esa-sceva/satcom-chunk-collection huggingface dataset:
### Sensing Platforms for the Mapping of Burned Areas
#### 1.2.3 UAV Platform  
Drones or UAVs (unmanned aerial vehicle) are currently used in a wide spectrum of applications, ranging from aerial photography to military purposes. The ability to equip these aircrafts with scientific instruments opens a totally new approach to spatial analysis [44]. Applications range from forest characterization for tree species and wildlife monitoring [50; 51], estimation of post-fire vegetation recovery [7; 43; 44], and landslide photogrammetric analysis [52; 53].  
Low altitude flights provide ultra-high spatial resolution and allow single feature analysis of the considered phenomenon. In addition, the cost of data collection is much lower and safer compared to ground data collection. The unmanned aircraft used in this work falls in the \"light\" UAV class (\(<\)25 kg take-off weight) and allows a hoovering time of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nr.** & **Name** & **Start WL** & **Middle WL** & **End WL** & **Spectral Rn.** & **Spatial Res.** & **Purpose** \\ \hline
11 & 10 & 1360 & 1375 & 1390 & 30 & 60 & Detection of thin cirrus for atmospheric correction \\  & & & & & & & \\
12 & 11 & 1565 & 1610 & 1655 & 90 & 20 & & \\  & & & & & & & \\
13 & 12 & 2100 & 2190 & 2280 & 180 & 20 & & \\  & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PRISMA Sensors Characteristics** \\ \hline Swath/FOV & 30 km/2.45\({}^{\circ}\) \\ \hline GSD & Hyperspectral 30 m \\ Panchromatic 5 m \\ \hline Spatial pixels & Hyperspectral: 1000 \\ Panchromatic: 6000 \\ \hline Spectral range & VNIR: 400–1010 nm \\ SWIR: 920–2505 \\ \hline Spectral resolution & \(\leq\)12 nm \\ \hline Spectral bands & VNIR: 66 \\ SWIR: 171 \\ \hline Radiometric quantization & 12 bit \\ \hline Absolute radiometric accuracy & Better than 5\% \\ \hline \end{tabular}
\end{table}
Table 2: Prisma hyperspectral and panchromatic sensors specifications. Modified after [49].  
30 min. Most of the commercial drones have relatively low carrying capacity and stability. Further UAV specifications are provided in [54]. In this study, the analysis of the area of interest was performed through a customized Canon Powershot S100 digital camera with a filter for the collection of NIR radiation [55].
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types
### LiDAR Sensors  
LiDAR detects signals from the crop, canopy, and ground. It is capable of penetrating the vegetation canopy, a goal of which is to measure the height, terrain, and plant diameters [113]. The LiDAR system estimates the plant parameter with high precision in all types of environmental conditions [114]. It accurately maps the 3D coordinates of a plant with two-dimensional data [115], as shown in Table 1.  
The airborne LiDAR system provides optimal results to estimate crop parametric data. Various features of LiDAR have been proven to be highly accurate in field tests. [PERSON] et al. [114] studied the ratio of the sum of the intensity of ground returns to the sum of the intensity of total returns for the estimation of crop parameters. The results showed significant values (R\({}^{2}\) = 0.610) with accurate estimation [116]. However, the type of data and management methods interrupt this accuracy when there is a high amount ofvariation. It was noticed that overestimation also occurs when there is dense vegetation. [PERSON] et al. [117] reported that the accuracy of the system was higher for few parameters and lower for others. This problem can be addressed by analyzing the effects of occlusions of dense vegetation. Another major problem of the system is inaccurate estimation of the model in time and space. This creates an urgent need to optimize the processing methods in new geographic locations and systems based on an understanding of the effects on particular crop parameters.
## 3 Results and Discussion
### FTIR Spectroscopy
The C=N stretching vibration peak of the quinazoline ring shifted slightly at 2819.86 cm\({}^{-1}\). The halogen groups also exhibited variation in their stretching vibrations at 1463.22 cm\({}^{-1}\) for fluoro and 843.47 cm\({}^{-1}\) for chloro moieties. The C-O-C exhibited merged stretching vibrations at 1100.15 cm\({}^{-1}\) for GEF pure and for TPGS. The HP \(\beta\)-CD depicted prominent C-O-C stretching vibrations at 1020.02 cm\({}^{-1}\). The other peaks of TPGS were also observed with slight changes in their frequency at 2881.4 cm\({}^{-1}\) for the ester and 1343.93 cm\({}^{-1}\) for C=C aromatic moieties. For the complexation of GEF-TPCS with HP \(\beta\)-CD (F3), the stretching vibrations were observed mostly for GEF pure with slightly changed frequencies. The stretching vibrations for 2\({}^{\circ}\) amine were shifted at 3364.57 cm\({}^{-1}\) and for C-H at 2920.19 cm\({}^{-1}\) as compared to GEF pure. The halogens exhibited shifting in their frequencies at 1498.80 cm\({}^{-1}\) (for fluoro) and 849.93 cm\({}^{-1}\) (for chloro) as compared to GEF pure. C-N stretching of the morpholine ring for F3 was found at 1387.84 cm\({}^{-1}\). The C-O-C peaks exhibited the peak values for drugs and carriers at 1017.07 cm\({}^{-1}\), endorsing the development of the inclusion complex as a ternary formulation. The pattern of IR spectra for the GEF-TPCS HP \(\beta\)-CD inclusion complex (F3) confirmed the encaging of GEF-TPCS SDs inside the bucket-like cavity of HP \(\beta\)-CD, as substantiated by the host-guest complexation of desloratadine with HP \(\beta\)-CD [48]. Figure 3: The FTIR spectra of individual ingredients and solid dispersions. GEF pure (**a**); TPGS alone (**b**); F1 (**c**); SLS alone (**d**); F2 (**e**); HP \(\beta\)-CD alone (f) and F3 (g).
# Analyzing Key Features of Open Source Software Survivability with Random Forest
## 1 Introduction
By investigating how issue retention rates reflect survivability, this study aimed to establish a robust framework for evaluating OSS sustainability and guiding effective project management strategies. This research applied a systematic methodology comprising data collection, analysis, and feature evaluation to achieve this objective. Data were collected from GitHub APIs and SonarCloud, capturing a wide range of information, including issue occurrences, resolution times, code complexity, and community activity metrics such as project stars, forks, and watchers. These datasets provided a comprehensive foundation for understanding how various factors contribute to the accumulation of unresolved issues. Kaplan-Meier survival analysis was used to assess issue resolution times and generate survival curves, which visualize the probability of unresolved issues persisting over time. These survival curves enable the quantification of maintenance trends and offer a clear picture of how well an OSS project manages its challenges. To delve deeper into the relationship between project characteristics and issue retention rates, random forest models were employed. This machine learning technique is particularly suited for capturing complex, nonlinear relationships and interactions among multiple variables. By leveraging random forest models, this research evaluated the impact of community engagement, technical complexity, and project characteristics on unresolved issues. Hyperparameter optimization was conducted using RandomizedSearchCV to improve prediction accuracy, and RobustScaler was applied to minimize the effects of outliers in the dataset. These methodological refinements ensured that the model captured meaningful insights while maintaining reliability. Feature importance and permutation importance analyses revealed the relative influence of each factor, while partial dependence plots (PDP) provided a nuanced understanding of how specific features, such as project popularity or contributor count and shape issue retention rates. This study contributes significantly to the field by presenting a data-driven framework for assessing OSS survivability. By centering the analysis on the issue retention rate, it provides a quantitative approach to understanding how unresolved issues influence long-term sustainability. This research identifies key factors that affect survivability, such as technical complexity, community engagement, and project characteristics, offering practical recommendations for improving project management. Additionally, the integration of survival analysis and random forest models demonstrates the strength of combining statistical and machine learning approaches to address the challenges of OSS maintenance. These insights not only aid project managers in formulating effective strategies but also pave the way for future research on enhancing the sustainability of OSS ecosystems.
## 2 Materials and Methods
### Data Preprocessing
The process of generating new spectra involved combining the original data with the generated white noise (see Figure 2), and this allowed for an expansion of the dataset and a balancing of the classes, ensuring that all categories were adequately represented. Applying the same level of standard deviation to all spectra, it is ensured that all samples have the same amount of added random variability. This avoids possible biases or excessive differences between the generated spectra, which could affect the interpretation and comparison of the results. These standard deviation values are in line with typical noise fluctuations observed in many scientific experiments and spectroscopic measurements. This new balanced dataset, like the previous one, would consist of 343 features representing the flux values, and an additional column representing the spectrum label. In this case, a final count of 3000 spectra was achieved, with an equal distribution of 1000 spectra for each object type. This balanced dataset ensures that each type of star is adequately and proportionally represented, which is crucial to avoiding biases and enabling more accurate analysis and modeling. Table 3 presents the distribution of stellar spectra in the balanced dataset, categorized by star type. It illustrates the composition of each class, distinguishing between original spectra obtained from observations and those synthetically generated to achieve balance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stars** & **Original** & \multicolumn{2}{c}{**Generated**} \\ \hline Symbiotic Stars & 201 & 20.1\% & 799 & 79.9\% \\ Planetary Nebulae & 574 & 57.4\% & 426 & 42.6\% \\ Red Giants & 1000 & 100\% & 0 & 0.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Class distribution in the balanced dataset, showing the proportion between original stellar spectra and synthetically generated ones for each star type. Figure 2: The figure displays the original spectrum of the symbiotic star (blue) and the spectrum resulting from the addition of noise (green), following a normal distribution with a mean of 0 and a standard deviation of 0.05.
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 4 A New Service Proposed
### Mts Api  
The system proposed in this study, implemented using the MTS API, features an access point that facilitates data exchange between two interested parties. This service receives a POST request via the HTTP protocol. The request must contain three elements: (i) the destination to which the data should be sent, (ii) the method to access that destination, and (iii) the information the sender wishes to transmit. Observing the default format of the data model is crucial, as the API interprets the data in the request body in JSON format, using the respective keys to identify the content.  
After receiving the data, the \(postData\) procedure is activated. This function is tasked with recognizing the end client's destination and the method used in the HTTP protocol. This is due to the need to make a new request to send the information. The \(makeRequest\) method is activated to execute this new request, which separately receives the destination IP address and the data to be transmitted as arguments. Once this new request has been completed, the API obtains the response provided by the server and forwards this response back to the client. This chain of actions can be seen in Figure 3.  
The functionality will be accessible via an external port called NodePort using the Kubernetes Kube-proxy service. In this context, Kube-proxy establishes a port linked to the service using NAT [37]. As seen in Figure 3, the expectation is that the client will check the availability of the desired service in the Service Registry and, if available, receive all the relevant information. Consequently, the data format the MTS API expects must contain the full IP address, i.e., the destination IP, port, and endpoint.  
The Service Registry task involves receiving data about the applications and services available on the server in question and enabling interested client applications to obtain these details, allowing access to these applications or the ability to direct their data appropriately. To perform this function, SR uses the NoSQL database MongoDB [38].  
To make a MEC application accessible to those interested, the application must carry out a registration process with the SR. The SR waits for the application to transmit data, such as its IP address, port number, and the endpoints it offers. The application must  
Figure 3: Operation of the proposed API.  
establish at least one endpoint. Once the information arrives, the SR checks the validity of this data and stores it in the database.  
Service Registry is a service accessible externally by the client to check the existence of registered services. The client uses an access point for searches, receiving a JSON with service information and an authentication token generated with Python's JWT library using a specific key in the code.
## 3 Analysis of Test Results
### Acoustic Emission Characteristics  
#### 3.4.1 Energy Rate Characteristics  
Figure 9 illustrates the quantitative variation curves of the peak energy rate/impact rate. It can be observed that with increasing soaking time, there is a gradual decrease in energy rate per impact. The energy rate per impact for dry sandstone is 10,522 counts. In contrast, the peak energy rates per impact for sandstone soaked for 7, 14, 30, 60, and 90 days are 9460, 4522, 1028, 717, and 198 counts, indicating a reduction of 10.1%, 57%, 90.2%, 93.2%, and 98.1% compared to dry sandstone, respectively. This result indicates that the severity of rock sample failure decreases with increasing soaking time. In rock, the energy released during tensile cracking exceeds that released during shear cracking [36; 37]. The cumulative energy data indicate that with increasing soaking time, there is a gradual reduction in the rate of change in cumulative energy released by acoustic emission.  
Figure 8: Failure modes of samples soaked for 7, 14, 30, and 60 days: (**a**) actual damage photographs; (**b**) damage schematic diagrams.  
As observed in the fitted curve in Figure 9, the average cumulative energy was recorded every 100 s from the start of the uniaxial compression test until the fracture of samples at different soaking times. The secant line of the fitted curve shows a steady decrease in its slope, indicating a transition from brittle to plastic failure mode. Extensive experimental data and research indicate that tensile cracking occurs over a shorter time, while shear cracking takes longer. This trend aligns with the observed transition from tensile to shear failure in the rock samples.
The lifetime of a sensor node is mainly determined by the power supply since battery replacement is not an option in sensor networks. The longer the lifetime of a sensor, the more stable the network. In order to save power, redundant activities should be reduced if not eliminated. For example, implosion problem should be avoided which is caused by using flooding protocols where multiple copies of the data might be transmitted to the same destination from different intermediate sources; communications should be constrained since it has been shown [14, 15, 21] to be the most power-consuming process. Efficient and intelligent usage of battery becomes a most challenging problem. In order to reduce redundancy and thus save power, localized algorithms seem attracting since only a subset of nodes in the network are invoked for a specific task. Localized algorithm is a special kind of distributed algorithm. How to choose the subset of sensor nodes to participate in a specific task is, however, a non-trivial problem. The stringent resource issue also indicates low bandwidth supply and unreliable wireless links. How to achieve complex data dissemination tasks within the limit of the wireless communication networks is also a challenging problem. In this article, the development of localized algorithms in sensor networks is discussed, including the localized networking services and the collaborative information processing applications. In order to develop efficient localized networking services, they have to be tightly coupled with the application. In another word, the network routing protocol design is moving toward application layer.
## 4 Numerical Results of the Proposed Reconstruction Method
### Influence of Sensor Number and Placement on Reconstruction Performance  
In the previous section, the selection of the number of basis vectors is discussed from two aspects: reconstruction accuracy and noise effect. For the velocity field, the increase of the eigenvector is beneficial to the improvement of the reconstruction error, but the excessive increase will make the error more sensitive to noise, so the number of basis vectors is determined as six. However, for the pressure field, the reconstruction accuracy is more susceptible to measurement errors due to the large data gradient. Hence, the selection of the number of basis vectors should comprehensively consider the accuracy of feature extraction and the impact of noise level, and finally, the number of basis vectors is determined as eight. After determining the number of base vectors, the factors that affect the accuracy of reconstruction need to be further explored. By observing Equation (10), M is the key factor affecting the solution of reconstruction coefficient \(\mathbf{a}\), and the number and location of sensors determine M. Thus the effect of the number and placement of sensors on the accuracy of reconstruction will be further discussed in this section.  
Figure 11: Diagram of velocity fields in five planes (five different colors) to be reconstructed.  
Figure 12: Velocity nephograms of CFD with boundary condition of 90\({}^{\circ}\), 15 m/s.  
Figure 14: Relative pressure nephograms of CFD with boundary condition of 90\({}^{\circ}\), 15 m/s.  
Figure 13: Velocity nephograms of reconstruction with boundary condition of 90\({}^{\circ}\), 15 m/s.  
Figure 15: Relative pressure nephograms of reconstruction with boundary condition of 90\({}^{\circ}\), 15 m/s.  
In particular, as shown in Figures 17 and 18, there is a large variance in the distribution of the \(\Phi(K_{\lambda})\) and \(RE\) for six sensors in the velocity field and eight sensors in the pressure field. Specifically, the relative errors and condition number can change by orders of magnitude with the same sensors, but simply placed in different locations. From both figures, it can be seen that fewer sensors can produce both exceptional results and extremely poor performance depending upon the sensor locations, which illustrates that extremely high variability can be generated in reconstruction using random and sparse measurements.  
Thus, the sparse sensor placement is critical for accurate 3D wind field reconstruction, which suggests that developing a principled way to determine optimal sensor placement is of great importance.  
Figure 17: The average and variance (red bars) over 1000 trials of logarithm of the condition number of \(K_{\lambda}\) and relative errors (velocity field).  
Figure 18: The average and variance (red bars) over 1000 trials of logarithm of the condition number of \(K_{\lambda}\) and relative errors (pressure field).
## 6 Performance Evaluation
### Results by Epidemic, GSaR, TCCB, TC, and PRoPHETv2
#### 6.3.1 Case (a)
However, as depicted in Figure 10b, the overhead ratio is higher and drastically increases as TTL \(>8\) h. GSaR induces more overhead ratio and a lower delivery rate when the TTL increases from \(1\) to \(8\). As depicted in Figure 10c,d, both latency and hops in GSaR are higher as compared with other policies. In the case of NCCU with a higher node density, nodes have higher chances of contacting other nodes. A message replicated more times can induce higher hops. In GSaR, the policy of deleting a message probably decreases the chances of reaching the message earlier to the destination and thus causes higher latency. Because Epidemic, TCCB, TC, and PRoPHETv2 do not use control-based replication, their replication policies in the NCCU trace generate many message copies for long-TTL messages, which results in a higher overhead ratio. Figure 10c depicts that PRoPHETv2, TC, and TCCB have lower latency because of many messages created in both the policies. With a higher node population in NCCU, more communities are formed in TC. As a result, the amount of message forwarding increases, resulting in a higher overhead ratio and delivery rate. There is an interesting case as TTL \(>8\) h in Figure 10b. Both PRoPHETv2 and TCCB have huge overhead. This is possibly due to buffer replacement operations frequently occurring by comparing Epidemic with PRoPHETv2 and TCCB. Older messages in Epidemic are replaced frequently, and the number of replicas created for such messages is less than those in the PRoPHETv2 and TCCB cases. In PRoPHETv2 and TCCB, messages with a higher TTL have higher chances of remaining in the buffer, and with a higher node population, older message are replicated more times. Although the node population is higher in NCCU, the MPMF creates fixed replicas for each message. As depicted in Figure 10, the delivery rate in the case of MPMF is similar for long-TTL messages as compared with other policies. In addition, the MPMF induces a minimal overhead ratio as in Figure 10b.