esa-sceva/satcom-chunk-collection huggingface dataset:
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Technological Considerations
#### 5.1.5 Flarm  
FLARM, short for flight alarm, is a system used in general aviation that informs the pilot of any nearby aircraft (manned or unmanned) or parachutes that have FLARM on board and locates them relative to the FLARM-equipped aircraft's built-in Global Navigation Satellite System (GNSS) receiver and altimeter [68; 69; 70]. FLARM can be meaningfully compared with ADS-B to consider why it may be chosen in lieu of--or additional to ADS-B--as a means of DAA. FLARM only has a range of up to 5 nm (9.26 km). Therefore, it cannot detect high velocity aircraft with enough time to safely respond, does not integrate with traffic collision avoidance systems, only works in the region it is purchased, and does not provide suggestions to pilots as to how to resolve any collision [68]. Despite these disadvantages, it also has some advantages compared with ADS-B; namely, it is cheaper, quicker, uses less energy, and can operate independently from ATC [68]. As noted earlier, forest environments are typically in remote areas, away from controlled airspace or significant aviation activity. The ability of FLARM to operate independently of ATC is, thus, a major advantage, and the disadvantages of range and inability to detect high velocity aircraft are unlikely to pose any major hindrance to the typically low altitude operations that take place in forestry. Thus, FLARM can provide a low-cost addition as part of a combination of DAA measures to help detect other aircraft.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 3 Data Services
### Giovanni
Data lineage information is available and the datasets used in the processing steps are listed for download.
## 4 Simulations and Real Data Processing
### Comparison with the ACCF Method
When the acceleration is considered, the velocity estimation results by using CCAE on stretched signals, CCAE on UR signals and ACCF on UR signals are compared in Figure 8. It can be seen that the RMSE of the ACCF method is also larger than the proposed method. In this experiment, the threshold SNRs of the three test methods are \(-26\) dB, \(-16\) dB and \(-4\) dB, respectively. To estimate the velocity, the estimated acceleration should be used to eliminate its influence. Thus, the performance of the velocity estimation is related to the performance of the acceleration estimation. As the performance of estimated acceleration is worse, the performance of velocity estimation of the ACCF method also becomes worse. From a comparison of Figures 6 and 8, for the proposed CCAE method, it can be seen that the RMSE of the estimated velocity with acceleration is a bit higher than that without acceleration. This is because the velocity is calculated by subtracting two i.i.d variables. This can be confirmed by comparing Equations (29) and (31). In Figures 6-8, there exist performance gaps between the simulation and the theoretical results of the CCAE method. The differences are mainly caused by the noise approximation, which is analyzed in Section 3.4. The RMSE gap in Figures 7 and 8 is bigger than that in Figure 6. The reason is that acceleration estimation is implemented by performing CCAE twice. This leads to a noise term subtracted from two double Gaussian noises. When approximating this subtraction noise to be a Gaussian distribution, the error is increased. Thus, the RMSE gap between the simulation and the theoretical results in Figures 7 and 8 is larger than that in Figure 6. In summary, for the CCAE method, the RMSEs of the estimated acceleration and velocity are both close to the theoretical RMSEs, which proves the effectiveness of the proposed method. Figure 8: RMSE comparison of velocity estimation by using CCAE on stretched signals, CCAE on UR signals and ACCF on UR signals (with acceleration). Figure 7: RMSE comparison of acceleration estimation by using CCAE on stretched signals, CCAE on UR signals and ACCF on UR signals (with acceleration). The total time costs of the 1000 Monte Carlo simulations for all of the SNR cases are listed in Table 4. It is seen that the time cost of the CCAE on stretched signals is much less than that of CCAE on UR signals and ACCF on UR signals. The reason is two-fold: first, the data size of the UR signal is larger than the stretched signal; second, the number of FFT operation required by the ACCF method is more than that of the CCAE method.
# An Overview of CubeSat Missions and Applications
## 5 CubeSats as Astronomical Exploration and Research Enabling Platforms
### Gamma-Ray Burst Detectors  
Another application of CubeSats for astronomical observations is for gamma-ray burst detection, with one notable mission being BurstCube [222]. The BurstCube CubeSat expedition to a 6U CubeSat was developed by NASA and launched in March 2024, in order to detect and study gamma-ray bursts (GRBs), one of the most intense events in the universe and often associated with supernovae or the formation of black holes. BurstCube's cadmium zinc telluride (CZT) detectors (see Figure 13b), which are sensitive to high-energy gamma rays, enable it to swiftly detect and pinpoint GRBs despite its compact size (6U) and low cost. It mainly acts as a supplement to larger observatories, such the Fermi Gamma-ray Space Telescope, by providing early detection and alerts for GRBs, especially those that occur outside the coverage regions of larger spacecraft. BurstCube's architecture enables the rapid transmission of gamma-ray data, which enables Earth-based astronomers to schedule follow-up studies at other wavelengths. A previous mission relative to this subject was GRBAlpha CubeSat. GRBAlpha is a 1U Cubesat project developed by the Technical University of Kosice that was launched in March 2021, dedicated to detecting gamma-ray bursts (GRBs) from deep space to demonstrate that gamma-ray detection on a nanosatellite platform is possible [223]. GRBAlpha's CsI(Tl) scintillation detector, developed via international collaboration, may record high-energy events associated with the death of massive stars and the formation of black holes since it can detect gamma rays with energies between 50 keV and 300 keV. Since its successful launch in March 2021, GRBAlpha has advanced the objective of employing several nanosatellites to expand global GRB monitoring by proving that small-scale, affordable GRB detection is feasible. GRBAlpha demonstrates how CubeSats might complement traditional observatories in the study of powerful cosmic occurrences in addition to assisting GRB research by transmitting real-time data on transient gamma-ray bursts.
## 3 Results and Discussion
### Flood Mapping from Satellites
On the other hand, the lack of preflood images acquired with the same configuration of measurement of the flood images prevented AUTOWADE from applying  
Figure 9: Panel (**A**): RGB false color image produced using the S1 data acquired on 19 October 2021 (red) and 25 October 2021 (green/blue). Panel (**B**): Flooded areas map generated by AUTOWADE using the S1 images shown in Panel (**A**). Cyan: floodwater; blue; permanent water. change detection. This implied a decrease in the accuracy of the flood map and made it difficult to distinguish permanent water from floodwater. Nevertheless, to the best of our knowledge, the map produced using CSG data, shown in Figure 11, represents the first flood product generated using this sensor. By comparing Figure 10, panel B, with Figure 11, it can be noted that more water is present in the former figure, although it was produced using SAR data acquired one day later. This is due to the lack of a preflood CSG image underlined above, which hampered a profitable application of a change detection approach.
## 6 Simulation Setup and Results
### Uplink Use Case
#### 6.7.4 Dss2  
As SINR measurement graphs are similar to DSS1, they are not presented here. Detailed analysis shows that with DSS2, the offered service was marginally better (see Figure 13). Satellite links behave almost optimally, however, just like DSS1, DSS2 fails to protect FWA links. When further examining the reason for this, we concluded that since DSS2 is based on spectrum sensing at the receiver, and the receiver of the GSO link is 36,000 km away, it cannot sense the presence of FWA links at the surface of the Earth. Furthermore, as the GSO link has zero PER, it does not need to initiate any slot reallocation procedure. On the other hand, the powerful GSO-ESIM transmitter creates a huge amount of interference on the Earth's surface, impacting all links that are not capable of adapting their spectrum allocation. The only way to resolve the remaining issues using DSS2 is to also integrate the DSS module that supports spectrum sensing and dynamic spectrum access in FWA nodes, making them also adaptive and interference-aware. In this way, they would be able to simply move out of the spectrum that is affected by the GSO link.
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 2 Literature Review
### PV Array to Inverter Sizing Strategies
#### 2.2.3 A Climate Classification
  
*Some articles were accounted for in the classifications starting with A, but the majority was concentrated in classifications B and C, as shown in the Table 4 summary. Regarding the theoretical equation considered to calculate the size ratio of the PV array-to-inverter rated power, there are a number of perplexing approaches. Several publications used different brand names for the pairing of the PV array and inverter, including v, which is the nominal ratio power [91], IPR (inverter power ratio) [92], r (PV/inverter sizing ratio) [93], DF (dimensioning factor) [72], and others. The system size ratio formula in the literature expressed the power ratio as inverter/PV array and PV/inverter. Regarding the STC PV power capacity, the majority of studies assessed the sizing ratio of the inverter rating power to be between 10% and 40% [72; 91; 94]. The value of the system size ratio is dependent on how much the tilted PV array's orientation fluctuates, and it tends to rise as the installed PV array's tilt angle rises, according to other authors' summaries, including the orientation of the tilted PV array factor described in [3]'s conclusion. [PERSON] et al. [69] and [PERSON] et al. [95] explored the effect toward size ratio according to the different inverter transfer efficiencies from the viewpoint of the used inverter. Both authors came to the conclusion that the use of higher inverter efficiencies caused the sizing ratio to be nearer to 1.0, particularly in high irradiance.
# Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
## 5 Results
### Analysis of Single Patches  
In the following we consider the S-1 swath corresponding to stack 7 in Table 2 and we provide a performance analysis of our proposed algorithm over selected patches, in order to analyze specific details in the images. As shown in Figure 5, we select four small patches of \(512\times 512\) pixels, extending by about 25 km \(\times\) 25 km on ground. Patches (a) and (b) are characterized by the presence of urban areas, that is, the municipalities of Porto Velho and Ariquemes, respectively, while patches (c) and (d) identify stable regions of cropland mixed with remaining rainforest areas. Similarly to Section 5.1, we summarize the results of the patches in Figure 6 and Table 5, which describes the OA and AA for each patch, in both the input configurations, case _(ORIG)_ and case _(SADH)_.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & **Class** & **Stack 6** & **Stack 7** & **Stack 8** & **Stack 9** \\ \hline \multirow{3}{*}{**pixels**} & _ART_ & 10060 & 34890 & 6009 & 1054 \\  & _FOR_ & 14626324 & 7846234 & 9822268 & 10087671 \\  & _NFR_ & 1655335 & 6194973 & 4193972 & 4280135 \\ \hline
**Case** & **Metric** & **Stack 6** & **Stack 7** & **Stack 8** & **Stack 9** \\ \hline \multirow{3}{*}{_ORIG_} & _OA_ & 88.48\% & 82.50\% & 85.03\% & 84.84\% \\  & _AA_ & 61.05\% & 81.64\% & 80.50\% & 71.95\% \\ \hline \multirow{3}{*}{_SADH_} & _OA_ & 91.90\% & 84.26\% & 86.49\% & 87.66\% \\  & _AA_ & 65.11\% & 85.59\% & 85.06\% & 82.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall accuracy (OA) and average accuracy (AA) for the four swaths in Figure 4. Each swath is associated to a stack, according to the enumeration in Table 2.  
Figure 5: Classification map of the stack number 7 summarized in Table 2. Black: _invalid pixels_ (INV), blue: _artificial surfaces_ (ART), green: _forests_ (FOR), red: _non-forested areas_ (NFR). White polygons delimit four patches of \(512\times 512\) pixels used for the classification accuracy analysis. They are named (a), (b), (c), and (d).
## 3 Proposed Research Framework  
The key research objectives considered in this work are given below:  
* RO1: Designing a common standard for patient risk scoring during traversal to the hospital;
* RO2: Hybrid communication architecture for transmitting delay-intolerant health risk data;
* RO3: Traffic-aware real-time routing of health risk data to the selected hospital.  
The proposed method enables the assurance of the QoS of the network with topology and ensures that the ambulance travels in a route which reaches the hospital as soon as possible. This work ensures the shortest path along with the tele-assistance to be given to the patient in the ambulance.
## 4 IoMT Authentication Taxonomy
### Authentication Levels  
Because IoMT systems are complex and distributed, it is challenging to propose generic authentication solutions for various IoMT systems nodes. Therefore, IoMT authentication is primarily considered at three levels: device-level, user-level, and network-level, as shown in Figure 8.  
Figure 6: Article selection process.  
Figure 7: Taxonomy of authentication in IoMT.  
At the device level, authentication attempts to identify devices to the IoMT system [24; 25; 26; 27; 28; 29]. [PERSON] et al. [30] developed an end-to-end protocol for authenticating resource-constrained IoMT devices. Using the Diffie-Hellman key establishment scheme, they offloaded the heavy security computation to the trusted neighboring nodes [30]. [PERSON] et al. [31] introduced a lightweight, provable, and secure scheme for IoT-healthcare systems. They developed a secure authentication protocol among sensor entities and servers using the non-verification table (NVT) technique.  
Most of the reviewed papers considered user authentication in IoMT systems. The authentication at this level mainly attempts to identify and authenticate patients or healthcare providers at the application layer [32; 33; 34; 35]. [PERSON] et al. [36] designed an energy-efficient scheme that supports key agreement, authentication, and access control mechanisms. It further preserves the doctors' and patients' privacy through the transfer of ownership. The transfer ownership technique prevents the old user from knowing the new user's identity. [PERSON] [37] developed a selective-group technique for IoT authentication in the healthcare system. The group authentication scheme is based on a threshold mechanism to select users for authenticating them to IoMT.  
At the network level, the communicating entities must register and authenticate users or devices to secure the overall IoMT network [38; 39; 40; 41]. In [42], [PERSON] et al. considered both the user and sensor registration in the system while developing their authentication scheme. Moreover, [PERSON] and [PERSON] [43] proposed a smart contract using blockchain. They adopted an interplanetary file system (IPFS) within the cluster which implements the smart contracts at the beginning of authenticating patients and the IoMT devices.
## 3 Results and Analysis of the Filed Measurements
### Indoor Environment of Canteen Kitchen
#### 3.2.2 Temperature, Relative Humidity and Air Velocity on Typical Test Day  
The statistical analysis showed that the thermal condition of the canteen kitchen was very bad. From Figure 5, we found that the temperature in the cooking zone of the canteen kitchen was between 29.20 \(\pm\) 0.29-39.10 \(\pm\) 0.29 \({}^{\circ}\)C and reached the temperatures peak at 8:00, 11:03 and 17:18. The maximum temperatures of measuring points were 71, 72, and 73 in the cooking zone were 35.90 \({}^{\circ}\)C, 36.1 \({}^{\circ}\)C, and 39.10 \({}^{\circ}\)C, respectively. It was different from the result reported by [PERSON] [16] that restaurants reached the highest peak at 12:50 at noon and 19:00, which may be due to the different work nature of restaurants and canteen kitchens. Canteen kitchens in colleges needed to provide three meals at a fixed period, while restaurants generally provided two meals and needed to start cooking according to the customers' orders. Like the restaurants, the variation of relative humidity was contrary to the trend of temperature. The minimum relative humidities of measuring points 71, 72, and 73 in the cooking zone were 46.90% RH, 46.50% RH, and 43.40% RH, respectively, which were 12.00% RH, 18.50% RH and 18.30% RH lower than the indoor area relative humidity.  
Figure 6 shows the air velocity at different measuring points for a typical day. According to BSEN16282 [18], the permissible mean air velocity should not be higher than 0.48 m/s when the indoor air temperature of a commercial kitchen is lower than 32 \({}^{\circ}\)C. We can see from Figure 6 that the air velocity of the measuring points in the indoor area met the standards' value, while the air velocity of the measuring points 71,72 and73 in the cooking area were 0.53 \(\pm\) 0.13 m/s, 0.52 \(\pm\) 0.15 m/s, and 0.48 \(\pm\) 0.13 m/s, respectively. Chefs may expose to higher air velocity during cooking, which was consistent with the questionnaire results that 91.60% of the chefs felt different degrees of draft sensation.  
Figure 4: The seasonal variation of the temperature and relative humidity in different comfort ranges of the indoor environment in the kitchen.
# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 2 Proposed Methodology  
As shown in Figure 1, a novel and comprehensive deep learning-based framework for the early detection of breast cancer is proposed in this study. It makes use of the complementary strengths of two different medical imaging datasets: The Breast Ultrasound Images dataset and the MIAS (Mammographic Image Analysis Society) dataset. The approach begins with independent preprocessing of both datasets to ensure consistency in format, resolution, and class labeling. To increase model generalization and data diversity, a fusion of the datasets is then carried out. The fused dataset is then subjected to an additional preprocessing phase to unify input dimensions and normalize image values, ensuring compatibility with DL architectures. The unified dataset is divided into training and testing sets after preprocessing, preparing the way for a dual-path modeling approach. Several cutting-edge transfer learning techniques are used in the first path, such as CNN, MobileNet, Xception, DenseNet121, EfficientNet, ResNet50, and VGG16, which are fine-tuned on the training data to learn high-level image representations. In parallel, the second path introduces
# The Microverse: A Task-Oriented Edge-Scale Metaverese
## 4 Case Study: A Smart Public Safety Surveillance Microverse for Smart Communities
### Design Rationale  
The SPSS microverse system employs a sophisticated network of surveillance cameras strategically deployed throughout the town to gather real-time data from multiple vantage points. These cameras are connected to an advanced data processing and analysis server infrastructure. Furthermore, the SPSS infrastructure serves as a gateway to a virtual environment, accessible through a metaverse application developed using the Unreal Engine 5 (UE5) platform.  
Following the hierarchical microverse framework architecture, the physical layer functions as the principal repository of data, whereby surveillance cameras are employed to oversee and watch various public areas, thoroughfares, and crucial sites. The entire SPSS network is under one administration that manages physical devices, logical networks, and users. The gathered data, including live video feeds and sensor information, are delivered to the server nearly instantaneously. The server processes the data that the user provides. Then, it saves them in a structured database, which guarantees the establishment of a complete repository containing various community activities and events. Furthermore, the sensor and video content are available for user queries such as forensic analysis [64].  
The metaverse layer, constructed using the Unreal Engine, functions as a dynamic reflection that accurately represents the current state of the community in real-time. Instantaneous replication of observed occurrences or deviations inside the tangible realm is promptly reflected within the digital domain. The synchronization of various elements not only serves to improve the situational awareness of public safety personnel but also establishes a framework for effective decision-making and coordination of response efforts.  
The SPSS microverse surpasses traditional monitoring methods by leveraging the immersive capabilities of the metaverse. Law enforcement agencies and emergency responders can virtually traverse a digital depiction of the community, allowing them to gather valuable insights into existing crises and enhance their methods for real-world interventions.  
This case study illustrates the capacity to integrate the physical and virtual realms to augment public safety in smart communities. Integrating data from the metaverse and the physical world, this novel methodology lays the groundwork for a forthcoming epoch in intelligent urban governance. In this microverse paradigm, amalgamating up-to-the-minute data and virtual observations culminates in developing safer and more adaptable urban settings. As a small-scale, proof-of-concept prototype considering one network domain, this case study does not involve the network slicing layer and microchannel security networks. We leave them to our future work.