esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 5 Challenges and Future Research Directions
As compared to the OFDM system, the filter bank multicarrier (FBMC) system does not require a CP, so it makes the use of radio resources more efficient. Therefore, MC for FBMC can be a future problem. In NOMA, if a different user uses a different modulation format, then MC for NOMA can be a challenging task. As compared to the OFDM system, orthogonal time frequency space (OTFS) has significantly high error performance over delay-Doppler channels with a wide range of Doppler frequencies. MC for OTFS can be a future research problem for designing advanced wireless communication systems. Due to the high peak-to-average power ratio (PAPR), it is difficult to use OFDM on the uplink. To overcome this problem, single-carrier frequency division multiple access (SC-FDMA) is used on the uplink. Therefore, the MC algorithm for SC-FDMA needs to be developed. MC for multicarrier code-division multiple access (MC-CDMA) can also be a critical research problem for future wireless communication. Conceptualization, A.K. and S.M. ; methodology, A.K. ; validation, S.M. ; formal analysis, A.K. and S.M. ; investigation, A.K. and S.M. ; writing, original draft preparation, A.K. and S.M. ; writing, review and editing, A.K., S.M., G.G., H-C.W. and C.Y. ; visualization, A.K., S.M., G.G., H-C.W. and C.Y. ; supervision, S.M., G.G., H-C.W. and C.Y. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the Ministry of Electronics and Information Technology, Government of India, under Project 13 (2)/2020-CC&BT and in part by the Empowerment and Equity Opportunities for Excellence in Science schemes by the Science and Engineering Research Board through the Department of Science and Technology, Government of India, under Project EEQ/2018/000201. Not applicable. The authors would like to thank [PERSON], Memorial University, Canada for the precious help and suggestions. The authors declare no conflict of interest.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 1 Introduction
This revealed key features and their values (high or low) that impacted security outcomes, improving threat mapping for various 6G scenarios. Additionally, we analyzed the correlations between these features, which allowed us to differentiate between malicious and benign activities, offering a clearer understanding of potential security threats. We then mapped these insights to identify the relevant security threats and performance issues that each 6G scenario may face. Based on these findings, we proposed targeted mitigation strategies that address the unique vulnerabilities and requirements of each usage scenario. Furthermore, we offer security enhancements derived from XAI analysis, enabling security administrators to gain actionable insights and implement targeted security measures tailored to the specific needs of each 6G usage scenario. By making AI-driven decisions more transparent and understandable, our work fosters greater trust in AI-powered security solutions. This not only strengthens the security of healthcare systems, but also supports better-informed decision-making in critical healthcare environments, ensuring the reliability and protection of medical data and devices. The key contributions of this paper include the following:  
* Comprehensively mapping the emerging vulnerabilities of 6G usage scenarios within the healthcare domain, using the authoritative ITU-R IMT-2030 framework as a foundation. * Leveraging and synthesizing SHAP, LIME, and DiCE to identify and interpret model behavior--uncovering the hidden patterns and feature sensitivities that expose critical security flaws in 6G-enabled IoMT environments. * Cross-validating multiple XAI methods to assess the consistency and reliability of feature importance in model predictions. * Aligning XAI insights with the specific needs and challenges of each 6G use case to identify relevant threats and performance issues. * Proposing targeted mitigation strategies to address the unique vulnerabilities and requirements of each 6G usage scenario's. * Providing XAI-driven security enhancements to empower administrators with actionable insights and targeted security measures tailored to each 6G use case. The remainder of the paper is organized as follows: Section 2 reviews the relevant literature on 6G security in healthcare. Section 3 discusses the security challenges associated with 6G IoMT applications. Section 4 outlines the materials and methods used in this research, including the application of XAI techniques. Section 5 presents the experiments and results, focusing on the identification of key features and vulnerabilities. Section 6 discusses the cross-validation of XAI methods and the consistency of feature importance. Section 7 proposes targeted mitigation strategies tailored to the unique requirements of each 6G usage scenario. Finally, Section 8 concludes the paper.
# A Mathematical Study of COVID-19 Spread by Vaccination Status in Virginia
## 3 Results
### Modeling with Different Vaccine Coverage Levels  
The sensitivity analysis depicted in Figure 3 suggests that the cumulative cases and effective reproduction number (\(R_{t}\)) are highly dependent on the number of individuals who become vaccinated (\(M_{1}\) and \(M_{2}\)). To further explore this, we increase/decrease the vaccination levels in the model (2) with parameters given in Table 2 in order to (a) predict what the spread of COVID-19 would have been during period 23 January through 18 September 2021 and (b) forecast the spread of COVID-19 through 31 December 2021. For the purpose of forecasting COVID-19 spread after 18 September, we use the transmission value of the final interval (\(\beta(t)=\beta_{8}=0.453\)).  
The results of simulations with values of \(M_{1}\) and \(M_{2}\) chosen between \(10\%\) more and \(10\%\) less of the overall Virginia population are contained in Figure 4. The model predicts a \(5\) times increase in new cases from 18 September through 31 December if \(10\%\) less of the population had been vaccinated. The model also predicts that the Delta surge experienced in August and September 2021 could have been largely avoided if an additional \(10\%\) of the population had been vaccinated.  
Figure 4: Simulations of the two-stage vaccination model (1) with parameters from Table 2 and differing values of overall vaccine uptake (\(M_{1}\) and \(M_{2}\)). The transmission rate during the period September 18 through 31 December 2021 is assumed to be that of the final fit period (i.e., \(\beta(t)=\beta_{8}=0.453\)). On the left, forecast values through 31 December 2021 are shown for cumulative cases, hospitalizations, and deaths given current vaccination uptake, 10% more vaccination, and 10% less vaccination.  
Figure 3: Sensitivity analysis using Equation (11) on the cumulative cases of COVID-19 in Virginia (left column) and \(R_{t}\) (the effective reproduction number, right column) with respect to vaccination parameters (\(M_{1}\), \(M_{2}\), \(\alpha_{1}\), and \(\alpha_{2}\)) and recovery rates (\(\gamma_{0}\), \(\gamma_{1}\), and \(\gamma_{2}\)). Note the negative impact of the parameters on the cumulative cases, but a non-trivial impact on the effective reproduction number as population sizes change in each vaccination stage through time. Further notice the small influence that \(M_{1}\) has on both outputs, until later in the simulation.
## 3 Analysis Results
### Spatial and Temporal Distribution of TEC Anomalies
Obvious disturbance  
Figure 4: Variation of TEC direct observations with the date (X axis) and distance from the epicenter (Y axis). Figure 3: TEC spatiotemporal distribution map. The dates of panels (**a–e**) are 28 July (11 days before the earthquake), 29 July (10 days before the earthquake), 1 August (7 days before the earthquake), 2 August (6 days before the earthquake), and 8 August (the day of the earthquake). (The red star represents the position of the epicentre.) anomalies appeared within about 3000 km kilometers from the epicenter within 2-3 UT on 29 July, which were located in areas farther to the north from the spatiotemporal distribution map in Figure 3b and should have little relationship with the Jiuzhaigou earthquake. Additionally, a section of obvious TEC-lowering anomalies existed within 500 km from the epicenter at 12 UT, which gradually became farther away with time to the epicenter. Further analysis revealed that on August 1, there were obvious disturbances in different time periods from UT12 to UT18 within 2500 km from the epicenter (Figure 5c). On 2 August, the anomalies were concentrated around UT12, and the anomalies were more apparent in the area around 500 km from the epicenter. Strong positive and negative perturbations on August 7 mainly appeared around UT22, as shown in Figure 5d. The anomalies were evident from 1 to 9 h before the earthquake on 8 August, and at about 5-9 h before the quake, the anomalies gradually moved closer to the epicenter with the passage of time. In the process of studying the spatial distribution of ionospheric TEC anomalies, [PERSON] et al. [53] found that the distribution of TEC anomalies on the daily scale tended to move closer to the epicenter 8-9 days before the earthquake, which may be related to the stress accumulation around the fault making anomalous phenomena cluster near the epicenter. This may be the advantage of GEO's high spatial and temporal resolution, which allows us to observe abnormal changes on an hourly scale. However, in the following 1-5 h before the quake, the anomalies showed a tendency to become farther away with time. It may be that the perturbed electric field caused by the earthquake increases the equatorial fountaining effect [54; 55], which intensifies the equatorial ionization anomaly (EIA). It can be seen in the sub-panel at UT12 in Figure 3e that the northern crest of EIA is enhanced. Previous studies using GPS TEC data have detected the changes in TEC near the epicenter before the earthquake on 8 August, while they did not find this pattern of anomaly variation over time and distance around the epicenter [32; 33; 34; 35]. Therefore, the utilization of high-resolution TEC data from BDS GEO observations is uniquely advantageous in revealing ionospheric perturbations. Figure 5: \(Cont.\)  
Figure 5: TEC variation with the date (X axis) and distance from the epicenter (Y axis). The red dashed line represents the time of the earthquake. The dates of panels (**a–d**) are 24 July to 8 August, 28 July to 29 July, 2 August to 2 August, and 7 August to 8 August.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 4 Photocatalytic Oxidation
### Humidity  
Humidity significantly affects the photocatalytic degradation of volatile organic compounds (VPCs). In high humidity conditions, water molecules can compete with pollutant molecules for active sites on the photocatalyst, thereby reducing adsorption efficiency and photocatalytic reactions. Excess water may also act as recombination centers for charge carriers, diminishing photocatalytic activity [90]. Conversely, high water concentrations can promote the formation of hydroxyl radicals that oxidize pollutants, potentially enhancing photocatalytic efficiency. This dual effect complicates the identification of an optimal humidity level for maximizing the removal efficiency of indoor pollutants. It appears that a specific amount of water is necessary to initiate photocatalysis; however, exceeding this concentration may decrease efficiency due to competition for active sites [91].  
Moreover, the impact of humidity on photocatalytic efficiency varies with the type of pollutant. For example, higher humidity levels may enhance the degradation of acetaldehyde but reduce the efficiency of ethanol, benzene, and toluene. This variation is due to the differing interactions between the pollutant molecules and the photocatalyst. Photogenerated electrons can be trapped at the semiconductor's active sites, hindering the direct oxidation of adsorbed carbonyl compounds like acetaldehyde. However, in high humidity, these compounds can be indirectly oxidized by hydroxyl radicals generated from water oxidation. In contrast, pollutants like ethanol efficiently adsorb onto the catalyst's active sites, resulting in reduced oxidation efficiency in the presence of water due to limited interactions with hydroxyl groups [91; 92].  
This pollutant-specific effect of humidity underscores the challenges of applying photocatalysts in indoor environments where multiple pollutants may coexist. The varying responses necessitate a tailored approach to optimize photocatalytic degradation efficiency for each target pollutant, complicating the implementation of photocatalysis in diverse indoor air quality management scenarios [91; 92].  
Contradictory results have emerged even when studying the same pollutant. For instance, while one study reported that higher humidity levels decreased the efficiency of photocatalytic oxidation for NO [93], another found an increase in degradation efficiency with rising humidity [77].  
Research on the photocatalytic removal of formaldehyde indicates that the optimal relative humidity for TiO\({}_{2}\) is approximately 45% [94] (Figure 2a). In contrast, a study on g-C\({}_{3}\)N\({}_{4}\)-TiO\({}_{2}\) revealed that pollutant degradation decreased from 60% to 38% as relative humidity increased from 20% to 80% [95] (Figure 2b).
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 2 Materials and Methods
### Pile Test Site  
The test site for in situ CPT testing and experimental measurement of piles is part of the city bypass with bridges designated as SO--201 to SO--204 (Figure 1). A geological survey involving boreholes and dynamic penetration tests (DPTs) was performed to describe the geological conditions in the designed route. For the research purpose, additional CPTu probes with continuous record of data were realized using piezoelectric cone with sensor for measurement of pore pressure \(u_{2}\). Probes were situated at already constructed bridges SO--201 to SO--204, near the foundations of pillars (Figure 1).  
Detailed geological characterization is mentioned in the survey report [29]. The terrain of the locality is mainly planar with elevation of 153.8 to 154.3 m above sea level. Top-soil layers consist of eolian deposit, namely washed-out clayed loess. Fluvial sands and gravels were located in lower positions and they act as an aquifer for groundwater. The typical geological profile is described below:* Topsoil with humus with thickness of 0.3-0.5 m;
* Silt with intermediate plasticity and firm consistency with thickness of 0.3-0.7 m;
* Loess-eolian clay with low to intermediate plasticity and stiff to hard consistency with tubules with thickness of 7.6-10.1 m. Some positions are washed-out;
* Fluvial deposits-clayey sand, sand to gravel, clayge gravel and gravel with loose to medium density with the thickness of 0.9-3.1 m;
* Neogene deposits-clay with intermediate to high plasticity and firm to stiff consistency, sandy clay to clayge sand.  
Groundwater level was located in the fluvial sand and gravel deposits during survey. Collapsibility of loess was investigated by the oedometric tests where the coefficient of collapsibility \(I_{m}\) was in the interval from 0.20 to 3.63%. Soil is considered collapsible if \(I_{m}>1.0\%\). Most of the quaternary clay layers were declared as collapsible [29]. Loess represents a risk factor for ultimate and serviceably limit state of the structure because of the lower structural stability of the soil skeleton [30; 31; 32; 33]. Therefore, a pile foundation was proposed for realized bridges with load testing of selected piles. Additionally, monitoring of the structure during service life using geodetic methods, laser scanning, or various types of sensors can be adopted [34; 35; 36; 37].
## 3 Results and Discussion
### Quantitative Evaluation  
The results achieved by the network after training and validating the CNN using the five-fold cross-validation are summarized in Table 1. Figure 3 presents the ROC curve for each of the cross-validation folds, which also reports their corresponding AUC values.  
As can be seen, the achieved results demonstrate that the system is able to generalize well, obtaining similar and stable results across the different folds. Each of these sets achieved balanced accuracies greater than 91%, and AUC values above 0.97, which confirms that the system is very reliable when performing the classification. After calculating the average of the metrics obtained over all of the different cross-validation folds, the system achieved 92.53% sensitivity, 96.33% specificity, 93.76% precision, 93.14% F1-score, 94.43% balanced accuracy, and an AUC value of 0.988.  
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Fold Test**} & \multirow{2}{*}{**Actual Classes**} & \multicolumn{3}{c}{**Predicted Classes**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**Balanced Accuracy**} \\ \cline{3-3} \cline{5-10}  & & **Normal** & & & & & & & \\ \hline
1 st fold & Normal & 851 & 16 & 96.71\% & 98.15\% & 96.89\% & 96.8\% & 0.997 & 97.43\% \\ \hline  & & 98.15\% & 1.85\% & & & & & & \\ \hline  & COVID-19 & 17 & 499 & & & & & & \\ \hline  & & 3.29\% & 96.71\% & & & & & & \\ \hline
2 nd fold & Normal & 839 & 28 & 94.00\% & 96.77\% & 94.54\% & 94.27\% & 0.990 & 95.38\% \\ \hline  & & 96.77\% & 3.23\% & & & & & & \\ \hline  & COVID-19 & 31 & 485 & & & & & & \\ \hline  & & 6\% & 94\% & & & & & & \\ \hline
3 rd fold & Normal & 834 & 33 & 93.02\% & 96.19\% & 93.57\% & 93.29\% & 0.989 & 94.61\% \\ \hline  & & 96.19\% & 3.81\% & & & & & & \\ \hline  & COVID-19 & 36 & 480 & & & & & & \\ \hline  & & 6.98\% & 93.02\% & & & & & & \\ \hline
4 th fold & Normal & 815 & 52 & 88.95\% & 94.00\% & 89.82\% & 89.39\% & 0.976 & 91.48\% \\ \hline  & & 94\% & 6\% & & & & & & \\ \hline  & COVID-19 & 57 & 459 & & & & & & \\ \hline  & & 11.05\% & 88.95\% & & & & & & \\ \hline
5 th fold & Normal & 839 & 30 & 90\% & 96.55\% & 93.98\% & 91.94\% & 0.986 & 93.27\% \\ \hline  & & 96.55\% & 3.45\% & & & & & & \\ \hline  & COVID-19 & 52 & 468 & & & & & & \\ \hline  & & 10\% & 90\% & & & & & & \\ \hline Average & Normal & 96.33\% & 3.67\% & 92.53\% & 96.33\% & 93.76\% & 93.14\% & 0.988 & 94.43\% \\ \hline  & COVID-19 & 7.47\% & 92.53\% & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross-validation results for each of the folds, where sensitivity, specificity, precision, F1-score, AUC, and balanced accuracy are reported. The average of these metrics over the different folds are also shown.  
Figure 3: **(Left)**: ROC curve for each cross-validation set. (**Right**): zoomed in at top left. AUC values are shown in the legend.
###### Abstract  
Maritime Autonomous Surface Ship (MASS) has been developed recently, and demonstration projects have been carried out internationally. Considering the full autonomous level is unlikely to be addressed shortly, remote control centre and Remote Operator (RO) will play a vital role in the MASS system. Although competence of watchkeeping at the ship's bridge is inevitable for RO to avoid ship accidents caused by human errors, international requirements have not been introduced yet. This paper presents a way to develop the regulatory framework on the competence of RO based on the International Convention on Standards of Training, Certification and Watchkeeping for Seafraers (STCW) by exploring the concept of Situation Awareness (SA). Goal-Based Gap Analysis (GBGA) is constructed based on the human-behaviour model and the required information for SA. A case study through the mini focus group discussion with interviews by a total of three (3) veteran instructors of training ships is conducted utilising the information including the results of previous demonstration projects on the remote control. The results show the relationship between required information and ship sense, shortage of these factors when RO is operating, additional competence and possible regulatory requirements for RO. The findings support the usefulness of GBGA and pave the way to develop a regulatory framework for RO further.  
maritime autonomous surface ship (MASS); remote operator; situation awareness; goal-based gap analysis; competence; STCW +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Flow Volume and Velocity Measurements  
As expected by the model's performance based on DSC and ROC statistics, the models demonstrated similar performance for measuring flow volumes with the ground truth. Whilst Nested and Attention U-Nets were able to detect these arteries, the boundaries created for them may not be consistent with the ground truth, achieving a high DSC. The Bland-Altman analyses implied that when compared with the ground truth, the models achieved good agreement with most data points within the standard deviation limits. This was true for the Standard and Attention U-Net, showing potential for fine-tuned single artery accuracy for this application. Specifically, comparisons with the ground truth further exhibited that each artery had similar impacts of segmentation accuracy in the three deep learning models. In general, Nested U-Net showed the most promise among these three models in breaking the barrier between accurate segmentation results and flow calculation.