esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing Methods
### Multi-Look Processing of the SAR Images
#### 3.3.2 The Experiment of Multi-Look Processing  
Based on the analysis presented above, corresponding experiments were designed to evaluate the performance of multi-look processing in practical applications. In the experiment, a CBS was utilized to conduct 256 consecutive observations of a corner reflector  
Figure 5: The relationship between the phase noise standard deviation, coherence coefficient, and number of looks.  
target within the scene, with a time interval of 2.5 ms. After imaging processing, 256 SAR images were obtained. For these SAR images, different numbers of looks (L) were employed for multi-look processing, and the phase distribution of the corner reflector target under different look numbers is illustrated in Figure 6. Additionally, Table 1 presents the mean and STD of the phase distribution of the corner reflector following multi-look processing. The results indicate that the phase values obtained after multi-look processing exhibit a lower STD, thereby demonstrating that this processing technique can effectively suppress phase noise, enhance phase stability, and ensure the accuracy of deformation monitoring.  
Due to the inherent characteristics of SAR imaging, the resulting SAR images often exhibit relatively low Signal-to-Noise Ratios (SNRs). Consequently, multi-look processing is employed to mitigate the effects of noise and enhance the accuracy of subsequent analyses. In the context of this study, the time interval between observations made by the CBS is only 2.5 milliseconds, enabling numerous observations of the target area within a short time frame. This characteristic facilitates the implementation of temporal multi-look processing, which effectively reduces phase noise without incurring the resolution loss typically associated with spatial multi-look processing. By leveraging this approach, the quality of the final images is significantly improved, ensuring that the results are both reliable and precise.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**L** & **1** & **4** & **8** & **16** & **32** \\ \hline
**Mean(rad)** & \(-1.7175\) & \(-1.7173\) & \(-1.7171\) & \(-1.7171\) & \(-1.7173\) \\
**STD** & 0.0730 & 0.0580 & 0.0464 & 0.0417 & 0.0387 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean and STD of the phase under different numbers of looks.  
Figure 6: Phase distribution of the corner reflector under different numbers of looks.
## 2 Mathematical Model
### Active Hydroneumatic Suspension Vehicle Model  
To study the dynamic response characteristics of the auxiliary transport vehicle under different road conditions, a quarter-active hydropneumatic suspension vehicle's vibrational model is established, as shown in Figure 2b.  
When modeling the auxiliary transport vehicle, the following conditions should be satisfied:  
* The four wheels exhibit identical hydropneumatic suspension configurations and dynamic characteristics, while road excitation profiles demonstrate statistical equivalence. This permits simplification to a quarter-car equivalent model;
* The model considers only the vertical displacement of the sprung mass; the pitch, roll, and longitudinal vehicle dynamics are neglected;
* The tire-ground interaction is idealized as a linear spring, with a stiffness coefficient of \(k_{u}\), neglecting tire damping and road-contact loss effects;
* Road excitation is modeled as a vertical displacement input applied directly to the tire-road contact point.  
The mathematical differential equations for the entire vehicle's two-degree-of-freedom model are as follows:  
\[\begin{split}& m_{s}\hat{x}_{s}+k_{s}(x_{s}-x_{u})+c_{s}(\hat{x}_{s}- \hat{x}_{u})-F_{s}=0\\ & m_{u}\hat{x}_{u}-k_{s}(x_{s}-x_{u})-c_{s}(\hat{x}_{s}-\hat{x}_{ u})+k_{u}(x_{u}-x_{r})+F_{s}=0\end{split} \tag{4}\]  
Let the state vector be  
\[x=\begin{bmatrix}x_{s}&\hat{x}_{s}&x_{u}&\hat{x}_{u} \end{bmatrix}^{\text{T}} \tag{5}\]  
Figure 2: Vehicle’s vibrational model: ((**a**) structure of the hydropneumatic spring; (**b**) quarter-vehicle’s vibrational model).  
Then,  
\[\dot{x}=Ax+Bu+Cw \tag{6}\]  
The system matrices \(A\), \(B\), and \(C\) are as follows:  
\[A=\begin{bmatrix}0&1&0&0\\ -\frac{k_{x}}{m_{x}}&-\frac{c_{x}}{m_{x}}&\frac{k_{x}}{m_{x}}&\frac{c_{x}}{m_{x}} \\ 0&0&0&1\\ \frac{k_{x}}{m_{u}}&\frac{c_{x}}{m_{u}}&-\frac{k_{x}+k_{y}}{m_{u}}&-\frac{c_{x }}{m_{u}}\\ \end{bmatrix}B=\begin{bmatrix}0\\ -\frac{1}{m_{x}}\\ 0\\ 0\\ \frac{1}{m_{u}}\\ \end{bmatrix}C=\begin{bmatrix}0\\ 0\\ 0\\ \frac{k_{x}}{m_{u}}\\ \end{bmatrix}\]  
where \(u=[F_{s}]\) denotes the actuator's force per suspension subsystem; \(w=[x_{r}]\) indicates the wheel's road displacement input.
# Distribution of Volatile Compounds in Different Fruit Structures in Four Tomato Cultivars
## 2 Results and Discussion
### Differences in the Volatile Profiles among the Cultivars
#### 2.4.2 The Volatile Profile of Septa and Columella (SC) and Locular Gel and Seeds (LS)  
Then the proportion of total volatile concentrations flowed by SC and LS. According to Table 3, the levels of 2-methyl propanol, 3-methyl butanol, and 2-methyl butanol were high in LS of \"Tasti-Lee,\" \"Tygress,\" \"FL 47\" and \"Cherokee Purple\" fruits (Table 3), which could impart \"alcoholic,\" \"grassy,\" \"sweet,\" \"whiskey,\" \"malt,\" \"burnt,\" \"malt,\" \"wine,\" and \"onion\" notes in the tomato fruits (Table 2). It was in consistent with results of [PERSON] et al. [16]. Besides, the distribution of other volatile compounds within the four tissues varied among the cultivars. For \"FL 47\" and \"Cherokee Purple\" fruit, the SC tissue possessed higher levels of 4-methyl pentanol and 3-methyl pentanol. Conversely, 1-penten-3-one, 6-methyl-5-hepten-2-one, 2-ethylfuran and butyl acetate were present higher concentrations in SC tissue of \"Tasti-Lee\".  
The aromatic proportion and aroma series of SC and LS behaved significantly differently in different cultivars. The cocoa series showed a higher intensity in LS from \"Tasti-Lee\" and \"Tygress\" and in SC from \"FL47\" (Figure 2). As shown in Table 2, the cocoa flavor was mainly imparted by 2-methylfuran and 2-methyl butanal. The SC of \"Tasti-Lee\" showed high levels of floral and fruity series compared with other tissues, while these series were present in high levels in LS of \"Tygress\" and \"Cherokee Purple\" (Figure 2). The results indicated that floral and fruity series showed a higher intensity in SC and LS. Floral aroma was produced by 6-methyl-5-hepten-2-one, geranyl acetone, and 1-penten-3-one. In tomato fruit, 1-penten-3-one was a fatty-acid-derived volatile. 6-methyl-5-hepten-2-one and geranyl acetone were apocarotenoid volatiles which could be directly synthesized from their carotenoid precursors by the action of carotenoid cleavage dioxygenenses (CCD) [1,31]. 6-methyl-5-hepten-2-one directly comes from lycopene, and \(\zeta\)-carotenoid is the direct precursor for geranylactone [4,32].  
In addition to floral notes, they also made considerable contributions to fruity flavor notes (Table 2). 2-methyl-2-butanal, butyl acetate, 2-methylbutyl acetate, and 1-nitro-pentane were also main contributors to the fruity series. Furthermore, previous studies found that the fruity aroma could also be strengthened in some octanal [33], nonanal [34], and limonene [35,36] grape samples due to their lemony flavor, which could be found in our study as well.
## 3 Localisation of a City Bus with GNSS  
GNSS localisation is the most common method for providing the positions of autonomous and semi-autonomous vehicles [45], including city buses [21; 22]. It has multiple advantages, which include the low cost of GNSS receivers, especially when compared to other sensors, such as LiDARs or cameras. Another significant advantage of GNSS systems is the fact that they provide the latitude and longitude coordinates that are expressed in a global reference frame associated with the Earth centre. An example of such a frame is the World Geodetic System (WGS84), which is used by the Navstar GPS. Thanks to this, the coordinates provided by GNSS receivers do not depend on their position during the startup and initialisation.  
Although the accuracy of a standard GNSS receiver is sufficient for most applications, some cases might require higher precision. It can be achieved by using an additional receiver that makes it possible to apply the DGNSS (Differential GNSS) techniques, including the RTK (Real-Time Kinematics) GNSS method [46]. This approach significantly improves localisation accuracy, as it allows for the elimination of errors caused by the influence of the atmosphere on signal propagation. The accuracy gain resulting from utilising the DGNSS methods differs between the receiver types and manufacturers. However, the exemplary u-blox F9P [47] module has a declared horizontal accuracy of 1.5 m in the standard mode and 0.01 m in the RTK mode (assuming optimal conditions). For that reason, we decided to utilise this method of localisation, as it allows us to determine the bus position with the necessary accuracy and reliability at an acceptable cost. Moreover, poses estimated using RTK GNSS are very repetitive [48], which is desired for precise manoeuvres, such as docking to the charging station.
## 4 Results and Discussion
### Comparison between 0D and 2D Models  
The coefficients resulting from the fitting procedure described in Section 2 are reported in Table 2. Equations (  
The correspondence between the LHTS thermal power and its state of charge is not univocal. As a matter of fact, the LHTS thermal power depends both on the current state of charge (SOC) and on the initial state of charge (\(SOC_{0}\)). However, \(SOC_{0}\) affects the results only in case a new charging or discharging phase starts. For instance, if there are two consecutive charging or discharging phases (only interrupted by an idle period), the value of \(SOC_{0}\) should not be updated at the beginning of the second charge/discharge simulation. Instead, the previous value must be retained. This issue is due to the fact that when a partial charge is performed, only the PCM close to the fins becomes liquid, while the PCM far from the fins remains solid. If the subsequent operational phase is a discharge, the current state of charge (\(SOC_{0}\)) is not updated at the beginning of the second charge/discharge simulation. Instead, the current state of charge (\(SOC_{0}\)) is updated at the beginning of the second charge/discharge simulation. The current state of charge (\(SOC_{0}\)) is updated at the beginning of the second charge/discharge simulation.  
a peak of thermal power occurs at the beginning of this latter process because the first layer of PCM encountered in the heat propagation is liquid (Figure 5).  
A similar consideration is valid when a charge is performed, starting from a partial discharge of the system (Figure 6). The 0D model for the LHTS discharge thermal power (8) is slightly less accurate when \(SOC_{0}\) is much smaller than 1 (i.e., when a discharge is preceded by a very short charging phase). However, it is rather unlikely to discharge a thermal storage starting from such a low content of energy. Similarly, the 0D model for the LHTS charge thermal power (9) is less accurate when the \(SOC_{0}\) is much larger than 0 (i.e., when a charge is preceded by a very short discharge phase). As a matter of fact, the 2D curves in Figure 5 are both shifted and contracted with respect to one other, while the 2D curves in Figure 6 are mainly shifted. This dissimilarity might be due to the fact that the HTF inlet temperature during the charging phase (75 \({}^{\circ}\)C) is much closer to the PCM average phase change temperature (70 \({}^{\circ}\)C) compared to the HTF inlet temperature during discharge (48 \({}^{\circ}\)C). In fact, as indicated by [24], the HTF inlet temperature affects the LHTS thermal power.
## 5 Hysteretic Energy Dissipation Performance
### Restraint Ratio Threshold of CSA-BRB Energy-Dissipating Type  
Equation (7) proposed the formula for the restraint ratio threshold of the energy-dissipating CSA-BRB \(\omega=1.6\). The first set of examples in Table 3, i.e., group CSA-20-\(\zeta\) in Figure 11, is used. For the example, CSA-20-3.13, the restraint ratio threshold \(\left[\zeta\right]_{\omega}=3.14\) is calculated by Equation (7). Since the restraint ratio is less than the restraint ratio threshold, the components cannot complete the entire hysteresis load process, and the hysteresis curve is not total. It does not meet the requirements of energy-dissipating members, which is consistent with the numerical analysis results, as shown in Figure 12g. With the increase in the restraint ratio, the example CSA-20-3.24 is selected, and its restraint ratio threshold \(\left[\zeta\right]_{\omega}=2.97\) is calculated by Equation (7), which is smaller than its restraint ratio. Therefore, this example can complete the hysteretic load process. The curves are plump and meet the requirements of energy-dissipating type, consistent with the numerical analysis results, as shown in Figure 11h. For the other three sets of cases in Table 3, the FE calculation results are consistent with Equation (7). As a result, using Equation (7), the restraint ratio threshold of CSA-BRB may be computed more precisely. Table 4 shows the results of the FE hysteresis energy consumption calculations for the four sets of instances, along with a measure of how full their hysteresis curves are.  
Figure 12: Hysteretic curves for numerical examples (No. 1) in Table 3.  
Similarly, in the preliminary design stage of CSA-BRB, it is necessary to estimate the restraint ratio threshold value threshold conservatively. As a result, Figure 13 depicts the link between the restraint ratio \(\zeta\) and stability factor \(\varphi\) for each of the four sets of calculations. The restraint ratio threshold value threshold can be obtained. As shown in Figure 13, when the restraint ratio of the CSA-BRB (\(\zeta\geq 3.30\)), the CSA-BRB can meet the requirement of hysteretic performance for energy-dissipating members without damage, and the hysteretic curve is whole, satisfying the requirement of the cumulative plastic deformation capacity factor (\(\mu_{c}\geq 200\)) in AISC341-05. Therefore, \(\zeta\geq 3.30\) it can be used as the restraint ratio threshold value threshold for energy-dissipating bracing for its preliminary design.
## 2 Materials and Methods
### Parameter Selection  
The selected parameters for the database population are split into two main categories. The first one includes the parameters that are used to quantify the specifications of a UAV and, as such, are related to layout, aerodynamics and performance (\"technical\" parameters). They are used to extract design trends regarding the platform itself, in a similar way to the work shown in [22; 24; 25]. The most notable of these parameters are the wingspan and length, the cruise and maximum speed, the gross take-off weight (GTOW), payload weight and empty weight, the propulsion type and its generated power, the endurance, service ceiling and the operational range. The second category involves UAV characteristics that are used to classify the aerial vehicles from an operational perspective (\"operational\" parameters). Indicatively, some of these parameters are the region of origin, EIS, NATO UAV category, primary mission type, etc. In general, technical parameters are expressed using continuous numeric variables, whereas operational ones are either grouped in classes or defined as logical ones, respectively creating different types of charts (continuous plots for the first and bar/pie charts for the latter)
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
###### Abstract
However, it requires complicated electronics with many digital Integrated Circuits (ICs) to realize reconfigurability. As is seen in Figure 3 from [47], the number of ICs at the back side of the RIS is greater than the number of unit cells. As was reported in [47], the fabricated \(10\times 10\) cm\({}^{2}\) RIS sample consumed 8 W of dc power. In [48], which describes a non-reciprocal transmitting PMS (\"meta-prism\"), each unit cell comprises two antenna elements on the opposite sides of the PMS, two variable gain amplifiers, and an electronically tunable phase shifter. Although such a \"meta-prism\" allows for independent control of the signal amplitude and phase, such functionality was achieved with the cost of increased complexity. Conventional beamforming methods assume full control over the amplitude and phase distributions over the antenna aperture. On the other hand, our fully reactive PMS design [28; 46] achieves full 360\({}^{\circ}\) reflection phase control, albeit with a fixed reflection magnitude. On the hardware level, this allows us to eliminate active components and reduce the PMS cost and complexity; however, it complicates the relation between the desired radiation pattern and the aperture phase distribution. Therefore, in the beamforming methodology that is developed next, we apply optimization methods (e.g., Nelder-Mead, PSO, pattern search [49; 50; 51]) to fine-tune the initial Fourier transform-based aperture phases and attain the desired far-field pattern. With the optimization framework developed in this paper, we define and minimize a constrained nonlinear multivariable cost function that encodes the desired multi-beam radiation pattern. The required aperture phase distribution is obtained iteratively after many rounds of cost function minimization steps. Finally, we apply AI methods such as trained ANN to sidestep the time-consuming numerical optimization techniques and pave the way to real-time beamforming on inexpensive hardware. Figure 2 shows a conceptual diagram of our methodology. Here, we use FFNN to predict phase distributions and control voltages for an electronically controlled PMS that we have designed earlier [28; 29]. Two different types of ANN are proposed based on the FFNN model. The first trained ANN, Beamforming Neural Network (BFNN), relates the desired main beam characteristics to the required phase distribution over the MS. Consequently, the second ANN formed by Controlling Neural Network (CTRLNN) blocks, relates the phase distribution to the necessary control voltages applied to the cells of the MS. We train the FFNN on a dataset obtained for typical beamforming scenarios by using the optimization methodology outlined above. After training, the FFNN is able to perform beamforming tasks with about the same accuracy as the original optimization algorithm. Because trained ANNs can produce results in a much shorter time [40] compared to time-consuming global optimizations, this approach is beneficial for real-time beamforming.
## 8 Applications of Calcium Carbonate Nanoparticles
### Dye Removal  
[PERSON] (2018) synthesized 15-50 nm, cobalt tungstate, and CaCO\({}_{3}\) nanocomposite for the removal of organic pollutants (dyes) from the wastewater. The morphological and chemical properties were analyzed by vibrating sample magnetometer, UV-Vis diffuse reflectance spectroscopy, XRD, and SEM where the latter two instruments helped in the confirmation of the average crystallite size of nanocomposites, whose size was in the range 15-50 nm. The high absorption with 3.2 eV bandgap of synthesized nanocomposite was identified with the DRS spectrum. The synthesized nanocomposite exhibits photocatalytic activity which was used for the degradation of methyl violet, methylene blue, phenol red, and eosin Y dyes. The developed nanocomposite degraded the MV dye up to 99%, within 200 min [102]. [PERSON] et al. reported the synthesis of CCNPs from finger citron residue (FCR) that have already been discussed above in the synthesis section and utilized them for the removal of Congo red (CR) from the wastewater [82]. Here Nickel-doped porous CaCO\({}_{3}\) monoliths were developed from the FCR and applied for the removal of anionic dye CR by adsorption method in a batch experiment. The adsorption of CR by such NPs showed a pseudo-second-order kinetic model and Langmuir adsorption isotherm. It was found that due to the differences in positive and negative charge effects between CR and Nickel, a higher amount of CR dye was adsorbed on the Nickel-doped porous CaCO\({}_{3}\) monoliths which authenticate that later are a promising adsorbent for the removal of the anionic dyes from wastewater.  
The removal of various organic and inorganic pollutants from different water and wastewater indicates that the main process for the removal of pollutants was adsorption. In some cases, CCPs were without surface functionalized while in some cases the CCPssurface were modified by various capping agents. These capping agents were used in order to make the CCPs more specific for a specific pollutants. The adsorption is a simple technique which is practiced from a century to remediate the pollutants. In this process inorganic pollutants like heavy metals gets reduced on the surface of the CCPs. Due to this reduction, the toxic heavy metals gets converted into nontoxic form which is beneficial for the flora and fauna. Moreover, adsorption process do not involve any sophisticated instruments, and machinery so it is considered easy, and economical. The only problem faced is the disposal or recycling of the CCPs loaded with the toxic heavy metals.
## 3 System Design
### Blockchain Network and Smart Contract Deployment  
A permissioned blockchain network is deployed using Hyperledger Fabric v2.2 as shown in Figure 1 to facilitate secure and verifiable policy evaluation and access management [8; 25]. The network comprises two peer organizations (Org1 and Org2) and one ordering service organization. Chaincode (smart contract) is developed in Go and instantiated on the channel named mychannel. The smart contract includes three core functions: RegisterUser, CreatePolicy, and EvaluateAccess. These functions enable user registration with attribute-based details, dynamic policy creation for mining licenses, and real-time access evaluations, respectively [27].  
Recent advances have explored scalable permissioned blockchain frameworks optimized for privacy-preserving IoT authentication and lightweight data storage [28]. These improvements are especially relevant in enterprise contexts, such as mineral resource governance, where scalability and secure data management are critical.  
To ensure modular and maintainable development, the chaincode structure follows best practices as described in the Hyperledger Fabric documentation [8]. Access control conditions are defined using logical combinations of user attributes such as role, location, and affiliation [24].  
The blockchain network uses a permissioned consortium-based architecture built with Hyperledger Fabric. For implementation details, including Docker containerization, TLS security, and organizational setup.  
Deployment is orchestrated through Docker-based containers, providing modularity, isolated service management, and future extensibility [29]. Communications among peer nodes are secured using mutual Transport Layer Security (TLS). Endorsement policies are configured to require multi-organization approval, enhancing system trust through collaborative validation.  
The blockchain network uses a permissioned consortium-based architecture built with Hyperledger Fabric. For implementation details, including Docker containerization, TLS security, and organizational setup.  
Figure 1: Blockchain and Smart Contract Architecture.  
All chaincode invocations and queries are executed via the Fabric CLI (peer commands), with TLS certificates and Orderer addresses explicitly configured in evvecution context [30].  
1. The FABRIC_CFG_PATH environment variable is exported during simulation execution to ensure the correct linkage to peer configuration files (e.g., core.yaml).
2. MSP artifacts for each peer are derived from the organizations/peerOrganizations directory to ensure identity recognition and endorsement compatibility across the network.
3. Separate endorsement policies are dynamically configurable depending on the type of resource being accessed. This allowed fine-tuned control over policy enforcement and reflects a scalable and adaptable network design.