esa-sceva/satcom-chunk-collection huggingface dataset:
# Rule-Based DSL for Continuous Features and ML Models Selection in Multiple Sclerosis Research
## 1 Introduction
As previously discussed, domain experts encounter several challenges when attempting to utilize ML models, including the design of their own training rules, the navigation of the complexities of cross-directional features in MS, and the selection of the most appropriate ML model. Furthermore, the DSL serves as a bridge to enhance communication and improve coordination between ML experts and domain specialists. It provides a common language that both groups can understand and use effectively. For instance, neurologists can use the DSL to specify important features and training objectives using straightforward syntax. This clear specification allows ML engineers to implement the desired models and analyses without ambiguity, ensuring that the resulting ML solutions accurately reflect the domain experts' requirements and expectations. This collaborative approach ensures that the resulting ML models are both technically sound and clinically relevant. This paper aims to contribute to the development of ML in MS by proposing a novel DSL. Our DSL is designed for these stakeholders, including MS experts and ML researchers. Each group has specific requirements:  
1. MS experts require an intuitive interface to select features and goals without deep programming knowledge. They require explainability and interpretability to validate the model's decisions. 2. ML researchers seek flexibility and control over model parameters and feature selection and require advanced customization options to experiment with different ML techniques and optimization strategies. By addressing these requirements, our DSL ensures that all stakeholders can effectively contribute to the ML model development process in MS research. This paper is structured as follows: Section 2 provides an overview of the background knowledge on MS, detailing how ML has been implemented in MS and the methodology of DSL. Section 3 delves into our DSL design, presenting the meta model and illustrating how to define models in three distinct ways using examples. In Section 4, we define several models to test their results on real data and compare the outcomes. Section 5 reviews related work, and Section 6 summarizes our findings and outlines future directions for research.
# Progress of Organic/Inorganic Luminescent Materials for Optical Wireless Communication Systems
## 1 Introduction
Unfortunately, these enhancements extended the modulation bandwidth by \(\sim\)30 MHz, which is insufficient for VLC [7]. Thus, it is deductible that novel materials for light conversion with faster radiative recombination, high brightness, and notable optical features are needed to strengthen the wireless communication technologies. In this quest, some organic semiconductors, such as oligoglourenes [4; 22] or composed polymers [23], were employed as light converters for VLC due to their low-energy band gap (yellow emission), high photoluminescence quantum yield (PLQY), fast radiative recombination lifetimes, and facile integration with commercial blue-emitting nitride-based LEDs. Under this premise, two-color white light emission can be produced, for instance, for VLC dualink applications [17]. However, CRI values of \(\sim\)57% are obtained, indicating the difficulty of obtaining a stable and high color purity in the white color tonality [7]. Figure 1: Schematic representation of a typical VLC system: luminescent active layer (for instance, yellow-emitting quantum dots) on a blue-light LED transmitting optical signals to reach Tx and Rx lens, as well as introducing long-pass filter to suppress remanent blue-light coming from illumination source. Optical response is collected by photodetector to produce an electrical response related to function of quality of data transmission. Reproduced and modified with permission [13]. Copyright 2018, American Chemical Society. Therefore, the preparation of light converters based on green- and red-PL emission is primordial to combination with blue-LEDs, extending the broadband luminescence of the final device to increase the CRI. More specifically, among the red-emissive organic tools with high PLQY and favored radiative recombination rates, the actual state-of-the-art elements are limited to show high-quality blue-light emitters with the strong optical power required to generate a suitable signal-to-noise ratio for the modulation of bandwidth in VLC. In conclusion, research groups are focused on the preparation of green-light emission-based converters, which can absorb the blue-light from the LEDs. This process allows access to low wavelengths from the visible spectrum, transferring carriers to red-light emitters to cover a wider energy spectrum, including red-IR range [22]. In this review, we exhibit the recent progress of promising luminescent materials with valuable photophysical properties, highlighting organic polymers, organic semiconductors, metal-organic framework/mixed matrix membranes, and semiconductor-based nanocrystals with high optical performance. We provide new insight about how the intrinsic features of these materials could be tuned to promote improvements in the CRI and color quality of WLED, OLEDs, and other prominent devices, providing a step forward to facilitate more efficient data/information transfer from optical wireless technologies.
# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 3 Results
### Leading Edge Slope (LES)  
In addition to NBRCS, the current CyGNSS ocean wind speed retrieval algorithms use a second observable, the LES of the waveform of reflected GPS power. In view of the findings relating to NBRCS, similar investigations of CyGNSS LES data were performed, temporally and in relation to SVN/block and geometry, with very similar results (Appendix A). However, some additional dependencies were found, which are presented here. It needs to be noted that, although the analysis shows LES values converted to dB (i.e., 10 log10), these are not directly representative of any power property and, as such, are not directly comparable to equivalent NBRCS values.  
Global maps of LES for a given bistatic configuration show similar geographical patterns as for NBRCS in Figure 4 (not shown). However, the geographical distribution of average LES (Figure 7 top) reported by a single receiver (FM03), averaged across all transmitters, reveals the presence of an additional spatial pattern. Well-defined areas of high and low LES are observed across the global ocean, with differences reaching \(\sim\)1.5 dB. These patterns show strong resemblance to the contours of the geoid (Figure 7 bottom). This is likely related to how the LES is estimated from the DDMs and suggests that the calculation needs to be further refined in order to prevent the sea surface height from contaminating the LES observable and, subsequently, the L2 ocean wind products.  
Figure 7: (top) Geographical distribution of leading edge slope (LES) at U10 \(\sim\)7m/s reported by FM03 averaged across all SVNs over August 2018–December 2019; (bottom) global map of geoid (EGM 2008) [22].
# Frequency Response of Optically Pumped Magnetometer with Nonlinear Zeeman Effect
###### Abstract  
Optically pumped alkali atomic magnetometers based on measuring the Zeeman shifts of the atomic energy levels are widely used in many applications because of their low noise and cryogen-free operation. When alkali atomic magnetometers are operated in an unshielded geomagnetic environment, the nonlinear Zeeman effect may become non-negligible at high latitude and the Zeeman shifts are thus not linear to the strength of the magnetic field. The nonlinear Zeeman effect causes broadening and partial splitting of the magnetic resonant levels, and thus degrades the sensitivity of the alkali atomic magnetometers and causes heading error. In this work, we find that the nonlinear Zeeman effect also influences the frequency response of the alkali atomic magnetometer. We develop a model to quantitatively depict the frequency response of the alkali atomic magnetometer when the nonlinear Zeeman effect is non-negligible and verify the results experimentally in an amplitude-modulated Bell-Bloom cesium magnetometer. The proposed model provides general guidance on analyzing the frequency response of the alkali atomic magnetometer operating in the Earth's magnetic field. Full and precise knowledge of the frequency response of the atomic magnetometer is important for the optimization of feedback control systems such as the closed-loop magnetometers and the active magnetic field stabilization with magnetometers. This work is thus important for the application of alkali atomic magnetometers in an unshielded geomagnetic environment.  
frequency response; nonlinear Zeeman effect; optically pumped magnetometers +
Footnote †: journal: Applied Physics Letters
# Mobility-Multihoming Duality
## 6 Results of Evaluation
### TCP Throughput  
Figure 10a on page 27 shows the throughput observed. With the target throughput of 10 Mbps, the observed throughput remained consistent. While the 200 ms scenario experienced a slightly wider variation in observed throughput, the spread was, at most, 0.04 Mbps (0.4%). Some of this variation was due to the packet pacing used by iperf2 to maintain the target throughput.
# Developing a Dead Fish Recognition Model Based on an Improved YOLOv5s Model
## 4 Results and Analysis
### Ablation Experiment  
The design starts with the YOLOv5s model, first incorporating the DWMConv module, then embedding the EMA within the C3 module, and finally adding the C3-Light module. This setup aims to verify the impact of each improvement on performance. The experimental results are shown in Table 1, where \(\surd\) indicates that the module is used and \(\times\) indicates that the module is not used.  
It can be observed that the addition of the DWMConv module results in a decrease in precision by 0.5%, while recall increases by 2%. The average precision mAP\({}_{0.5}\) and mAP\({}_{0.5\sim 0.95}\) improve by 2.2% and 2.9%, respectively. However, the model's floating-point computation increases by 1.8G, and the number of parameters increases by 10.1%. Next, embedding the EMA mechanism leads to an increase in precision of 4.9% and a decrease in recall of 0.7%. The average precision mAP\({}_{0.5}\) and mAP\({}_{0.5-0.9}\) show improvements of 2.5% and 2.1%, respectively, while the floating-point computation decreases by 1.5G and the number of parameters decreases by 11.1%. Finally, the addition of the C3-Light module results in precision and recall rates of 93.6% and 77.5%, respectively, with average precision mAP\({}_{0.5}\) and mAP\({}_{0.5-0.9}\) reaching 87.5% and 49.8%. The model's computation decreases by 0.2 G compared to YOLOv5s, and the number of parameters decreases by 9.69%. In summary, the sequential addition of the DWMConv module, C3_EMA module, and C3-Light module effectively improves the model's performance.  
Figure 9 demonstrates the change curve of mAP\({}_{0.5}\) during the training process, and it can be seen that the mAP\({}_{0.5}\) of the model is greatly improved and the volatility disappears after the addition of DWMConv convolution, which indicates that the method of multi-scale feature fusion is effective. The mAP\({}_{0.5}\) is further improved after adding C3-EMA. After adding the C3-Light lightweighting module, the curves are highly overlapped with those when adding the C3-EMA module, which shows that C3-Light does not degrade the accuracy of the model while performing the lightweight operation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**DWMConv** & **C3-EMA** & **C3-Light** & **P/\%** & **R/\%** & **mAP\({}_{0.5}\)** & **mAP\({}_{0.5-0.9}\)** & **FLOPs/G** & **Params/106** \\ \hline \(\times\) & \(\times\) & \(\times\) & 89.7 & 75.9 & 83.0 & 44.5 & 15.9 & 7.02 \\ \(\surd\) & \(\times\) & \(\times\) & 89.2 & 77.9 & 85.2 & 47.4 & 17.7 & 7.73 \\ \(\surd\) & \(\surd\) & \(\times\) & 94.1 & 77.2 & 87.7 & 49.5 & 16.2 & 6.87 \\ \(\surd\) & \(\surd\) & \(\surd\) & 93.6 & 77.5 & 87.5 & 49.8 & 15.7 & 6.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of ablation experiment.  
Figure 8: The precision, recall, and mAP\({}_{0.5}\) curves of the model before and after improvement.
# Compilation of a City-Level & Four-Digit Industry Code MRIO Table Based on Firm-Level Data  
Jieping Chen  
School of Economics and Management, Tongji University, Tongji Building A, Siping Road 1500, Shanghai 200092, China School of Business, East China University of Science and Technology, Meliong Road 130, Shanghai 200237, China  
Shanlang Lin  
School of Economics and Management, Tongji University, Tongji Building A, Siping Road 1500, Shanghai 200092, China School of Business, East China University of Science and Technology, Meliong Road 130, Shanghai 200237, China  
[PERSON]  
Scholars have attempted to compile various multi-region input-output (MRIO) tables for different countries. However, due to city-level data scarcity and methodology constraints, almost no MRIO table covers a large number of cities with more disaggregated sectors in countries with large economies, such as China. Based on two large-scale firm-level datasets, the China Annual Survey of Industrial Firms (CASIF) survey and the China Customs Data (CCD) database, from 2000 to 2013, this paper uses China as a case study and presents a new compilation method to construct an MRIO table covering 284 prefecture-level administrative cities and 334 four-digit sectors, which is by far the most comprehensive MRIO table with the largest number of cities and the most segmented industries in China. Unlike existing MRIO tables constructed based on provincial single-region IO (SRIO) tables, we use information along with various linear constraints implied by sector-level and firm-level statistics. This paper expands on the direct decomposition method by developing auxiliary econometric models necessary for estimations and consistency adjustment. In addition, a comparative analysis shows the reliability of our method, which guarantees better coherence and comparability with the MRIO officially published by the National Bureau of Statistics of China (NBS). Therefore, our proposed methodology provides the possibility of producing more disaggregated MRIO tables in other similar contexts.  
multi-region input-output (MRIO); city level; expanded direct decomposition method; four-digit sectors (China)
## 3 Results and Discussion
### Sensitivity Investigation  
Sensitivity investigation was performed to find the most substantial input variable in predicting relative energy dissipation (%) of the spillway (Table 8). For this investigation, the stochastic M5P based model was used because it produced the best results. It is apparent from Table 8 that four input combinations were considered and in three of them, one input variable was omitted. Results from the table conclude that spillway slope in degrees (\(\alpha\)) is the most influential input variable in predicting the relative energy dissipation (%) of the spillway in comparison to other input variables used in this study. The second essential parameter is \(\rm H_{dam}/d_{c}\) in the prediction of the relative energy dissipation with respect to CC and RMSE statistics.  
For validation of the best model, the stochastic M5P model, it was also used for prediction based on the data set that resulted from the original equation by [PERSON] [29]. The developed empirical formula, by [PERSON] [29], for calculating the energy dissipation of stepped spillways in the skimming flow is listed as Equation (21), where \(f\) is the friction factor and \(\alpha\) is the spillway slope. Figure 11 shows the scatter plots of the results obtained from the stochastic M5P model and resulting data from Equation (21). Figure 11 illustrates the agreement between the predicted values and the resulting data from Equation (21) and it is clear that the stochastic M5P model has powerful generalization ability for the prediction of stepped spillways energy dissipation in skimming flow.  
Figure 10: Contours of energy loss for stepped spillways with 30 degrees in slope.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Combination**} & **Input Parameter** & \multicolumn{2}{c}{**Stochastic M5P**} \\ \cline{3-4}  & **Removed** & **CC** & **RMSE (\%)** \\ \hline N, \(\rm H_{dam}/d_{c}\), \(\alpha\) & 0.9993 & 0.8246 \\ \(\rm H_{dam}/d_{c}\), \(\alpha\) & N & 0.9862 & 3.691 \\ N, \(\alpha\) & \(\rm H_{dam}/dc\) & 0.7953 & 13.515 \\ N, \(\rm H_{dam}/d_{c}\) & \(\alpha\) & 0.7818 & 13.90 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Sensitivity investigation based on the stochastic M5P model.
# Observing APOD with the AuScope VLBI Array
## 5 Data Analysis
At first glance, the distinct bent shape in Scan 169 can lead to the assumption that this is caused by elevation-dependent effects, such as troposphere delays. However, investigations showed that this signature can be explained by a constant offset in the satellite's along-track position present in the a priori orbit data that were used to determine the theoretical delays. The second panel in Figure 12 depicts the o-c time series derived by the same observation data, but with different constant along-track offsets (from \(+10\) to \(-10\) m) applied when modeling the computed delays. Typically, the largest uncertainty in orbit determination is present in the along-track direction, especially in the case of LEO satellites due to the predominant effect of the atmospheric drag, which is constantly decelerating the satellite (e.g., [44; 45]). Considering the low quality of the used orbit data (see Section 2.2), deviations of 10 m (or even more) along-track can be expected. When applying a constant along-track offset of about \(-8\) m on the a priori orbit, the bent shape disappears. The remaining constant offsets between the two scans can be explained by uncorrected clocks. Such o-c values could in principle be used to estimate all parameters that are usually determined by the geodetic VLBI technique, plus satellite orbit parameters. Due to the severe inaccuracies in the a priori orbit data, it is mandatory to estimate orbit parameters. Otherwise, unmodeled orbit errors would propagate into other estimates. Basically, VieVS provides features to estimate satellite  
Figure 12: Residual delays (o-c) and modeled delay corrections of Scans 168 and 169 of Experiment a332. From top to bottom: (1) observed delays (ionosphere free linear combination) minus computed delays (o-c), (2) o-c residuals with the APOD position shifted by values between \(-10\) and \(+10\) m in the along-track direction (shift amounts are color-coded, no shift indicated by black line), (3) observation elevations at Stations 1 (blue crosses) and 2 (red diamonds), and (4) applied corrections for the hydrostatic (red squares) and wet parts (green crosses) of the atmosphere and the sum of both (blue dots). position offsets in terms of piece-wise linear functions, which refers to a kinematic orbit modeling approach. The presented APOD observations were used to validate and test these features. However, for kinematic orbit modeling, the available VLBI observations are insufficient. Firstly, the total number of available tracks is by far too low. Secondly, a global tracking network would be required, rather than a regional one, to acquire observations distributed over whole orbit arcs. A global network would also yield more observations in total.
## 4 Evaluation Study
### Setup
#### 4.1.1 Attack Scenarios  
We focused on seven attack scenarios, which are critical for CAVs and IoV systems: GNSS signal spoofing, GNSS signals jamming, CAN DoS attack, alteration of CAN messages, alteration of camera stream, false sensor reading, and malware/ransomware attack. Table 2 presents a brief description of them, and Figure 3 illustrates the IoV environment and the compromised components for each scenario.  
Figure 2: The activity diagram of the primary functionality (behavior) of CAVeCTIR.
## 5 Experimental Evaluations  
In this section, we will look at the overall effectiveness of COME-UP. After examining the influence of mobility prediction on latency, energy consumption, and server utilization, the LDPM is rated for its effectiveness. Furthermore, we compare COME-UP to two baseline approaches, DMPO [8] and TAOM [9], to see which one performs better. In addition, we examine the system's performance with and without the user direction prediction in the suggested framework to see which is better. When the framework takes into account the user direction, it is referred to be COME-UP, and when it does not, it is referred to as COME-WP.
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
###### Abstract  
Unmanned pavement construction is of great significance in China, and the primary issue to be solved is how to identify the boundaries of the Pavement Construction Area (PCA). In this paper, we present a simple yet effective method, named the Bidirectional Sliding Window (BSW) method, for PCA boundary recognition. We first collected the latitude and longitude coordinates of the four vertices of straight quadrilaterals using the Global Positioning System--Real Time Kinematic (GPS-RTK) measurement principle for precise single-point positioning, analyzed single-point positioning accuracy, and determined the measurement error distribution models. Next, we took points at equal intervals along one straight line segment and two curved line segments with curvature radii of 70 m to 300 m, for simulation experiments. BSW was adopted to recognize the Possible Irrelevant Points (PIP) and Relevant Points (RP), which were used to identify PCA boundaries. Experiments show that when the proposed BSW algorithm is used and the single-point positioning accuracy is at the centimeter level, PCA boundary recognition for straight polygons reaches single-point positioning accuracy, and that for curved polygons reaches decimeter-level accuracy.  
boundary recognition; pavement construction area; Bidirectional Sliding Window; precise single-point positioning; Global Positioning System; Real Time Kinematic; Possible Irrelevant Points; Relevant Points +
Footnote †: journal: Applied Sciences