esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 2 Actions for Collision Avoidance
### Stage of Collision for Vessels  
The stage of collision can be divided into four stages using the relative distance of ships. Table 2 shows the responsibility of stand-on and give-way vessels in each stage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Meeting** & **Region** & **Action by Own Ship** & **Action by Target Ship** \\ \hline Head-On & A &
\begin{tabular}{c} Alter own course to starboard so that each shall pass on the \\ port side of the other \\ \end{tabular} \\ \hline Crossing & B & Give-way & Stand-on \\  & D & Stand-on & Give-way \\ \hline Overtaking & C & Stand-on & Keep out of the way of the \\  & & & vessel being overtaken \\ \hline \hline \end{tabular}
\end{table}
Table 1: Collision avoiding actions by ship in three meeting situations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Stage** & **Distance** & **Actions of Stand-On Vessel** & **Actions of Give-Way Vessel** \\ \hline
**1** & No risk of collision &
\begin{tabular}{c} Above \\ 6–8 Nm \\ \end{tabular} & Keep a proper look out & Keep a proper look out \\ \hline
2 & & 4–6 Nm & Keep on own course and speed &
\begin{tabular}{c} Take action \\ immediately \\ \end{tabular} \\ \hline
3 & Risk of collision exists & 2–4 Nm & \
\begin{tabular}{c} Alert other ship by sound \\ signal, and take necessary \\ action \\ \end{tabular} &
\begin{tabular}{c} Take action \\ immediately \\ \end{tabular} \\ \hline
4 & & \begin{tabular}{c} Below \\ 2 Nm \\ \end{tabular} & \
\begin{tabular}{c} Take the most \\ effective action \\ immediately \\ \end{tabular} &
\begin{tabular}{c} Take the most \\ effective action \\ immediately \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stage of collision.
## 7 Transcription-Coupled Repair (TCR): RNA Polymerase as the DNA Damage Sensor
### Mfd-Dependent TCR  
TCR-Mfd is triggered when the elongating RNAP stalls at a lesion, however, stalling obscures the damaged DNA segment from processing by repair proteins. To repair such a lesion, the stalled RNAP must first be displaced. Mfd binds to the stalled RNAP immediately upstream of the arrest site and displaces the stalled RNAP from the DNA strand by pushing it forward using an ATP-dependent DNA translocase activity, orchestrated by domains 5 and 6 [25; 75]. Single-molecule fluorescence imaging analysis revealed that as the RNAP is evicted from the DNA, the RNA is also released [139]. However, the displaced RNAP still associated with the slowly translocating Mfd [139]. Notably, Mfd appears to fulfill additional roles that do not involve presence of lesions, since its association with RNAP takes place absent DNA damage [140]. Further, a recent single molecule study showed that Mfd translocates on genomic DNA autonomously and remediates RNAP stalled at pause sites, in the absence of lesions [141; 142]. Lastly, roles for Mfd beyond functioning in a damaged DNA context is highlighted by an activity that rescues RNAPs whose forward progress is inhibited by protein blocks on DNA [143; 144]. Roles played by Mfd outside of a lesion-DNA context will not be further discussed here.  
The mechanisms that enable translocation by Mfd to displace RNAP remain to be clarified and likely involve a number of distinct structural intermediates in both Mfd and RNAP. It has been proposed that translocation of Mfd in the presence of bound RNAP could impose torque on the upstream DNA, causing rewinding of the upstream edge of the transcription bubble and collapse of the elongation complex [24]. The translocation activity of Mfd is regulated by the autoinhibitory domain D7 and domains D1-D3 [145; 146]. Binding of Mfd to RNAP via interaction between RID and the \(\beta\) subunit of RNAP results in the relief of auto-inhibitory interaction and activation of ATPase and translocase activity.  
To complete the repair process, Mfd recruits the core NER machinery. Isolated Mfd does not bind to UvrA, since the D2 UvrA-binding surface is masked owing to an interaction with the D7 element [50]. Interaction of Mfd with the stalled RNAP unmasks the UvrA binding element, leading to an interaction only when a lesion is encountered [26]. Engagement of UvrA to the Mfd-RNAP complex is mediated by ATP binding and hydrolysis by both Mfd [26] and the distal ATP-binding site of UvrA [27]. UvrA and UvrB together have been shown to facilitate displacement of Mfd from DNA [140; 147]. Importantly, UvrB loading is required for the removal of Mfd from the DNA. This is evident by the fact that Mfd-UvrA\({}_{2}\)-UvrB complex formed by UvrB that is deficient in loading are impaired in the handoff of damaged DNA from Mfd to UvrA\({}_{2}\)B\({}_{2}\)[140]. Once UvrB is loaded onto the site of the damage and the 'pre-incision complex' is formed, the repair reaction proceeds identically to those seen in the latter steps of GGR.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 4 Results and Analyses  
### Impact of Different Attention Mechanisms on Model Detection Performance  
To evaluate the performance of different attention mechanism modules, this paper conducts comparative experiments by replacing the EMA attention mechanism in the model with four other attention mechanisms: SE [17], CA [18], ECA [19], and GAM [20]. The results are shown in Table 2.  
As shown in Table 2, the EMA mechanism demonstrates superior performance for cotton ball disease detection, achieving 84.4% [EMAIL_ADDRESS] and 60.2% [EMAIL_ADDRESS]:0.95. This represents an improvement of 2.3, 3.5, 1.3, and 4.8 percentage points over the SE, CA, ECA, and GAM mechanisms, respectively. Notably in [EMAIL_ADDRESS]:0.95, EMA surpasses SE, CA, ECA, and GAM by 2.8, 5.1, 0.9, and 1.3 percentage points, respectively. The precision of the model with the EMA mechanism is also higher by 0.5, 3.6, and 2.4 percentage points compared to SE, CA, and GAM, respectively, and slightly lower than ECA's precision of 90.9%. However, EMA achieves 2.3 percentage points higher recall than ECA. Recall quantifies the proportion of correctly identified positives among all actual positives. High recall indicates effective identification of diseased cotton bolls with minimal false negatives. Due to its effective feature weighting capability, the model can more comprehensively focus on the key features of cotton bolls, effectively improving detection accuracy. This dual-attention architecture significantly outperforms the SE and CA mechanisms, which focus on a single dimension, as well as the ECA mechanism, which uses 1D convolutions to optimize channel attention.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 5 Evaluation
### Test Data
#### 5.2.3 Transfer Speed  
As mentioned in Section 3.2, the transfer speeds for Bluetooth and Wi-Fi Direct are 2 Mbps and 250 Mbps, respectively. This is a parameter of paramount importance if we want to achieve a realistic behavior. Assuming the message size to be 100 KB, this means that we can send 2.5 messages per second using Bluetooth, and 300 messages per second with Wi-Fi Direct.
## 3 Results
### Temporal \(\Delta S_{4}\) Analysis around Earthquakes  
In this section, the results of a more detailed temporal analysis of the \(S_{4}\) changes are presented. The purpose is to study the temporal evolution of the \(S_{4}\) changes around the earthquakes in the period under study. The way to measure this evolution is by averaging all the pixels in the \(S_{4}\) deviations maps that fall inside a circle of a given radius around each earthquake and computing this average for each day during a period of some days before and after the earthquake day.  
Figure 10: Some of the larger earthquakes around Vanuatu at the beginning of July 2017, showing their epicenters, time, depth and surface felt magnitude shakemaps. It can also be seen the submarine trench and the tectonic fault between Australian and Pacific plates.  
Figure 12 presents different plots for different minimum earthquake magnitudes in rows, and different integration radii, in columns. Each plot shows the averaged \(\Delta S_{4}\) value of all the pixels inside a circle centered in the earthquake epicenter from 15 days before to 15 days after the event, and then averaged for all the earthquakes in the period from 1 March, to 29 August 2019. Only earthquakes that are surrounded by at least 40% water pixels are computed in this average. This filter is applied to remove false alarms due to earthquakes that occur on land, but close enough to the sea to have enough pixels with valid data. A low number of data points to average may produce unrealistic results. The total number of earthquakes in this period, and those remaining after filtering with the water coverage, are shown in each graph.  
Figure 11: \(S_{4}\) variations maps for selected days around Vanuatu islands at the beginning of July 2019, with a resolution of 0.5” per pixel. Stars represent the earthquakes’ epicenters with magnitudes \(\geq\) 4.5 in the period from \(-\)6 to +6 days from the current day.  
The results in Figure 12 are divided in rows according to the earthquake magnitude lower limit. For the largest magnitude interval, M6 and larger (Figure 12a-c), it is observed that for the 10 days before earthquakes, the value of seven days \(\Delta\)S4 is positive and larger in magnitude than the rest of the days, particularly, the ones after the earthquakes. Similar behavior is observed using different integration radii (50 km, 100 km, 200 km), only showing a small decrease in the peak when integrating larger radii. This is the expected response, as the larger the area to estimate scintillation, the more uncorrelated noise in the circle.  
In the next figures for smaller magnitude intervals (from M5 and M4), a decrease in the peak amplitude is observed, which reduces with decreasing earthquakes' magnitude and larger radii. Furthermore, please note that the position of the peak has moved towards the right, indicating that the scintillation positive anomalous increments occur now closer to the event of earthquake. Since the seven day window introduces a 3.5 day, a positive \(\Delta\)S4 peak at \(D=3\) means that the precursor occurs just the day before the earthquake (Figure 12d).
## 1 Introduction
[21] investigated a dynamic model of a domestic refrigerator developed in this study and used it to analyze both steady-state and hot-load cooling operations. The results show that the power consumption value is well captured, with the average power consumption deviating by less than 10% in all tests. Power consumption is within \(\pm\)5% for all tests. For load processing, the water temperature deviation did not exceed 1.2 \({}^{\circ}\)C throughout the cooling period. [PERSON] et al. [22] propose a rigorous model to represent the operation of an air-cooled chiller of a large office building and some simple correlations to predict the global heat exchange coefficient and the pressure drop of the evaporator. The rigorous model shows that large variations on the order of 10% are observed in the prediction of the global heat exchange coefficient of the evaporator. We have found that the structures of mathematical models in the literature, the results for the properties of the processes, and the formulas have an extraordinary degree of dispersion, the limits of their usability are usually not disclosed, and their comparison is difficult. The developed mathematical models describe the operating processes that take place in the system elements of the heat pump only partially or with certain cutbacks. Within the scope of this work, the goal is to formulate a complete mathematical model of the shell-and-tube evaporator that describes the steady-state behavior of the system with sufficient accuracy. In the vast majority of models, researchers assume that the heat transfer coefficients and tube friction factors are constant. They do not take into account that the change in the process is a function of temperature, quantity, and vapor quality, or they use inaccurate, outdated equations created in previous decades for other refrigerants. The mathematical model presented also takes into account the pressure drop of the single-phase and two phase refrigerant and the change in the heat transfer coefficient. The equation of state and thermodynamic properties of other refrigerants can be integrated into the presented model. However, the model is limited to shell-and-tube heat exchangers and does not support plate heat exchangers, for example. The refrigerant R134a is selected because of its very favorable thermodynamic properties. In view of future restrictions (e.g., EU F-Gas Regulation), it can be relatively easily replaced by non-flammable (A1) HFO/HFC alternatives.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 5 Empirical Results
### Test Route  
The test route was a 1-km loop north of the University of Texas at Austin campus in Austin, TX. The route included a variety of light-to-moderate urban conditions, from open-sky to overhanging trees to built-up areas. The Dean Keeton corridor, toward the left in Figure 11, was the most challenging stretch along the test route for GNSS positioning. It passes below a pedestrian bridge and is flanked on both sides by buildings ranging from 30 to 65 meters tall set back 28 meters from the center of the roadway.  
To study the code-phase-based positioning error characteristics over time-separated sessions in the same area, and to perform multi-session mapping with GEOSLAM, multiple laps of the test route were driven over six separate campaigns. The first two campaigns were conducted on 21 December 2017 and  
Figure 11: An overview of the 1-km test route. The Dean Keeton corridor, toward the left, is spanned by a pedestrian bridge and flanked by buildings on both sides. A total of 75 laps of the test route were driven over six separate campaigns.  
15 January 2018, while the other four campaigns were conducted in pairs of two on 3 June 2018 and 4 June 2018. The GNSS error charts are presented for a total of 75 laps of the test route, while multi-session mapping with GEOSLAM was performed over eight laps/sessions of data from the four latest campaigns.  
Imagery collected over the four June 2018 campaigns exhibits appreciable visual diversity, offering a real-world challenge to multi-session GEOSLAM operation. Figure 12a,b show the variation in lighting and visual features between the data collected on 3 June 2018 and 4 June 2018.
## 1 Introduction  
### Credit Card Fraud  
In the contemporary era, technology permeates nearly every field of our lives, from education and healthcare to finance, economics, industry, trade, politics, and entertainment. The methods through which consumers conduct transactions have undergone profound transformation and expansion in recent years. As a result, the surge in electronic commerce (e-commerce) and online credit card transactions for purchases and payments can be directly linked to the evolution of modern lifestyles, technology advancements, and the ubiquitous presence of online applications.  
According to industry research, merchants are projected to incur losses of USD 130 billion from fraudulent transactions between 2018 and 2023 [1]. Many financial institutions allocate a security budget ranging from 20% to 30%, known as extended detection and response (XDR), considered a top priority in their security investments [2]. Credit card fraud occurs when a fraudster unlawfully uses someone's credit card, either online or physically, by stealing credit card information for unauthorized transactions. Fraudsters continually innovate fraud methods to breach credit card systems and conduct unauthorized transactions. Credit card fraud, whether offline or online, can occur without the authorization or permission of the cardholder. Offline credit card fraud takes place when a fraudulent individual physically uses a credit card at a point-of-sale (POS) terminal. On the other hand, online credit card fraud occurs when a fraudster steals credit card information and conducts unauthorized transactions or payments over the internet [3; 4].  
As widely acknowledged, AI and ML methodologies have showcased their efficacy and efficiency in detecting anomalies and fraud across diverse domains, including credit card transactions. Leveraging machine learning-based fraud detection presents numerous benefits, including automated fraud identification, real-time processing, minimized verification durations, and the capacity to uncover latent correlations within data. Conversely, conventional fraud detection methods entail the manual establishment of decision rules, consuming considerable time, necessitating multiple verification procedures (potentially inconveniencing users), and primarily detecting overt fraudulent activities [3; 4].
## 3 Threat Detection System for the Security of Critical Infrastructures  
The function of the C&C system in a multi-sensor system for the security in public spaces, which is the integration and orchestration of the different tasks of the human and technological components of such a system.  
Figure 1 illustrates the role of the C&C described in this paper in a reference architecture of a multi-sensor system for early threat detection deployed at a critical infrastructure. This consists of various site-specific installations of a threat detection system, each integrat  
ing sensors deployed in pre-defined corridors of the critical infrastructure, which supply threat information to a security control room.  
In this architecture, the C&C has two functions: collecting and visualizing threat information generated at the various sites for the operators in the security control room; and local coordination of the detection activity performed by the sensor systems, with real time data fusion for alarm management by the security agents on the field. For the second function, the C&C leverages person identification and tracking of two intelligent video-systems, a 2D Video system and a 3D Video system, with cameras disposed near the sensors, and of an indoor localization system for the coordination of security agents informed of the threat by means of smart glasses. Such an integrated system is named INSTEAD (Integrated SysTem for Early Thread Detection) and consists of a framework for development of multi-sensor systems to be deployed in restricted indoor areas where people/passengers follow a regular and pre-determined path, such as in underground/train/airport corridors. The sensors are external components, and the role of the C&C in the INSTEAD framework is to identify the commuters to whom the supplied sensor results refer by using the data sent by the video-systems. The security operators, wearing smart glasses, may then receive real time information on the threat person so that he/she could be eventually stopped in the area for a deeper inspection.  
Figure 1: The C&C system deployed in a security control room at a critical infrastructure. The C&C receives messages from different sites. Site i displays the DEXTER deployment for the big city trial, as also shown in [7].  
As an example, Figure 1 highlights the deployment with MIC (microwave imaging curtain) detector, a radar system that generates two-dimensional and three-dimensional images in real time of explosives and firearms carried by persons, and EXTRAS (Explosive TRAce detection Sensor), a detector of explosive traces on surfaces through the implementation of spectroscopy techniques. Such a deployment has been realized within the DEXTER (Detection of Explosives and firearms to counter TERrorism) project of the NATO Science for Peace and Security (SPS) Programme.
## 4 Applications of Microwave Heating in Coal Gas Drainage  
Microwave energy can be used to heat a coal seam through waveguides and antennas. Firstly, a borehole is drilled into the coal seam from the floor roadway. Secondly, the waveguide is connected to the antenna and inserted into the borehole along with the extraction pipe. A Teflon protective tube is installed between the antenna and the borehole wall. Finally, the borehole is sealed (Figure 25a) [11,15]. Gas extraction is conducted after the microwave generator is turned on. The microwaves pass sequentially through the rectangular waveguide, waveguide converter, coaxial waveguide, and antenna. The antenna radiates the microwaves into the coal seam and heats it.  
Under microwave irradiation, pyrite heats up rapidly and conducts heat to the coal matrix. The increased coal temperature results in gas desorption. Simultaneously, the expansion of the hot area induces coal matrix fracturing, which significantly increases the coal's porosity and permeability. This greatly enhances gas extraction.  
According to this study, the rotating heating model can cause a more rapid temperature increase than the static heating model within a given time, resulting in more extensive  
Figure 24: Percentage of damage area at each intermediate pressure.  
Figure 25: Schematic diagram of microwave heating stimulation of a coal reservoir: (**a**) static radiation and (**b**) rotating radiation.  
damage to the coal matrix. Therefore, rotary joints are added to adjust the rotation speed (Figure 25b). By rotating the rectangular waveguide, the electromagnetic field changes with time. Compared to the static radiation method, this disturbance radiation method can potentially enhance the efficiency of coal seam gas desorption.
## 4 Steam Electrolysis and Thermo(-Electro-)Chemical Cycles  
Nowadays, the most widespread technology is water electrolysis, preferably integrated in an RES production grid. Currently, the electrolysis plants installed in the EU account for 24 MW from solar sources and 500 MW from wind energy. Regarding further developments of this methodology, some problems are still present, and, in particular, it would be necessary to reduce investment costs and increase the electrolyzer's lifetime [83]. Another issue is the resistance under numerous start and stop cycles.  
Regarding the other routes, pilot plants up to a TRL of 5-6 have been developed for thermochemical and hybrid water splitting cycles and also for steam reforming [84]. Almost always, the RES is from CSP, and, in this respect, the integration with high-temperature storage systems represents a major challenge.  
Regarding water electrolysis, production costs can be evaluated at about 5-6 EUR/kg for plants connected to an electric grid, with a target of 1-3 EUR/kg [85]. Off-grid productions instead present significantly higher costs of around 8-10 EUR/kg.  
Regarding thermochemical cycles, the absence of industrial-scale plants means that production costs are estimated through techno-economic analyses. FCH-JU reported 10 EUR/kg in 2017, with the target of reaching 8 and 6 EUR/kg for, respectively, 2020 and 2023 [86]. Similar values are also expected for high-temperature electrolysis.  
Steam reforming coupled with CSP presents the most promise in terms of cost efficiency. In fact, from data obtained with a pilot plant, it was possible to establish costs below 4 EUR/kg for hydrogen and electric-power co-production [87].