esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Training Samples  
For the application of the classification algorithms, samples were selected based on the number of pixels located in each polygon selected as a classification target. This selection process was carried out manually, relying on knowledge of the study area and visual analysis of the images. Consequently, a shapefile layer was created with the same DATUM reference as the drone images, using the WGS 84 coordinate reference system.  
The classification of developed and underdeveloped forage cacti was based on the biometric values of the crops, namely: height, canopy size, and number of cladodes. The values were measured in 72 samples and presented in Figure 3.  
The study area consisted of three cultivation plots, in which six types of samples were defined for the classification of the processed image (Figure 4). Sample selection per class was balanced manually aiming to obtain the most faithful results possible in the classification and resulted in the following quantities: bare soil (7327 samples), soil with cover (6353 samples), forage cactus (7837 samples), underdeveloped forage cactus (or with impaired development) (6603 samples), moringa (7203 samples), and gliricidia  
Figure 3: Mean biometric data and standard deviation of forage cactus at the irrigated area.  
(6143 samples). It is worth noting that sample selection was based on the presence of all sample elements (classes) in the study area.  
After applying the algorithms, with a data distribution where 70% of the samples were used for training and 30% for testing, confusion matrices were generated and analyzed for each applied classification algorithm. Thus, it was possible to analyze metrics derived from the classifications performed by the algorithms, such as precision, recall (sensitivity), F1 Score, accuracy, and finally, the Kappa index [39]. According to [PERSON] and [PERSON] [39], Kappa values from 0.0 to 0.2 indicate'slight agreement', 0.21 to 0.40 indicate 'fair agreement', 0.41 to 0.60 indicate'moderate agreement', 0.61 to 0.80 indicate'substantial agreement', and 0.81 to 1.0 correspond to 'almost perfect' agreement. This allows evaluating the performance of each algorithm for each individual class of selected samples, presenting an overall average of the values, and assessing the general performance of the algorithm.  
Precision measures the proportion of samples classified as positive that are truly positive. Recall, or sensitivity, measures the proportion of positive samples correctly classified relative to the total positive samples. In other words, it is the model's ability to find all positive samples [40]. The F1 Score is a helpful metric for finding a balance between precision and recall in classification tasks. A high F1 Score indicates that the model is succeeding in both positive instances and minimizing false positives.  
Furthermore, a comparative study was conducted, both on the classification algorithms and the RGB and multispectral sample spaces.
## 2 Discussion
### Sonoluminescence Light Manipulation with Carbon Nanodots  
Carbon nanodots--nanoscale particles of carbon that have been used as a fluorescent material, exhibit high biocompatibility and photostability, as well as low toxicity and eco-friendliness [47, 84, 85]--are another type of nanoparticle that can be used to modify the spectrum of sonoluminescence light [59]. In the cited paper, sonoluminescence was achieved in a single-bubble experimental arrangement, where the emission of light was accompanied by the sonochemical reaction \(H_{2}\mathrm{O}\to H^{\bullet}+OH^{\bullet}\). The spectrum of light emitted by a single air bubble in water typically extends from UV to visible wavelengths, but its intensity decreases with increasing wavelength, thus explaining the observations of light-emitting bubbles as bright blue spots in otherwise dark water. Yet, at relatively low levels of driving acoustic pressure waves, a characteristic optical spectral line corresponding to \(OH^{\bullet}\) appears in the sonoluminescence spectrum when deionised water is mixed with a noble gas such He, Ar, and Xe [34, 35]. It is also well-known that certain chemical reactions between \(OH^{\bullet}\) and other chemicals (e.g., luminol, as demonstrated in the multibubble experiment in [86]) can help achieve control over sonoluminescence spectra. In the case  
Figure 5: Sonoluminescence light spectra in (**a**) pure water and (**b**) in water containing ZnO-NH\({}_{2}\) nanoparticles for different levels of ultrasound intensity. (**c**,**d**) The same sonoluminescence light spectra as in (**a**,**b**), but measured in an argon-saturated environment at ultrasound intensities of 0.9 \(W\)/cm\({}^{2}\) and 1.2 \(W\)/cm\({}^{2}\), respectively. One can see a significant increase in the number of counts (i.e., the number of photons received by the photodetector) in the presence of ZnO-NH\({}_{2}\) nanoparticles compared with data obtained using water. Reproduced from [58] under the terms of a Creative Commons license.  
of carbon nanodots, in one of the experiments reported in [59], it was demonstrated that while the carbon cores of carbon nanodots are changed due to the combined mechanical and thermal effects of sonoluminescence, the chemical groups attached to the surface of carbon nanodots also become modified, which, in turn, changes the optical properties of the nanodots and shifts the broad peak of the sonoluminescence spectrum towards the visible range.
## 4 Case Study
### Results and Discussion
#### 4.3.4 Analysis of the Importance of Weather Impact  
According to Figures 14-16, the impact of weather factors on flight operation metrics, economic metrics, and transportation capacity shows significant differences. Figure 14 indicates that among all weather factors affecting flight operation metrics, temperature_point and wind_direction_speed have the highest importance, at 0.35 and 0.32, respectively. This suggests that temperature and wind speed are the key factors determining flight operational performance. Other factors, such as FEW, visibility, and wind_direction_change, have some impact, but their importance is relatively lower.  
Figure 14: The importance of weather factors to flight operation indicators.  
Figure 15: The importance of weather factors to flight economic indicators.  
Figure 15 illustrates the importance of weather factors in economic metrics. Temperature_point and wind_direction_speed remain the most critical factors, with importance scores of 0.32 and 0.30, respectively. These results indicate that temperature and wind speed are not only essential for normal flight operations but also have a direct impact on the economic benefits of flights. In comparison, the importance of FEW, visibility, and SCT decreased, but they still warrant attention.  
Figure 16 reveals the impact of weather factors on flight transportation capacity. Among all weather factors, wind_direction_change and wind_direction_speed have the highest importance, at 0.7 and 0.65, respectively. This suggests that wind direction and speed changes are the main factors affecting flight transportation capacity. Other factors, such as SCT, FEW, and BR, also significantly impact transportation capacity, while temperature_point and visibility have relatively lower importance.  
In summary, wind_direction_speed and temperature_point are the critical weather factors influencing flight operation and economic metrics, while wind_direction_change has the most significant impact on flight transportation capacity. These findings imply that airlines should pay particular attention to these key weather factors when planning flights and assessing risks to ensure flight safety, efficiency, and economic performance.
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 3 Methodology
### Safety Risk-Based Cooperative Adaptive Cruise Control (SRI-Based CACC)  
In the context of our control concept (see Figure 1), the CACC is responsible for calculating the acceleration required to achieve the reference speed. The reference speed can be set by the driver or in the event that the actual risk is deemed to be too high, updated by the system. The CACC function is based on a standard MPC controller, and it considers the relative velocity, the relative distance, and the HV velocity as input variables. The Safety Risk Index (SRI) block is responsible for resolving the optimization problem of minimizing the difference between the tolerable and actual risk by manipulating the value of the reference speed. Our proposed control could overwrite the reference speed set by the driver if the mentioned SRI is higher than the predefined target value.  
To be able to react to changes in network performance, we have to propose a new control structure that takes into consideration the QoS parameters, like PDR and end-to-end latency (E2E). In this research, we adopted the risk estimation function (1) proposed by [PERSON] et al. [20] to quantify the safety risk of a particular traffic scenario.  
\[SRI=f(v_{HV},v_{RV},PDR,E2E) \tag{1}\]  
We modified the CACC reference speed calculation algorithm to take into consideration the Safety Risk Index (SRI). For calculating SRI, we used host vehicle (HV) and remote vehicle (RV) velocities and the PDR and E2E values.  
When determining the network performance metrics, we only considered the PDR value, as our previous experience suggests that latency has a much less negative impact on safety risk. Accordingly, latency is assumed to be ideal, so E2E is set to zero. Furthermore, as an important extension of the previous concept, the network monitoring module cannot determine the PDR until the first packets are received, so it predicts with the ideal value of 100%. After the first successful reception, the network monitoring module starts to calculate the PDR as a last second mean value, which is a sliding window method-based aggregation. In order to improve the applicability of the method, we have defined a sufficiently low target for SRI in order to prevent a too aggressive intervention in low-risk scenarios. If we surpass this target, then the proposed function limits the host vehicle's velocity to meet these predefined criteria, thereby improving safety. Additionally, we have defined further constraints for the minimum safe velocity calculation. In the case of the newly introduced method, we set 10 m/s as an absolute minimum value. If the vehicle receives a correct packet with a valid velocity value higher than the absolute minimum, then this is used as the minimum value for the a velocity calculation.
## 4 Discussion
### Met-ADS and IPEC-J2 Cell Line Viability  
Due to the more efficient results obtained by the release tests performed on Met associated with ADS, compared with Bio, only Met-ADS was chosen for the in vitro and ex vivo trials.  
Prior studies demonstrated morphological and functional similarities between the IPEC-J2 cell line and intestinal epithelial cells [20]. IPEC-J2 cells mimic the intestinal physiology more closely than any other cell line of tumor origin. Therefore, they are ideal tools to study epithelial transport and the effect of nutrients on a variety of parameters reflecting epithelial functionality [18; 21; 22].  
In this study, IPEC-J2 cells were used as an in vitro model to study the effects of Met-ADS in order to exclude any cytotoxicity issue at the intestinal epithelium level before ex vivo trials. Results from IPEC-J2 experiments did not reveal statistically significant toxicity in IPEC-J2 cells treated with Met-ADS, suggesting that Adsorbene did not cause detrimental effects on the intestinal epithelial tissue after 3 h of contact [23]. Once the safety of the Met-ADS products on intestinal epithelial cells was assumed, the absorption of the Met released by the ADS was tested in porcine explants _ex vivo_.
## 2 Fundamental Principles
### Finite Pixel Width  
The diffraction grating within the spectrometer spectrally separates the interferometric signal in terms of wavelength, directing spectral components of the signal onto discrete pixel detectors across the pixel array. The optical intensity of each of these spectrally dispersed spots is sampled across each pixel. The optical spot intensity is integrated over the pixel area which in effect imposes a rectangular function as a convolution of the Gaussian beam spot. This signal decay factor as a function of the finite pixel width \((R_{PW})\) manifests as a non-normalized Sinc function as shown in Equation (14) along with the Rect function [47]:  
\[R_{PW}=sinc^{2}\left(\frac{\pi z}{2\cdot z_{max}}\right),Rect\left(\frac{k}{ \delta k}\right)=\begin{cases}0&\text{for }|k|>\frac{\delta k}{2}\\ \frac{1}{2}&\text{for }|k|=\frac{\delta k}{2}.\\ 0&\text{for }|k|<\frac{\delta k}{2}\end{cases} \tag{14}\]  
The result of this is a 4 dB drop in the signal-to-noise ratio (SNR) near the Nyquist limit [47]. However, the finite pixel width does not introduce a decay of the noise level due to the statistical independence of the shot noise between neighbouring pixels of the array [46].
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 2 Literature Review
### Multi-Agent System (MAS)  
A MAS consists of a set of agents embedded in a shared environment interacting with each other to achieve a common goal. In this context, an \"agent\" refers to an intelligent entity characterized by autonomy, activity, reactivity, and mobility [22]. In detail, an agent operates autonomously, making decisions without direct human intervention, while also exhibiting proactiveness by taking goal-driven actions rather than just reacting. It dynamically responds to changes in its environment through reactivity, adapting its behavior in real time. Additionally, some agents possess mobility, allowing them to move across systems or physical spaces to perform tasks efficiently. Together, these features--autonomy, proactiveness, reactivity, and mobility--enable intelligent agents to function effectively in dynamic and complex environments. Due to the inherent nature of the MAS, it is well-suited to tackle large-scale and/or complex problems. Similarly, public transportation systems, such as buses, urban trains, and airplanes, operate within extensive networks with strong internal coupling, making the MAS an attractive approach for researchers in the railway sector.  
For instance, Castro & Oliveira proposed a MAS to solve airline operation problems, specifically addressing crew recovery [23]. Their approach specialized the traditional Airline Operations Control Centre as an agent responsible for crew allocation, considering associated costs like hotel expenses. Another application of the MAS is found in the work of [PERSON] et al., who introduced a MAS for bus assignment that adapts to changes in passenger flow, highlighting the potential advantages of the MAS in transportation research [24]. Similarly, [PERSON] et al. presented a systematic approach for the allocation and reallocation of human resources--such as train drivers, train managers, and service staff--in passenger railways [25]. Their MAS emphasized a negotiation protocol for crew assignment and multi-layer structural control within railway operations. [PERSON] et al. also demonstrated a MAS framework for real-time train dispatching in a bottleneck section in the UK [1].  
In previous research, the MAS has typically been applied as a conceptual modeling technique, with limited discussion on the interactions among agents within the system. Additionally, most recent railway traffic management research has focused on developing algorithms specific to particular network layouts, limiting their scalability and applicability to larger networks. To the best of the authors' knowledge, there is no existing work that discusses the application of the MAS to the train rescheduling problem, particularly with regard to the scalability of conflict resolution approaches. This paper introduces a novel MAS to address the large-scale train rescheduling problem, offering improved scalability and potential for extension across entire railway networks.
## 4 Underwater Sensor Networks
This information could be used for research or educational applications and by policy-makers or agencies in charge of the management of the coastal area. Obtained measurements showed that several sources of physical forcing (such as wind, tide-associated currents and river discharge) were responsible of the spatio-temporal patterns of the dissolved oxygen, salinity and turbidity in the estuary. The river discharge was identified as the main forcing agent of the hydrology inside the estuary. Using network values, they related episodes of elevated turbidity, together with episodes of low salinity and dissolved oxygen to the increase in water supply from a dam located upstream. The real-time monitoring network can be applied to generate early warnings for navigation, turbidity increases, hypoxia events, and toxic algal blooms, and for earlier decision making processes to prevent or mitigate undesirable events. [PERSON] \(\dot{\mathfrak{o}}\)n _et al._ propose an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors in [31]. The system mitigates the data incompleteness problem by employing Artificial Intelligence techniques to improve the quality of data. The system performs as follows. Magnitudes such as temperature, salinity, visibility and density are physically related one to the other, and due to proximity, they are also related to nearby values in depth and location. Under these conditions it is possible to infer and extract an approximate of the missing information from the existing data. Authors evaluate the performance of ANN-based architecture using real data to validate the approach. The proposed architecture is able to provide very low errors. Their solution is able to keep the quality of the data at near-zero cost, saving money from the costly maintenance operations. It is especially suitable for cases where there are long time failures and big portions of missing data.
# Snap-Through Buckling Mechanism for Frequency-up Conversion in Piezoelectric Energy Harvesting
## 2 STB Mechanisms  
The investigated bistable mechanism, also referred to as STB mechanism, consists of two doubly-clamped curved beams clamped to each other at mid-span, as shown in Figure 1. With reference to Figure 1b, each curved beam has an as-fabricated shape which can be expressed by means of the following equation:  
\[w(x)=\frac{h}{2}\left[1-\cos\left(2\pi\frac{x}{L}\right)\right] \tag{1}\]  
where \(w(x)\) is, for each curved beam, the distance of the beam from the straight line connecting its two clamped ends, \(h\) is the initial rise of the curved beams, namely \(h=w(L/2)\), and \(L\) is the span length.  
The STB mechanism is characterized by two configurations of stable equilibrium: the initial undeformed one and the reversed configuration. Indicating with \(Q=h/t\) the ratio of the initial rise to the thickness of the single curved beam, it can be analytically demonstrated that bistability is ensured as long as \(Q>2.31\) and the curved beams are clamped at mid-span in order to by-pass the asymmetrical buckling mode. As the STB mechanism is subjected to a force \(F\) applied at mid-span, the nonlinear static behavior of the device is characterized by a nonlinear force-displacement curve (with \(F\) changing its sign if STB occurs). During the transition from one state to the other, \(\bar{w}(x)\) indicates the deformed shape of the mechanism and the displacement \(d\) can be defined as the difference \(d=w(L/2)-\bar{w}(L/2)\). The force-displacement curve, for \(Q>6\), can be represented as shown in Figure 1c and the following analytical expressions hold, as reported in [34; 35]:  
\[F_{\text{top}}\approx 1480\frac{EIh}{L^{3}} \tag{2}\]  
\[F_{\text{bot}}\approx 740\frac{EIh}{L^{3}} \tag{3}\]  
\(F_{\text{top}}\) is the force threshold value triggering the STB from the initial configuration to the reversed one and \(F_{\text{bot}}\) is the force value triggering the STB in the opposite sense, \(I=bt^{3}/12\) is the moment of inertia of the single curved beam, \(E\) is the Young's modulus of the material, and \(b\) is the width of the beam.  
The STB mechanism of the typology described in this section is employed for the FuC of a bimorph piezoelectric cantilever beam. The designed and manufactured prototypes are described in the following section.
## Materials and Methods
### Satellite Imagery
#### 2.2.2 RapidEye  
RapidEye and PlanetScope imagery products are currently acquired and supplied by the same private company, Planet Labs Inc. RapidEye provides optical imagery from a constellation of five earth-imaging satellites. It has a large area coverage with frequent revisit time (one-day interval), which allows one to get cloud-free images for almost all areas. The images have high spatial (5-m pixel size) resolution. RapidEye image products can be obtained in different forms (RapidEye basic (1B): least processed product; RapidEye Ortho (3A): orthorectified and other required corrections made at individual 25 km \(\times\) 25 km tiles; and RapidEye Ortho Take (3B): large-scale orthorectified product based on RapidEye Image Takes), depending on the users' needs and image processing capability (Planet, 2017). Recent RapidEye Ortho Take (product level 3B) images of the study sites were obtained from the same source as PlanetScope. Only images with a cloud cover \(<\)50% and images from December 2015 to May 2017 were used. The minimum number of images per location was one, and the maximum was 12 images; on average, 4.5 RapidEye images were available for interpretation for each location.