esa-sceva/satcom-chunk-collection huggingface dataset:
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 3 Selected Beamforming Techniques
### Phased Array  
The _phased array_ beamformer (PHA), often referred to as _delay-and-sum_ (DAS) in the literature, is the most straightforward form of beamforming. The underlying idea is to correct the diverse phase delays at which the signal will arrive at each antenna element. To this end, the weights \(\mathbf{w}_{\mathrm{PHA}}\) become those that maximize the response of the array at the LOSS DoA, that is, those that maximize the power of the output beamformer \(y(n)\). These weights are given by  
\[\mathbf{w}_{\mathrm{PHA}}=\frac{1}{L}\mathbf{a}_{0} \tag{5}\]  
which are nothing but the steering vectors of the impinging LOSS signal. This beamformer can thus be understood as the spatial matched filter to the spatial signature of the received signal when the spatial noise is uncorrelated among antenna elements. However, its simplicity is counteracted by a poor performance when it comes to canceling the signals impinging on the array from particular directions.
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 5 Discussion
### Dynamic Analysis of GDP Spatial and Temporal Changes
#### 5.2.4 Analysis of Cities with Rapid GDP Decline  
Figure 11 is a trend chart of GDP temporal and spatial changes. As can be seen from the figure, the GDP data of most parts of mainland China are on an upward trend, and the obvious increase is the general trend in GDP change from 2013 to 2023; only some regions have a more obvious downward trend in GDP, such as Aksu in Xinjiang, Daqing in Heilongjiang, Ordos in Inner Mongolia, Yan'an in Shaanxi, etc.  
In order to find the areas with a declining GDP in the figure and explore the law behind the obvious downward trend in the GDP, area tabulation was performed in ArcGIS to obtain data on the obvious decline in the GDP at the prefecture-level city level. The specific situation of the top ten cities with an obvious GDP decline in the absolute area at the prefecture-level city level is shown in Table 9. From the data, it can be seen that most of the areas with an obvious GDP decline are resource-based cities, such as Daqing City, Aksu Prefecture, Tangshan City, Xinzhou City, Ordos City, etc. Among them, the rise and fall of Daqing City in Heilongjiang Province is related to the reserves and production of oil. With the reduction in oil reserves, the development of heavy industry is hindered, and the GDP shows a downward trend. In addition, there is Yan'an City in Shaanxi Province, which is represented by returning farmland to forest and grassland and the relocation of immigrants. Yan'an City is located in the hinterland of the Loess Plateau and the ecological environment is extremely fragile. In recent years, in order to alleviate the phenomenon of soil erosion, Yan'an City has increased its efforts in environmental protection, planted trees and grass reasonably, and reduced the amount of NTL. In general, the regions with a downward trend in GDP are all cities that were mainly developed by heavy industry in the early stage and faced transformation due to the reduction in mineral resource output in the middle and late stages. The damage to the ecological environment and the reduction in mineral resources in these cities have restricted the development of the local urban economy.  
Figure 11: GDP trends in mainland China from 2013 to 2023.
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 5 Conclusions  
Drones and AI are seen as vital pillars of the 4 IR that serve many smart applications including smart aerial journalism. To the best of our knowledge, this is the first work that considers an optimized propagation model that integrates both drone and AI to empower smart journalism, and make it a more sustainable and eco-efficient process. This has been done by utilizing a NN-RBFN framework to train the neural network to obtain an accurate channel propagation model to deliver robust aerial media missions, such as: investigative reporting (e.g., humanitarian crises), footage of news events (e.g., man-made and/or natural disasters), or even more livestreaming of short-term, large-scale events (e.g., Olympic Games). To obtain novelty and make a noticeable shift from existing work, there are several key contributions of this work: (1) Testing and evaluating set propagation models, which are representatives of their types from an aerial perspective using NN-RBFN approach; (2) The optimized propagation model would exhibit advantages of accuracy, strong connectivity, and wide media coverage; (3) Validating obtained results against other well-known optimized propagation models as proof-of-concept using 3D Remcom Wireless Insite tools.  
The simulation has considered 5G MIMO antenna to improve diversity performance of the channel capacity, and thus enhance the reliability of wireless communication via the drone and reducing power consumption. Simulations have been done using MATLAB, and for validation, it uses 3D Remcom Wireless Insite tools on Souq Okaz for proof-of-concept. The simulated work shows that the proposed framework gives a satisfactory result, which reflects the usefulness of such an integration for aerial journalism. Furthermore, journalists see it as a safer and cost-efficient means of reporting and recording missions, especially in highly vulnerable coverage in hard-to-reach zones and/or short-term, large-scale events. As future work, this concept can be developed further by implementing this concept in real scenarios and then comparing simulated scenarios against experimental results.  
Conceptualization, methodology, project administration, investigation, funding acquisition, F.A.A.; software, F.A.A. and B.O.S.; validation, F.A.A. and B.O.S.; formal analysis, F.A.A. and B.O.S.; resources, M.A. ([PERSON]); data curation, F.A.A.; writing--original draft preparation F.A.A.; writing--review and editing, F.A.A., M.A. ([PERSON]), M.A. ([PERSON]  
Figure 15: Complexity analysis of the proposed NN-RBFN framework against PSO and SOM optimization approaches.  
ami) and B.O.S.; visualization, B.O.S.; supervision, B.O.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deanship of Scientific Research at Taif University, Kingdom of Saudi Arabia through Taif University Researchers Supporting Project Number (TURSP-2020/265).
## 3 Results and Discussions
### Route Validity Time
Similarly, the CM method aims to adapt a routing protocol with its surrounding network scenario to enhance its robustness against the dynamic topological changes and multi-scenario phenomenon in VANET. Routing adaptation is achieved by tuning the protocol's parameter values for the best network performances. Accordingly, two optimization methods (TOM and DEM) are utilized to best tune the routing parameters. Further, these methods are used to enhance AODV and GPSR mechanisms and contribute to CM-AODV and CM-GPSR routing protocols. CM-AODV and CM-GPSR added two procedures to their successors, AODV and GPSR routing mechanisms: optimization and switching procedures. The optimization procedure focuses on finding the best-tuned values of the routing parameters. This procedure utilizes a messaging mechanism to perform the optimization scenarios [27].
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 3 Results
### Reflecting Off of Mirrors  
Measurement errors for the configurations where light is reflected off of a mirror are shown in Figure 8. Figure 8a shows data for a clean first surface mirror, Figure 8b for the dusted first surface mirror, Figure 8c for clean regular mirror, and Figure 8d for a dusted regular mirror. Different traces show measurement errors for configurations with different distances between the ranging device and mirror, ranging from 5 cm, shown in blue, to 55 cm, shown in magenta, and other configurations in 5 cm increments in between.  
The blue (5 cm) traces in all graphs in Figure 8 show a significant positive measurement error in the first few data points. Even larger errors appear in setups with a dusted mirror at 15 cm or further from the ranging device. There the measurement error grows increasingly negative as the target is moved away. The effect is less noticeable when the mirror is positioned further from the ToF ranging device and becomes unnoticeable beyond 40 cm.  
To help explain the disparity between the error plot for clean and dusted mirror, Figure 9 shows the shape of the reflected pulse on a system with a dusted mirror without a detectable target in sight (blue) and with a weak reflection from a target (orange). The signal is sampled after the TIA. One can notice that some signal is present even when there should be none (ranging at an infinitely distant target).  
Figure 8: Measurement error measured on a clean (**a**) and dusted (**b**) protected first surface mirror, and clean (**c**) and dusted (**d**) regular mirror. Different traces correspond to configurations with different distances between ranging device and mirror’s reflecting surface. Blue trace corresponds to the configuration where this distance is 5 cm, and magenta trace for when it is 55 cm all other distances are displayed in the legend key.
## 3 Results and Discussion
### Thermal Properties  
The thermal conductivity is considered as an important index of masonry insulation, which is why it was investigated in this study.  
Figure 18 depicts the thermal conductivity results as obtained for different fiber contents and for three cement contents. From the graph, it can be noticed that the increase in cement content leads to a slight increase in the value of thermal conductivity. The effect of JF content on thermal conductivity is shown in Figure 18. The values of thermal conductivity ranged between 0.2465 W/m\(\cdot\)K and 0.4350 W/m\(\cdot\)K. The highest value corresponds to the sample with a cement content of 12% without fibers, whereas the lowest value corresponds to the sample with a cement content of 4% and a fiber content of 0.2%. The graph indicates clearly that an increase in fiber content leads to a decrease in the value of thermal conductivity. In comparison to the non-reinforced blocks, the thermal conductivity  
Figure 16: Results of wet compressive strength according to the fiber content.  
of CEBs reinforced with 0.05%, 0.10%, 0.15% and 0.20% Juncus fibers and stabilized with 12% cement was 16.6%, 20.40%, 25.4% and 34.7% lower, respectively. These results could be explained, on the one hand, by the increase in blocks' porosity with incorporation of JF, making the CEBs more insulating, and on the other hand, the insulating effect of Juncus fibers, since they have a low thermal conductivity of 0.0756 W/m\(\cdot\)K in comparison to the earth-cement matrix. It is clear from these findings that the reduced thermal conductivity of JF produces less conductive blocks. These findings are consistent with the results reported by several researchers [56; 57; 58] in their works conducted on the incorporation of vegetable fibers into earth blocks. For example, the research work carried out by [PERSON] et al. on earth composites reinforced with alfa fibers of different contents (0%, 7%, 11% and 15% vol) identified a decrease in thermal conductivity of approximately 13.1% [27].
## 2 Research Background
### Failure Mode and Effects Analysis (FMEA)  
FMEA is a highly structured approach through which all potential failure modes of a system and their effects can be identified, evaluated, and prioritized. The technique can result in cost/time-savings when employed during early design stages by exposing probable operational challenges and eliminating cascading failures. In this technique, the risk score for each failure mode is obtained by multiplying the individual scores for three risk factors of severity (S), occurrence (O), and detectability (D). This composite risk is called \"Risk Priority Number (RPN)\" and is calculated by:  
\[\text{RPN}=\text{S}\times\text{O}\times\text{D} \tag{1}\]  
where the risk factors S, O and D are rated on a scale 1-10 for each failure mode using the guidelines presented in reference [11]. Therefore, the RPN values for different failure modes will range between 1 and 1000. Engineers should assign a threshold RPN value to classify failure modes. For instance, in the current FMEA technique, precautions are taken for all failure modes whose RPN values exceed 100. Thus, a failure mode with RPN = 100 is considered as 'corrective action required', whereas another failure mode with RPN = 98 is classed as 'consider corrective action'.  
The general principle governing the multiplication of risk factors to obtain RPNs and prioritization of failure modes has been largely criticized [12]. For example, the three risk factors S, O and D are assumed to have the same importance; or various combinations of S, O and D may produce an identical RPN value, whereas the risk implication may be totally different and may result in high-risk events going unnoticed; etc. Extensive work has been done in the recent years to improve the FMEA process (see, e.g., references [13; 14; 15]). According to a detailed literature review performed by [16], Artificial Intelligence (AI), fuzzy rule-based systems, Grey theory, and Multi-Criteria Decision Analysis (MCDA) models such as Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) are the most prominent methods used for overcoming the FMEA limitations. Furthermore, the cost-based FMEA models pioneered by [PERSON] [17] have also gained popularity.
## 3 Proposed Methods
### Adaptive Pre-Distortion System
#### 3.1.2 Median Based Method (MBM)  
The overall workflow is schematically illustrated in Figure 5. For the channel-equalized signals, it can be shown that the nonlinear coefficients \(\hat{b}_{n}\)\((n\in 0,1,2)\) can be estimated by solving the following equation using \(x\) and \(y\):  
\[y=\hat{b_{0}}+\hat{b_{1}}(\hat{x}-I_{\text{DC}})+\hat{b_{2}}(\hat{x}-I_{\text{DC }})^{2}. \tag{10}\]  
However, relying on a single equation set results in strong susceptibility to noise, leading to significant estimation errors. To mitigate this issue, we propose MBM. Specifically, multiple sets of independent equations are solved to generate a sequence of coefficient  
Figure 4: Principle of LSM.  
Figure 5: Principle of MBM.  
candidates. The final estimate coefficient is then obtained by selecting the median of these candidates.  
\[\hat{b_{n}}=\text{Median}(\hat{b_{n}^{0}},\hat{b_{n}^{1}},\ldots,\widehat{b_{n}^{ N^{\prime}-1}}). \tag{11}\]  
where \(N^{\prime}=\frac{N}{3}\). This approach effectively reduces the impact of noise by leveraging the statistical robustness of the median operator, which inherently suppresses outliers caused by impulsive noise or transient distortions. However, this algorithm has a computational complexity of \(O(N^{2})\), and therefore higher than that of LSM.
###### Abstract  
Shadows in hyperspectral images lead to reduced spectral intensity and changes in spectral characteristics, significantly hindering analysis and applications. However, current shadow compensation methods face the issue of nonlinear attenuation at different wavelengths and unnatural transitions at the shadow boundary. To address these challenges, we propose a two-stage shadow compensation method based on multi-exposure fusion and edge fusion. Initially, shadow regions are identified through color space conversion and an adaptive threshold. The first stage utilizes multi-exposure, generating a series of exposure images through adaptive exposure coefficients that reflect spatial shadow intensity variations. Fusion weights for exposure images are determined based on exposure, contrast, and spectral variance. Then, the exposure sequence and fusion weights are constructed as Laplacian pyramids and Gaussian pyramids, respectively, to obtain a weighted fused exposure sequence. In the second stage, the previously identified shadow regions are smoothly reintegrated into the original image using edge fusion based on the p-Laplacian operator. To further validate the effectiveness and spectral fidelity of our method, we introduce a new hyperspectral image dataset. Experimental results on the public dataset and proposed dataset demonstrate that our method surpasses other mainstream shadow compensation methods.  
hyperspectral images; shadow compensation; exposure fusion; edge fusion; p-Laplacian +
Footnote †: journal: Medical Image Analysis  
1
Footnote 1: [[https://doi.org/10.3390/appl14093890](https://doi.org/10.3390/appl14093890)]([https://doi.org/10.3390/appl14093890](https://doi.org/10.3390/appl14093890))
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 9 Innovative Tools to Increase the Agility of Small Satellites  
### The BIROS High-Torque Wheels  
Three High-Torque Wheels (HTWs) [14], mounted as secondary payloads in the BROS payload segment as shown in Figure 18 (and operated as devices of the BIROS AOCS, see the blue marked circles on the left side in Figure 16), were successfully tested on 19 March 2018 over the Lausitz region in East Germany.  
Figure 19 shows the curves for the torque moment and the slew rate of the BIROS HTWs as well as the resulting angular movement and the slew rate of the BIROS satellite around one of its axes.  
Figure 20 shows a thermal image sequence acquired on 19 March 2018 at 21:15 local time over the Lausitz region in East Germany during several attitude maneuvers of the BIROS HTWs and AOCS. During these maneuvers, the Line of Sight (LoS) of the BIROS main payload performed angular movements or entered into stable, quiet states within the \(\sim\)8 min duration of all these maneuvers, which were conducted in the following sequence (please see Figure 20 from the bottom to the top):  
* looking 15\({}^{\circ}\) forward in a quiet state;
* slew 15\({}^{\circ}\) from forward to nadir;
* looking nadir in a quiet state;
* slew 15\({}^{\circ}\) from nadir to backward; and
* looking 15\({}^{\circ}\) backward in a quiet state.  
Figure 19: Curves for the torque moment and the slew rate of the HTWs (left diagram) as well as the resulting angular movement and the slew rate of the BIROS satellite around one of its axes (right diagram).  
Figure 18: The BIROS High-Torque Wheels (HTWs): one of them is shown on the right side and three of them mounted in the BIROS payload segment are shown on the left side (in the yellow color).  
The observed region is a coal mining region, where many former mining pits are artificial lakes now. The right side of Figure 20 shows three magnified thermal image fragments of the scene--without geo-coding--containing the same four major and five smaller artificial lakes whose surface temperatures at 21:15 h local time are higher, i.e., appear brighter, than the mean temperature of the surrounding land surface.  
BIROS is the first small satellite capable of recording TIR and MIR data of an Area of Surveillance (AoS) with an \(\sim\)180 m spatial resolution and three observation angles (nadir and \(+/-15^{\circ}\) along the rack from nadir) within 8 min.  
Data of this type can be also used for thermal stereo imaging of clouds, for instance, at night.
## 3 Interoperability Issues
### Generating Correspondences between IFC and CityGML: Identification of Standard Common Entities  
To generate associations between IFC and CityGML, according to their structure, the entities and the objects that identify the same component in the different models need to be compared. To verify these correspondences, a study regarding common entities between IFC and CityGML at different levels of detail has been carried out.  
From the research of [38] a unified model which contains all the IFC and CityGML features has been taken into account considering every building element and its representation in the different LoDs. Moreover, starting from the study of [39], which describes how a CityGML input model is automatically reconstructed according to the IFC standard, the identification of common entities between IFC and CityGML standards at different levels of detail has been carried out (Table 3, \"x\" means not association available).  
Figure 9: CityGML building model in LoDs 0 to 4 [37].  
For the present test, the LoDs 2-3-4 are considered as the three levels in which the building elements could have a 3D representation and description both in GIS and BM environment.  
Analysing the features mainly used in the construction of an architectural model in the IFC and CityGML standards, it was noticed that the relation of the classification, as it is possible to notice from the Table 3, is \"1 to many\", namely for each class provided by CityGML different IFC elements correspond.  
For example, in the case of the entity \"Roof\", it has been noticed that _IfcRoof_ corresponds to _RoofSurface_ from LoD 2 to LoD 4. In addition, LoD 4 makes a further subdivision between interior and exterior surfaces, so CityGML differs _CeilingSurface_ from _RoofSurface_, respectively.  
Thanks to this test investigation, it has been possible to devise the correspondences of the San Lorenzo Church model useful for the data format and standard transformation, avoiding loss of geometries and information.
## 5 Evaluation
### Comparison and Summary of DQN-EC and Experiment Results  
Similar to DQN-EC, RouteNet [24, 25] improves the delay and jitter to the source and destination by up to 1.4-times compared to OSPF. In [26], the authors compared between the deep learning system and OSPF; in this case, the overall network throughput was improved by approximately 2.3-fold, and the average hop delay was measured to be approximately 12-times lower in the proposed deep learning system. In addition, the authors of [27] indicated that the results of a methodology experiment showed approximately a 3-fold improvement in network throughput and average delay per hop compared to OSPF. In [34], when the network load level is between 2 and 2.5, the average delivery time OSPF converges to the maximum, whereas Q-routing records an average delivery time similar to that of OSPF when the network load level is 4, which is an improvement of approximately 1.8-fold. In [35], CQ-routing recorded the same average packet delivery time as OSPF until the route had 1500 learning times. However, as the learning count reached 3000, OSPF continued to increase, recording an average packet delivery time of 70 s; however, CQ-routing converges at close to zero. In [37], DRQ-routing learns the optimal routing policies 1.5-times faster than spectrum-aware Q-routing at low and intermediate network loads, and the average packet delivery time differs by up to 30 s compared to Q-routing. In addition, in [39], RL-routing improved the average file transfer rate by approximately 3-fold and the average utilization rate by approximately 2-fold compared to those of OSPF.  
The DQN-EC proposed in this paper constructs an experimental environment by using a DQN, deep reinforcement learning, the addition of elements necessary for decoding in erasure coding, and the selection of OSPF, the subject of the comparison of the experiment results. The block transmission time, which has a similar meaning as the average packet delivery time, improved 2.5-fold when the probability of a link failure was 15%. Network throughput improved 0.4-times, and bottleneck bandwidth was measured up to 100 Mbps lower than OSPF, reducing bottleneck bandwidth. The data may be recovered twice as fast as when OSPF is used and the number of decoding requests is 10.  
Compared with the above related studies, the purpose of calculating the optimal routing path, which is the final goal of DQN-EC, is the same. However, DQN-EC incorporates a large-capacity distributed clustering system using erasure coding into the network topology. In addition, it is possible to quickly recover the data because a switch that has failed in the network is avoided, and the transmission link is not simply reduced, but instead the bandwidth of the transmission link is detected and the block is transmitted using an efficient routing path when applying the decoding.