esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing an Adaptive Underwater Visible Light Communication System +
## 2 Literature Survey
Power and BER curves for PPM were obtained to study the effect of receiver angular displacement on the received signal. Table 1 on the next page briefly discusses relevant studies in the literature (second column) and summarizes contributions of our work (third column), which addresses gaps in the current research. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Focus** & **Research Gap Addressed** \\ \hline [PERSON] et al. [8] & Angle diversity and rate-adaptive technique for visible wireless communication. & Angle diversity technique at the receiver end for UVLC. \\ \hline [PERSON] et al. [9] & Adaptive beam steering technique free-space optical systems in high-speed trains. & 1. Adaptive beam by changing receiver’s position in UVLC. 2. Servo motor and Arduino integration. \\ \hline [PERSON] [10] & 1. Smart transceiver design. & 1. Adaptive UVLC system is implemented using SDR technology. 2. The receiver calculates the power and adapts to the direction of maximum beam intensity. 3. PPM modulation scheme. \\ \hline [PERSON] et al. [11] & 1. Misalignment addressed. 2. Adapting beam divergence angle. 3. Monte-Carlo-based simulation. & 1. Real- time UVLC system implemented by adapting the beam divergence with changing receiver’s position. 2.Li-Fi- and SDR-based UVLC system model is introduced. \\ \hline [PERSON] et al. [7] & Comparing various underwater technologies and the modulation schemes used. & Using PPM as modulation scheme for adaptive UVLC network. \\ \hline [PERSON] et al. [12] & 1. Addressing the misalignment in UWOC. 2. Considering NLOS UWOC. 3. OOK modulation scheme used. & 1. SDR-based technology. 2. Li-Fi receiver. 3. PPM modulation. \\ \hline [PERSON] et al. [13] & 1. Diffraction beam shaping technique for UWOC. 2. Adjusting transmitter distance dynamically. & 1. SDR-based. 2. Adjusting receiver position using servo motor and Arduino. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of relevant studies and research gap.
## 7 Conclusions and Prospects
### Prospects  
The deflection monitoring data repair method for steel truss bridges, as outlined in this paper, has yielded specific outcomes. However, there is still potential for enhancement and optimization, which will be realized through further development and integration of monitoring data processing and visualization technologies. Future research could be initiated in the following areas:  
(1) Bridge deflection values are constrained by the laws of physics and structural limits; however, the prediction process in the paper lacks physical constraints, such as the imposition of allowable deflection limits. This may lead to prediction bias in extreme cases, and this will be improved in subsequent studies.  
(2) The primary objective of this paper was to examine the deflection monitoring data of specific sections of the bridge to ensure its overall integrity. To this end, future research initiatives should be pursued that encompass diverse monitoring types and extend to theentire bridge. This comprehensive approach will ensure the comprehensiveness of the bridge monitoring data.  
(3) A more thorough examination of data repair and anomaly localization techniques could facilitate the optimization of neural network model design, enhancing the accuracy and efficiency of anomalous data repair.  
Y.C.: Conceptualization, Formal analysis, Methodology, Resources, Soft- ware, Funding acquisition, Project administration; R.L.: Conceptualization, Data curation, Writing--original draft, Methodology, Visualization; J.W.: Data curation, Writing--original draft, Methodology, Visualization; F.P.: Resources, Visualization, Investigation, Validation; F.L.: Resources, Data curation, Investigation, Validation; H.C.: Methodology, Investigation, Validation, Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Project of Construction Technology Research and Development of Fujian Province, grant number 2017-K-56, and Project of Construction Technology Research and Development of Fujian Province, grant number 2017-K-57.  
Not applicable.  
The datasets presented in this article are not readily available due to technical and time limitations. Requests to access the datasets should be directed to the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company Fuzhou Zuohai Holding Group Co., Ltd. Author [PERSON] was employed by the company Railway Bridge Science Research Institute, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 2 Faults in DC Microgrids
### Low-impedance Faults  
Low-impedance faults or short-circuit faults occur when a pole comes in contact with another pole or ground trough with low resistance, resulting in a significant voltage drop and high-magnitude current. In the case of an LIF, the contribution of the individual current sources to the fault depends mainly on the converter topology. Since DC microgrids are connected to the utility grid through a voltage source converter (VSC) and the largest contribution to the fault comes from the grid, its fault response is briefly described.  
The VSC fault response is divided into three stages: capacitor discharge, diode free-wheeling and grid-side current feeding [39]. During the capacitor discharge stage, its voltage drops from the nominal value to zero. Since the cable and fault resistances are low, the capacitor current reaches magnitudes that are about a hundred times higher than the rated current. When the capacitor is discharged, the diodes begin to conduct, marking the beginning of the diode freewheeling stage. The current remains high and the diodes are in danger of destruction. In the last stage, the grid starts feeding the fault, as the VSC behaves like a full-bridge rectifier. It is desirable to detect this fault before the fault current reaches its peak, and this can be achieved as its magnitude strongly deviates from that in normal operation.
## 7 Approaches to Prevent and Manage Infections Caused by Biofilms
### Antimicrobial Lock Solutions  
Antimicrobial lock solutions (ALS) have become an essential approach for preventing and managing biofilm-related infections in catheters. ALS employs a targeted method by administering elevated doses of antimicrobial drugs directly into the catheter lumen, establishing conditions that deter bacteria colonization and biofilm development. This focused approach improves treatment effectiveness and markedly decreases catheter-related bloodstream infections [328; 329]. ALS operates via many strategies to counteract biofilms. By introducing a concentrated antimicrobial solution into the catheter lumen [330; 331], ALS infiltrates the biofilm matrix, disrupts the EPS, and exposes embedded bacteria to elevated concentrations of antimicrobial chemicals [332; 333].  
The composition of ALS is contingent upon the implicated pathogens and the clinical situation. Antibiotic-based ALS often uses compounds like vancomycin and daptomycin. These solutions are aimed against Gram-positive bacteria, including _S. aureus_ and CoNS, which are prevalent in biofilm-related illnesses [334; 335]. Also, caspfungin has proven effective against drug-resistant _C. auris_ and Candida-bacterial polymicrobial biofilms during catheter-lock therapy when used in solutions with low ionic concentration [336].  
Non-antibiotic ALS, including ethanol and taurolidine, provide alternate strategies that reduce the likelihood of antibiotic resistance. Ethanol interferes with microbial membranes and solubilizes biofilm matrix, while taurolidine has extensive antibacterial efficacy and prevents biofilm development [337; 338]. Adjuvants such as ethylenediaminetetraacetic acid (EDTA) enhance the effectiveness of antibiotics by chelating vital metal ions that stabilize biofilms [339]. Combination therapy, such as minocycline-EDTA-ethanol solutions, has shown enhanced effectiveness in eliminating biofilms produced by _C. auris_[340].  
Notwithstanding their benefits, ALS encounters certain obstacles, notably the risk of antimicrobial resistance linked to extended use of antibiotic-based treatments. While non-antibiotic ALS reduces this danger, it must be meticulously given to prevent systemic toxicity, particularly in ethanol-based formulations. Future developments in ALS formulations will likely emphasize hybrid strategies that integrate antibiotics with non-antibiotic compounds or use nanotechnology to enhance effectiveness and reduce side effects. An overview of strategies to prevent and manage biofilm-related infections is shown in [Table 3].
# A New Low-Cost Compact Antenna for the 2.45 and 5.8 GHz ISM Bands
###### Abstract  
This paper presents the design of a high-performance dual-band antenna for industrial, scientific, and medical (ISM) band applications. The proposed prototype consists of a low-cost patch antenna, 40 mm \(\times\) 24 mm in size (i.e., 0.36\(\lambda_{0}\)\(\times\) 0.19\(\lambda_{0}\), with \(\lambda_{0}\) the wavelength corresponding to the low frequency), with a relatively wideband for both operational bands (up to 140 MHz at 2.45 GHz and 510 MHz at 5.8 GHz), and a radiation efficiency of over 90%. The antenna has a quasi-omnidirectional radiation pattern with gains of 2.41 dBi and 5.22 dBi at 2.45 GHz and 5.8 GHz, respectively. The design methodology is detailed and illustrated by simulation results showing the optimization steps and the characteristics associated with the antenna. Experimental results based on a fabricated prototype are presented and compared with simulation results from the design stage. Finally, the proposed antenna prototype is also compared with similar antennas available in the literature.  
antenna; patch antenna; ISM applications Article
## 3 Results and Discussion
### Volatile Compounds
#### 3.4.2 Norisoprenoids  
Norisoprenoids, primarily C\({}_{13}\)-norisoprenoids, are derived from the breakdown of grape carotenoids, such as lutein, \(\beta\)-carotene, violaxanthin, and neoxanthin. This process occurs through a complex series of mechanisms involving multiple intermediates. Their levels are largely influenced by soil, climate, and various pre-fermentation practices [45; 46]. Yeast activity during fermentation can further influence the formation of free volatile norisoprenoids [47].  
Among the 16 norisoprenoids identified in this study, only a few displayed significant variation among yeasts. A certain dichotomy was observed, since \(\beta\)-cyclocitral was more abundant in SCE wines, while both isomers of \(\beta\)-damascenone had higher concentrations in SPA wines from both harvest years (Table 5). The initial breakdown of carotenoids during vinicitation is catalysed by grape carotenoid cleavage dioxygenenses (VvCCDs) encoded by specific genes [46]. However, during fermentation, other yeast enzymes supported by acidic environment can modify various intermediates, influencing the final levels of norisoprenoids in wine, as shown previously for _trans_-\(\beta\)-damascenone [47]. It is possible that _S. paradoxus_ exhibited a stronger activity of enzymes involved in the reduction of its carbonyl precursors during fermentation, including alcohol dehydrogenases, aldose reductases, and NADPH reductases [47], which resulted in higher levels of \(\beta\)-damascenones in SPA wine. _Trans_-\(\beta\)-damascenone is the most prominent C\({}_{13}\)-norisoprenoid in wine due to its low odour detection threshold and appealing odour. It contributes to wine aroma with stewed apple and honey notes. The significantly higher concentrations of _trans_-\(\beta\)-damascenone observed in SPA wines in this study suggest that these aromas are likely to be more pronounced in wines produced by _S. paradoxus_ fermentation compared to those produced by _S. cerevisiae_. Wines from the 2022 harvest, aside from a virginare isomer, contained higher concentrations of norisoprenoids than 2021 harvest wines.
# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 5 Conclusions and Future Work  
Our proposed framework successfully overcomes the limitations of existing methods by integrating causal discovery and reinforcement learning to address delivery risks in supply chains. By uncovering the root causes of delivery delays, the model goes beyond mere prediction, offering a roadmap for proactive interventions that directly mitigate these risks. The inclusion of causal strength quantification ensures that the most impactful relationships are prioritized, allowing decision-makers to focus on the key drivers of delays. In our experiments, this approach consistently outperformed traditional models, providing clearer insights into the causal mechanisms behind delivery risks. These insights not only support short-term operational improvements but also inform long-term strategic decisions. Additionally, the framework's adaptability ensures its continued effectiveness as supply chains evolve, making it a valuable tool for maintaining operational efficiency and resilience.  
Looking ahead, several avenues for future research and improvement can be explored. First, the uncertainty in the calculation of causal strength could be further examined, addressing questions such as how confident we are in the existence of strong causal relationships between variables like \(X_{i}\) and \(X_{j}\). The model could also be fine-tuned to handle diverse types of data or adjust for varying supply chain conditions, increasing its robustness across different industries such as economics, healthcare, or manufacturing. Additionally, experimenting with alternative scoring functions and hyperparameters during the reinforcement learning process could further enhance the accuracy of causal discovery. The methodology could be expanded to include multi-modal data inputs or adapted for real-time data streams, providing dynamic and responsive insights that evolve with changing supply chain conditions. These advancements would broaden the applicability of our approach, making it a versatile tool for optimizing performance across a wide range of domains.  
Authors equally contributed to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** The datasets generated and/or analyzed during the current study are available in Kaggle. [[https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis](https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis)]([https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis](https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis)) (accessed on 31 August 2024)  
**Conflicts of Interest:** The authors declare no conflicts of interest
## 3 Guard Interval Analyses
### The 1 st-Order IIR Channel  
Owing to passive and active cancellation techniques, the multipath effect of the loop can be significantly suppressed [15]. Suppose that the loop's multipath effect is suppressed; the loop coefficient is only \(a(1)\). The guard interval under the 1 st-order IIR channel can be expressed as  
\[\overline{s}_{v}(0)=a(0)y_{v}(0)+a(1)y_{v-1}(0). \tag{26}\]  
Under the 1 st-order IIR channel, the guard interval complexity is related to the maximum delay of the loop as \(\mathcal{O}(Nlog_{2}(N)+l_{g}^{2}+3l_{g}+2)\), i.e., \(\mathcal{O}(Nlog_{2}(N)+6)\).  
The expectation of the guard interval is  
\[p_{v}(0) =\frac{\sigma_{d}^{2}}{N}\sum_{n=0}^{N-1}\left[\frac{a(0)a(0)^{*}+ a(1)a(1)^{*}}{A_{n}A_{n}^{n}}\right]\] \[=\sigma_{d}^{2}\frac{a(0)a(0)^{*}+a(1)a(1)^{*}}{a(0)a(0)^{*}-a(1) a(1)^{*}}, \tag{27}\]  
where the guard interval is only connected to the forward path \(a(0)\) and the loop \(a(1)\).  
Similarly, to ensure system stability, it is necessary to satisfy \(a(0)a(0)^{*}-a(1)a(1)^{*}>0\). In addition, the IIR channel coefficients also have to satisfy \(a(0)a(0)^{*}-a(1)a(1)^{*}\gg 0\) for the guard interval expectation to be in a reasonable range. It is demonstrated that for IIR-OFDM, suppressing loops maintains the system stability and reduces the guard interval expectation. However, loops cannot be completely suppressed and the performance of IIR-OFDM under IIR channels will outperform that of classical OFDM.
# Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles
(1) Although previous studies have developed various approaches to reduce the number of model parameters, they have largely focused on classification [9] and detection models [7; 8; 11]. In studies on SSWM, semantic segmentation models [12] have proven to be effective at weed mapping tasks [13; 14]; however, their optimization cannot be guaranteed based on the results of the aforementioned studies. One of the greatest obstacles for implementing such deep learning networks for widespread deployment on embedded devices is their high computational and memory requirements. In general, semantic segmentation models are difficult to optimize for small devices because they use 2D information that requires significant computation, making them less suitable for the research described in this paper. (2) The aforementioned studies only considered model design and optimization, while ignoring the optimization of the inference process, which may have the potential to achieve better efficiency. (3) Most previous studies have only conducted model design and validation on computers or embedded devices. However, in the context of SSWM applications, a real-time spraying machine requires modules for map visualization, flight control, image collection, image processing, and spraying. Based on the objectives of this study, it was necessary to construct a basic hardware environment to support modules for map visualization, route planning, image collection, and image processing. Therefore, the main goals of this study were to (1) develop a hardware system integrating map visualization, flight control, image collection, and image processing; (2) perform model design to achieve a compact size while maintaining reasonable prediction accuracy; and (3) exploit the optimization of the inference process, which can further improve inference speed. There are two main contributions of this work. First, we propose a lightweight network architecture for weed mapping tasks, which improves inference speed while maintaining reasonable accuracy. Second, we propose combining precision calibration with model design to improve speed, which has not been discussed in other related papers.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Simulation in Simulation Software  
In order to support the simulation based on VxWorks, we used Wind River Workbench 3.3.6 supporting the system of VxWorks 6.9 as the simulation software to simulate the protocol stack. The software interface is shown as follows:  
The simulation software creates the virtual machine running the VxWorks 6.9 system, which is called Vxsim. By using this, we can build a network of Vxsims with VxWorks 6.9 systems installed to simulate the space-based optical backbone network. As shown in Figure 10, we built a network topology of 16 nodes.  
Figure 10: Simulation software interface.  
Figure 9: Advanced orbiting systems (AOS) frame switching scheme diagram. LFIB: label forwarding information base.  
As shown in Figure 11, there are 16 nodes with nine ports each in the simulation network. As can be seen, each node has two markers: nodeID and IPv6 Address, which are defined as follows:  
1. NodeID: used to mark a node, while managing and controlling the network.
2. IPv6 Address: used to mark a node, while transmitting IPv6 services.  
It can also be seen, that there is a unidirectional link from node 11 (0x 000B) to node 7 (0x 0007), which means that data could be transmitted from the former to the latter, but the reverse is not true. Then, we simulated the protocol stack in the simulation network. We ran the protocol stack in each node and then recorded the information shown in the simulation software.
## 6 Conclusions
### Nomenclature  
\begin{tabular}{l l} \(\mathrm{C_{N}}\) & Battery nominal capacity \\ \(U_{L}\) & terminal voltage \\ \(U_{1}\) & voltage over RC network \\ \(U_{ac}\) & open circle voltage \\ \(R_{1}\) & polarization resistance \\ \(C_{1}\) & polarization capacitance \\ \(l^{+}\) & battery charge current \\ \(l^{-}\) & battery discharge current \\ \(k\) & time step index \\ \(\eta\) & coulomb efficiency \\ \(h_{\mathrm{k}}\) & hysteresis voltage \\ \(\gamma\) & positive constant to tune the rate of decay \\ \(S(k)\) & experimental data (battery voltage or SOC) at step \(k\) \\ \(\hat{S}(k)\) & estimated value (battery voltage or SOC) at step \(k\) \\ \(k\,\mathrm{l}\,k\)-1 & prior estimation state \\ \(\omega_{k}\) & system process noise \\ \(v_{k}\) & measurement noise \\ \(\xi\) & identification parameters \\ \(\mathrm{X}\) & a n-dimension vector for state space \\ \(\lambda\) & scaling parameter \\ \(W_{l}^{m}\) & weights of mean \\ \(W_{l}^{c}\) & weights of covariance \\ \(\alpha\) & small positive number \\ \(\beta\) & state distribution parameter \\ \(\kappa\) & scaling factor \\ \(Q_{k}\) & covariance matrix of the state noise \\ \(R_{k}\) & covariance matrix of the measurement noise \\ \(\mathrm{K_{k}}\) & Kalman gain \\ \(\epsilon_{\mathrm{k}}\) & residual error \\ \(V_{\mathrm{k}}\) & residual error sequence covariance matrix \\ \(\mu_{k}\) & fading factor \\ \multicolumn{2}{l}{**List of abbreviations**} \\ ANN & Artificial Neural Network \\ BMS & Battery Management System \\ BSF & Battery Size Factor \\ CDKF & Central-Difference Kalman filter \\ ECM & Equivalent Circuit Model \\ EKF & Extended Kalman Filter \\ EMF & Electro-Motive Force \\ EV & Electric Vehicles \\ FL & Fuzzy Logic \\ FUDS & Federal Urban Driving Schedule \\ HPPC & Hybrid Pulse Power Characterization \\ KF & Kalman filter \\ MAE & Mean Absolute Error \\ OCV & Open Circuit Voltage \\ RLS & Least Square Method \\ RMSE & Root Mean Squared Error \\ SOC & State of Charge \\ STF & Strong Tracking Filter \\ SVM & Support Vector Machine \\ UKF & Unscented Kalman Filter \\ \end{tabular}
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 6 Future Directions and Challenges  
This paper provides a comprehensive analysis of e-healthcare systems based on VLC and concludes by identifying key areas for future research and development. While VLC offers significant potential for transforming wireless communication in healthcare environments, several challenges must be addressed to fully leverage its capabilities. This section highlights future research directions and the technical obstacles that need to be overcome for the successful implementation of VLC in e-healthcare systems.