esa-sceva/satcom-chunk-collection huggingface dataset:
# The Difficulty of Measuring the Roughness of Glossy Surfaces Using the Triangulation Principle
## 2 Materials and Methods  
The conducted experiments were aimed at monitoring the suitability of the contact-free laser profilometer for measuring the roughness parameters of glossy surfaces. The tested samples were made of stainless steel and aluminum alloy. The roughness on the cut surface was measured in a contact-free and contact way. Both methods were applied to the same cut areas measured. A Mitutoyo contact roughness meter (Surftest SJ-400, Tokyo, Japan) and a new commercial system on the market, a non-contact laser profilometer (KVANT, Bratislava, Slovakia), were used to measure and evaluate the samples.  
In evaluating the surface roughness, the parameter Ra, the mean arithmetic deviation of the profile, and the parameter Rz, the tallest height of the profile unevenness, were monitored. In practice, the Ra and Rz parameters are those most monitored, capable of providing sufficient information about the quality of the measured surface [26]. The measured values of the sample materials were graphed as dependencies for both the Ra and Rz quality parameters of roughness.
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Fuel Cell Powered UAVs

Table 3: _Cont._to other power sources, which makes them a promising option for achieving extended endurance in UAVs while maintaining a given weight [57]. Nevertheless, it should be acknowledged that fuel cells may have lower energy density compared to lithium batteries due to the additional volume required for hydrogen storage in the form of a tank. In a separate study, the development of a UAV for mobile crane inspection was explored, with a focus on proton exchange membrane fuel cells [90] and lithium-ion batteries [91]. The research involved an economic analysis and a life cycle assessment to compare the two power sources. One of the main conclusions drawn from the study was that from a commercial standpoint, fuel cells, being a niche product, tend to be more expensive compared to lithium-ion batteries. This fact emphasizes the trade-offs between fuel cells and batteries in terms of specific energy, energy density, cost, and commercial viability. The selection of the appropriate power source for UAV applications relies on specific requirements, mission profiles, and considerations such as flight endurance, weight limitations, cost constraints, and the availability of infrastructure.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 6 Pectin Applications
HM pectin can be used as a gelling agent, stabilizer, emulsifier, and thickener in the food industry for the production of jams and jellies, while LM pectin can be used as a fat replacer in spreads, ice cream, fruit preparations for yoghurt, heat-reversible bakery glazing, emulsified meat, or low-calorie products such as diet carbonated beverages [1]. The texture and stability of milk-based products can be modified by the addition of pectin [31]. Indeed, the gelling and thickening properties of pectin stabilize beverages containing acidified milk (desert creams based on acid and non-acidic milk). Moreover, HM pectin can specifically stabilize acid casein particles, through HM pectin-casein interactions, in acidified dairy beverages. The pectin acts in this case as a defloculant and stabilizes the caseins by steric hindrance. In the presence of pectin, the particles no longer aggregate, and the formation of sediment is avoided. Pectin can also be used to stabilize cloudy beverages [152]. The stabilizing property of pectin prevents the separation of a heterogeneous medium (ice cream mix, preparation of fruit with pieces, cocoa drinks, etc.) and can be explained by the increasing viscosity and a network that is efficient enough to keep particles in suspension but weak enough not to be perceptible (gel liquids). The addition of pectin to a complex product such as jam can alter the perception of flavor, which varies depending on the type of pectin and aroma. The use of HM pectin decreases the olfactory intensity. The intrinsic viscosity, which indicates the hydrodynamic volume occupied by the polymer under given conditions, is a characteristic that represents the thickening power of pectin. This intrinsic viscosity of pectin is influenced by DM (related to molecular weight). The thickening power also depends on extrinsic conditions (temperature, type of solvent, pH) [50; 153]. Pectin can be used in emulsion-based foods such as low-fat mayonnaise and dressings, low-fat dairy products which emulate some characteristics of whole milk products and other beverages, emulsified meat products, and low-fat spreads [138].
###### Abstract  
In order to strengthen disaster prevention control under deep resource development and space utilization, it is necessary to construct a damage intrinsic model under complex stress states to predict the mechanical behavior of deep-rock mass under cyclic loading. An indoor uniaxial cyclic loading test on sandstone was carried out in this paper. By analyzing the mechanical properties and energy transformation of the failure process, it was assumed that the failure of rock micro-units follows a Weibull density function, and the damage intrinsic relationship was constructed using the Mogi-Coulomb strength criterion. The constitutive rationality was verified via the nonlinear fitting of the experimental curve and theoretical curve, and the model parameters were analyzed. This study indicates that the cyclic loading procedure has a strengthening effect on the elastic modulus. The brittleness of the rock increases with the cycle amplitude, the axial strain accumulates continuously, and the hysteresis loop area increases gradually and moves to the right. The energy conversion of the loading process is mainly split into the energy storage phase before the damage and the release phase at the time of damage, and the dissipation energy percentage curve shows the groove evolution law. The damage intrinsic model based on the Mogi-Coulomb strength criterion accurately reflects the ontological relationship of sandstone under cyclic loading, and the model parameters have clear physical significance. This study has important theoretical and engineering meaning for predicting the deformation and destruction of rocks.  
Mogi-Coulomb strength criterion; stepped loading and unloading; damage constitutive model; sandstone damage; model parameter
## 4 Calibration of Differential Code Biases
Results from three 24 h data sets, each collected with two receivers on days 151 **(a,d)**, 153 **(b,e)** and 154 **(c,f)** of 2018.  
frequency bands: L1 + L2 + L5, filter type: forward, elevation mask: \(5^{\circ}\), ionosphere correction: IF combinations, ephemeris/clock: broadcast, GPS + Galileo). While the frequency bands and constellations to be used in processing the input data can be selected in RTKLIB, there is no record in the output data specifying exactly which observations have been used in a certain epoch. The obtained position was compared to the known position of a roof antenna (standard deviation of antenna position below 3 cm in each coordinate) to form the RMS errors given in Table 6. Calibration of the DCBs practically cuts the RMS error for each coordinate in half, resulting in an overall RMS error of \([0.6;0.8;1.5]\) m compared to \([1.2;1.5;2.9]\) m with uncalibrated DCBs. RTKLIB achieves an intermediate accuracy with an overall RMS error of \([0.8;1.1;2.1]\) m. These results verify the DCB calibration and support the approach to work with different types of pseudo-range observations simultaneously.
## 3 Methodology
### Training Parameters  
The Table 3 provides a detailed description of the training hyperparameters used for the proposed _ECGDnet_ denoising model. For training, a batch size of 128 was selected, with an initial learning rate of \(1.0\times 10^{-4}\), optimized using the Adam optimizer configured with \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=1.0\times 10^{-8}\). The model was trained for 50 epochs, utilizing a cosine annealing learning rate schedule with a warm-up phase spanning 5 epochs. The loss function combines a primary loss of Mean Squared Error (MSE) and a secondary loss of L1, weighted at 1.0 and 0.1, respectively. To prevent overfitting and improve generalization, regularization techniques such as weight decay (\(1.0\times 10^{-4}\)), gradient clipping (threshold of 1.0), and a dropout rate of 0.1 were applied. Early stopping was incorporated, monitoring the validation loss with a patience of 10 epochs and a minimum delta of \(1.0\times 10^{-4}\) to terminate training when improvements stagnated. These hyperparameters collectively aim to balance training stability, efficiency, and performance. The proposed neural network architecture was implemented using the PyTorch (version 3.9) deep learning framework and trained for 50 epochs on an NVIDIA A100 GPU with 80 GB of memory. Leveraging the high-performance computing capabilities of the State-of-the-Art A100 accelerator, the extensive 50 epoch training regimen allowed the model to thoroughly optimize its parameters and capture complex relationships within the sequential data. This rigorous experimental setup, combining the flexibility of PyTorch with the parallel processing power of the GPU, ensures the reliability and reproducibility of the research findings, enabling future applications of the self-attention-based architecture.
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 2 Data and Methods
### Development of Stochastic Model
#### 2.3.4 Multi-GNSS Weighting Schemes  
Following the considerations mentioned above, we propose five different multi-GNSS weighting schemes (Figure 4). In two schemes, namely GREC_L10 and GREC_L50, equal \(\sigma_{P}\) and \(\sigma_{L}\) are applied for all GNSS, and therefore there are actually no inter-system differences in observation weighting. The GREC_L10 scheme represents the stochastic model developed for GPS (\(\sigma_{P}\) = 0.30 m and \(\sigma_{L}\) = 10 mm). GREC_L50 represents the scheme for which the station averaged \(m0\) is close to 1 in GPS code-base only solution (\(\sigma_{P}\) = 0.235 m and \(\sigma_{L}\) = 50 mm). Therefore, in the GREC_L50 scheme, slightly larger weights are put on pseudoranges and significantly smaller weights are put on carrier phase when compared to the GREC_L10 scheme.  
In the next two schemes, namely GREC_GxN and GREC_GxS, the stochastic model developed for GPS is also used, but for other GNSS \(\sigma_{P}\) and \(\sigma_{L}\) are multiplied proportionally to the signal noise of each system (Table 4. Standard deviation of carrier phase and code receiver noises. or general SISRE results (Figure 3), respectively. Compared to the GREC_L10 scheme, in the GREC_GxN scheme GLONASS pseudoranges are downweighted, Galileo pseudoranges are upweighted and BeiDou pseudoranges are slightly upweighted as well. Carrier phases of other GNSS are only slightly downweighted when compared to GPS. In the GREC_GxS scheme both pseudoranges and carrier-phases of other GNSS are downweighted compared to GPS, and the smallest weights are put on BeiDou GEO observations, due to the poor quality of orbit and clocks products for GEO satellites.  
Finally, a GREC_N&S scheme is proposed in which the a priori error of observable \(\sigma\) is calculated as a cumulative error of the corresponding orbit quality expressed by SISRE and signal noise \(N\), as follows:  
\[\sigma=\sqrt{N^{2}+SISRE^{2}} \tag{4}\]  
In this scheme we found Galileo pseudoranges to have the highest weights among all GNSS, while GLONASS and BeiDou GEO pseudoranges are downweighted when compared to other pseudoranges. For carrier phases the smallest \(\sigma_{L}\) are obtained for GPS satellites and they are more than two times larger for GLONASS, more than three times larger for Galileo. Carrier phase weights are four and almost five times larger when compared to GPS for BeiDou MEO, and BeiDou IGSO, respectively. For BeiDou GEO \(\sigma_{L}\) is 10 times larger than for BeiDou MEO, which means that in this scheme carrier phase observations to BeiDou GEO carrier phases have weights at the level of pseudoranges, so consequently they have a limited contribution to the solution.
## 5 Simulation and Results
### Clustering Network and Dynamic Maintenance Function Simulation  
#### 5.2.1 Simulation of Clustering Network Function  
In this experiment, the network consists of 16 nodes, with the initial node distribution shown in Figure 7. To complete the mission, these nodes are divided into 3 clusters. To achieve this, we employed an improved K-means++ algorithm for partitioning. By considering both the node positions and the balance of node numbers within each cluster, the capacity-constrained K-means++ algorithm ensures an even distribution of nodes in space, minimizing interference between clusters. Figure 8 illustrates the partitioning result of this algorithm, which demonstrates that the node numbers within each cluster are effectively balanced, thereby achieving the goal of load balancing.  
After the partitioning, a weighted algorithm was used to select the cluster head and backup cluster head for each partition, ultimately forming the network topology. Figure 9a shows the network topology formed after selecting the cluster heads and backup cluster heads, with the cluster head nodes being 9, 5, and 7, and the backup cluster head nodes being 11, 3, and 15. With the proposed clustering algorithm, all UAV nodes are interconnected through the cluster head nodes within the clustered structure, achieving efficient deployment and stable communication. Figure 9b displays the network topology  
Figure 8: Node distribution after partitioning.  
Figure 7: Initial node distribution.  
after running for a period of time. It can be seen that even when the positions of the nodes change, the constructed network topology still ensures connectivity, demonstrating the effectiveness of the proposed algorithm.
## 2 Materials and Methods
### Method
#### 2.2.3 Partial Least Squares Regression  
Partial least squares (PLS) regression is a robust statistical method that integrates core functionalities of linear regression analysis, principal component analysis, and canonical correlation analysis. It enables simultaneous regression modeling, data structure simplification, and association analysis between two sets of variables. As an effective multivariate data analysis strategy, PLS is particularly suitable for datasets with multicollinearity--where independent variables are highly correlated--a common scenario in meteorological factor studies [83; 84]. Many researchers have already used the PLS method to identify the effective climate parameters influencing the variables under study [85; 86; 87]. By combining the advantages of principal component analysis (PCA) and multivariate regression, PLS becomes an ideal choice for addressing these issues. Additionally, VIP, also known as \"variable projection influence\", is a crucial tool for variable selection in PLS models. VIP scores reflect the importance of independent variables to model fitting, calculated as the weighted sum of squared PLS weights based on the variance explained by each extracted latent variable [88]. The VIP score formula is as follows:  
\[\text{Vj}=\sqrt{\text{P}\text{\sum}_{\text{a}=1}^{\text{A}}\left[\text{SS}_{ \text{a}}(\text{W}_{\text{aj}}/\|\,\text{W}_{\text{a}}\|)^{2}\right]/\text{ \sum}_{\text{a}=1}^{\text{A}}(\text{SS}_{\text{a}})} \tag{4}\]  
where Vj represents the VIP score of the jth variable, \(\text{SS}_{\text{a}}\) denotes the sum of squares explained by the ath latent component, and \((\text{W}_{\text{aj}}/\|\text{W}_{\text{a}}\|)^{2}\) signifies the relative importance of the jth variable with respect to all variables on the ath latent component, where p is the number of independent variables. VIP scores assess the importance of independent variables in explaining the dependent variable (Y). According to [PERSON] (1995), if a variable's VIP score is below 0.8, it can be considered less contributive to the dependent variable [89]. This approach helps identify the most influential independent variables, thereby enhancing model interpretability and effectiveness. VIP scores provide researchers with a systematic method to understand which predictors best explain the variation in the dependent variable and estimate their contribution to the PLS regression model, thus promoting data structure comprehension and optimizing variable selection during model building. To explore the influence mechanism of meteorological factors on NDVI changes, this study adopts PLS for multivariate modeling. VIP is used to evaluate the relative contribution of each meteorological factor to NDVI changes. The key driving factors that have a significant impact on NDVI are then identified and further used for future scenario prediction modeling.
# Acoustic Emission-Based Method for IFSS Characterization in Single-Fiber Fragmentation Tests
## 3 Results and Discussion
### Fiber Fragmentation Characterization  
After each test, the fragments were measured microscopically and observed in the calibrated area of the sample. The fragments were generally located within a few micrometers of the calibrated length, and no fractures were observed outside the study area. This procedure allowed the direct determination of reference fragment lengths for each sample.  
In Figure 7, it is observed that the AE events are mainly concentrated before the plastic regime of the material, a similar behavior is observed in [25; 26; 33]. This occurs because the first stage of the test begins when the load transferred from the matrix to the fiber exceeds its tensile strength. When the plastic regime is reached, the creep of the specimen prevents the matrix from sustaining additional stress, reducing the transferred stress to the fiber and preventing the generation of new fragments [34].  
In optical measurement, carbon fiber can cause confusion due to the low contrast it presents relative to the failure zones. Polarized light facilitates observation thanks to the birefringence generated by the stress field in the resin surrounding the fiber. At the interface, qualitative patterns can be identified as indicating the level of adhesion. The observed evidence of an extended detachment region, where relative fiber slippage occurs, is referred to as a debond zone, partially generated by the rupture of molecular bonds within the interphase [33]. Previous studies have reported this phenomenon in unloaded samples with particularly weak interfaces, such as carbon fiber with epoxy resin [16; 19]. These findings align with fragment lengths ranging from 300 \(\upmu\)m to 900 \(\upmu\)m, and these ranges have also been observed in unsized carbon fibers [35].  
Figure 7: Load vs. head displacement curve and envelope of the AE measured during the tests. The upper section shows the signals recorded in 16 SFFT samples, while the lower section displays the results for the two samples without fiber.
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
## 3 QKD Protocols
### BB84 Protocol  
The theoretical unconditional security provided by QKD is capable of fulfilling communication security needs. The pioneering QKD protocol, proposed by [PERSON] and [PERSON] in 1984 (BB84) [10], has spurred significant theoretical and empirical research interest due to its foundational role in ensuring secure communication by leveraging the principles of quantum mechanics. The BB84 protocol's ability to detect eavesdropping and ensure secure key exchange has made it a cornerstone of QKD research and development. Theoretical ad vancements have focused on reducing protocol complexity and enhancing system security. The B92 protocol [37] and the six-state protocol [38] are simplified and improved iterations of the BB84 protocol. The decoy-state protocol [39; 40; 41] further refines the BB84 protocol, enabling resistance to photon number-splitting (PNS) attacks. To date, real QKD systems have also been developed, achieving QKD in free-space air channels [42; 43] and in optical fiber channels [44; 45].  
Our study introduces the concept of mono-authentication within QKD-BB84 protocol, a significant innovation aimed at streamlining the authentication process. Unlike traditional methods that authenticate at multiple stages, mono-authentication consolidates the authentication step to the end of the communication session. This not only simplifies the process but also minimizes potential vulnerabilities that adversaries could exploit during intermediate authentication steps. By focusing on a single authentication instance, mono-authentication enhances the overall security and efficiency of QKD systems.
# Survey of Blockchain-Based Applications for IoT
## 8 Blockchain Scalability and Efficiency
### Blockchain at the Edge  
Blockchains can be combined with edge computing to improve the efficiency and security of computation offloading in IoT environments. [PERSON] et al. suggested a blockchain-enabled computation offloading method, named BeCome [64], that leverages blockchain technology to maintain data integrity during task offloading while employing advanced algorithms to optimize resource allocation and minimize energy consumption. Through extensive simulations, the study demonstrates that BeCome outperforms traditional methods in terms of resource utilization, load balancing, offloading time, and energy efficiency [64].  
Another approach is to offload both consensus mining tasks and data processing tasks in blockchain-empowered mobile edge computing (MEC) environments using deep reinforcement learning. [PERSON] et al. [65] proposed the DRGO (Deep Reinforcement Learning with Genetic Optimization) algorithm, which optimizes real-time offloading decisions by formulating the problem as a Markov Decision Process (MDP). To efficiently offload mining tasks, the algorithm leverages edge computing devices (ECDs) as mining nodes, where blockchain mining tasks are dynamically distributed to nearby edge nodes to reduce latency and computational load. This innovative approach effectively balances computation offloading for blockchain mining and data processing tasks, making it well-suited for dynamic and resource-intensive MEC environments. However, one drawback is that the algorithm is designed around a Proof of Work (PoW)-based consensus mechanism, which is generally resource-intensive and less suitable for the permissioned or lightweight blockchain networks commonly used in MEC settings.
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 3 Methodology
### Empirical Method for Tunnel-Induced Horizontal Displacements  
During the excavation of tunnels, horizontal displacements arise simultaneously with vertical displacements, with lateral movements directed toward the center of the trough. In greenfield conditions, past experience with the observational method [34; 35], justified the theoretical assumption that considers displacement vectors directed toward the tunnel axis, thus relating the horizontal surface movements u\({}_{x}\) to vertical ones w [28; 36], as follows:  
\[\mathrm{u_{x}(x)=w(x)\cdot x/z_{0}}. \tag{6}\]  
From the latter, the maximum horizontal displacement is attained for x = i\({}_{x}\), and substituting Equation (4) into Equation (6), one obtains the following:  
\[\mathrm{u_{x,max}=0.61\cdot K\cdot w_{max}}, \tag{7}\]  
which, for typical values of K in sandy soils or clays, leads to a maximum horizontal displacement in the range of (0.15 \(\div\) 0.28)\(\cdot w_{\rm max}\) and (0.25 \(\div\) 0.40)\(\cdot w_{\rm max}\), respectively.