esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### In-Orbit Results
#### 5.2.3 Window of Stationarity  
To analyse the first-order stationarity of the interference measurements, the window of stationarity is estimated at the \(-10\) dB level for the CV (see Section 3). The measurements have been divided into four regions (Figure 12a): South Pacific and South Atlantic (named the Antarctic), America, the Arctic, and Europe. In this section, when we refer to Europe, we include the north of Africa and the Middle East. The estimation of the window of stationarity over the frequencies has been divided into two parts: the frequencies that are  
Figure 11: Example of a five second measurement with different types of signals over Greenland. (**a**) Average power spectrum (\(m_{2\lambda}\)) and coefficient of variation. If there was no time variability in the mean, it would be zero. (**b**) Normalised LME second moment for different time windows for all frequency bins in 3D. (**c**) Normalised LME second moment for different time windows for all frequency bins.  
within the band-limited interference (434.83-435.19 MHz); and the ones outside but within the 3 dB bandwidth (434.75-434.83 MHz and 435.19-435.25 MHz).  
In Figure 12b, the window of stationarity for frequencies outside the band-limited signal is shown. Most measurements have a window of stationarity below 6.83 ms, particularly the Antarctic measurements. The only regions that have stationarity windows longer than 27.31 ms are Europe and the Arctic.  
In Figure 12c, the frequency bins analysed are the ones within the band-limited interference. For the Antarctic measurements, there is no big change. For the rest of the regions, the window of stationarity increases for more measurements. About 29% of the Arctic measurements have a window of stationarity longer than 27.3 ms. For America and Europe, it is around 9% and 31%, respectively. There is a considerable difference for measurements over America between Figure 12b, since the largest window of stationarity in Figure 12b is 6.83 ms, and, in Figure 12c, it is longer than 27.3 ms.  
Figure 12: Window of stationarity results of in-orbit measurements. (**a**) Measurement locations grouped into four categories: Antarctic (blue), America (yellow), Arctic (black), and Europe (magenta). (**b**) Histogram of stationarity window per region (outside band-limited interference bandwidth). (**c**) Histogram of stationarity window per region (within band-limited interference bandwidth).
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 9 Efficient Signature Verification in Post-Quantum VerITAS
### Proof System for the Lattice Hash
#### 9.3.2 Matrix-Vector Product Proof  
The goal of the matrix-vector product proof is to verify that \(h=A\cdot v\). This is accomplished using [PERSON] algorithm combined with a sum-check protocol. The steps are as follows:  
1. Generate a random vector \(r\gets F^{\pi}\).
2. Prove that \(r^{\top}h=\big{(}r^{\top}A\big{)}v\), which ensures the correctness of the matrix-vector product.
3. Use Aurora's univariate sum-check protocol to efficiently verify the computation.
###### Abstract  
Water is an essential necessity for maintaining the life cycle on Earth. These resources are continuously changing because of human activities and climate-related factors. Hence, adherence to effective water management and consistent water policy is vital for the optimal utilization of water resources. Water resource monitoring can be achieved by precisely delineating the borders of water surfaces and quantifying the variations in their areas. Since Lake Van is the largest lake in Turkey, the largest alkaline lake in the world, and the fourth largest terminal lake in the world, it is very important to determine the changes in water surface boundaries and water surface areas. In this context, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automatic Water Extraction Index (AWEI) were calculated from Landsat-8 satellite images of 2014, 2017, 2020 and 2023 in June, July, and August using the Google Earth Engine (GEE) platform. Water pixels were separated from other details using the Canny edge detection algorithm based on the calculated indices. The Otsu thresholding method was employed to determine water surfaces, as it is the most favored technique for calculating NDWI, AWEI, and MNDWI indices from Landsat 8 images. Utilizing the Canny edge detection algorithm and Otsu threshold detection approaches yielded favorable outcomes in accurately identifying water surfaces. The AWEI demonstrated superior performance compared to the NDWI and MNDWI across all three measures. When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector Machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. In 2017, the highest producer accuracy, user accuracy, overall accuracy, kappa, and f score evaluation criteria were all 100% with the SVM algorithm and AWEI. In 2020, the SVM algorithm and NDWI produced the highest evaluation criteria values of 100% for producer accuracy, user accuracy, overall accuracy, kappa, and f score. In 2023, using the SVM and CART algorithms as well as the AWEI, the highest evaluation criteria values for producer accuracy, user accuracy, overall accuracy, kappa, and f score were 100%. This study is a case study demonstrating the successful application of machine learning with Canny edge detection and the Otsu water surfaces thresholding method.  
Google Earth Engine; Canny edge detection; Otsu threshold; NDWI; AWEI; MNDWI; machine learning algorithms
# Climate Perspectives in the Intra-Americas Seas
## 2 Large-Scale Climate Forcing
### Large-Scale Circulation  
The low-level circulation in the tropics is driven by the ascending branch of the Hadley circulation as a result of latitudinal heating gradients and plays a major role for the poleward transport of heat. The analysis of the so-called trade winds and the overall global circulation goes back to the 18 th century [62]. Near the equator, moist and warm tropical air rises until reaching the subtropics, where it descends towards the equator near the surface [63]. In the region, the circulation is featured by the dominating easterlies, as shown in Figure 1, middle panel. Low-level winds are forced by SST gradients and their strength is highly sensitive to SST changes [64]. Energy and mass exchange can be described by the relationship between SST and winds. The vertical velocity, which is responsible for the energy and mass transfer, is forced by diabatic heating and at the same time, the vertical velocity drives the large-scale flow within the boundary layer [65]. As a result, changes in the large-scale flow are a response to near surface heating and therefore, surface winds are driven by SST. Despite the relative simplicity, this interaction is highly complex as it involves a series of non-linear feedbacks. Further interactions between the lower atmosphere and the ocean surface can be described in terms of footprinting mechanisms. The latter is given as the tropical atmosphere is also sensitive to the subtropical SST response to atmospheric variability at different time lags [66]. Here, we highlight the fact that SST-atmosphere connectivity is not only local, and it is featured by mid-latitude tropical feedbacks as well. This is relevant to better understand that the large-scale motions and variability can be triggered by non-local interactions. A good example is the case of ENSO, for which extra tropical precursors have been identified [67]. Another way to consider the role of surface processes in the region is by means of the wind stress, given the sensitivity of the tropical circulation to this variable. Wind stress corresponds to the force (per unit area) that the airflow exerts over a surface and is a measure of the transfer of momentum between the atmosphere and the ocean, and therefore provides a quantitative estimate of the air-sea coupling [68]. Wind stress plays a major role as a key mechanism to trigger instabilities [69] as well as influence rainfall [70]. The different mechanisms and interactions acting in the region are fundamental to determine the characteristics of rainfall. In fact, it is the large scale that defines one of the most relevant structures associated to rainfall, as explained in the next subsection.
# Cooperative Guidance Law for the Mother-Cabin of the Anti-UAV Cluster Mother-Son Missile
###### Abstract
Based on the current state of research, hard kill methods such as firearms, artillery, directed energy weapons and UAV cluster countermeasures have been proposed to directly destroy targets, as well as soft kill methods such as communication jamming, photoelectric decoys and electromagnetic bomb to analyse cluster warfare capabilities.Most current plans aim to develop a defense system with a combination of hard and soft killing means with echelon deployment, and use a variety of means to effectively counter UAV clusters [34; 35; 36]. These strategies have been developed due to the effectiveness of individual means and the constraints of use conditions. The effective range of these weapons for UAV detection, interference, and killing is typically between 5 km and 25 km, and the suggested defense systems can only achieve medium- to short-range protection. Another efficient strategy is to use conventional air defense systems to intercept the carrier aircraft and completely destroy the cluster. However, there is less research on countermeasures for UAV clusters in the medium- and far-range formation cruise phase after the release of the carrier aircraft. The main contributions of this study are as follows:  
1. We propose a novel pattern of intercepting UAV cluster: the use of mother-son missiles to in-tercept UAV cluster composed of medium to large UAVs over the range of medium and far distances. 2. For the proposed operational pattern, we have designed a special guidance law for the mother-cabins of the mother-son missiles with the following characteristics: zero LOS constraint, smooth convergence of each missile velocity, and converge to the same velocity only at the time of impacting the targets. This design allows the controller to have a small and smooth output. In addition, given the weak cluster mobility of the UAV cluster, the controller designed in this paper is simple and easy to implement.
## 4 Results  
The proposed method successfully achieved online damage detection on the optics of the FOA based on ResNet, with a success ratio exceeding 95% and a failure ratio less than 5%. In order to further characterize the performance of the trained ResNet-18 model, we calculated the accuracy (\(P\)) and recall rate (\(R\)) at the object level, and the numbers of true positives (\(TP\)), false positives (\(FP\)), false negatives (\(FN\)), and F1 score were determined using Formula (4) as follows:  
\[\begin{split} P&=TP/(TP+FP)\\ R&=TP/(TP+FN)\\ F1&=2 PR/(P+R)\\ \end{split} \tag{4}\]  
In this case, \(TP\) corresponds to true damages correctly labeled by the model, and \(FP/FN\) corresponds to false/real damages incorrectly labeled by the model as real/false damage. After training, the recall rate of ResNet-18 on the test set is 95.7%, the accuracy rate is 92.5%, and the \(F1\) score is 0.94. \(FP/FN\) mainly results from tiny damages whose size  
Figure 17: Comparison between predicted values and actual values of damage areas.  
of pixels is at the same level as the background noise of images. As depicted in Figure 18, the predicted damage for each element is highlighted and circled in red. Furthermore, this technology has been seamlessly integrated into a proprietary optics management software in our central control room and has been applied in practical settings for several years. This integration has undoubtedly enhanced the efficiency and effectiveness of damage detection and maintenance processes within the FOA system.  
The examination reveals that the prediction of each incremental damage trend, and the overall growth of damage areas on optics can be visually represented. Moreover, this technology was successfully implemented in the high-power laser facility of CAEP. It allows for the examination of multiple online images of a single element captured at various times. By selecting any damage point in Figure 18, the software displays the evolving trend of that specific damage, as illustrated in Figure 19.  
Figure 19: _Cont._  
Figure 18: An example of the results showing the online damage image detection of a shield window.  
Figure 19b presents similar content to Figure 16b, depicting the prediction for overall damage trend variations. By combining future experiment plans and relevant experimental parameters with predictions, the system can effectively manage all elements in optimal conditions. When the damage area approaches a critical point, maintenance warnings are issued to prevent catastrophic damage. Experimental results demonstrate an accurate prediction ratio for damage area, exceeding 90%. This robust predictive capability ensures proactive maintenance and enhances the overall operational efficiency and safety of the system.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review  
With advancements in AI and the IoT, their applications in PV systems have become increasingly diverse and sophisticated. AI techniques enhance operational efficiency by detecting faults early, predicting maintenance needs, and improving energy forecasting. Meanwhile, IoT facilitates real-time data collection, remote monitoring, and automated control, ensuring optimal system performance.  
This section explores AI-driven applications in PV systems, covering fault detection and diagnosis, predictive maintenance, and energy forecasting. Additionally, it examines IoT-based advancements, such as solar monitoring systems and optimization techniques like MPPT, solar tracking, and automated cleaning systems. These technologies collectively contribute to smarter, more efficient solar energy management.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Edge Computing** & **Cloud Computing** & **Fog Computing** \\ \hline \multirow{2}{*}{Data-Processing Location} & Local, close to data source (solar panels) & Centralized, in remote data centers & Decentralized, close to edge devices (inverters) \\ \hline Latency and Response Time & Very low latency, real-time processing & Higher latency due to distance to cloud servers & Lower latency than cloud, but not as fast as edge \\ \hline \multirow{3}{*}{Scalability} & Limited scalability in remote areas & Highly scalable for large-scale PV systems & Scalable with local processing, complementing cloud \\ \hline \multirow{3}{*}{Security and Privacy} & High security, minimal data transmission to cloud & Secure, but data transmission increases risk & Enhanced security by reducing data transmission to cloud \\ \hline \multirow{2}{*}{Energy Efficiency} & Optimizes energy by processing locally & Minimizes local energy use but consumes more at data centers & More energy-efficient than cloud for localized processing \\ \hline \multirow{2}{*}{Use in PV Systems} & Real-time monitoring of energy and performance & Stores and analyzes data from PV IoT devices & Reduces latency in monitoring and control, ensures faster decisions \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of edge, cloud, and fog computing in PV systems.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 6 Conclusions and Future Research Directions
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Technology** & **Task** & **References** \\ \hline DSRC & Operational concept for vehicle safety is presented. & [192] \\  & Field test shows that lane and position of surrounding vehicles can be identified & [193] \\  & with 100\% accuracy when the distance between vehicles is less than 50 m. & \\ \hline C-V2X & V2X framework is discussed in detail. & [165] \\ C-V2X & C-V2X is not able to satisfy all the latency requirements, therefore, shortened & [133] \\  & transmission time interval is proposed. & \\ \hline
5G-NR V2X & Framework of 3 GPP-Version 16 for AV is discussed. & [194] \\  & Field trial of mm-Wave with 5G-NR, a stable data rate of 2.8 Gbps within 500 ms & [195] \\  & latency is achieved. & \\  & Field Trial of 5G-NR V2X satisfying the latency requirements for truck platooning. & [196; 197] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._latency communication (uRLLC) and, hence, it is suitable for V2X communication. In conclusion, this survey identifies 5G technology as one of the important advancements in the development of autonomous cars and can serve as a backbone of future intelligent transport systems.
# Hom Antenna on Chip Operating at 180 GHz Using the SiGe CMOS Process
## 3 Simulation and Measurement
### Future Outlook  
Chip antennas, as highly integrated miniature antennas, have played a significant role in the field of wireless communication. They are widely used due to their small size, low cost, and ease of integration into various electronic devices, particularly mobile devices, Internet of Things (IoT) gadgets, and wearable technologies. With technological advancements and increasing demands for wireless communication, the future outlook included higher frequencies and wideband operation, with the evolution of 5G and future 6G technologies, chip antennas need to support higher frequencies and wider bandwidths. This will require the development of new materials and design techniques to achieve efficient antenna performance while maintaining their miniature size. And another future outlook is multifunctionality and Multiband, Chip antennas will need to support multiple wireless communication standards and bands simultaneously, including Wi-Fi, Bluetooth, LTE, and future technologies. This will make antenna designs more complex, but also enable a single device to support more wireless functionalities. The third is intelligent and adaptive antenna technologies, with the advancement of Artificial Intelligence (AI) and machine learning technologies, chip antennas can become smarter, capable of automatically adjusting their parameters based on the wireless environment and communication needs. This adaptive capability will further enhance the efficiency and reliability of wireless communication. The fourth is increased integration; Future chip antennas will be even more integrated, not just as standalone components but potentially integrated with other electronic components (such as microprocessors, sensors) on the same chip. This will help further reduce the device size and improve overall performance. The last one is Environmental Friendliness and Sustainability, With increasing awareness of environmental protection, future chip antenna designs will pay more attention to using recyclable or biodegradable materials to minimize environmental impact.
## 3 Assessment of the Developed Algorithm Efficiency in Some Case Studies
### Lake Huron
This experiment is indicative that very time consuming and expensive determinations of spectral specific coefficients of absorption, \(a\)*(\(\lambda\)) and backscattering, \(b_{b}\)*(\(\lambda\)) in some cases can be replaced by less cumbersome measurements of both the concentration vector \(\mathbf{C}(\textit{chl},\,\textit{sm},\,\textit{doc})\) and spectral volume reflectance, \(R(\lambda)\) or subsurface spectral remote sensing reflectance, \(R_{\textit{rsw}}(\lambda)\).
## 2 Conventional Channel Estimation Methods
### Semi-Blind Channel Estimation Method
#### 2.3.1 Subspace-Based Semi-Blind Channel Estimation  
Similar to the subspace-based blind algorithm, the semi-blind subspace-based algorithm also leverages the statistical characteristics of the signal. It aims to find the channel parameters that best match the signal or noise subspace to achieve channel estimation. In the scheme presented by [PERSON] et al. [40], the received signals are initially projected into the subspace with minimal interference. Following this, a low-complexity modified power method is employed to recursively establish the subspace base. Finally, an initial estimation of the projected channel is performed using a small number of pilots. Through experiments, the scheme demonstrates its ability to effectively reduce the impact of pilot contamination. In [41], [PERSON] et al. utilized out-of-band emission to suppress redundancy in their subspace-based scheme. Subsequently, they employed the LS estimation on pilot subcarriers to solve the scalar ambiguity. Through experiments, their scheme can perform channel estimation for the remaining subcarriers with a high accuracy equal to that of the pilot subcarriers.
# Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
## 3 Interferometric Data Processing
### Phase Unwrapping  
Based on the results of the high-accuracy phase filtering, the minimum cost flow method was used to unwrap the phase with a coherence higher than 0.3. The unwrapping result is shown in Figure 10.  
Figure 8: Comparison of the details of three-phase filtering methods showing local details: (**a**,**e**) original interferogram, (**b**,**f**) [PERSON] method, (**c**,**g**) [PERSON] method, and (**d**,**h**) our method.  
Figure 9: Interferogram after phase filtering with the multi-scale phase-filtering method in the wavelet domain.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 3 Theoretical Background and Considerations
### Frequency Hopping Spread Spectrum (FHSS)  
FHSS operates by changing the carrier frequency of a signal at regular intervals according to a specific hopping sequence. The signal \(S(t)\) is transmitted across different frequencies over time, making it difficult for an unauthorized party to intercept or jam the communication. The mathematical model of the transmitted signal can be expressed as:  
\[S(t)=\sum_{o=1}^{N}A_{o}\cos(2\pi f_{o}t+\phi_{o})\cdot\mathbf{1}_{[t_{o},t_{o +1})}(t) \tag{1}\]  
where:  
* \(A_{o}\) is the amplitude of the signal during the \(o\)-th hop,
* \(f_{o}\) is the carrier frequency for the \(o\)-th time interval,
* \(\phi_{o}\) is the phase for the \(o\)-th time interval,
* \(\mathbf{1}_{[t_{o},t_{o+1})}(t)\) is an indicator function that is 1 if \(t\in[t_{o},t_{o+1})\) and 0 otherwise,
* \(t_{o}\) and \(t_{o+1}\) are the start and end times of the \(o\)-th hop, respectively,
* \(N\) is the total number of frequency hops.  
Frequency Hopping Sequence  
The sequence of frequencies \(\{f_{o}\}\) used in FHSS is typically generated using a PRNG. Let \(\{r_{o}\}\) be a sequence of pseudo-random numbers generated by a PRNG. The frequency for the \(o\)-th hop can be determined as:  
\[f_{o}=f_{\min}+(f_{\max}-f_{\min})\frac{r_{o}}{R} \tag{2}\]  
where:  
* \(f_{\min}\) and \(f_{\max}\) are the minimum and maximum frequencies in the hopping band,
* \(r_{o}\) is the pseudo-random number for the \(o\)-th hop, and
* \(R\) is the range of the PRNG (i.e., the maximum possible value of \(r_{o}\)).  
The key challenge with PRNG-based sequences is that, given enough output samples, an adversary can potentially reconstruct the sequence generation process, posing a security risk.