esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
In this study, the reservoir temperature (RT) was predicted using several approaches: an MLP trained with a classical optimization method, MLPs trained with three distinct metaheuristic methods, and the AOSMA-MLP, which was proposed for the first time. The dataset comprised 161 samples, encompassing hydrogeochemical data and RTs from geothermal resources across various regions in Anatolia, Turkey. Understanding RTs is essential for determining the most effective use of geothermal resources, whether for direct heating applications or electricity generation, depending on the RT. The dataset was divided into training and test sets, constituting 80% (128 samples) and 20% (33 samples) of the data, respectively. This division ensured consistency across the four models proposed in the study. The metaheuristic methods employed were the whale optimization algorithm (WOA), ant lion optimizer (ALO), slime mould algorithm (SMA), and the newly proposed adaptive opposition-based slime mould algorithm (AOSMA). Characteristics of the MLPs trained using these metaheuristic optimization methods are detailed in Table 2, including the number of hidden neurons (HNO), which was kept consistent across all models. For the classical optimization-based training of MLP, the one-step secant backpropagation method was utilized. Table 2 outlines various parameters, including the number of attributes (NA), samples (NS), training samples (NTRS), test samples (NTS), hidden neurons (HNO), dimension (Dim), weight (W), bias (B), and neural network structure (NNS). Additionally, Table 3 lists the parameters of the employed metaheuristic optimization methods, with the maximum number of iterations and the number of search agents standardized at 200 and 30, respectively, to facilitate equal evaluation conditions across all methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**NA** & **NS** & **NTRS** & **NTS** & **HNO** & **Dim** & **W** & **B** & **NNS** \\ \hline
7 & 161 & 128 & 33 & 15 & 136 & 120 & 16 & 7-15-1 \\ \hline \end{tabular}
\end{table}
Table 2: Properties of the MLP.  
The training durations for the metaheuristic optimization methods, namely, ALO-MLP, WOA-MLP, SMA-MLP, and AOSMA-MLP, were recorded as 13.07, 8.06, 8.17, and 8.26 min, respectively, highlighting the efficiency and potential applicability of these advanced computational techniques in geothermal resource evaluation.
# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 4 Discussion
### Adaptation to Diverse Traffic Environments  
The model's validation focused on Zhongyuan Avenue, a high-traffic arterial road, demonstrating its effectiveness in this specific context. However, its performance in other urban settings requires further testing, including intersections with complex pedestrian flows, roundabouts, and low-speed residential zones. In future work, we will focus on scenario-specific calibrations to enhance the model's generalizability across diverse urban traffic scenarios. This includes adjusting \(h_{f}\) for intersection priority rules, pedestrian density, and other dynamic factors. Additionally, the current simulations primarily focus on vehicle-to-vehicle interactions but exclude pedestrians and cyclists, limiting their applicability to mixed traffic environments. To address this gap, we plan to extend the model with modules for vulnerable road-user trajectory prediction. For example, pedestrian crossing intent inferred from smartphone data or cyclist speed profiles could be incorporated into the IDM's safety distance logic. This extension will enable comprehensive risk assessments, ensuring the model accurately captures interactions between vehicles, pedestrians, and cyclists in complex urban environments.
## 4 Case Study and Discussion
### Simulation Setup  
#### 4.1.1 Parameters of a Wind Farm and an ESS  
Operational data related to the coordinated model were required to simulate the coordinated wind farm model with an ESS as shown in Figure 1. The key parameters for the coordinated model operation are listed in Table 1.  
The wind farm in Jeju Island is a 10,790 kW Haengwon offshore farm [25]. The ESS in the wind farm was assumed to be installed with an optimized capacity of 2450 kWh [26]. The operation conditions for the ESS, regarding the minimum and maximum charge level, turnaround, and charging and discharging efficiency were assumed to be 90%. In addition, the variable O&M cost of ESS was adjusted to a five-minute time step unit [27]. The REC multiplier for the discharging power in peak periods followed the current RPS policy [22].
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 4 Experiment
### Settings  
#### 4.1.1 Datasets  
The data used in this experiment is from the Global Ocean Argo Grid Data Set (BOA_Argo) [23]. The grid dataset provides annual average, monthly average and yearly ocean temperature from January 2004 to December 2017 covering the global ocean (180\({}^{\circ}\) W 180\({}^{\circ}\) E, 79.5\({}^{\circ}\) S 79.5\({}^{\circ}\) N) and salinity data. Spatial resolution is \(1^{\circ}\times 1^{\circ}\) horizontally and 58 layers in the vertical standard layer.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 2 Transparent Antennas: A Review
### Transparents Conductive Oxides
#### 2.2.2 Multilayer  
In Reference [18], the electrical properties of a multilayer ITO film consisting of ITO/Cu/ITO with a maximum optical transmittance of 61% are investigated to validate the feasibility at the ultrahigh frequency (UHF) band. A transparent monopole antenna is printed on a glass substrate with an ITO/Cu/ITO film, featuring an \(R_{\text{s}}\) of 4.7 \(\Omega\)/sq with an 85- (ITO), 13- (Cu), and 85-nm (ITO) thickness. The glass substrate is Corning glass with a relative permittivity of 5.7 and a loss tangent of 0.0002. The transparent monopole antenna operating at the UHF band (500 to 1300 MHz) is discussed. The ITO/Cu/ITO films feature a theoretical gain of \(-1.96\) dBi, while the gain of the ITO film is \(-4.10\) dBi.  
Meanwhile, since ITO has a fragile characteristic at room temperature, other representations of ITO such as Indium Zinc Tin Oxide (IZTO) have been investigated to overcome the inherent weakness of ITO [19]. In Reference [20], the feasibility of a multilayer film-based transparent patch antenna with IZTO is studied at a Wi-Fi service band at 2.45 GHz, as depicted in Figure 5a. The IZTO/Ag/IZTO multilayer film features a sheet resistance of 2.52 \(\Omega\)/sq and an optical transmittance of above 80%. These antennas used the transparent acryl substrate with a multilayer film with a 100-nm thickness. To evaluate the performance, the reflection coefficient of the multilayer film patch antenna is retrieved in Figure 5b. The multilayer film patch antenna features a peak gain of \(-4.23\) dBi with an efficiency of 7.76%.
## 2 Unanticipated Obstacle Scenario and Obstacle Avoidance Principle
### Obstacle Avoidance Principle  
For AVs, the dual primary objectives of obstacle avoidance are to guarantee vehicle safety and fulfill transportation tasks successfully. The factors that need to be considered in motion planning for AVs in unanticipated obstacle scenarios include:  
Figure 1: Unanticipated obstacle scenario at intersection inlet lane. (**a**) The ego vehicle approaches the intersection; (**b**) the ego vehicle waits in the lane; (**c**) the ego vehicle is blocked by the obstacle vehicle.  
1. Driving safety: Safety takes precedence when carrying out any driving task, especially when obstacle avoidance involves a violation. It is critical to avoid collision caused by the violation.
2. Impact on the transportation environment: Indefinite standstills can lead to traffic congestion and negatively impact traffic flow. Violations undertaken to address conflicts can impact the normal operation of surrounding vehicles. The extent of violations should be minimized.
3. Legal liability: Violations will result in appropriate penalties that are closely related to traffic safety. Less penalized violations should be prioritized to accomplish obstacle avoidance.  
[PERSON] [22] incorporates ethical considerations into motion planning and control. Traffic law compliance and obstacle avoidance represent two crucial aspects of autonomous vehicles (AVs), corresponding to consequentialism and deontology, respectively. Based on this idea, the obstacle avoidance principle in unanticipated scenarios is proposed. First of all, it is essential to ensure the safety of AVs. Secondly, the impact on the traffic environment is taken into account. Finally, the legal liability associated with AVs should be considered.  
The same principle is equally applicable to the vehicles that play different social roles, taking an on-duty ambulance as an illustrative example. While the ambulance has the discretion to deviate from traffic laws, it does not imply that reckless driving is permissible. Ensuring safety remains imperative. Moreover, its actions should consider the potential implications on the traffic ecosystem, which indirectly influences its safety and efficiency.
# Combining Keyframes and Image Classification for Violent Behavior Recognition
## 3 Proposed Method
### The Overall Framework  
Our model incorporates keyframe extraction, image classification, and violent-behavior recognition (Figure 1). The keyframe extraction algorithm uses the frame difference method to calculate the local maximum value of the sliding window (continuous N frames) and selects the local maximum frame as the keyframe in every continuous K frames. Compared with the current mainstream violence recognition models, this design reduces the number of processed frames to 1/K of the total video frames. ResNet18 is used as a keyframe-feature-recognition network, which outputs the probability that a given keyframe contains violent behavior. Compared with 3 DCNN-based and VGG-based sorts, our model parameters are smaller. This approach reduces the hardware requirements of the system. Furthermore, our model is easier to train compared to 3 DCNN-based and LSTM-based sorts. The violent-behavior-recognition module determines the video category with reference to the number of keyframes belonging to the category of frames containing violence. We control the recognition efficiency of the system by setting the number of keyframes and control the recognition sensitivity by setting the threshold. Therefore, our model is robust and can be flexibly adjusted. In applications, the threshold and processing efficiency can be selected according to the requirements of the actual situation.
## 4 System Modelling
### Path Reservation
#### 4.8.2 Path Reply  
After computing the shortest path, the server will send back path reply packet (Prep) to the source vehicle as well as to all the servers which have a coverage area of at least one route segment included in the shortest path, the servers will update their RRM accordingly.
## 3 Results: Medical History-Taking Challenges
### Observation 3: Only 4.81% Physicians Use Digital Medical History  
The findings for **RQ3** were that only **4.81%** of physicians exclusively rely on digital formats for maintaining patient records. Shockingly, approximately half of the surveyed physicians (50%) still adhere solely to paper-based records, while 23.08% maintain a hybrid system utilizing both paper-based and digital records. Equally concerning is that 22.12% of the surveyed physicians do not store any patient medical history at all. Not only in developing countries but also in developed countries, there is no complete dependence on digital health data management. A study conducted in Kuwait with a total of 98 participants from different organizations responded to the survey. The study results revealed that 43% of hospitals were using paper-based medical records, compared to 53% that used both paper-based and electronic media [12].  
Paper-based records are traditional, hard to manage, error-prone, and resource-intensive. On the other hand, electronic health records (EHRs) are recognized as more efficient and user friendly, enabling doctors to save valuable time. The low adoption of digital history management systems underscores the pressing need for their widespread  
Figure 2: The correlation matrix analyzed relationships between specific questions (Q1, Q2, Q3, Q6, Q7, Q8, Q9, Q14, Q15, Q16, Q17, Q18, Q20, Q21).  
implementation. Furthermore, it is worth noting that enhanced utilization of EHR systems and their delegation were found to be independently associated with heightened levels of physician productivity [13]. Web-based technology and the use of Internet of Things (IoT) sensors can help improve healthcare data management, considering satisfaction, effectiveness, and efficiency. The hypothesis suggested that those maintaining paper-based records or not storing any data might encounter challenges in history taking \"(5) every time\".  
Figure 3 shows the relationship between history-taking approach and history-taking challenge level rated by the physicians. It is surprising to note that among those not storing any data, none face challenges \"(5) every time\". Nevertheless, a total of 82% encounter challenges \"(4) almost every time\" or \"(3) occasionally\". Among those using paper-based documents, 8% face history-taking challenges every time, 28% face challenges almost every time, and 55% face challenges occasionally. The graph indicates that among users of digital records, 60% \"(2) almost never\" face challenges in history taking, while 40% face challenges occasionally.
## 3 Data and Methods
### Method
#### 3.2.1 Pixel Offset Tracking  
POT technology is used to estimate satellite orbit offset and surface deformation offset by correlating SAR images from two different periods in a correlation window. The POT method applies the normalized cross-correlation (\(NCC\)) to calculate the azimuthal and range offsets [25]. The formula is as follows.  
\[NCC(\mu,\
u)=\frac{\sum_{x,y}\left(f(x,y)-f_{r}\right)\left(g(x+\mu,y+\
u)-g_{s }\right)}{\sqrt{\sum_{x,y}\left(f(x,y)-f_{r}\right)^{2}}\sqrt{\sum_{x,y}\left( g(x+\mu,y+\
u)-g_{s}\right)^{2}}} \tag{1}\]  
where \((x,y)\) is the center coordinate of the template window, \((\mu,\
u)\) indicates the center coordinate of the search window, \(f_{r}\) is the average pixel value of the template window, and \(g_{s}\) is the average pixel value of the search window. When the \(NCC(\mu,\
u)\) value is closer to 1, the similarity between the two images is greater. When the cross-correlation coefficient is at the maximum, it indicates that the registration of the two images is complete. Therefore, the offset between the template window and the search window can be calculated. The calculation formula is as follows.  
\[D_{offset}=D_{defo}+D_{orbit}+D_{topo}+D_{ions}+D_{noise} \tag{2}\]  
where \(D_{offset}\) is the offset obtained after registration and \(D_{defo}\) is the offset due to surface deformation. \(D_{orbit}\) is the offset due to flight orbit and satellite attitude, which can be removed by fitting a bilinear polynomial to the geometric transformation parameters. \(D_{topo}\) is the offset due to terrain undulation. \(D_{ionos}\) is the ionospheric offset, which is negligible at low and middle latitudes where the ionospheric influence is small. \(D_{noise}\) is the offset due to system noise, which is negligible. This method relies on speckle features in SAR images, which can overcome the problem of low-coherence or even incoherent images and is more suitable for glacial areas.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 5 Results
### Impact Taking into Account Different Transport Modes  
In this test, we analyse how the shortest route changes depending on the transport mode selected, by car, bicycle, or on foot, and how the pollution affects on that route. With this, we can see how different modes of transport can influence the pollution exposure and the route choice. This analysis was carried out during 16 July 2023, Sunday at noon. For this scenario, we use SO\({}_{2}\) concentration as an indicator of pollution.  
As we can see in Figure 9, the pollution is lower compared with a working day. We can highlight that depending on the route modality, there are differences in the pollution exposure. In particular, in the cycling mode, since we are constrained to specific types of routes, pollution fluctuates more compared with walking and driving. Between walking and driving, walking is more uniform than driving. However, there are more fluctuations due to the number of junctions and higher road type diversity.  
Figure 8: NO\({}_{2}\) and PM2.5 mapping at 22:00 p.m., 19 July 2023, Wednesday.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Hour** & **NO\({}_{2}\)** & **PM2.5** \\ \hline
8:00 & 24.5 & 19.6 \\
10:00 & 18.2 & 13.5 \\
12:00 & 16.4 & 12.3 \\
14:00 & 16.1 & 11.1 \\
16:00 & 10.3 & 10.2 \\
18:00 & 14.0 & 9.2 \\
20:00 & 14.2 & 8.4 \\
22:00 & 24.5 & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evolution of the average concentration of NO\({}_{2}\) and PM2.5 (in \(\upmu\)g/m\({}^{3}\)) during a working day, 19 July 2023, Wednesday.  
In this case, the time slot used for the analysis is safe in terms of air pollution. Thus, the user can choose any kind of transport mode. It is worth mentioning, as shown in Figure 9c,d, that cycling is a better alternative than walking.
# A Review on Bitumen Rejuvenation: Mechanisms, Materials, Methods and Perspectives
## 4 The State-Of-The-Art
### Rejuvenation Probed by [PERSON] et al. in 2012 [52] explored the possibility of using a waste cooking oil (WCO) as rejuvenator in the aged bitumen, in order to reduce the expense of highway renovation. They added various amounts of a WCO mainly composed by Oleic acid (C18:2n9c, 43.7%), Palmitic acid (C16:0, 38.4%) and Linoleic acid (C18:2n6c, 11.4%) to an aged bitumen monitoring the changes in standard parameters like penetration index, softening point, Brookfield viscosity and dynamic shear viscosity. The comparison among pristine, aged and rejuvenated bitumen gave self-consistent results furnishing clear clues: the aged bitumen, which is stiffer than the virgin one as result of oxidation processes, can be substantially driven to the performances typical of the virgin bitumen by progressive addition of waste cooking oil. The restoring of the virgin performances takes place at WCO content of 3%, but they can even be improved (n penetration index, softening point and viscosity) by further addition of WCO up to 5%. See, for example, Figure 2 where the penetration value versus various rejuvenator amounts is reported. These results were interpreted at the molecular basis with the aid of Fourier transform infrared spectroscopy (FT-IR) which probes the amount of oxidized functional groups, and specifically C=O and S=O. The increase of polar functional group signal intensity, and specifically C=O and S=O ones, in FT-IR spectra when the bitumen is aged correlates with its stiffness increase. This suggests that the loss in bitumen performances is due to the oxidation process. Even, this increase correlates with an increase in asphaltenes fraction, i.e., the most polar component of the bitumen. On the other hand, the addition of WCO implies a substantial restoring of the pristine C=O and S=O signal, together with the pristine asphaltene/maltene ratio, although the process seems not to be complete/quantitative. Typical IR spectra, together with the most important band attributions, are reported in Figure 3. In conclusion, WCO appears to have a rejuvenating effect because it reduces the oxidation (asphaltene content) compared to an aged bitumen, even if it is not capable of restoring the correct ratio maltene asphaltenes. However, it is interesting to point out that the rejuvenated bitumen has less tendency to ageing compared to original bitumen and has lower volatility than the virgin bitumen reasonably, due to the lower volatility of bio-oil as a consequence of its high content in saturated hydrocarbons. Unfortunately, the Authors do not give details on the FT-IR experiments.
The reason lies in the fact that the ketone group is present in pongamia oil itself, and therefore, it is not necessarily due to ageing process. This result should be interpreted as an invitation in being prudent when performing FT-IR experiments and when interpreting its results.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method
### Depth-Sensitive Feature Enhancement
#### 3.2.4 Feature Enhancement with Depth Prototype  
We redistribute the depth prototypes back to each pixel \(i\) by weighting the prototypes \(P[d]\) again with \(Attention(i,d)\). In other words, each pixel gathers relevant cues from all depth levels based on its learned attention distribution. This re-projection step injects global depth context into local features, thereby strengthening the model's spatial-depth reasoning capability.  
The depth prototypes are then projected back to enhance the feature map, creating an updated representation:  
\[F_{enhanced}=\sum_{d=1}^{D}Attention(i,d)\times P[d],i\in\{1,\dots,N\} \tag{7}\]  
Each pixel \(i\) not only retains its local feature representation but also absorbs contextual information from the global depth prototype, allowing for enhanced depth reasoning and spatial consistency. Next, each pixel \(i\) retrieves or reclaims information from every depth prototype \(P[d]\) by applying the same attention distribution \(Attention(i,d)\). As a result, each pixel's final representation \(F_{enhanced}[i]=\sum_{d=1}^{D}Attention(i,d)\times P[d]\) incorporates both its original local features and the global depth cues distilled into \(P[d]\). Physically, this second step \"injects\" scene-wide depth awareness back into each local position, reinforcing the model's three-dimensional perception.  
Finally, the enhanced feature map \(F_{en}\in R^{C\times H\times W}\) undergoes a convolutional refinement to integrate depth-sensitive information. As a result, the enhanced depth feature is generated by combining the initial depth-sensitive feature with the reconstructed features through concatenation. This mechanism ensures that the depth-sensitive prototypes effectively capture spatial-depth relationships, significantly improving feature quality for 3D object detection tasks.
## 3 Results
### Volumetric and Depth Dependent En-Face Visualization  
To gain a better understanding regarding volumetric structures of leaf specimens, three-dimensional scanning method of OCT was utilized. The depth dependent en-face visualizations were involved to confirm the consistency of the examined position in depth range through the obtained 3D rendered images. Figure 5a,b illustrate the three dimensionally constructed front and rear surfaces of rice leaf specimen. Figure 5c-f emphasizes the magnified visualizations of the regions of interest indicated with red square boxes on Figure 5a,b. Figure 5g,h represents the en-face visualizations taken at a depth of 50 \(\upmu\)m underneath the epidermal cell layer. The en-face representation exhibits the internal cellular patterns of mid-vein, aerenchyma, and bulliform cells with a high resolution confirming the potential applicability compared to existing inspection methods. Aerenchyma is a spongy tissue that forms spaces for air channels enabling exchange of gases [49; 50]. As emphasized through en-face visualizations, the channels of air-filled cavities provide a low resistance internal pathway for the exchange of gases, such as oxygen and ethylene between the plant above the water and the submerged tissues.  
Figure 4: Comparison of two-dimensional SS-OCT images of the front and rear surface of the rice leaf specimens: (**a,b**) shows the front and rear surface of the rice leaf specimens. The red dotted line indicates the region of interest along with the scanning direction. The sub-figures (**c,d**) exhibit two-dimensional SS-OCT images of (**a,b**). Lateral and vertical scale bars: 500 \(\upmu\)m. AC: Aerenchyma, BUL: Bulliform cells, LEC: lower epidermis cells, MV: mid-vein, PAR: parenchyma, SV: small vein, UEC: upper epidermis cells, V: vacuum region and VB: vascular bundle.