esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Seismic Inversion and Petrophysical Property Prediction  
Figure 10 shows the inverted primary wave impedance (AI), shear wave impedance (SI), and the density for the seismic section inline 10,154. The inversion analysis shows a good fit between the inverted logs and the original logs (Figure 11). The simultaneous and sequential prediction results are shown in Figures 12-14.  
Figure 9: Cross plots showing the modeling results (PosV\({}_{clay}\) 0.2, Neg Por10 p.u. scenario). The sands’ good thickness makes them relatively suitable for lithofluid discrimination, even at reduced porosity (although the sensitivity is reduced).  
Figure 11: Machine-learning prediction at well 15/9-18A.  
Figure 10: Inverted seismic section showing (from top to bottom) the P-wave impedance, S-wave impedance, and density for the seismic section (inline 10,154).  
Figure 14: Time slices of the water saturation inversion taken at 2400 ms—From the left, sequential and simultaneous approaches, respectively.  
Figure 12: Time slices of the shale volume inversion taken at 2400 ms—From the left, sequential and simultaneous approaches, respectively.  
Figure 13: Time slices of the porosity inversion taken at 2400 ms—From the left, sequential and simultaneous approaches, respectively.  
The inversion was then carried out after effectively tweaking the impedance parameters with the help of the cross plots from the well information [50; 51]. The time slices taken at 2400 ms, with a window centered on the target and a fixed size of 10 ms, highlight the simultaneous and sequential prediction results. The black broken lines show the reservoir bounds. The previously known reservoir area around one of the wells has a low shale volume, high porosity, and low water saturation, which correlates well with our results (Figures 12-14).  
There is a strong correlation (or dependence) between lithology and porosity, therefore, predicting V_clay_ first and subsequently using V_clay_ as an input for porosity prediction improved our results. Overall, our results show that the sequential prediction approach produces a higher accuracy result (with the RMS error between the predicted and the original data being 0.31) than the simultaneous algorithm (with an RMS error of 0.45).
## 5 Research Gaps and Prospects
### Data Ecosystem Development: From Ad Hoc Integration to a Standardized Data Framework
Flight control systems will increasingly incorporate autonomous functions, such as visual-inertial fusion for intelligent localization and obstacle avoidance, along with online path replanning capabilities in unknown environments, enabling UAVs to operate more stably in complex terrain and dynamic conditions [137; 138; 139; 140; 141]. (2) How can intelligent platforms be designed to deeply integrate edge computing, enabling real-time onboard analysis and decision-making to mitigate data overload from the source? Embedding high-performance AI inference chips (e.g., NVIDIA Jetson series, Google Edge TPU) within UAVs is expected to shift computation from the cloud to the onboard system, allowing for in situ image preprocessing, object detection, and change monitoring. This migration alleviates transmission bandwidth and latency issues, enabling near-real-time decision-making [187]. In privacy-sensitive or bandwidth-constrained environments, federated learning frameworks can distribute model training and updates across multiple UAVs and ground servers, preserving data isolation while optimizing global model performance, thus enhancing the overall level of UAV-RS intelligence.
# Using IoT for Cistern and Water Tank Level Monitoring
## 1 Introduction  
In neighborhoods of a city where drinking water is scarce during the dry season or due to the effect of the law of gravity in hills or high areas, it is necessary to know the water level in a cistern or a tank to optimize water consumption in the home. A problem lies in the need to open the cistern lid, visually check the water level in the tank, and corroborate the water pressure brought by the municipal water in charge of supplying and refilling the cistern. For this purpose, clearing the garage, opening and lifting the cistern lid, observing the water level, and replacing the cistern lid is necessary.  
In drinking water shortages, this task may need to be performed daily to eliminate any uncertainty about the water level at a given time. Typically, this check might be required every three to four days. If the water level is not monitored, there is a risk of running out of water because of not knowing its levels. Many households need to remember to check their cisterns, and if they do not manage water effectively, they could run out of drinking water.  
Considering the issues outlined earlier and utilizing the standard water tanks and cisterns shown in Figure 1, e.g., water tank capacity of 1100 L and cistern 5000 L, the research question targeted by this paper is as follows: How can an IoT system enhance efficiency in domestic water management by delivering precise data on water levels in cisterns and tanks, as well as on flow rates and water quality?  
Given the problems described above, we propose a method of knowing the water level in a tank in real time without looking at it. This monitoring can be observed through an Internet interface (Node-RED dashboard). We also intend this method to know the water pressure to calculate the tank's time to refill. The quality of the drinking water, e.g., whether it is too cloudy or contains mud, rust, or other residues, can be monitored.  
To achieve the above, we propose publishing four types of measurements on the Internet to be sensed from a drinking water cistern, thereby avoiding visually checkingthese variables. As mentioned, we propose measuring the following: 1. water level in the cistern, 2. water level in the water tank, 3. water flow or tap water pressure in the area, and 4. quality of the water that comes from a public drinking water pipeline. A water tank is filled when the water level is below 50% of its capacity. A submersible pump with a hose brings water from the cistern to the tank.  
This paper is organized as follows: Section 2 reviews various solutions for automatic water level monitoring systems and related projects. Section 3 details our Monitoring of Water Architecture, including a description of the water tank and cistern system and the corresponding electronic circuit diagrams. Section 4 presents specific experimental results. Section 5 discusses results obtained in experiments. Finally, Section 6 concludes this study.
## 5 Experiments
### Time Analysis  
The experimental data presented in Table 1 elucidates the comparative mapping times of various SLAM algorithms across three diverse scenarios. While FastSLAM records the swiftest mapping times, algorithms that integrate optimization techniques, such as PSO, exhibit markedly better performance in accuracy and robustness, albeit with modest increases in runtime. Both FastSLAM-PSO and FastSLAM-MO-PSO show longer mapping times compared to FastSLAM, but demonstrate significantly enhanced performance. This trade-off signifies the algorithms' capacity for a more exhaustive exploration of the solution space, culminating in improved mapping results. Notably, FastSLAM-MO-PSO, which embraces a multi-objective optimization strategy, surpasses FastSLAM-PSO, indicating that a multi-objective framework excels at balancing the competing requirements of mapping precision and computational expedience.  
Moreover, the results indicate that PSO-based algorithms, namely FastSLAM-PSO and FastSLAM-MO-PSO, exhibit shorter runtimes compared to their DE counterparts, FastSLAM-DE and FastSLAM-MO-DE. This suggests that PSO is more adept at steering the optimization process within the SLAM framework. The dynamic velocity and position update mechanisms of PSO likely contribute to its efficiency, as they empower particles to adapt more fluidly to the evolving landscape of the solution space. Conversely, DE's mutation and crossover operations may introduce unwarranted complexity, leading to extended runtimes, particularly in more intricate scenarios.  
In conclusion, the experimental analysis across varying scenarios consistently illustrates that the integration of multi-objective optimization, notably PSO, into the SLAM process yields superior mapping performance compared to conventional and single-objective approaches. The enhanced accuracy and robustness of FastSLAM-MO-PSO, despite its slightly longer runtime, highlight the advantages of adopting a multi-objective optimization strategy for SLAM algorithms. This strategy not only elevates the quality of mapping outcomes, but also lays a more dependable foundation for autonomous navigation in complex environments.
## 3 Results  
As mentioned previously, the maturity index (MI) is mixture-specific. While several alternative relationships were explored for relating NDT response to the maturity temperature-time product, it was concluded that the best fit model was of the logarithmic form \(y=a\ ln(x)+b\)[28]. For all 11 mixtures included in this study, the logarithmic form also provided the best fit. The corresponding R\({}^{2}\) are shown in Table 2. The R\({}^{2}\) for almost all cases was above 0.9 and \(p<0.05\), indicating (i) a very good fit between NDTs and the maturity temperature-time product and (ii) consistent model forms for all concrete mixtures. It should be mentioned that such NDTs provide consistent response in concrete independent of the sample's dimensions and shape [33] since these effects are factored in Equations (1)-(3). Also of note is that for Mixtures 3 and 4, there were not sufficient compressive strength data to provide reliable relationships. The consistency on the model form between mixtures implied that the \"master curve\" concept may potentially be feasible for UPV and/or dynamic modulus versus MI for a group of similar concrete mixtures. The shift function for each mixture-specific maturity equation to the generalized form (i.e., master curve) was then developed in function of mixture-specific concrete ingredients and proportioning.
## 6 Computer Aided Design of EMSs  
The revised coupled models discussed in the last section can be beneficially practiced to determine a realistic behavior of EMSs, including energy conversion drives. Moreover, these complete models are often used to design and optimize these systems. This is accomplished through computer-aided design (CAD) tools.  
For several decades, the research trend has been to move from real prototypes to virtual emulation tools. It becomes possible that some studies of a physical system can be simulated using computers. By understanding and mimicking the behavior of a particular system on a computer before physical creation, the number of tests and experiments can be greatly reduced. Due to these advantages, a number of virtual systems has been developed. This work was carried out for CAD purposes or to simulate the behavior of the system. Some of these simulations were performed to build a complete model, while others were performed to simulate a specific type of application. Examples of such virtual models can be found, e.g., in the case of electromagnetic systems, see for example [35; 36; 37; 38].  
The reliability of the behaviors provided by the virtual systems strongly depends on the level of emulation of the physical systems. However, due to the complexity of the system and the uncertainty of the process, it is often difficult to build a realistic virtual system compared to its specific physical counterpart. The complexity of a system is related to its operation as well as to its environmental conditions. Process uncertainty is related to the accuracy of the virtual model (mathematically and numerically). In such a case, we need composite 3D virtual models taking into account all the physical phenomena involved in the functioning of the system, see for example [39; 40; 41]. Note that the process described here is relatively cumbersome and usually involves some offline matching practice.
# A Ka-Band Cylindrical Paneled Reflectarray Antenna
## 4 Results and Measurements  
As shown in Figure 5, a prototype of the segmented reflectarray was realized. The 14 reflectarray panels (seven on each side of the reflector) were fixed to the feeding structure using two metallic support pairs. Unfortunately, some manufacturing errors occurred in the fabrication of these parts causing discontinuities and misalignment between the dielectric panels 5-7. These discrepancies, not symmetric in the two halves of the antenna, severely affected the radiation pattern as well as the radiation efficiency. In order to evaluate their impact on the antenna performance, a full-wave simulation reflecting the actual geometry of the antenna was performed. Considering that the PPW used in the prototype has an efficiency, \(e_{PPW}\), equal to 0.9, the expected gain of the whole structure could be computed as  
\[G_{ex}=10 log_{10}\Big{(}\frac{4\pi L\times D}{\lambda^{2}}e_{RFA}e_{PPW}\Big{)} \tag{6}\]  
where \(e_{RFA}\) is the cylindrical reflectarray efficiency. In the nominal case, \(e_{RFA}\) is equal to 0.65, thus leading to an expected gain of 37.5 dBi and a radiation efficiency of 58.5%. Full-wave simulations show that in the presence of the gaps and misalignments \(e_{RFA}\) was reduced to 25% producing a gain drop of about 4 dB. This value was confirmed by the measurements that showed a gain of about 33 dBi (excluding the losses in the PPW).  
Figure 6 shows the simulated and measured radiation pattern of the whole structure at the center frequency (20 GHz). As it can be observed, the nominal simulated radiation pattern had a half-power beam width of about 2.8\({}^{\circ}\). The manufacturing errors did not affect this parameter but they caused an  
Figure 4: Comparison of field distribution (magnitude and phase) along the aperture of the parabolic cylindrical reflector and of the cylindrical reflectarray.  
Figure 5: Manufactured segmented reflectarray. The unwanted gap between adjacent segments due to manufacturing errors are also shown.  
increase of the radiation at low radiation angles as well as an asymmetry. The same figure shows the radiation pattern simulated with the unwanted gaps introduced by the aforementioned manufacturing errors. Good agreement between simulated and measured results were observed.  
The measured gain and reflection coefficient vs. frequency are reported in Figure 7 along with the simulated curves. Simulations took into account the unwanted gaps. As it can be observed, the gain of the reflectarray (including the losses in the PPW) is higher than 31 dBi within the entire Ka Rx band (19-21 GHz) while return losses remain below 10 dB over a wider bandwidth. As expected, the gain bandwidth is limited by the narrow band effect of the reflectarray elements.  
Measurements was performed in the anechoic chamber of the Laboratorio de Sonido Polytechnic University of Madrid (Spain). The 70 cubic meters chamber has a cutoff frequency of 150 Hz and is ISO 3745 certified. An 18-40 GHz broadband horn antenna with 15 dBi of gain was used during the tests.
## 3 The Multi-Agent Framework
### Storage Consumption  
Storage consumption involves two types of consumption. The first type of storage consumption is for maintaining packets in sensors' waiting lists. For example, if a sensor has several packets to send, but in a time slot, it can only send one packet, it has to keep the remaining packets in its waiting list and transmit them in the succeeding time slots. It is important for a sensor to keep as few packets in its waiting list and as short of a period of time as possible, so that both the sensor's storage consumption and the packet transmission latency can be reduced. For a single sensor, \(v_{i}\), its storage consumption for maintaining packets can be calculated using Equation (3).  
\[Stor_{i}^{(1)}=S^{(1)}\cdot(\sum_{j=1}^{|W_{i}|}t_{ij}^{(1)}) \tag{3}\]  
where \(W_{i}\) denotes the waiting list of sensor \(v_{i}\), \(t_{ij}^{(1)}\) demonstrates the number of time slots during which sensor \(v_{i}\) maintains a packet \(j\) and \(S^{(1)}\) is a constant to represent the storage coefficient for keeping packets.  
The second type of storage consumption for an individual sensor is to keep (both physical and cooperative) neighbours, which can be calculated by employing Equation (4).  
\[Stor_{i}^{(2)}=S^{(2)}\cdot(\sum_{j=1}^{|N_{i}|}t_{ij}^{(2)}+\sum_{l=1}^{|N^{ \prime}_{i}|}t_{il}^{(3)}) \tag{4}\]  
where \(N_{i}\) and \(N^{\prime}_{i}\) are the physical and cooperative neighbours of \(v_{i}\), respectively, which have been described above; \(t_{ij}^{(2)}\) indicates the time slots during which sensor \(v_{i}\) keeps \(v_{j}\) as its physical neighbour; \(t_{il}^{(3)}\) indicates the time slots during which sensor \(v_{i}\), _i.e._, agent \(ag_{i}\), keeps \(ag_{l}\) as its cooperative neighbour; \(S^{(2)}\) is a constant to represent the storage coefficient for keeping neighbours.  
The reason for taking time into account when computing storage consumption is that in Equation (3), the time used to keep packets can indirectly reflect transmission latency, and in Equation (4), the time used to keep neighbours can, to some extent, reflect the computational overhead associated with taking each neighbour into consideration whenever a sensor sends a packet.  
Likewise, for the whole sensor network, the storage consumption is the sum of each sensor's storage consumption.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Authentication
#### 3.6.1 Authenticated Channel  
The Authenticated channel ensures authenticity on the channel level. According to [54], a secure authenticated channel should have the following characteristics:  
* Mutual authentication of the peers;
* Key confirmation (at least one peer is able to verify that the common secret is indeed common);
* Forward secrecy (old session keys cannot be calculated even when long-term secret keys (such as certificate Secret keys are known).  
Implementation examples of an Authenticated channel are channels established through Kerberos authentication [53] and the two-way Secure Sockets Layer (SSL) authentication [55].  
Figure 14: Conceptual overview of Authentication Process pattern [49].
###### Abstract
Continuous measurement is extremely important and useful, since obtaining the spatial extension of local structural data at local points (at distances of 25-50 m or even 500-1000 m) is an ambiguous and difficult task. It is also challenging to obtain a realistic average value of such local data. Using traditional sampling methods, local faults cannot be determined, although with quick feedback to the contract, these faults could be corrected during or right after construction work. This study, therefore, investigated the applicability of GPR in the field of quality control. We focused on the determination of the air void content of asphalt layers. There are several empirical studies on the relationship between the dielectric constant and air voids in the literature. Most of the formulae are based on an exponential relationship, although in theory, it is clear that air voids have lower and upper asymptotes as a function of the dielectric constant. Therefore, we proposed a new logistic function to describe this relationship and examined how robust each model is in real-world conditions with a few calibration data points.
# Correlation Plenoptic Imaging: An Overview
## 3 Experimental Demonstration of CPI
In Figure 9, we report the experimental refocused images obtained, respectively, for element 3 of group 2 (having \(d=0.198\) mm) placed at \(z_{b}-z_{a}=-46\,\)mm (measurement A in Figure 7), and for element 4 of group 1 (having \(d=0.354\) mm) placed at \(z_{b}-z_{a}=41\) mm (measurement C in Figure 7). The corresponding standard ghost images are much more blurred than the one reported in Figure 8a, and therefore have not been reported. The SNR in Figure 9a is lower than in Figures 8b and 9b because the displaced coherent images retrieved for \(z_{b}=z_{a}/2\) are two times larger than the object, and distributed over a region wider than the illuminated area; the coherent images are thus affected by a poor SNR, which reflects on the final refocused image. To avoid this issue, the divergence of the light source should be designed to account for such displacement and enlargement. The experimental parameters used to obtain the results in Figures 8 and 9 are identified by points A, B and C in Figure 7. In all three experiments, correlation plenoptic imaging provides a relevant DOF advantage: CPI enables refocusing the object associated with A and B in a 7 times larger range than in standard imaging, and in a 2.5 times larger range than with standard PI having \(N_{u}=3\). For the object associated with point C, the refocusing range of CPI is four times larger than the DOF of standard imaging and two times larger than the DOF of a standard PI device characterized by a spatial resolution that is three times worse (i.e., \(N_{u}=3\)).
## 2 RS Systems
### Passive
#### 2.1.3 Microwave Radiometers  
Microwave radiometers can be divided into real and synthetic aperture systems. Microwave radiometers can also be divided into two groups of polarimetric and nonpolarimetric. Currently, there is only one polarimetric microwave radiometer (i.e., Windsat), which can measure both OSW speed and direction. The other microwave radiometers are nonpolarimetric and can only measure OSW speed [29]. The measurements of microwave radiometers are target radiances, which are usually referred to as Brightness Temperature (BT). The propagation of electromagnetic radiation at microwave frequency through a material is determined by its electric conductivity, magnetic permeability, and electric permittivity [29; 30]. Some of the applications of microwave radiometers include the measurements of OSW, SST, Ocean Salinity (OS), Sea Ice (SI), soil moisture, snow cover extent, ice-sheet melt, and atmospheric water vapor [31; 32]. Due to the low amount of passive radiated energy from Earth sensed by the spaceborne microwave radiometers, the spatial resolution of these satellites is usually low (e.g., 20-50 km).