KPH mode, but in the last attitude, the total lift of the HDK mode does not rise but fall, and finally, the total lift of the HDK mode is lower than that of the KPH mode. The aerodynamic characteristics change dramatically during the take-off phase, and the aerodynamic characteristics of the two take-off modes are quite different, and these changes and differences are difficult to observe during real training and at the competition site. The KPH mode has an obvious aerodynamic advantage over the HDK mode. During the take-off process, the athlete should increase the force generated by the knee joint extension and appropriately reduce the speed of the hip joint extension, control the using force order of the lower limb joints, and push the hip joint extension by the knee joint extension in order to avoid issues, such as the hip joint angle being too large, the hip joint extension angle being too fast, the center of gravity being too far back, and other problems. Studying the aerodynamic characteristics during the take-off phase provides valuable insights for athletes to achieve favorable flight postures after take-off, offering scientific guidance to improve their training strategies and enhance their competitive performance.  
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