esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 A Critical Discussion on the Techniques of Characterization
### UV-Vis Absorption for Probing Bitumen Screening towards UV Radiation  
Some nanoparticles, due to their semiconductor nature, can absorb radiations whose energies are above the semiconductor energy gap. This energy threshold of absorption can be changed by changing the type of nanoparticles: for example, as expected, the widely-used nano-SiO\({}_{2}\) cannot counteract the effect of photo-oxidation due to UV aging [66] since its energy gap is too high (7.5-9.6 eV [132; 133]). It is, indeed, considered more as an insulator rather than a semiconductor.  
The energy threshold of absorption can also be changed by changing nanoparticle size and the efficiency of absorption by the inner structure governing the physical nature (direct or indirect) of the photon absorption [134]. This actually provides researchers with a further method to tune the absorption of radiation. For this reason, the capability of nanoparticles to absorb photons must be exactly determined for a wise design of a performing bitumen, and this requires suitable techniques for nanoparticle characterization.  
This aspect can be in-depth explored by a simple UV-Vis spectroscopy technique, which can probe the photophysical behaviour through very simple data analysis, allowing the determination of both the energy gap and the efficiency of absorption. [26; 135].  
The possibility to tune the probability of photon absorption by changing the nanoparticle structural characteristics could be, in our opinion, a powerful way to further enhance the resistance of a nanoparticle-modified bitumen to the photo-induced ageing, which deserves much attention. For examples, the presence in traces of a metal within nanoparticles can induce a lowering of the nanoparticle energy gap (sensitization) thus extending the wavelength range of the photons which are absorbed. [26; 135]. More absorbed photons means that the resulting nanoparticle-containing bitumen can be more resistant to photo-induced ageing. Here the same consideration on costs holds: it is obvious that obtaining nanoparticles with enhanced photophysical properties involves huge costs, but progresses in specialized technologies for their productions and an eventual increasing market can  
Figure 8: 3D images of the whole cylinder, as discussed in the text. The spatial distribution inside the sample of the large virgin and small RAP aggregates is shown with different colors (red and green, respectively).  
make, with time, the use of such kind of nanoparticles affordable, with unprecedented benefits, in our opinion, in terms of durability of bituminous materials.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 4 Simulation Results  
In this section, we evaluate and compare the performance of four different detection schemes in a typical indoor GOMIMO system through numerical simulations.
## 2 Dam Sliding Model  
### Conventional and Improved Sliding Safety Factors  
To evaluate the dynamic sliding stability of concrete gravity dams subjected to earthquakes, the concept of Sliding Safety Factor (SSF) has been used as the ratio of the shear strength to the shear force on a dam tending to slide along its base [3]. When the SSF is larger than unity during the entire duration of an earthquake, it is supposed that the dam structure is stable against rigid-body sliding. The conventional formulation of the SSF is defined as:  
\[\text{SSF}=\frac{cA+N(t)\tan\phi_{i}}{V(t)} \tag{1}\]  
where \(c\) is the cohesive strength of the dam-foundation interface, \(A\) is the total area of the dam bottom face, \(N\) is the total vertical force at the dam base as a function of time \(t\), \(\phi_{i}\) is the internal friction angle of the dam-foundation interface, and \(V\) is the absolute value of the external shear force along the dam base, which is also a function of time \(t\). This sliding criterion represents a simple ratio between the shear strength of the interface at the dam base and the force to slide a dam body.  
Although, mainly due to its simplicity, the SSF in Equation (1) is popularly used in static and dynamic stability evaluation, it only accounts for the sliding stability of a dam under the assumption of undamaged dam-foundation interface, and becomes inaccurate or invalid once the fracture propagates along the dam-foundation interface.  
To address this problem, the improved SSF has been proposed [17; 18]. In the formulation, it is considered that the total shear strength of the interface area is divided into two parts, the shear strength of the undamaged area and one from the fractured (so, damaged) area. Each of them follows Coulomb's friction law with different friction angles, and the SSF is written as:  
\[\text{SSF}=\frac{(1-\eta)(cA+N(t)\tan\phi_{i})+\eta N(t)\tan\phi_{f}}{V(t)} \tag{2}\]  
where \(\eta\) is the ratio of the damaged area to \(A\), the total area of the dam bottom face, \(N\) is the vertical force at the dam-foundation interface, \(\phi_{i}\) is the internal friction of the undamaged interface region, and \(\phi_{f}\) is the coulomb friction angle along the fractured surface of the dam-foundation interface. While the internal friction angle is a material property [19], the coulomb friction angle, \(\phi_{f}\), represents the surface friction caused by roughness and interlocking along the fractured dam-foundation interface. The ratio \(\eta\) is assumed to be constant to represent a pre-existing fractured region caused by strong uplift hydro-pressure or a preceding earthquake. In Equation (2), it is assumed that the total vertical force at the dam base is divided into the undamaged and the fractured region components in proportion to the ratio of their areas. When a structure is dynamically excited with horizontal and vertical loads, the vertical reaction at its base region cannot be decomposed in proportion to subregional areas. Hence, the vertical force of each area should be evaluated separately for the precise frictional force computation.
## 7 Hardware Complexity Optimization for MIMO
### Joint DSP for MCF-Based SDM  
Last but not least, this subsection focuses on a joint DSP scheme for the MCF-based SDM transmission system to reduce the overall cost and power consumption of integrated receivers. There is a strong correlation between the phase fluctuations of the different sub-channels, and the master-slave phase recovery causes no BER degradation. The block diagram of a joint DSP method for an MCF-based SDM transmission system with master-slave phase recovery is presented in Figure 30, which utilizes the phase recovered from a single \"master\" sub-channel to eradicate phase recovery blocks in the \"slave\" sub-channels, thus decreasing the overall DSP burden at the receiver side [44].  
Figure 28: The Q-value vs. step size using both two-stage and single-stage FD-RLS algorithms as a function of transmission distance in an FMF-based transmission system [15].  
Figure 29: The convergence speed comparison of two FD-LMS algorithms and single-stage FD-RLS algorithms at different OSNRs [7].  
In addition, the BER versus OSNR observed for master-slave phase recovery using the joint DSP scheme for MCF-based SDM transmission system is presented in Figure 31, which is equivalent to that found with independent phase recovery [45].
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel without Assistance
#### 3.2.2 Coherent Integration Only with Serial Implementation  
The serial implementation is already given in Reference [23], and shown in Figure 6, with its corresponding timing diagram in Figure 7. However, the timing diagram in Reference [23] is not totally correct because it shows that the coherent integration for different secondary code delay is computed first and then non-coherent accumulations can be done, which is not correct or would require additional storage. In Figure 7, the correlations and further coherent accumulations are computed first, and then the operation is repeated for different delays of the secondary code. Note, however, that the processing time in Reference [23] is correct and identical to the one given below.  
The processing time for one frequency bin is thus,  
\[\begin{array}{l}T_{\text{FB}}=KN_{\text{FFT}}N_{\text{S}}^{2}+2(N_{\text{FFT }}+L)-(N_{\text{P}}+N_{\text{Z}})\\ =KN_{\text{S}}^{2}(2N_{\text{P}}+N_{\text{Z}})+3N_{\text{P}}+N_{\text{Z}}+2L\\ =N_{\text{P}}\big{(}2 KN_{\text{S}}^{2}+3\big{)}+N_{\text{Z}}\big{(}KN_{\text{S} }^{2}+1\big{)}+2L.\end{array} \tag{11}\]  
Regarding the amount of memory necessary, the memory-based accumulator requires  
\[\begin{array}{l}M=2N_{\text{P}}B_{\text{C}}\text{ bits},\\ =2N_{\text{P}}(B_{\text{R}}+\lceil\log_{2}(KN_{\text{S}})\rceil)\text{ bits},\end{array} \tag{12}\]  
where \(B_{\text{C}}=B_{\text{R}}+\lceil\log_{2}(KN_{\text{S}})\rceil\) as previously.  
Figure 5: Timing diagram of the pilot channel acquisition shown in Figure 4, with \(N_{\text{S}}=4\) for the illustration.
## 2 Materials and Methods  
### SSS Coating and Preparation  
The development of biomimetic materials has promoted research into surface-modified hydrophobic concrete [33]. The high WCA of this material type has a strong repulsive force against water. The SSS coating is a biomimetic functional coating that mainly consists of modified polydimethylsiloxane, which has good adhesion and weather resistance under conditions such as high-speed water flow, sand-carrying water flow, and a high-altitude climate. The performance parameters of the SSS coating are shown in Table 1.  
A composite protective layer constructed by placing a layer of primer between the coating and the substrate can enhance interfacial strength and provide concrete with long-term and effective protection [45]. To adapt to complex high-pressure water environments, we added a layer of the EMS coating between the SSS coating and concrete. EMS can promote the penetration of the SSS coating and react with it to form a composite protective layer with stronger adhesion ability, as shown in Figure 1a.  
The preparation of the composite protective layer is shown in Figure 1b-e. The SSS coating is in a colorless and transparent flowing state. It is kept in a vacuum negative-pressure environment for 1-2 min to remove internal bubbles from the SSS coating. Subsequently, the SSS coating is slowly poured onto the concrete surface with the EMS coating attached. The uncured SSS coating has excellent flowability, can self-level within 5 min, and can cure itself within 24 h, ultimately forming a uniform thin layer with a thickness of about 0.5 mm on the surface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Performance Index** & **Parameter** \\ \hline
1 & Density & 0.3–2.0 g/cm\({}^{3}\) \\ \hline
2 & Nominal fracture strength 1  & 2.83 MPa \\ \hline
3 & Real fracture strength 2  & 18 MPa \\ \hline
4 & Modulus & 2–6 MPa \\ \hline
5 & Elongation at break & 20–700\% \\ \hline
6 & Water sliding angle (WSA) & 5–30\({}^{\circ}\) \\ \hline
7 & Water contact angle (WCA) & 90–120\({}^{\circ}\) \\ \hline
8 & Self-healing time & Visual self-healing within 1 min \\  & Functional self-healing 99\% within 72 h \\ \hline
9 & Operation life & \(\geq\)10 years \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance parameters of the SSS coating.
# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
## 2 Related Work
The minimum achievable throughput per ground user (GU) is maximized by co-optimizing cache placement, UAV resource allocation, and trajectory with limited cache capacity and flight time. The authors of [15] considered a scenario where infrastructure is rendered unusable due to a disaster situation and use UAVs to service vehicles in the affected area to meet the quality of service for users. The authors of [16] used UAVs to help with infrastructure operations. Since deploying multiple UAVs incurs greater costs, the number of UAVs dispatched is optimized to provide coverage of specific areas. In [17], the authors investigated communication with multiple UAVs with an onboard network and proposed an efficient collaborative UAV sensing and sending protocol. Ref. [18] proposed a joint optimization problem for UAV deployment and content placement to minimize the average request latency and proposed a Q-learning algorithm to solve the content placement problem. In [19], the authors proposed a UAV network that transmits data from the vehicle to the core network, where one UAV serves the vehicle and the others are used to relay the data, saving energy by limiting the maneuverability of the UAVs in a certain area.
## 6 Case Studies  
The IEEE 33-bus distribution system [30] is taken as an example to verify the effectiveness of the planning model and solution method in this paper. The topology of the IEEE 33-bus distribution system is shown in Figure 5.  
Switches 1-32 are section switches, and switches 33-37 are interconnection switches. The voltage level is 10 kV, \(DG\) can be installed at nodes 7, 14, 24 and 25, and the rest are conventional load nodes.  
The maximum allowable \(DG\) capacity for each node to be selected is 2000 kW. The \(DG\) types to be selected are \(WTG\) and \(PVG\), and the rated capacity of a single \(WTG\) is 100 kW. Cut in wind speed, rated wind speed and cut out wind speed are 4.3 m/s, 17.9 m/s and 7.7 m/s, respectively. The investment cost per unit capacity of \(WTG\) is 7800 USD/kW, the operation and maintenance cost per unit generating capacity is 0.30 USD/(kW \(\bullet\) h), and the active management cost per unit power is 0.08 USD/(kW \(\bullet\) h). The maximum allowable reduction rate of \(WTG\) is 20%. The rated capacity of a single \(PVG\) is 200 kW, and the rated light intensity of \(PVG\) is 500 W/m\({}^{2}\). The investment cost per unit capacity of \(PVG\) is 8200 USD/kW, the operation and maintenance cost per unit power generation is 0.30 USD/(kW \(\bullet\) h), the active management cost per unit electricity is 0.08 USD/(kW \(\bullet\) h). The maximum allowable reduction rate of \(PVG\) is 20%. The economic service life of \(WTG\) and \(PVG\) is 20 years. The discount rate is 0.06. The unit cost for ADN to purchase electricity from the superior power grid is 0.48 USD/(kW \(\bullet\) h). The network loss cost per unit electricity is 0.7 USD/(kW \(\bullet\) h). The cost caused by the switch changing the once on/off state is USD 50. The permeability of \(DG\) ranges from 0.3 to 0.7 [18]. The switching times of network reconfiguration are constrained to 5 [23]. The IEEE 33-bus network load and line impedance are presented in [30].  
The planning scheme experiments are performed on a computer with Intel(R) Core(TM) i7-8565U CPU and 8.00 GB RAM using MATLAB R2020b and Gurobi 9.1.2.  
There are four cases to verify the proposed method.  
Case 1: Network reconfiguration is not considered and deterministic planning model is adopted.  
Case 2: Robust planning model is adopted without considering network reconfiguration.  
Case 3: Network reconfiguration is considered and a deterministic planning model is adopted.  
Case 4: Network reconfiguration is considered and a robust planning model is adopted.  
It is worth noting that the deterministic model in Cases 1 and 3 refers to the assumption that wind speed, light intensity and load do not fluctuate during the planning process, that is, a basic scene is utilised during the planning process.  
The planning scheme and various costs obtained from the simulation are shown in Table 1 and Figure 6, respectively.  
Figure 5: The topology of IEEE 33-bus distribution system.
# Granular Classification for Imbalanced Datasets: A Minkowski Distance-Based Method
## 2 Information Granules and Minkowski Distance
### The Representation of Information Granules  
Clustering methods and a method called the principle of justifiable granularity [33] are usually used to transform data into information granules. In light of the unique distribution of imbalanced data, other methods should be considered for constructing and representing information granules. Since information granulation is a process of extracting knowledge from data by organizing close or similar data points. The organization of data points  
Figure 2: Three typical Minkowski distances, i.e., Euclidean, Manhattan and Chebyshev distances.  
involves the calculation of the distances. Therefore, we use Minkowski distance to represent information granules. Assuming that there is a series of \(n\)-dimensional normalized data points, viz., \(\mathbf{D}=\{\mathbf{x}_{t}\in[0,1]^{n}|t=1,2,\cdots,N\}\), which are close to a fixed point, i.e., \(\mathbf{v}\), the information granule generated on \(\mathbf{D}\) can be represented by  
\[\Omega=\big{\{}\mathbf{x}_{k}\ |\ \|\mathbf{x}_{k}-\mathbf{v}\|_{p}\leq\rho, \mathbf{x}_{k}\in\mathbf{D}\big{\}}, \tag{5}\]  
where the fixed point \(\mathbf{v}\) is the center of the information granule \(\Omega\), \(\rho\) is the predefined radius of \(\Omega\), and \(\|\mathbf{x}_{k}-\mathbf{v}\|_{p}\) means the Minkowski distance between \(\mathbf{x}_{k}\) and \(\mathbf{v}\). The information granule \(\Omega\) can be treated as a hollow geometry, which covers the points whose distance to \(\mathbf{v}\) is within \([0,\rho]\). For convenience, we represent the information granule simply with \(\Omega=(\mathbf{v},\rho)_{p}\).  
When we change the values of parameter \(p\) in (5), different geometric shapes of the corresponding information granules are produced. Specifically, when \(p\) equals \(1\), \(2\) and \(\infty\) the Minkowski distance is determined as Manhattan, Euclidean and Chebyshev distances, respectively. Referring to (1), when the dimension (\(n\)) of points in \(\mathbf{D}\) is \(2\), \(3\) and higher values, the corresponding information granule \(\Omega\) appears in the form of a circle, a sphere, and hypersphere, respectively. Similarly, seeing (3), when \(p\) is set to \(1\), the geometric shapes of the constructed information granule would appear in the shape of a diamond, an octahedron and \"hyper-diamond\". When \(p\) is set to \(\infty\), the corresponding geometric shape of the corresponding information granule becomes a square, a cube and a hypercube. It is worth noting that when the parameter \(p\) of Minkowski distance is set as \(2\), \(1\) and \(\infty\), the resulting information granules of different dimensions will appear as a series of corresponding regular shapes, see Figure 3. Given the above, the information granules constructed with Minkowski distance can be easily represented by two symbols, i.e., \(\mathbf{v}\) and \(\rho\), which effectively reduces the difficulty of presenting high-dimensional imbalanced data.
## 2 6G Developments
#### Potential Frequency Bands for 6G  
For 6G networks, the potential frequency bands are under study and are yet to be finalized [21]. Primarily, mmWave bands (24 GHz to 100 GHz), sub-THz bands (100 GHz to 1 THz), and super high-frequency THz bands (1 THz to 10 THz) are being researched and explored [8]. Based on these studies, a decision on the identification of bands for IMT-2030 will be taken at the World Radio Communication (WRC) Conference to be held in 2027 [22]. Earlier in WRC 2019, mmWave bands in 24.25-27.5 GHz, 37-43.5 GHz, 45.5-47 GHz, 47.2-48.2 GHz, and 66-71 GHz were assigned to IMT usage [8], which are insufficient to support the current and future applications. Many of these frequencies are used for remote sensing, radio astronomy, radar, and other applications. The sub-THz frequencies have immense potential for achieving ultra-high data rates, low latency, and high capacity. However, there are several limitations and challenges, such as spectrum scarcity and limitations to achieving the spectral efficiency (-100 bits/Hz to achieve 1 Tbps  
Figure 1: Six usage scenarios for IMT-2030 and four overarching aspects, specified by the ITU-R in June 2023 [19].  
speed) as per IMT-2030 requirements. In the year 2024, ITU-R SG 5 and, subsequently, WP 5D, agreed to study and explore the technical feasibility of IMT in bands above 100 GHz [9]. It is being studied and contended that the THz band can be a potential candidate for the applications that require Tbps speed. At these frequencies, there will be constraints for optical sensing, adverse health effects, and others at such extremely high frequencies. TV white spaces [23,24] can also enable 6G technology for extended coverage, energy-efficient IoT, and dynamic spectrum access. These low-frequency propagation characteristics can support rural connectivity, smart cities, and wireless backhauls.
# Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection
## 4 Discussion
The class-specific analysis revealed that the grassy shoot and sett rot classes achieved near-perfect classification performance, with AUC values approaching 1.000 across all models, reflecting their distinct and separable feature patterns. In contrast, classes such as smut, pokkah boeng, and brown rust demonstrated significantly lower F1-scores, particularly for models like random forest and SVM. This is due to features overlapping and insufficient representation in the training data, which remain critical barriers to the accurate classification of these challenging classes. Advanced strategies, such as oversampling, feature augmentation, or hybrid ensemble approaches, are required to improve the model performance for these underrepresented categories. Notably, the stacking model achieved the highest F1-scores for all embedding methods, with F1-scores of 0.8767 and 0.9272 for smut and pokkah boeng, respectively, when paired with InceptionV3 embeddings. These results underscore the utility of ensemble methods in mitigating class imbalance issues and leveraging complex feature spaces. For \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **CA** & **F1-Score** \\ \hline InceptionV3 with stacking & 0.9564 & 0.9562 \\ EfficientNet-b6 [46] & 0.9339 & 0.9094 \\ DeiT3-Small [45] & 0.9379 & 0.9096 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model performance comparison. computationally efficient scenarios, SqueezeNet embeddings coupled with stacking models also showed promising results, particularly for less complex classes like grassy shoot and sett rot. Additionally, stacking, despite requiring slightly more time than other conventional ML methods, outperformed all other DL approaches in achieving the best results. Its combination of pre-extracted features with ensemble learning offers a balance between computational efficiency and predictive performance. This highlights its potential as a robust alternative to computationally intensive DL models like CNNs and RNNs, especially when pre-trained feature extractors are employed. This suggests that hybrid approaches, such as stacking with optimized feature representations, present a viable alternative for large-scale sugarcane disease monitoring systems.