esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Explainable Analysis
#### 3.5.1 Global Interpretation  
Global interpretation analysis was performed on the Transformer-SE model in the straight channel (R\({}^{2}\) = 0.914) and the Transformer-LA model in the hexagonal channel (R\({}^{2}\) = 0.999). SHAP statistics plots and heatmaps were generated for all features, as presented in Figure 10. The analysis of the two channel shapes--namely, the straight channel (Figure 10a) and the hexagonal channel (Figure 10b)--revealed substantial differences in feature importance and their contributions to the model output.  
Firstly, from the SHAP statistics plot (left), it can be observed that for the straight channel, \"Dh\" contributes most significantly to the model output. Other features, such as \"RE\", \"Qw\", \"Tinv\", \"PR\", \"D\", and \"pin\", also exhibit notable contributions but to a lesser degree compared with \"Dh\". In contrast, for the hexagonal channel, \"Tinv\" emerges as the most important feature, followed by \"Qw\", \"PR\", and \"pin\". The importance of features like \"D\", \"Dh\", and \"Td\" is relatively low.  
From the SHAP heatmap (right), it can be observed that the model output (f(x)) of the heat transfer coefficient for the straight channel steadily increases as the number of instances grows, indicating a strong correlation with the feature values. In contrast, the model output (f(x)) of the heat transfer coefficient for the hexagonal channel exhibits a more fluctuating pattern, suggesting a more complex relationship between the features and the output.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline CNN & 0.996270 & 55.751 & 35.456 \\ +SA & 0.996367 & 68.737 & 40.859 \\ +SE & 0.996446 & 62.744 & 38.157 \\ +LA & 0.997125 & 60.147 & 36.806 \\ LSTM & 0.998251 & 46.512 & 26.816 \\ +SA & 0.987895 & 146.027 & 97.554 \\ +SE & 0.983603 & 161.526 & 98.774 \\ +LA & 0.997325 & 58.975 & 36.614 \\ Transformer & 0.992415 & 97.613 & 39.735 \\ +SA & 0.989617 & 131.531 & 68.774 \\ +SE & 0.998861 & 56.820 & 35.138 \\ +LA & 0.999257 & 39.561 & 26.005 \\ \hline \hline \end{tabular} The Transformer–LA with the best performance has 2 num_layers, 64 hidden_dim, 0.1 dropout_rate, 0.001 learning_rate, and 8 batch_size.  
\end{table}
Table 6: Prediction results of the heat transfer coefficient of the hexagonal flow channel based on deep learning with attention mechanism.
## 4 Open Challenges and Future Research Directions
### Interoperability and Trust Management  
IoE systems consist of highly heterogeneous environments where numerous devices from different manufacturers operate together. These systems involve various protocols, standards, and security policies, which raise significant compatibility and trust challenges. In particular, there is a pressing need to develop cross-domain security models that ensure seamless and secure operation across multiple ecosystems [47; 88; 120; 127].  
Open Research Areas* Development of secure and standardized data sharing protocols between heterogeneous IoE platforms [93; 120].
* Integration of blockchain-based provenance systems with federated identity management solutions [84; 93; 130].
* Implementation of behavior-based dynamic trust scoring mechanisms for cross-domain trust management [93; 127].
* Design of lightweight and context-aware access control mechanisms to enhance interoperability [171; 172; 173; 174].  
_Research Direction:_ The integration of blockchain-based provenance systems with federated identity management and context-aware trust scoring mechanisms could significantly improve inter-platform trust management. In particular, cross-chain data sharing scenarios can benefit from the combination of identity federation and behavior-driven access policies, enabling the establishment of a robust, interoperable trust chain across diverse IoE systems [171; 172; 173; 174].
## 1 Introduction
Some of CH's prominent features include in situ gelling, mucoadhesion, hydrophilicity, and permeability enhancement; this enables its utilization in many drug delivery systems [12].The simplicity of structural changes is one of the significant advantages of CH. This allows optimization to provide appropriate biomaterials for various therapeutic applications [13; 14]. That said, CH's effectiveness is debated as to whether it acts as a bactericidal or a bacteriostatic agent [15; 16]. Meropenem (MEM) is a synthetic \(\beta\)-methylcarbapenem antibiotic that is categorized under the carbapenem subclass, which belongs to the \(\beta\)-lactam antimicrobial class. As an antimicrobial agent, it possesses an extensive spectrum of activity, with the broadest coverage among all the \(\beta\)-lactams [17]. The use of polymeric carriers impregnated with antimicrobial agents on titanium plates is an innovative approach to prevent infections, thereby reducing the need for additional surgeries and minimizing the risk posed to patients. It is used as an antibiotic of choice in such applications, in contrast with cephalothin, carbenicillin, amoxicillin, cefamandole, tobramycin, and vancomycin, which are more often used in coatings on bone implants [18; 19; 20; 21; 22]. As far as we know, no previous studies to date have been reported regarding PLGA and/or CH augmented with MEM as a coating for titanium plates to combat bacterial adhesion and colonization that we could use as a comparison; however, some studies were performed in the fields of dental implantology and orthopedics [23; 24; 25]. Thus, this study is designed to verify and investigate its efficacy. Such modified titanium plates are intended to replace their current counterparts as a prophylactic against infection in routine maxillofacial procedures and especially in reconstruction cases in which the patient may have multiple comorbidities. Ultra-high-performance liquid chromatography (UPLC) is a well-known method for separating and quantifying chemicals. It is also a simple, quick, and sensitive analytical approach that can distinguish both organic and water-soluble substances [26]. The present baseline study was carried out to quantify the drug release profiles of sterile and non-sterile coated titanium plates using UPLC to evaluate the possibility of delayed drug release within the copolymer layer over time.
## 2 Materials and Methods
### Determination of Site Suitability Criteria and Constraints  
All the candidate sites should be located in government-controlled areas (parcels). Since CyCLOPS is a strategic infrastructure, and the objective is to provide nationwide coverage, CUT cooperated with the Department of Lands and Surveys (DLS) to determine candidate state parcels within the Republic of Cyprus. The selection was refined and finalized after consulting with the Geological Survey Department (GSD), and taking into account crucial information on Cyprus's geological, geodynamic, and geotechnical regime. The acquisition of official site permits, including the required works for equipment safety (i.e., fencing), was mandatory for installing the permanent segment. The choice of state land was obvious, since the infrastructure has a strategic nature (nationwide coverage), it has to be permanent, and has to provide continuous monitoring. The choice of state land minimises the risk of having to uninstall or move the infrastructure due to ownership changes, through long-term lease of the government parcels.  
Evidently, the site selection process must fulfil all the requirements and criteria to maximise both GNSS and CRs performance. A review of the most critical performance factors per sensor follows.
# Blockchain-Based Quality Assurance System for Academic Programs
## 1 Introduction
The proposed approach represents the first attempt to develop a blockchain-based system to secure and manage quality assurance processes. Moreover, this research investigates three well-known cryptographic schemes, which are the RSA digital signature algorithm (DSA) with AES encryption, the Elliptic Curve DSA with AES encryption, and the Elliptic Curve Integrated Encryption Scheme (ECIES), to provide additional security services at the application layer. The cryptographic schemes are implemented and compared in terms of time consumption to find the best one to be integrated into the QAS. The current research employs the interplanetary file system (IPFS) to address the storage space limitations of blockchain. The IPFS is a distributed file sharing and storage protocol that aims to provide safe and faster storage environments on the web. The IPFS uses cryptographic hash functions to produce a unique hash code for each file and its related blocks. The content-based hash can be used to search for files that might be distributed on several nodes in the network. In comparison with the traditional distributed storage techniques that require storing multiple copies of the same file on different servers in the network, IPFS can significantly reduce the redundancy of data stored across the network. This represents a considerable saving in resources. Furthermore, the IPFS can enhance the performance of data retrieval, because it uses a distributed hash table (DHT) to locate the nearest copy of the file so that it can be efficiently retrieved. Also, the IPFS can avoid the risk of a single point of failure and hence preserve the availability of data [20]. Additionally, by integrating the IPFS with a blockchain, such as Ethereum, the scalability issues of blockchain can be alleviated, since only essential transaction metadata are stored on the blockchain, while large files are stored off-chain. The use of the IPFS can save blockchain space and enhance the overall network performance [21]. The following sections present relevant research works, the proposed model, experimental results and discussions, and finally, the conclusion and future research directions.
## 4 ML Applications for RIS-Assisted Communications
### Federated Learning Applications  
As depicted in Figure 11, FL is an approach where each UE device has its own model, whose parameters are sent from the RIS to the BS. A global ML model updates all parameters using a global aggregation method. In [98], the authors propose an AirCom-based FL model [99], which jointly optimizes the device selection, the aggregation beamformer at the BS, and the phase shifts at the RIS to maximize the number of devices that participate in the model aggregation under MSE requirements. The proposal consists of a two-step framework. In the first step, sparsity is considered for device selection, and then the maximum feasible device is found by solving a group of MSE problems of minimization. Then, an alternating optimization framework is used to design the aggregation of beamformers efficiently. Authors in [100] propose an offline enhancement of the RIS FL scheme with AirComp. The FL procedure is decomposed utilizing the look-ahead information and a Lyapunov framework. A BCD algorithm is used for phase-shift tuning and decoupled at the transceiver [100]. Then, a low-complexity algorithm based on an element-wise successive refinement is applied to make the practical implementation of an RIS with discrete shift constraint possible. The research in [101] studies the computation and communication of the resource allocation of an FL-RIS-assisted communication system to minimize the training latency. The authors propose a block coordinate descend technique (BCD), an alternative optimization to block the variables. Then, user selection and transmit power allocation are optimized via the maximization-minimization (MM) algorithm. Finally, semidefinite relaxation (SDR) and Gaussian minimization are used to obtain the RIS phase shift [101].  
Resource allocation has also been studied for FL Internet-of-things (IoT) applications [102]. In this framework, IoT devices collaborate by using an FL scheme to train an ML model, enabling the privacy of each device. However, the energy constraint can limit the interaction with the central server and IoT devices. To address this issue, the authors in [102] propose an iterative resource allocation algorithm to reduce the total energy consumption by jointly performing local training and model uploading. FL applications have also been used on RIS-assisted UAV communications. The framework reported in [103] proposes an FL network via over-the-air communications to achieve high-quality and ubiquitous network coverage under privacy and low-latency requirements [103]. The goal is to minimize the worst-case MSE by jointly optimizing the RIS phase shifts and the noise factor for noise suppression using the power transmitted by the UAV and the trajectory [103].
## 4 Results and Discussion
### Settlement Analysis  
Based on the settlement model built in the early stage, a 300 m buffer zone is built in ArcGIS for the Shenzhen University section of Metro Line 9. The purpose of establishing the buffer zone is to identify the engineering facilities that may be affected in the operation stage of metro, and to cut PS points with the buffer zone. From Figure 8, it is not difficult to find many engineering facilities with settlement risk in the study area. The PS point is divided into dangerous, abnormal, and safe points according to their settlement rate. The PS point with a settlement rate of more than \(-20.00\) mm/y is called a dangerous point, the PS point with a settlement rate of more than \(-8.00\) mm/y and less than \(-19.99\) mm/y is called an abnormal point, and the PS point with settlement rate less than \(-7.99\) mm/y is called a safe point.  
Figure 7: Map of Shenzhen.  
Through ArcGIS and InSAR technology, the settlement points processed by Sarcape are imported into ArcGIS software (v.10.8.1), and the pre-arranged vector box is used to cut and screen the settlement data. As shown in Table 5, a total of 11 dangerous and abnormal facilities are selected: Zhigong building, Zhiteng building, Zhixin building, Zhiyi building, Baishi Road, Zhaonan bridge, science and technology building, Binhai community, Qiaoyuan dormitory, Zhizhen building and southern Sports Square. According to the screening results, the point of maximum settlement rate is located in Baishi Road, which is \(-\)35.68 mm/y, and the engineering facility is located directly above Metro Line 9. At the same time, seven dangerous engineering facilities, two abnormal engineering facilities and two safety engineering facilities are selected, as shown in Table 5. In this case, 44 PS points were selected, and the maximum settlement rate was \(-\)22.55 mm/y.  
Figure 8: Settlement point of engineering facilities along Shenzhen section of Metro Line 9. The yellow numbers 1–11 in the figure are the 11 hazardous and unusual facilities. PS points with a settlement rate exceeding \(-\)20.00000 mm/y are called dangerous points, PS points with a settlement rate exceeding \(-\)8.000000 mm/y and less than \(-\)19.999999 mm/y are called abnormal points, and PS points with a settlement rate less than \(-\)7.999999 mm/y are called safe points.
# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 3 Variants of the HSA
### Multi-Objective Versions of the HSA  
[PERSON] et al. in [58] introduced a new approach for solving the security and industrial problems of microgrids (MGs) by creating several self-adequate self-governing sub-MGs via a clustering method. Hence, a multi-objective optimization algorithm was produced, where power waste minimization, energy profile development, and reliability improvement are recognized as objective functions. A hybrid algorithm based on HSA and GA, named HS-GA, is presented to solve the mentioned optimization problems. Furthermore, a load waft manner was provided to different model representations of MGs. The overall performance of the proposed HS-GA approach was assessed using several case studies. The obtained results confirmed the superior performance of the proposed HS-GA method.  
[PERSON] et al. in [66] proposed multiple solutions based on using HSA. An effectiveness comparison of the proposed methods was made in solving the clustering protocols of WSNs. Pareto optimality theory is incorporated for the multi-objective clustering problem in WSNs. Multiple objectives-based strategies with customized stopping measures in the proposed method give optimal solutions with decreased computational time demands and enhanced network performance in regard to power mode and data transfer from the sensor joints to the base location.  
[PERSON] and [PERSON] in [68] proposed a multi-objective tournament HSA coverage-aware load-balanced clustering algorithm (MH-CACA) to solve clustering problems in WSNs. To confirm the performance of the proposed MH-CACA, a comprehensive network was analyzed, and the obtained results confirmed that the proposed method has better results in terms of coverage charge, lifeless gateways, lifeless sensors, power consumption, and network lifetime compared to other similar methods.
## 3 Results
### Mode 1: Charging  
#### 3.1.1 Temperature Profiles during the Charging of the PCM  
In Mode 1, the influence of compressor power was evaluated using different compressor power as shown in Table 4. Figure 7 shows the PCM and refrigerant temperature evolution along time for compressor power at 30%, 45%, and 60% respectively. It was observed that the PCM at the top of the RPW-HEX was not fully charged (i.e., it was not completely solidified), while the middle and the bottom levels were completely charged resulting to temperature around \(-10\ ^{\circ}\mathrm{C}\). In addition, it was observed that the time for charging process to be completed (i.e., the condensing unit to switch off) was 17 min, 7.5 min, and 6.5 min for compressor power at 30%, 45%, and 60% respectively.  
As shown in Figure 7, the PCM located at the middle and bottom of the RPW-HEX start to solidify after around 1 min from the beginning of the experiments, while the PCM located at the top requires much more time to start the solidifying process. This means that the charging of the PCM is not uniform, and if the charging duration is not long enough, there are zones of the RPW-HEX where the PCM is not completely solidified. Although increasing the compressor power accelerates the solidification at the top region, it is not able to completely solidify since the charging process stops earlier because the evaporation temperature reaches the set-point.  
Another possibility was investigated to find out if it was possible to reach a complete PCM solidification in the entire RPW-HEX. To do this, the evaporation temperature was decreased down to the security set-point given by the freezing point of the HTF, i.e., -18 \({}^{\circ}\)C. As shown in Figure 8, by decreasing the compressor set-point, and for a low compression power (15%), the PCM was completely solidified after 50 min. Furthermore, the temperature distribution inside the PCM became more uniform over time, and a maximum temperature difference below 2 \({}^{\circ}\)C was achieved after 300 min, when the compressor switched off because it reached the security set-point. However, despite that fact that the RPW-HEX was completely charged and a uniform temperature distribution inside the PCM was achieved, this strategy has some major drawbacks, such as a very long charging process duration, and a decrease of the energy efficiency of the condensing unit.
## 2 Battery Modeling
### Parameter Identification of Lithium Battery  
The genetic algorithm (GA) is a common method for lithium battery parameter identification. It identifies the relevant parameters by finding the global optimal solution. However, ordinary GA can easily fall into the local optimal solution, which leads to large errors and even fails to identify the parameters. The adaptive genetic algorithm (AGA) has better global search ability and faster convergence speed, using adaptive crossover and mutation probability. Therefore, we used AGA to identify the parameters of the fractional order battery model.  
First, the AGA randomly generates individuals of the initial population. Each individual represents a solution to a problem, and the elements that make up the individual are called genes. In this paper, each individual of AGA is composed of seven genes, which are the values of \(R_{0}\), \(R_{1}\), \(C_{1}\), \(R_{2}\), \(C_{2}\), \(\alpha\) and \(\beta\). The fractional order battery model is used to calculate the terminal voltage of the battery, aiming to minimize the error between the terminal voltage estimated by the model and the actual terminal voltage. The algorithm flow of AGA is shown in Figure 3.  
Figure 2: \(U_{\text{OC}}\)–SOC fitting curve.  
AGA was applied to carry out the parameter identification for the FOM and IOM under DST at 25 \({}^{\circ}\)C. The obtained parameter values are shown in Table 1.  
Table 2 shows the mean absolute error (MAE) and root mean square error (RMSE) of the voltage prediction under DST for the FOM and IOM.  
From Figure 4 and Table 2, we can conclude that both IOM-AGA and FOM-AGA can adequately identify parameters through DST. However, the voltage of FOM is closer to the real DST than that of IOM, and its voltage MAE is only 9.8 mV. This is also the main reason for the higher accuracy of battery SOC estimation using the fractional order circuit model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**AGA** & \(R_{9}\)**/\(\Omega\)** & \(R_{1}\)**/\(\Omega\)** & \(R_{2}\)**/\(\Omega\)** & \(C_{1}\)**/F** & \(C_{2}\)**/F** & \(\alpha\) & \(\beta\) \\ \hline IOM & 0.0824 & 0.0121 & 0.0343 & 27,074 & 58,571 & / & / \\ FOM & 0.0763 & 0.2732 & 0.1066 & 42,521 & 120,952 & 0.9390 & 0.8158 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter values for FOM and IOM.  
Figure 4: Predicted voltages and errors under DST: (**a**) predicted voltages; (**b**) voltage error.  
Figure 3: AGA algorithm flow.
## 1 Introduction
In this study, the modular connections with blind bolts are suggested and their structural performance is investigated. In addition, the new modular system is designed the strength of its beam members is considerably lower than that of its column members to implement the strong column-weak beam concept. The results of the experimental work show that the proposed modular system can retain a sufficient level of ductility under extreme loading conditions. Finally, a simplified analytical model for the modular beam-column connection is proposed, and its effectiveness is validated by performing its push-over analysis and comparing its results with the test results. The outline of this paper is as follows. In Section 2, the composition of the new modular building unit is described. The main features of the two types of blind bolts are explained, and the modular beam-column connection using these blind bolts is proposed. Section 3 provides the details of the experimental program to investigate the structural performance of the proposed connection, including the test specimens, testing equipment and test procedure. In Section 4, the general behavior of the test specimens is described and their failure mechanisms are analyzed in detail to demonstrate the effectiveness of the proposed modular system. The simple analytical model for the modular connection is suggested and validated against the test results in Section 5. Finally, the summary and concluding remarks are provided in Section 6.