esa-sceva/satcom-chunk-collection huggingface dataset:
# Combating Pathogens Using Carbon-Fiber Ionizers (CFIs) for Air Purification: A Narrative Review
## 4 Discussion
### The Other Side of the Coin: How to Avoid Harmful Byproducts of Air Ionization?
Ozone gas has proven to inactivate norovirus and may be utilized in decontaminating empty areas; however, any spaces with people should be avoided due to its well-known toxic attributes [92]. Interestingly enough, [PERSON] et al. postulated that ozone has little to no bactericidal effects [18]. Corona discharge may create ozone, a harmful byproduct made up of three oxygen atoms, which is especially problematic when attempting to use an ionizer in closed environments or near humans [91]. It is crucial to note that both the quantity of electrons produced, which inadvertently influences the rate of ion emission, and the rate of ozone generation are directly linked to the amount of energy imputed into the system [89]. Some studies have attempted to decrease the ozone emission rate by placing a graphene coat over the metal wire [93]. Additionally, carbon brush ionizers have been noted to only discharge negligible amounts of ozone, defining a new age of biomedical engineering [5]. New innovative designs also include bipolar ionizers emitting negative corona discharge to avoid submicron ammonium nitrate buildup, which is a prominent concern when discussing ionizer micro-contamination [94].
# A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals
## 2 Problem Description and Modeling Approach  
### Problem Description  
Scheduling operations for multimodal freight transport in a synchronized manner involves effectively addressing the needs and objectives of multiple stakeholders with contrasting agendas. In this study, the problem revolves around the interactions between three distinct actors forming a multimodal chain, in an environment similar to the one depicted in Figure 1.  
The examined problem centers on synchronizing two interdependent processes: truck scheduling through an appointment system and berth allocation for inbound vessels. These processes share limited terminal resources and must be planned fairly and efficiently in a distributed environment where actors have conflicting objectives. Within this problem, the first actor is the vessels, which generate traffic from the seaside and expect quick access to a compatible berth and timely exit from the port to continue to subsequent destinations. The second actor is the logistics companies, which generate landslide traffic by dispatching trucks to the terminal to pick up or deliver cargo associated with the incoming vessels. The decision making of logistics companies is centered around reducing their deviation cost from their desired processing time-period arising from potential delays in vessel arrivals and departures or truck congestion. Finally, the terminal is acting as an intermediate point in the multimodal chain, orchestrating the transition between different transportation modes and processing the demand. The terminal is also expected to ensure that certain service levels are maintained by tracking Key Performance Indicators (KPIs). Such KPIs include the maximum arrival rate of trucks per hour, cargo levels in the storage areas, as well as limits related to resources such as internal vehicles to perform drayage operations.  
The system's actors are interconnected since they share resources such as berths, cargo handling equipment, and arrival windows. The requirement to synchronize processes among several stakeholders, who can have competing goals and little knowledge of one another's operations, further emphasizes the distributed aspect and need for distributed modeling methodology for this problem. Building on this, we further segment the problem setting it into two interrelated, distinct tasks: (1) truck scheduling at marine terminals, which is relevant for logistics companies; and (2) berth allocation for inbound vessels,  
Figure 1: Terminal layout for the examined supply chain.  
which directly impacts the vessels. Given the terminal's pivotal role as a coordinator, its own operations are affected by aspects of both tasks.
## 5 Experiment  
### Selected Cities and Their Data Description  
In this study, we selected cases from Singapore, Zurich, and Guangzhou to test the effectiveness of the constructed model. Singapore, Zurich, and Guangzhou are all representative cities in the world, facing their own challenges and opportunities in parking management and smart city construction. And the three cities have different geographical locations, economic development levels, and urban planning models. Singapore, as one of the financial centers in Asia, has highly intelligent urban management; Zurich is a typical city in Europe, emphasizing the coordinated development of transportation and environment; Guangzhou is a mega city in China, facing transportation and parking pressures brought about by rapid urbanization. Studying the data of these three cities can effectively help us verify the applicability and robustness of the model in different urban environments.  
From the perspective of parking, these cities are facing the problem of an imbalanced supply and demand of parking spaces. The high demand for parking in the urban centers of  
Figure 4: Schematic diagram of the PLGCRM module.  
Figure 5: The specific process of the multihead attention mechanism.  
Singapore and Zurich often leads to limited supply of parking spaces, resulting in difficulties in parking and traffic congestion. As a densely populated metropolis, Guangzhou has a more prominent problem of parking difficulties, especially in commercial and residential areas. By comprehensively reviewing the previous literature, we downloaded available parking space data from public data platforms: Zurich's Zurich (2018) available parking space data, Singapore's (2018) available parking space data, and Guangdong's (2018) available parking space data. VPS data are summarized at 5 min intervals, that is, there are 288 time steps for a day's VPS data, which includes street parking and off-street parking. During the data processing, this study encountered some outliers and missing values, which we solved for by applying the linear interpolation method. Table 1 summarizes the data spatio-temporal information of three cities.
# Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors
###### Abstract  
This paper investigates the factors influencing the probability of fatality in various types of maritime accidents, including grounding, capsizing, sinking, man overboard incidents, and fatal falls, with a focus on several contributing factors--alcohol consumption, meteorological conditions, and visibility. Through comprehensive analysis, the alcohol consumption was examined in order to show how it impairs judgment and physical abilities, significantly increasing the risk of fatal outcomes in these accidents. The paper explores the interplay between alcohol consumption and other contributing factors, such as time of day (daytime/night) and weather conditions, providing a comprehensive understanding of how these variables collectively influence fatality rates in EU maritime transportation. The findings underscore the critical need for stringent alcohol regulations and enhanced safety protocols to mitigate the heightened risks associated with alcohol-impaired maritime operations.  
maritime; alcohol; safety; accident; fatality; prediction model +
Footnote †: journal:  
critical injuries and their deaths later that day. The AIBN stated that alcohol impairment, high speed, and nighttime conditions were contributing factors. Alcohol likely impaired attention and decision making, with fatigue and poor night vision potentially contributing as well [2].  
In cases of accidents where alcohol is the primary contributing factor, crew members under the influence are responsible in 90% of instances. A notable example is the grounding of the M/V Wakashio off the southeast coast of Mauritius, which resulted in significant hull damage and a fuel oil spill that contaminated the coastline. The accident was likely caused by the master and chief officer navigating without detailed charts and altering the passage plan to receive a smartphone signal, reflecting a lack of awareness regarding safe navigation practices. Alcohol was identified as a contributing factor, impairing the crew's decision making and risk assessment, which led to the grounding [3]. Another alcohol-related accident occurred in December 2021, when the UK-registered cargo ship Scot Carrier collided with the Danish vessel Karin Hej in Sweden, causing the Karin Hej to capsize, which tragically resulted in the loss of two crew members. The investigation revealed that the second officer on the Scot Carrier had consumed alcohol and was influenced by alcohol, resulting in fatigue; moreover, he was distracted by a tablet during his watch, which contributed to the accident [4]. Thus, this paper aims to identify the effects of alcohol consumption on board maritime vessels by examining alcohol levels among individuals involved in accidents. Additional factors, such as darkness/visibility and meteorological conditions, were also considered. Based on data collected from almost 40 maritime accidents where alcohol was a contributing factor, a prediction model using real data is presented to assess the probability of fatality.
# A Unified Denoising Framework for Restoring the LiDAR Point Cloud Geometry of Reflective Targets
## 5 Conclusions  
In this study, we addressed the critical challenge of denoising LiDAR point clouds of reflective targets, which often suffer from severe noise and distortions. To this end, we propose a Unified Denoising Framework (UDF), which is capable of tackling the veiling effect, range anomalies, and blooming effect. The experimental results indicated that the UDF achieved an MSE of 0.15 cm\({}^{2}\), an MCD of 0.05 cm, and relative errors in height and width of 1.92% and 1.91%, respectively. In comparison to five representative algorithms, the UDF exhibited notable improvements in both denoising performance and the restoration of target geometric features. The UDF provides a robust solution for enhancing the quality of LiDAR point clouds in applications where accurate target geometry is essential, such as autonomous driving and infrastructure mapping. By restoring the geometric features of the target, this method can significantly improve downstream tasks, including feature extraction and object detection.  
However, the UDF has some limitations. The current method is primarily designed for reflective planar targets, and improvements may be required for when it is applied to non-planar or small targets. Future research could focus on enhancing the algorithm to address these challenges and explore adaptive parameter optimization techniques.  
Additionally, environmental factors such as humidity, fog, rain, temperature fluctuations, and air quality (e.g., dust) may also influence LiDAR measurements by affecting laser beam propagation and target reflectivity. Investigating the impact of these conditions on the blooming effect and the performance of LiDAR-based perception systems could further improve the robustness of the proposed method in real-world applications.  
Conceptualization, T.X.; methodology, T.X.; software, T.X.; validation, T.X. and C.W.; formal analysis, T.X.; investigation, T.X. and C.W.; resources, T.X.; data curation, T.X.; writing--original draft preparation, T.X.; writing--review and editing, T.X., J.Z., C.W., F.L. and Z.M.; visualization, T.X.; supervision, J.Z.; project administration, J.Z., F.L. and Z.M.; funding acquisition, J.Z. and F.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under the Special Fund for Research on National Major Research Instruments (Grant number 62327803).  
Not applicable.  
Data Availability Statement: Data are available upon request.  
The authors declare no conflicts of interest.
## 3 Results and Discussions
### Energy Cost Comparison  
Figure 10 illustrates the result of energy cost per packet based on the routing approaches for various speeds of the network devices. The proposed routing approach attains the lowest energy cost owing to exploitations of the HETX metric, which reduces packets flooding in the network. Therefore, a smaller number of RREQ packets induces in the network as a result of the minimization of the device energy cost. Frequent movement of intermediate devices in the network causes the variation of the device's energy levels while increasing mobility. The EMBLR routing approach also selects devices with better link quality and lower speed to decrease energy consumption and packet loss ratio; this awareness is excluded in the other routing protocols. The EMBLR routing approach recorded the lowest energy cost, followed by both MEQSA-OLSRv2 and MP-OLSRv2. Energy cost attained by MP-OLSRv2 is 20.45% and 19.22%, while the MEQSA-OLSRv2 is 11.56% and 9.56% higher than the EMBLR routing approach when the device speed varies from 10 to 60 m/s, respectively.  
Figure 8: Packet drop vs. various devices speed.
# How Reputation Systems Change the Psychological Antecedents of Fairness in Virtual Environments
## 3 Results
### Effects of Socio-Demographic and Psychological Characteristics on the Request  
This subsection is dedicated to the variables that affect the Request made by the participant of the bargaining game online, both when reputation is or is not considered. The GLMM technique allows testing whether the participant's request would change based on his socio-demographic and psychological characteristics, and depending on the experimental condition (i.e., reputation off or on).  
As shown by Table 5, when the reputational system is not involved in the online bargaining game, individuals with high levels of Extraversion, not only tend to offer more to their counterparts, but they also ask for more. At the same time, closed individuals appear to request more resources if compared with open ones, while people with high levels of Agreeableness appear to demand less. When the reputational system is provided, different dynamics of the request behavior emerge in the game. In fact, individuals with high levels of Extraversion and Closeness appear to behave oppositely, asking  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**Descriptive Statistics of the Study’s Variables**} \\ \hline \hline  & **Rep OFF** & \multicolumn{2}{c}{**Rep ON**} \\ \hline
**Variable** & **M** & **s.d.** & **M** & **s.d.** \\ \hline Age & 18.02 & 4.50 & 17.42 & 2.76 \\ Offer & 4.45 & 4.70 & 4.79 & 5.32 \\ Request & 6.32 & 7.33 & 5.86 & 7.21 \\ Fairness & \(-\)1.82 & 8.13 & \(-\)1.07 & 8.22 \\ Neuroticism & 7.41 & 4.31 & 7.46 & 3.78 \\ Extraversion & 11.01 & 3.08 & 10.94 & 3.63 \\ Agreeableness & 11.27 & 3.73 & 11.12 & 3.75 \\ Closeness & 5.35 & 3.43 & 5.45 & 4 \\ Conscientiousness & 8.41 & 3.78 & 8.02 & 4.14 \\ Self-Efficacy & 18.26 & 3.83 & 18.08 & 4.37 \\ Sense of Community & 23.71 & 4.58 & 23.40 & 5.36 \\ \hline \hline \end{tabular} M = Mean; s.d. = standard deviation; Rep OFF = Reputation system missing; Rep ON = Reputation system present.  
\end{table}
Table 3: Descriptive statistics. In the table are reported the participants’ socio-demographic and psychological characteristics, as well as their game behavior playing as proposers.  
for less respect to the previous scenario and individuals with low levels of these traits. On the contrary, agreeable people show the same behavior, requesting fewer resources also in this condition. Finally, when reputation is involved, other variables emerge as relevant in predicting the request behavior: Neuroticism, Self-Efficacy, and age. In particular, high levels of Neuroticism trait and Self-Efficacy promote higher requests, while age determines low demands.
Research works report on both remote [32] and global [33] calibration approaches, as they promise to be scalable and robust to concept drift or even sensor drift (in the case of continuous remote calibration). In other research works [34; 35; 36], the application of machine learning by means of a non-linear least absolute shrinkage and selection operator (LASSO) regression method is presented, in which both primary measurements of low-cost sensors and measurements of relative humidity, temperature, barometric pressure, wind speed, etc., are applied as independent variables of the LASSO function. This work focuses on the study of optimization of measurements from low-cost electrochemical sensors that are used to estimate Nitrogen Dioxide (NO\({}_{2}\)) and Ozone (O\({}_{3}\)) pollutants' concentration. This work's contribution centers on striking a balance between results and effort, minimizing computational burden, by the utilization of simplified mathematical methods and applications for achieving excellent results. The used sensors are the NO2-B43F model for NO\({}_{2}\) and the OX-B431 model for O\({}_{3}\) both manufactured by Alphasense. The innovation is in the application of the least absolute shrinkage and selection operator (LASSO) regression method [37] where the nonlinear equation involves only one independent variable, that of the measurements. This study aims to investigate whether the LASSO regression method can optimize the measured values with only one independent variable and the parameters of the LASSO equation. LASSO regression is an analysis method that modulates both variable selection and normalization in order to improve the prediction accuracy and interpretability of the resulting statistical model. In addition, specific methodology scaling was applied in the experiment. The object of the study was to apply the LASSO regression to measurements both of the time scaling (periodof 10 weeks of measurements) and seasonality scale (measurements of different months (summer, winter)) in order to determine whether the corrected measurements are affected by seasonality. The results are satisfactory in that the sensor readings after application of the method show values close to the actual reference values.
## 1 Introduction
Moreover, IRT excels in assessing the impact of trunk irregularities, such as cavities, on tree health [82; 89]. Temperature anomalies of these irregularities serve as indicators of potential issues related to water transport and structural integrity [89; 90], and thus, IRT offers non-destructive and efficient means of pinpointing areas that may necessitate further examination or intervention [82]. At the same time, in the context of foliar diseases, temperature variations linked to disease progression become evident through IRT monitoring, enabling the timely implementation of disease management strategies, including targeted pruning or treatment [91]. Despite the recent widespread utilization of IRT in various fields of plant ecology and physiology [92; 93; 94], its application in assessing the state of centennial olive trees remains relatively limited. Moreover, no prior studies have endeavored to establish connections between IRT data, tree phenotypic metrics, and productivity, as well as its two primary abiotic and biotic stressors: water stress and fungal diseases. In this context, our research aims to comprehensively assess the health state of traditional centennial olive trees in a typical Mediterranean environment, with a particular focus on their productivity while considering both biotic and abiotic stressors, seeking to establish meaningful relationships between these indices and key factors influencing olive tree vitality. We hypothesize that the integration of IRT with phenotypic traits and stressors will provide a valuable tool for assessing and monitoring the health and overall state of centennial olive trees, offering a comprehensive approach to understanding the complex interactions shaping traditional olive grove dynamics, contributing to our knowledge of how environmental, physiological, and pathological factors influence olive tree health. For this, we collected phenotypic metrics from traditional centennial olive trees on Naxos Island, Cyclades, Greece, and integrated them with in situ IRT metrics to achieve the following objectives: (a) assess intraspecific thermal and humidity profile variations, (b) investigate the intricate relationships between tree phenotypic traits, thermal and humidity indices, and stressors impacting olive trees, (c) develop models to gain insights into the factors influencing olive tree productivity and water stress, (d) examine whether the phenotypic parameters of olive trees, along with IRT metrics and water stress levels, can elucidate the presence of one of the most common biotic stressors, olive leaf spot disease, and (e) examine potential distinctions among centennial olive trees experiencing the effects of this fungal disease.
## 4 Test and Results
### Multistage TNEP Problem
#### 4.2.2 IEEE 24-Bus System
Test 4: The best solution found by the HEGA for the multistage TNEP with SCC and power losses has a total investment of US$227.264 \(\times\) 10\({}^{6}\), and no load shedding. This solution is found after solving 179,863 LPs with a computational time of 60.48 min.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 4 Simulations and Results  
The effectiveness of the proposed method in this paper is evaluated through simulations, which are performed using the MATLAB R2021b software platform. The effectiveness of the proposed spoofing interference extraction and confirmation method is examined in Experiment 1. The effectiveness of the suggested method for locating deception interference is assessed in Experiments 2 through 4. Experiment 2 compares and tests the localization effects of spoofing interference using the proposed approach and the two-step methods under different carrier phase measurement errors along five typical monitoring paths. Experiment 3 compares and tests the spoofing interference localization effects of our method and the better two-step method under different antenna positioning errors as well as varying carrier phase measurement errors. The spoofing interference localization effect is examined in Experiment 4, considering a varying number of observation points. The aforementioned simulation experiments examine the key factors that influence the effectiveness of the proposed method, and the results substantiate the effectiveness of the proposed method while providing guidance for its practical application.