esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Genesis of Amphibole Crystal Population
#### 4.1.1 The Source of Amphibole Crystal Population: Autocryst and Antecryst  
The amphibole crystal population plays a crucial role in the formation of the Sanggeda gabbros. The complex texture and composition of the amphiboles indicate that the intrusion is not the result of a single magma chamber undergoing continuous evolution. Equilibrium diagrams have been employed [52] to access the mineral-melt equilibrium between different amphibole types and the host magma. The Mg# of the amphiboles is plotted against the Mg# of the melt (calculated as molar ration: 100 MgO/(MgO + FeO)). The curves, calculated based on the iron-magnesium exchange coefficient for amphibole (Kd\({}^{\text{Amp-melt}}_{\text{Fe-Mg}}\) = 0.28 \(\pm\) 0.11) [53], represent the range where mineral and melt compositions are in equilibrium. All amphiboles plot below the curves (Figure 9), suggesting that they may not be in equilibrium with the melt.  
The fine-grained amphibole (Type 1) is considered an autocryst, euhedral and homogeneous, primarily present in the matrix, and is the product of host magma crystallization. The disequilibrium of the Type 1 amphibole shown in the diagram may result from the incorporation of numerous mafic minerals (e.g., amphibole phenocrysts) during magma recharge, which increases the Mg# of the magma components [54]. Other amphiboles (Type 2 to Type 4) plotted below the curves show clear disequilibrium with the melt. Considering their mineral characteristics, habits, and mineral chemistry, the dissolution phases developed at the edge of some amphibole grains suggest they are \"foreign\" crystals. Due to the rapid ascent and cooling of the host magma in the shallower crust, they are well preserved. The similar Mg# (Figure 5, 66-68) at the rims and the higher Th and U concentrations compared to the interior (Figure 7), especially for amphiboles as phenocrysts, may relate to the influence of the host magma during ascent, while internal compositional discrepancies originate from the magma reservoirs where the crystals formed. Despite noticeable differences in total REE concentrations among the various amphibole types, they exhibit similar REE patterns, indicating potential genetic relationships. These amphiboles did not crystallize from the host magma but are recycled crystals from earlier-stage melts. Therefore, it is more reasonable to consider these crystals as antecrysts from the same magma system rather than captured materials from unrelated wall rocks.  
Figure 8: (**a**) Chondrite-normalized REE and (**b**) primitive-mantle-normalized multi-element patterns for the Sanggeda intrusion. Whole-rock compositions from [42], lower continental crust compositions from [3], and chondrite, primitive mantle, oceanic island basalt (OIB), and normal-type mid-oceanic ridge basalt (N-Type MORB) values from [51].
## 6 Challenges and Future Research Directions
### Challenges in ML/DL-Based UWA Communication
It becomes impossible to rank algorithms or choose them for real-world deployment. The absence of unified benchmarks and reporting practices weakens the interpretability of results. It also limits reproducibility and cross-study insights, slowing progress in UWA communications. * **Robustness Challenges in Real-World UWA Environments:** Although several ML/DL models report high accuracy and BER reduction, they often perform under constrained experimental conditions. There is limited testing under unknown or changing channel conditions, including variations in Doppler spread, delay, ambient noise, or multipath severity. The absence of robustness testing, especially sensitivity to parameter perturbations, impedes real-world applicability. Furthermore, most studies do not assess generalization across different acoustic platforms or frequency bands, making cross-modem performance unreliable. The lack of domain-shift resilience and scarce labeled datasets further restricts adaptive performance across environments. * **Algorithmic Constraints vs. UWA Deployment Realities:** ML/DL models for UWA often demand substantial computation and high-quality training data, but these needs conflict with the strict energy budgets and latency constraints of real deployments--especially battery-powered AUVs and embedded sensors. Many architectures lack support for rapid physical-layer inference like low-latency channel estimation, which is vital for coherent demodulation. Sensitivity to assumptions like fixed Doppler profiles and ideal noise levels further amplifies the Sim2 Real gap, as sudden interference or biological noise causes unpredictable degradation. Without optimization for real-time and low-power settings, these models remain difficult to deploy in mutable marine environments. * **Adaptability Limits in Time-Varying UWA Channels:** Many reviewed models lack dynamic adaptability when faced with fast time-varying underwater channels. Traditional architectures are often statically trained and underperform as real-world conditions evolve, especially under abrupt changes in Doppler spread or delay characteristics. The absence of real-time update mechanisms--such as online learning,incremental learning, or meta-learning--prevents responsive adaptation, contributing to instability in unpredictable environments. These constraints inhibit timely physical-layer decisions, degrade modulation accuracy, and ultimately affect system reliability. Addressing these challenges is crucial for advancing AI-driven UWA communication systems. Future research should focus on developing lightweight and adaptive models, enhancing the generalization of techniques, improving robustness against multipath and Doppler effects, and designing energy-efficient algorithms for deployment in underwater networks. By overcoming these limitations, AI-powered underwater communication can become more reliable, scalable, and efficient, supporting applications such as marine research, deep-sea exploration, and naval security.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 1 Introduction
Energy efficiency is a chief consideration in the design of ADR schemes for LoRaWAN networks, as the battery life of end nodes can be a limiting factor in network operation. It is thus desirable to find additional mechanisms that deal with this phenomenon. Existing research predominantly emphasises metrics such as energy utilisation and link-level throughput, often at the expense of packet success probability within LoRaWAN networks. In this study, we advance SSFIR-ADR, an adaptive data rate mechanism that optimally allocates spreading factors, reducing packet collision to enhance the packet delivery ratio in the LoRaWAN network without compromising energy efficiency. Our proposed ADR scheme is designed to optimise packet delivery while taking into account energy efficiency for LoRa using confirmed traffic. To the best of our knowledge, such a framework has not previously been developed in the literature. The principal contributions of this work are outlined as follows:  
* The analysis of the impact of improving the standard LoRaWAN ADR by minimising interference and avoiding collisions. * The design of an ADR model with collision avoidance and minimised interference. * The proposal of a stochastic probability model to restrict packet retransmission that demonstrates an optimisation of the packet delivery ratio and energy saving. The remainder of the article is organised in the following manner: Section 2 discusses the technological background while Section 3 presents the related literature. Section 4 describes the system model and Section 5 presents the proposed scheme. Section 6 presents and discusses the simulation results. Section 7 concludes the paper.
# Studying the Process of Enzyme Treatment on Beef Meat-Bone Paste Quality
## 3 Results and Discussion
### Physicochemical Analysis
The described changes in collagen fibers, including breaks between fibrils and fibers, swelling, and an increase in air interlayers, are consistent with the effects of enzymatic collagen breakdown. Pepsin's action cleaves peptide bonds within collagen, leading to structural alterations. The disruption of collagen structure is known to impact the rheological properties of food matrices, resulting in reduced viscosity [51]. Thus, enzyme treatment of meat-bone paste improves consistency, strengthens moisture binding, and promotes protein loosening and fat breakdown in meat-bone paste, which leads to faster achievement of readiness during heat treatment. The application of proteolytic enzymes in meat processing has been widely studied for its ability to improve meat tenderness. Studies show that dipping meat in enzyme solutions after osmotic dehydration effectively increases tenderness, with papain-treated samples achieving the highest scores for tenderness and reduced hardness [52]. These enzymes work by breaking down both myofibrillar and collagenous proteins, significantly improving the texture of various meat cuts, including those with high connective tissue content [53]. For low-grade beef, the use of proteolytic enzymes has been found to be particularly beneficial in softening connective tissue. This enzymatic treatment, when followed by the application of transglutaminase, enhances moisture binding and retention, further improving the functional properties of the meat [54]. Such processes are essential for adding value to lower-grade cuts, making them more suitable for consumption and minimizing waste. Exogenous proteases, such as actinidin and zingibain, have shown potential in tendering meat from lower-quality sources. However, the effectiveness of these enzymes depends on optimizing conditions like temperature and pH for specific enzyme activity [55]. These findings support the growing role of enzymes in improving meat texture, functionality, and overall quality.
# A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment
## 3 A Coalition Formation Game Approach for Coverage Utility Maximization
### Analysis of the Mission Reward Maximization
Then, we derive the following equation according to Equations (13) and (14):  
\[u_{n}^{*}(g_{n},g_{-n})-u_{n}^{*}(g_{n}^{\prime},g_{-n})=\phi(g_{n},g_{-n})- \phi(g_{n}^{\prime},g_{-n}). \tag{15}\]  
The work for the proof has been done in [7]. Therefore, according to Definition 3, \(\mathcal{G}_{1}\) is an EPG with coverage utility \(U_{I}\) serving as the potential function. It can be concluded that \(\mathcal{P}_{1}\) has at least one NE point. Note that the designed potential function refers to overall coverage utility. Hence, the NE solution of \(\mathcal{P}_{1}\) turns to be the pure strategy NE point of \(\mathcal{G}_{1}\). which can be effectively helpful for the proof of the stability in the proposed model \(\mathcal{G}\). Motivated by the learning algorithm design in [7], we introduce a coverage optimization deployment algorithm based on binary log-linear learning (CODA-BLL) (Algorithm 1) to execute coverage maximization deployment. The proposed algorithm is mainly based on the binary log-linear learning, which was proved to guarantee convergence of Nash equilibrium [7; 29]. Given the current coalition selection profiles, the algorithm is executed one time to explore an optimal deployment for coverage utility per loop. Here, we design Equation (16) as the UAVs' position selection probability function, where \(\beta\) is the learning parameter (\(\beta>0\)):  
\[q_{n}^{g}=\frac{exp\{\beta u_{n}^{*}(g_{n},g_{-n})\}}{exp\{\beta u_{n}^{*}(g_{n },g_{-n})\}+exp\{\beta u_{n}^{*}(g_{n}^{\prime},g_{-n})\}}. \tag{16}\]
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 2 Rough Surfaces Parametrization
### Implementation Example for Generation of an Individual Gaussian Rough Surface  
The parameters to be chosen as inputs for the parametrization of a Gaussian rough surface are:  
* The RMS surface height: \(\sigma=\left[\left\langle h^{2}\right\rangle\right]^{\frac{1}{2}}\). As the height is defined with zero mean (\(\left\langle h\right\rangle=0\)), the standard deviation \(\sigma=\left[\left\langle\left(h-\left\langle h^{2}\right\rangle\right)^{2} \right\rangle\right]^{\frac{1}{2}}\) of the height is equal to the RMS height \(rms(h)=\left[\left\langle h^{2}\right\rangle\right]^{\frac{1}{2}}\); \(\sigma=\left[\left\langle\left(h-\left\langle h\right\rangle\right)^{2} \right\rangle\right]^{\frac{1}{2}}=\left[\left\langle h^{2}\right\rangle\right] ^{\frac{1}{2}}=rms(h)\).
* The correlation lengths, \(L_{x}\) and \(L_{y}\), along the local axes \(x\) and \(y\).  
The number of mesh points of the rough surface along the local axes x and y has then to be automatically fixed by the chosen mesh algorithm. The previously described Gaussian rough surface model is currently implemented for planar and cylindrical surfaces in 3D computations, and for their linear cross-sections in 2D, in the CIVA software [28] (platform for non-destructive evaluation simulation [29; 30; 31; 32; 33]). Examples of generated surfaces for small specimens with relatively high roughness are shown in Figure 1.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 8 Simulations and Real-World Results
### Impact of Different Signal Strengths  
Basically, a higher GNSS signal amplitude increases the correlation value from Equation (15) and thus influences Equation (50). From this, it is expected that changes in amplitude do not shift the mean of the resulting probability function but influence the variance. This is obvious because a stronger received GNSS signal must result in a more accurate estimate, which is shown in Figure 5.
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
###### Abstract  
With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.  
service recommendation; sparse data; feature interaction; deep forest; QoS prediction
## 3 Results and Discussion
### Scenario Simulation
#### 3.2.1 Increasing Water Level Fluctuation
Table 6 shows that in the coarse sand, the fluctuation of the nitrogen concentration changes most significantly with the increase in the fluctuation range of the water level, followed by the medium sand, and the fine sand has the slightest change. The impact of water level fluctuations on the three solutes is also different. The fluctuation range of the water level is expanded by 5 cm, and the fluctuation range of nitrite nitrogen, nitrate nitrogen and ammonium nitrogen in the coarse sand medium increases by 7.90%, 67.17%, and 37.49%, respectively. In the medium sand medium, the fluctuation range increased by 14.66%, 72.28%, and 7.26%, respectively. The fluctuation range of nitrite nitrogen in the fine sand medium decreased by 10.34%, while the fluctuation range of nitrate nitrogen and ammonium nitrogen increased by 72.17% and 1.62%, respectively. Table 6 shows that among the three media, the fluctuation of the water level has the most significant effect on the fluctuation of nitrate nitrogen, and the influence on the fluctuation of nitrite nitrogen and ammonium nitrogen is relatively small.
# Internet of Things Applications as Energy Internet in Smart Grids and Smart Environments
## 7 Conclusions  
The EI is an emerging concept for future grids constituted by smart grids, communication systems, intelligent systems, and smart elements. Communication infrastructure is one of the most important components of the EI concept. A widespread and secure communication framework has vital importance for both forming and operating the EI systems. On the other hand, IoT is an emerging technology coming up with several advantages, such as supporting wide application areas and heterogonous network structures, providing special security features, and ensuring the ability to communicate among many devices. Moreover, the IoT provides a suitable cyber physical interface to integrate the communication and data management systems with conventional and recent generation, transmission, distribution, and consumption levels of utility. The two-way transmission of energy and communication signals are ensured with highly reliable applications of IoT-based communication infrastructures. In this paper, we presented a complete overview for IoT applications that are regarded as promising key applications for the EI concept in the near future. We investigated researches related to IoT applications for smart grids and smart environments, such as smart cities, smart homes, smart metering, and smart energy management systems. In addition, we analyzed challenges and opportunities originating from these applications. Finally, we highlighted open issues and future research directions of IoT applications for the EI infrastructure.  
All authors are involved equally in developing the full research survey manuscript for its final presentation.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Adsorption Mechanism  
The adsorption mechanisms involved in the adsorption process could be attributed to electrostatic attraction, surface complexation, and hydrogen bonding, as illustrated in Figure 15. The Fe\({}_{3}\)O\({}_{4}\) nanoparticles tend to form complexes through surface complexation and electrostatic interactions with hydroxyl and carboxyl groups present on their surfaces. Consequently, chemical agents could easily form complexes with the MGONCs compos  
Figure 14: PSO kinetics fitting curve of GB and GD adsorption on MGONCs.  
Figure 13: PFO kinetics fitting curve of GB and GD adsorption on MGONCs.  
ite. Additionally, hydrogen bonding played a role in the adsorption process, wherein the free hydroxyl groups in the MGONCs act as hydrogen bond donors, while the oxygen centers in the chemical agents act as hydrogen bond acceptors. Although the strength of hydrogen bonding was relatively weak compared to other forms of interactions, the presence of numerous potential hydrogen bonding sites contributed to the overall adsorption process [33; 37; 39].
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 5 Numerical Results  
In this section, we present the simulation results to evaluate the performance of the proposed distributed scheme in multi-task Air-FL scenarios. Specifically, we consider two representative tasks: ridge regression on synthetic datasets and handwritten digit classification using the Modified National Institute of Standards and Technology (MNIST) dataset. These experiments aim to assess the effectiveness of the distributed scheme in managing interference and optimizing learning performance across multiple tasks. (While MNIST and synthetic regression tasks are used in our simulations for clarity and analytical convenience, the proposed distributed power control framework is compatible with more complex datasets and deep neural models. Our goal in this paper is to highlight the impact of interference-aware power optimization on FL performance under multi-task and multi-cell settings.)
# Considerations for the Design of a Wheelchair Dynamometer Concerning a Dedicated Braking System
## 2 Materials and Methods
### Selection of the Electric Drive and the Control System Design  
The essence of the designed braking system was to enable the control of friction braking force settings in a continuous and repeatable manner. Classic mechanical and mechanical-hydraulic systems, despite a wide range of applications in technology and great efficiency, were not used in this case due to their inability to meet the above requirements regarding control. In order to meet the set requirements, the structure created is therefore an electromechanical disc brake control system (EMB) with a hydraulic system generating brake pad pressure. In contrast to traditional braking systems, the use of an electro-mechanism allows an increase in the efficiency of the brake and precise control of the braking force settings, as well as relatively easy control via the integrated electronic panel [24; 25]. Based on the review of the literature, three types of estimation of the brake pad pressure force are distinguishable. These are methods based on the angular displacement of the electric motor, its current or a hybrid combination of the two [24]. We decided to implement system control via the angular displacement of the motor, which is an efficient method for the structure created.  
The schematics of the described method's control system are presented in Figure 3. The main component is the brake lever (13), the movement of which shifts the hydraulic piston (14). As a result, the brake piston (16) is shifted, which in turn causes the shoe to clamp onto the brake disc (17). The shift in the brake lever (13) is caused by the rotation of the electric engine (21) via the connector (18). The electric engine (21) is controlled by the control system (20). The control system (20) is supplied with pressure \(p\) from the liquid from the pressure sensor (15), which is installed between the hydraulic piston (14) and the brake piston (16). The system utilizes an electric engine consisting of a TSP-202005 servomechanism (TSP-Racing, provided by Conrad LLC, Poland), which was selected based on the brake  
Figure 2: View of the wheelchair dynamometer.  
lever clamping force demand calculations. The electric engine (21) is equipped with an encoder (19) that measures the current angular position \(\varphi^{\prime}\) and simultaneously transmits this value to the control system (20). This constitutes the basis for the generation and correction of the set position \(\varphi\) of the electric engine (21). As a result, the brake shoe clamp set on the controller (20) (setting m) is converted to the engine's angular displacement \(\varphi\), which is adjusted using a feedback loop based on the engine's actual angular displacement \(\varphi^{\prime}\) and pressure \(p\) in the supply line of the brake piston (16). The key issue in the system is the designation of the dependency between the braking torque \(M_{H}\) and the setting \(m\), i.e., the function \(M_{H}=f(m)\).