esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Analysis of Brine Concentration Distribution  
The injected freshwater initially dissolves and erodes the salt wall, creating high-concentration brine in the early leaching stage, which then diffuses due to the high-speed fluid. Figure 17 illustrates the brine displacement concentration contours at the vertical section (X = 0 m) during the first stage, showing a relatively uniform brine concentration on the salt rock surface at the same leaching time, with higher concentrations in the reflux cavern than those in the dissolution cavern. As leaching progresses, the brine concentration in the salt cavern increases quickly, redistributing due to convection, diffusion, and vortex effects, with convection being predominant due to continuous high-speed freshwater injection. Due to the limited vertical extent of the horizontal cavern, the gravitational effects are minimal, making fluid erosion the leading factor and resulting in brine concentrations ranging from 60 to 120 g/L during the first stage.  
Fluid erosion is initially predominant in the construction section, diminishing as leaching progresses, while dissolution takes precedence in the cavern formed during the first stage. At the onset of the second stage, the brine concentration decreases due to the low height of the dissolution cavern and the swift overall brine flow, as shown in Figure 18. The brine concentration in the dissolution cavern is notably lower than that in the cavern formed in the first stage. With cavity expansion, the vortices from both the sections gradually merge, leading to a gradual equalization of the brine concentration. In the reflux cavern, the brine concentration significantly rises compared to that in the first stage, reaching up to 270 g/L.  
Figure 19 illustrates the brine concentration contours at the vertical sections (X = 0 m) for each stage, revealing a gradual increase in the overall brine concentration within the cavern, with the concentrations nearing saturation by the final leaching stage. Initially, the highest brine concentrations are in the reflux cavern, while the lowest ones are in the dissolution section. By the fourth and fifth stages, the outlet concentrations exceed those in the reflux cavern, yet remain the lowest in the dissolution section. Due to stable flow field distribution, the brine concentration is relatively uniform across stages, with even distribution along the cavern walls and stable cavity expansion. At the fifth stage, the injection section exhibits the lowest concentration, increasing towards the outlet, as the concentrations approach saturation, indicating high construction efficiency.  
Figure 16: Contours of velocity magnitude at vertical section (X = 0 m) of each stage (The red arrow line represents the streamline of the brine).  
Figure 17: Contours of brine displacing concentration at vertical section (X = 0 m) during first stage (rainbow lines represent flow velocity lines).  
Figure 18: Contours of brine displacing concentration at vertical section (X = 0 m) during second stage (rainbow lines represent flow velocity lines).
## 7 IoT-Defined Application Protocols
### Constrained-Application Protocols (CoAP)  
It is a request/response protocol. The internet seeks are familiar with get-and-post-based HTTP (hyper-text transfer protocol) from the internet protocol (IP). CoAP was documented by the Internet Engineering Task Force (IETF) in 2014 [47] for constrained, i.e., low-power and small battery size, devices. CoAP enables edge devices and nodes to connect and share information with centralized systems using lower power. CoAP is a two-layered protocol that combines the characteristics of UDP with TCP. The first layer is the messaging layer that declares the connection's reliability as UDP or somewhat like UDP. The second layer is a request/response layer used for interaction between the communicating nodes. Note that CoAP can be used both within a single WSN-based IoT and outside the network when eternal nodes are essential for process control and/or data.  
CoAP packets/messages are of six distinct types, depending upon the mode of communication and information integrity. These message formats consist of nonconformable messages, conformable messages, acknowledgment messages, response messages, and reset messages. The messages are explained underneath.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 3 Scheme Framework  
### ABAC Basic Information  
ABAC model based totally on the subject, object, permission and environment [19] takes the subject attribute and object attribute as key factors to get entry to manage the decision and judge whether or not to supply the right of entry to the permission through the subject attribute set. The core concept of ABAC is attribute, which includes subject attribute, object attribute, permission attribute, and environment attribute. Subject attribute is the entity that can access and operate the protected resources, such as user, process, etc. Object attribute is the entity that can be accessed and operated, such as document, picture, and video data resources. The permission attribute refers to various operations on object resources, such as reading, writing, modifying, executing, etc. The environment attribute refers to the environment, time, context information, etc. in the process of the principal accessing resources. The basic concepts involved in the ABAC model are summarized in Table 1.
# On Analyzing Beamforming Implementation in O-RAN 5G
## 4 O-RAN Beamforming
### Open Research Issues
#### 4.2.1 Fronthaul Model Concept
The C-plane packet in this implementation carries more parameters, leading to a high signal cost due to the high number of bits, as shown in Table 2. On the other hand, in the channel state information beamforming method, the C-plane packet will carry fewer parameters and reduce the packet size by 40% by abandoning the BeamID parameter and generating precoding beam weights in O-RU (Category B) based on parameters transferred from O-DU such as UE identifier (ueld parameter--Section 5) and channel state information (cilsample and ciQsample parameters--Section 6). Additionally, the basic calculation formula can explain and compare the results of reducing the transferred parameters is to calculate channel capacity in the front-haul by using the celebrated Shannon formula  
\[C=\ B\ \log\left(1+\frac{P_{o}}{P_{N}}\right) \tag{1}\]  
where B is the channel bandwidth of the front-haul interface, \(P_{o}\) is the average signal power, and \(P_{N}\) is the average noise power. Reducing the transferred parameters leads to the use of fewer recourse blocks, which will raise the average output power of the signal. This concept is important in the channel state information beamforming method to achieve a lower packet error rate and high latency in order to support the complexity of generating the beamforming weight in O-RU, which required high performance of CSI data transferred from O-DU. The outcome results of this concept can prove that the channel state information beamforming method is considered a good choice in high channel traffic to improve the latency and channel capacity by using fewer recourse blocks and increasing the signal power compared to the other three beamforming methods mentioned above.
# Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network
## 3 Dataset Construction and Model Training
### Model Initialization and Training
The fine-tuning process generally uses the gradient descent method, the goal is to minimize the loss function and use the error back propagation (BP) to fine-tune the network. The fine-tune process is to compare the correct classification labels corresponding to different feature parameters (Table 1) in turn. If the expected output value is not obtained, the difference between the actual output and the expected output (error) is calculated, and the error is successively modified by modifying the weight of each neuron propagate to the input layer. * When iterations end, the training of the entire model is completed. After the training of the DBN model is completed, all training samples can be accurately classified under the correct label assigned, that is, the error between the predicted label and the real label converges to 0. Use loss \(L(\lambda)\) to evaluate the degree of convergence of the model: Loss tending to 0 means that the model  
Figure 4: DBN training flowchart. converges well. As shown in the loss curve of Figure 5, as the number of iterations increases, the loss tends to 0. This training uses 75% of the data to train the model, and its loss function and accuracy are shown in Figure 5. It can be seen from the figure that as the number of training increases, the model prediction accuracy gets higher and higher, and the error loss curve also declines, and finally remains at a stable value. At this time, an increase in the number of training may lead to overfitting. Finally, the 25% data in the sample set is used for testing, and the accuracy of model adaptation is obtained, which is 94.86% in combination. It shows that the recognition accuracy is already very high, close to the distribution of the sample data, and then the underwater acoustic transmission loss is calculated through the model matching and adaptive underwater acoustic propagation model.
## 3 Results and Discussion
### Impact of Camera Distance on the Defect Detection  
A concern about conducting the IRT in the field is the IR camera distance to the AST and its impact on the resolution and detection accuracy. In the field, it is not guaranteed that there will be enough space to evaluate the tank from the same distance that has been used in this study. Completing IRT using shorter distances is necessary when the ASTs are close to each other or when the tank is located next to a barrier. Figure 12 shows the thermograms captured from different distances at cycle #6 of the heating-cooling combination of 30-30 performed on the east side. The quantitative results are summarized in Table 7, which suggests that camera distances of 0.75 m to 1.3 m (2.5 ft to 4.3 ft) produce similar accuracies. A previous study found that a distance of 0.5 m to 1.5 m (1.6 ft to 4.9 ft) produced similar results [8], which agrees with the findings in this study. When the distance of the IR camera increases from 1.0 m to 2.0 m (3.3 ft to 6.6 ft), the smallest detectable AR and the maximum D/t both decrease by approximately 35%, while the inspected area increases by a factor of 3.8. When the distance of the IR camera increases from 1.0 m to 3.0 m (3.3 ft to 9.8 ft), the accuracy of the smallest detectable AR decreases by approximately 60% while the inspected area increases by a factor of 8.6. Therefore, the authors recommend using a camera distance of 0.5 m to 1.5 m (1.6 ft to 4.9 ft).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distance (m (ft))** & **Maximum D/t** & **Minimum AR** & **Area under Evaluation (m\({}^{2}\) (ft\({}^{2}\)))** \\ \hline
0.75 (2.5) & 0.73 & 0.80 & 0.15 (1.6) \\
1.0 (3.3) & 0.73 & 0.80 & 0.26 (2.8) \\
1.3 (4.3) & 0.73 & 0.80 & 0.44 (4.7) \\
2.0 (6.6) & 0.50 & 1.25 & 0.98 (10.6) \\
2.3 (7.5) & 0.50 & 1.25 & 1.66 (17.9) \\
3.0 (9.8) & 0.50 & 2.01 & 2.22 (23.9) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of defect detection at different distances.  
Figure 12: Thermograms captured during a single cycle with camera distances of (**a**) 0.75 m (2.5 ft), (**b**) 1.0 m (3.3 ft), (**c**) 1.3 m (4.3 ft), (**d**) 2.0 m (6.6 ft), (**e**) 2.30 m (7.5 ft), and (**f**) 3.00 m (9.8 ft).
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
### Connectivity Technologies
presented a survey on FSO systems classifying them based on several factors, including the environment, coverage, availability of Line of Sight (LoS), mobility, and link distance. [PERSON] et al. [70] provided another survey and discussed various FSO communication scenarios, including terrestrial, cooperative, multi-hop relaying, hybrid RF/FSO, satellite/aerial, and deep space, as well as discussing cross-layer design frameworks for link-layer retransmission protocols in FSO communication networks. The rest of this paper is organized as follows: In Section 2, we present the fundamentals of FSO signal transmission. In particular, we discuss geometrical loss, atmospheric attenuation, turbulence-induced fading, and pointing errors, which are critical for determining received signal levels. In Section 3, we present FSO-based backhauling for NTNs. In Section 4, we discuss open research problems and provide concluding remarks.
# Adsorption of Lead and Nickel on to Expanded Graphite Decorated with Manganese Oxide Nanoparticles
###### Abstract  
In this study, expanded graphite (EG) was decorated with manganese oxide nanoparticles (MONPs) by the hydrothermal method, and the newly formed composite (MONPs-EG) was applied as adsorbent for the removal of heavy metals from aqueous solutions. The comparative and competitive adsorption of Pb\({}^{2+}\) and Ni\({}^{2+}\) (0.01-1.00 mM) on MONPs-EG was investigated. Data from isothermal adsorption of single and binary systems suggested that both Pb\({}^{2+}\) and Ni\({}^{2+}\) were well described by the Langmuir isotherm, and the maximum adsorption capacities at 298 K were calculated at 0.278 and 0.113 mmol/g for Pb\({}^{2+}\) and Ni\({}^{2+}\), respectively. In binary systems, a dramatic decrease in adsorption capacity of Ni\({}^{2+}\) was observed, while the adsorption capacity of Pb\({}^{2+}\) was almost stable, indicating the favorable adsorption of Pb\({}^{2+}\) over Ni\({}^{2+}\) onto the prepared adsorbent. Kinetics studies of single and binary systems showed that a pseudo-second order model could explain the adsorption processes well. Thermodynamic analysis results demonstrated that the adsorption of these metal ions on the prepared adsorbent is spontaneous and exothermic in nature. The adsorption capacity of MONPs-EG increased significantly in the presence of humic acids. Overall, the results of this study suggest that MONPs-EG can be used effectively as an adsorbent for heavy metals removal from aqueous solutions.  
lead; nickel; adsorption; manganese oxide; expanded graphite; isotherms; kinetics; thermodynamics; partitioning coefficient +
Footnote †: journal: Applied Physics Letters  
Article
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 1 Introduction
These differentiated goals end up mutually influencing each other so that it is impossible to find a general optimization rule. As a result, new multi-objective optimization strategies have been recently introduced and developed [10; 11; 12]. The lack of a general optimization strategy and the pursuit of an adequate tradeoff between conflicting objectives can be seen as two major research goals also for the problem that is considered in this paper: security and energy efficiency have been largely treated separately so far; nonetheless, a joint optimization is usually better, in order to meet an equilibrium between opposite requirements. Often it is not taken into account how much more energy is consumed to provide secure services, and, similarly, the network is configured to provide maximum quality of service (QoS), not considering energy consumption or security. Resource allocation between heterogeneous networks is one of the main key drivers to provide QoS to users, but it can also be exploited to provide energy efficiency as well as security. The aim of this paper was to study resource allocation strategies to provide security and energy efficiency at the same time. In particular, we propose a new strategy of association between user terminals and small base stations (from femto to microcells) that provides the maximum secure throughput for the user and the minimum energy consumption suitably selecting which cells must be turned off.
## 3 Circuit Models
### CPWs Loaded with Pairs of Coupled SRRs
In this case, the transmission zeros are given by the two solutions of Expression (2), and the mutual coupling between resonators (\(M\)') enhances (or splits) the distance between the transmission zeros, _i.e._,  
Figure 11.3  
If the resonance frequencies of the isolated resonators (\(\omega_{\text{1}}\) and \(\omega_{\text{2}}\)) are distant enough (\(\left|\omega_{\text{1}}{}^{2}-\omega_{\text{2}}{}^{2}\right|>>0\)) and \(M\)' is small (this later condition is very reasonable in the considered structures), the two solutions of Expression (2) can be approximated by:  
Figure 11.4  
where we have considered that \(\omega_{\text{1}}>\omega_{\text{2}}\), and the positive dimensionless factor \(R=\left(\omega_{\text{1}}{}^{2}/\omega_{\text{2}}{}^{2}-1\right){}^{-1}\) has been introduced to simplify expressions (7). The validity of Expression (7) is subjected to large values of \(\omega_{\text{1}}{}^{2}-\omega_{\text{2}}{}^{2}\) (small \(R\)) and small values of \(M\)' (or \(k_{M}\)), avoiding negative expressions in the square root of \(\omega\)- in Expression (7). To validate the proposed model, the symmetric case has been first considered, where SRR and CPW dimensions are set to \(l_{1}=4.8\) mm, \(l_{2}=3.8\) mm, \(c=d=0.2\) mm, \(l=5.6\) mm, \(W=3\) mm, and \(G=1.01\) mm. The lossless electromagnetic simulations (inferred from _Agilent Momentum_) of the structure (\(S_{21}\)), considering different values of inter-resonator distance, \(d\)', are depicted in Figure 15 (the considered substrate is _Rogers RO3010_ with \(\varepsilon_{r}=11.2\) and \(h=1.27\) mm). The circuit parameters for the four considered cases (extracted from the method reported in [43]) are shown in Table 2 (analytical models for the SRRs are reported in [16,45,46], but these models are not accurate enough with the presence of the CPW). As \(d\)' increases, the mutual coupling \(M\)' decreases, and the resonance frequency increases. Note that the other circuit parameters do not significantly vary, and the agreement between circuit and electromagnetic simulations in the region of interest is very good, pointing out the validity of the model. The measured responses (shown in Figure 15) are also in good agreement (slight discrepancies are due to losses and fabrication related tolerances). Finally, we have considered an asymmetric structure with different SRR resonance frequencies. The geometry is as follows: \(l=5.6\) mm, \(W=3\) mm, \(G=1.01\) mm; upper SRR: \(l_{1}\)= 4.8 mm, \(l_{2}=4.6\) mm, \(c=d=0.2\) mm; lower SRRs: \(l_{1}=4.8\) mm, \(l_{2}=3.8\) mm, \(c=d=0.2\) mm. We have obtained the frequency response and the circuit parameters for four different values of \(d\)'. The pair of notches (at \(f_{\text{-}}=\omega_{\text{-}}/2\pi\) and \(f_{\text{+}}=\omega_{\text{+}}/2\pi\)) are depicted in Figure 16, and verify that their distance increases as \(d\)' decreases, and \(f_{\text{-}}<\)\(f_{2}\) and \(f_{\text{+}}>f_{\text{+}}\), in agreement with Expression (7).
## 3 Simulation Results and Discussion  
### Simulation Results  
The earthquake responses of different schemes of stone columns are analyzed. The ground acceleration of point 1, indicated in Figure 1c, and the EXPP at different depths at the counterpart place are observed, based on which the liquefaction mitigation effects of each scheme are evaluated. Taking the free field model FF-0 and the stone columns foundation model SC-1 to SC-3 for examples, the time histories of ground acceleration and EXPPR are displayed in Figures 3 and 4.  
For the free field model (FF-0), upon seismic loading, the EXPPR at depths of 2 m, 7 m, and 12 m climb up immediately and the peak values were reached in around 10 s, after which the pore water pressure was difficult to dissipate. The peak value of the EXPPR at each depth is approximate to 1.0, indicating that the sand has nearly fully liquefied. However, the peak value of the EXPPR at the depth of 2 m is slightly lower than that of deeper layers, which can be explained by the dense sand layer's stronger liquefaction resistance.  
A reduction in peak values of the EXPPR at the depths of 2 m, 7 m, and 12 m can be seen in all three stone column models (SC-1/2/3). In addition, the presence of stone columns accelerates the dissipation of the pore pressure that starts to drop right after the peak amplitude of the seismic excitation. There is a relatively minor difference in the EXPPRs at the three different depths for the same model. However, the reduction in EXPPR of the SC-1 is apparently smaller than that of the SC-2 and 3. In terms of ground acceleration response, for the free field model, the amplitude of the ground acceleration decreases rapidly in 10 s, representing a characteristic of liquefaction, the vibration isolation. With regard to the model SC-1/2/3, the \"vibration isolation\" effect is somewhat suppressed,  
Figure 3: Time histories of ground accelerations.  
but the PGAs go up to a certain degree. The SC-2 and 3 exhibit a similar trend with the free field model during the beginning of 8 s concerning the ground acceleration. A similar response of SC-1 with free field lasts slightly longer, for about 10 s. The earthquake response of models SC-4 to SC-9 is not analyzed in detail hereby. The PGAs at the same observation points of each model and also the peak value of the EXPPR at different depths are collected in Table 4.
# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 2 Materials and Methods
Two types of decision fusion are used:  
* Measurement-level fusion: All prediction vectors from coalition members are summed. The final class is the one with the highest aggregated score. * Abstract-level fusion: A simple majority vote is conducted among coalition members, based on their most confident class prediction. In addition to coalition-based methods, we also evaluate traditional ensemble fusion strategies--simple sum of prediction vectors for measurement-level or simple voting for abstract-level from all local models. By integrating these techniques, we explore both consensus-driven and diversity ensemble strategies for EEG signal classification under distributed constraints. This approach aligns well with clinical scenarios where data are fragmented across institutions or collection systems, and direct data sharing is restricted due to privacy, regulation, or logistical concerns.
## 3 System Model and Problem Formulation
### Problem Formulation  
Given an undirected graph _G_(_V,E_), source node set \(S\) and sink node \(t\), we assume _G'_ is a connected subgraph of \(G\) and energy consumption of _G'_ is given by:  
\[E\left(G\text{'}\right)=\sum_{e=E_{r}}\left(f\left(e\right)+t\left(e\right) \right)+\sum_{e=E_{e}}t\left(e\right) \tag{8}\]  
_E'\({}_{f}\)_ is an edge set where the end node of each edge is an aggregation point. _E'\({}_{n}\)_ is an edge set where the end node of each edge is a non-aggregation point.  
Our first objective is to find a near-optimal subgraph _G'_ that at least contains node set \(S\) and sink node \(t\) such that:  
\[G^{*}=\arg\min_{G\text{'}}E\left(G\text{'}\right) \tag{9}\]  
It has been shown that the aforementioned problem is NP-hard [8]. Without considering the imbalance of energy consumption, the constructed routing tree may result in premature death of some nodes, energy hole problems [30] and so on. On the basis of (9), we further consider the remaining nodal energy and dynamically adjust the routing strategy to balance nodal energy, effectively prolonging network lifetime, so our second goal is to find a feasible subgraph _G'_ considering nodal remaining energy such that:\[\begin{split}&\min E\left(G\,^{*}\right)\\ &\max l\left(G\,^{*}\right)\\ & s.t.\ E\left(G\,^{*}\right)\leq\varepsilon E\left(G\,^{*}\right) \end{split} \tag{10}\]  
Notice that \(\varepsilon\)(epsilon) denotes maximal permissible times of energy consumption of \(G\,^{*}\) to \(G\,^{*}\) and it is a variant that is set by us. Similar to the problem of finding an optimal subgraph \(G\,^{*}\), the above problem is also a NP-hard multi-objective optimization problem.