esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Results of the Proposed Four Hybrid Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Solutions**} & \multicolumn{4}{c}{**Fitness (RMSE)**} \\ \cline{2-5}  & **BSA-RF** & **MVO-RF** & **GEO-RF** & **PRO-RF** \\ \hline
30 & 0.1941 & 0.1893 & 0.1987 & 0.1901 \\
60 & 0.1868 & 0.1977 & 0.1974 & 0.1935 \\
90 & 0.1947 & 0.1870 & 0.1934 & 0.1861 \\
120 & 0.1940 & 0.1942 & 0.1925 & 0.1875 \\
150 & 0.1927 & 0.1917 & 0.1940 & 0.1928 \\ \hline \hline \multicolumn{5}{c}{Optimal hyperparameter combination} \\ \hline Nf & 19 & 21 & 20 & 17 \\ MaxDepth & 2 & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Development results of all hybrid RF models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**Performance Indices and Ranking Scores**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{5-10}  & **R\({}^{2}\)** & **Score** & **RMSE** & **Score** & **MAE** & **Score** & **WI** & **Score** \\ \hline BSA-RF & 0.9359 & 2 & 11.3203 & 2 & 7.8165 & 2 & 0.9824 & 2 & 8 \\ MVO-RF & 0.9407 & 3 & 10.8867 & 3 & 7.7471 & 3 & 0.9837 & 3 & 12 \\ GEO-RF & 0.9317 & 1 & 11.6807 & 1 & 8.0452 & 1 & 0.9809 & 1 & 4 \\ PRO-RF & 0.9423 & 4 & 10.7420 & 4 & 7.6514 & 4 & 0.9843 & 4 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance and ranking results of four hybrid RF models in the training phase. Figure 8 illustrates the regression results of the four hybrid RF models in the testing phase. As it can be seen in Figure 8a-c, large errors between the predicted EM values by the BSA-RF, MVO-RF and GEO-RF models and the measured EM values indicate that the data point move away from the diagonal, especially when the EM values are in the range of [50, 80] and [0, 20]. The PRO-RF model has obtained the most EM data points close to the diagonal based on the best predictive performance in the testing phase. Therefore, the PRO-RF model is the best hybrid RF model for predicting the rock EM in this paper.
## 4 Simulation Experiment
### Experiment Results
The average reconstruction error produced by our method is \(-5.7584\) dB, which is \(10.0992\) dB smaller than that of the ACC method, but larger than that produced by the PM method in the bright zone and the EDPM method in the bright zone. Figure 10 shows a comparison of the array effort of four methods with respect to the change in frequency. The array effort produced by the ACC method is about 0 dB at 10 frequencies, which is the largest of all methods. The array efforts produced by the PM method in the bright zone are smaller than the ACC method at most frequencies, but higher than the ACC method at 100 and 200 Hz. The array efforts at all frequencies produced by the EDPM method in the bright zone are smaller than those produced by the ACC method and the PM method in the bright zone. Our method produces the lowest array efforts at all frequencies of all methods, and its value ranges from \(-11.8789\) dB to \(-8.0994\) dB. Our method controls array effort in the process of sound field reconstruction, while the ACC and PM methods in the bright zone and the EDPM method in the bright zone do not \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Average Reconstruction Error (dB)** \\ \hline ACC & \(4.3408\) \\ PM method in the bright zone & \(-17.8140\) \\ EDPM method in the bright zone & \(-9.0047\) \\ Ours & \(-5.7584\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average reconstruction error comparison of different methods with respect to frequency. Figure 9: Reconstruction error comparison diagram of four methods with respect to the change in frequency. consider the optimization of array effort in the process of sound field reconstruction, so the array efforts generated by them are larger than those generated by our method. The average array efforts generated by four methods with respect to frequency are shown in Table 4. The average array effort results generated by different methods are consistent with Figure 10. The average array effort produced by the ACC method is 0 dB, which is the largest of all methods. The average array effort produced by the other three methods is smaller than the average array effort produced by the ACC method. The average array effort produced by our method is \(-9.4790\) dB and the smallest among all methods, which is \(9.4790\) dB smaller than that of the ACC method, about \(8.0712\) dB smaller than that of the PD method in the bright zone, and about \(4.8176\) dB smaller than that of the EDPM method in the bright zone.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems
### Gpu
#### 3.1.8 Dynamic State Estimation, Power Quality, and Dynamic Models  
After OPF applications, GPU usage in dynamic state estimations [191; 192; 193; 194; 195; 196; 197; 198; 199], power quality [202; 203; 204; 205; 206; 207; 208; 209], and dynamic models [210; 211; 212; 213; 214; 215] appear. Related to the dynamic state estimation of power systems, a lateral two-level dynamic state estimator based on the extended Kalman Filter method is implemented in a CPU-GPU platform [194]. The parameter identification, state prediction, and state filtering of the extended Kalman Filter are parallelized in the GPU as well as the computation of the Jacobian matrix and the solution of the system of linear equations with the state variables to estimate.  
Regarding power quality, [208] presented an optimized method for recognizing power quality disturbances based on the modified S transform and parallel stacked sparse auto-encoder. Finally, parameter identification of the dynamic models of electrical components is accelerated using GPU. The authors in ref. [211] presented an accelerated parameter identification of permanent magnet synchronous machines using a PSO algorithm parallelized in a GPU.
## 4 HVAC Control
### Design of the MPC
On the other hand, constraint (19) represents the white noise disturbance considered for the outside temperature of the enclosures being \(\delta\mathrm{T}\) the amplitude of the uncertainty for the temperature sensor. Therefore, we define the objective function \(J\) to be optimized in (11) as in Equation (20). The objective function has been constructed following the usual and recommended practice of normalizing the terms of the objective function, in order to avoid the terms whose values belong to a higher order of magnitude not presenting more weight than the remaining terms. \[J=\sum_{k=t}^{t+N_{p}}\sum_{i=1}^{N_{k}}\left(K^{i}_{T}\left\|1-\frac{T^{i}_{ k|t}}{T^{i}_{ref}}\right\|+K^{i}_{\Delta T}\left\|\frac{\Delta T^{i}_{k|t}}{T^{i}_{ ref}}\right\|+K^{i}_{\dot{Q}}\left\|\frac{\dot{Q}^{i}_{k|t}}{\dot{Q}^{i}_{ ref}}\right\|\right) \tag{20}\]  
The cost function (20) has several dimensionless coefficients: \(K^{i}_{T}\geq 0\) represents the weight penalizing the output deviation from the maximum temperature of reference \(T^{i}_{ref}\), \(K^{i}_{\Delta T}\) represents the weight penalizing the positive slack variable \(\Delta T^{i}_{t}\), and \(K^{i}_{\dot{Q}}\) is a tuning parameter used to smoothen the obtained optimal solution. The developed HVAC controller is used for two functions. Firstly, as a further component of the planner, it is used for the prediction of the consumption due to the air conditioning of the building. In this case, the MPC is focused on a nominal control design, i.e., we assume perfect model knowledge at the design stage. In this case, the MPC is built based on Equations (11)-(17) and (20). However, secondly, MPC is also used, once the planning has been completed, as a real-time control system, where the HVAC behavior is regulated. In this case, it is considered a robust controller where uncertainties in the estimation of certain parameters and disturbances in the inputs to the system are then considered, being built based on Equations (11)-(20). Once the optimization problem has been solved, for each room, the resulting optimal states and inputs are denoted as following:  
\[{T_{t}^{i}}^{*}=\left( \begin{array}{cc}{T_{t|t}^{i}}&{T_{t+1|t}^{i}}&\cdots&{T_ {t+N_{p}|t}^{i*}}\\ {\dot{Q}_{t}^{i}}^{*}=\left(\begin{array}{cc}{\dot{Q}_{t|t}^{i}}&{\dot{Q}_{t +1|t}^{i}}&\ldots&{\dot{Q}_{t+N_{p}-1|t}^{i}}\\ \end{array}\right)^{T}\end{array} \right)^{T} \tag{21}\]  
For closing the loop, the first input is applied to the system (10) during the time interval \([t,t+1)\):  
\[\dot{Q}_{t}^{i}=\dot{Q}_{t|t}^{i^{*}} \tag{22}\]  
At the next time step, \(t+1\), a new optimal problem in the form of (11)-(20), based on the new measurement of the state, is solved over a shifted horizon, yielding a moving receding horizon control strategy with control law.
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.4 Semantic Communication in 6G Networks  
Semantic communication, a novel approach, involves extracting information from a source and encoding it for communication through crowded channels. Unlike traditional error-prone bit-level transmissions [194], it prioritizes building a pervasive and comprehensible semantic knowledge base among humans and machines. This method aims to revolutionize classical communication systems, liberating networks from rigid data protocols. Semantic communication is expected to greatly improve efficiency, reliability, and the seamless intelligent connection of diverse elements [195].  
[PERSON] proposed classic information theory in 1948 [196], and [PERSON] emphasized the semantic problem, sparking research on how transmitted symbols convey precise meanings [197]. Semantic information theory, introduced in _The British Journal for the Philosophy of Science_ and refined in [198; 199; 200], focuses on understanding content and logical deduction ability [201]. [PERSON]. [202] demonstrated the uniqueness of semantic information representation. [PERSON]. [195] identified limitations in classic point-to-point semantic communication and proposed a federated edge intelligence-based, resource-efficient architecture. Another contribution suggested a semantic representation framework for an intelligent communication network, aiming for lower bandwidth, decreased redundancy, and improved intent detection [203].  
In recent applications, semantic communication has addressed block communication system bottlenecks in end-to-end communication systems. Studies involving image and text transmission [204; 205; 206; 207] and speech signal processing for automatic speech recognition [208] showcase its versatility.  
Despite rapid development, semantic communication faces challenges. Firstly, the precision of semantic building blocks is crucial for practical reliability. Secondly, designing effective error-tolerant mechanisms remains a challenge. Additionally, a straightforward and adaptable method for quickly detecting semantic information in resource-limited devices is needed. Challenges include difficulties in sharing semantic information models between entities and new security requirements [209]. Establishing censorship mechanisms to prevent malicious tampering and reliable storage for user privacy protection is crucial [195].
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 4 Performance Analysis
### Convergence Speed Test  
We evaluate the convergence time to achieve fairness, which refers to the duration required for a newly added flow to attain a relatively equitable throughput compared to other flows when link utilization approaches 100%. Figure 7a illustrates the network topology employed in the convergence speed test, while Table 3 presents the corresponding network configurations. The test procedure is as follows:  
1. S1's congestion control (CC) algorithm is set as the BRF protocol. At time zero, S1 establishes a connection with C1 and initiates data transmission. Subsequently, a new BRF flow is introduced every two seconds until no additional flows are added at eight seconds. The total duration of this experiment spans 60 s.
2. S1's CC algorithm is then switched to the BBR protocol, and step 1 is repeated.
3. Finally, S1's CC algorithm is changed to the CUBIC protocol, and step 1 is repeated accordingly.  
The outcomes of the convergence speed test are depicted in Figure 11, demonstrating that both BRF and CUBIC exhibit faster convergence speeds compared to BBR.  
The BRF protocol is built on the basis of an in-network control mode, and BRF has global information, so it can quickly allocate the appropriate bandwidth to the newly added  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Buffer = 1000 Packet** & **Buffer = 100,000 Packet** \\ \hline BRF & \(\approx\)0\% & \(\approx\)0\% \\ \hline CUBIC & \(\approx\)0\% & \(\approx\)0\% \\ \hline BBR & \(\approx\)1.1\% & \(\approx\)0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of packet loss rate.  
Figure 11: Convergence speed test results.  
flow. When a new flow joins, its initial bandwidth can be determined after 1 RTT, and the appropriate bandwidth of the flow can be determined after another measurement, so its convergence speed is about 2 xRTT.  
The window growth function of the CUBIC protocol is described in Equation (1) in Section 2.1.1, and the publication of its window growth time is in Equation (2). Since the window growth time of the CUBIC protocol is the third square root of the maximum window, it has a faster convergence rate.  
The BBR protocol spends most of its time in a probe bandwidth state to estimate bandwidth. The formula of the estimated bandwidth is Probe_BW = pacing_gain\"pacing_rate, and the pacing_gain values of the BBR protocol are usually 1.25 (increasing bandwidth), 0.75 (decreasing queuing), and 1 (running smoothly). Due to its slow bandwidth growth, it usually takes multiple RTTS to converge.
## 3 Full-Scale Measurement in Real Urban Streets for Model Validation  
### Site Selection for Full-Scale Measurement  
To examine the accuracy of the IMCRI in an actual urban environment, full-scale measurements were carried out on five different streets (Figure 4a-e) in Tianjin, China. All five streets were straight streets with parallel building facades. The height of the building facades and the width of the streets were constant along the streets. Therefore, the geometry of the streets can be simplified as rectangles. Because the IMCRI mainly focused on the diffuse modeling scheme, the measured streets were selected so that the morphologies of the building facades in the five streets were quite different. As shown in Figure 4, various building facade conditions were found, which included very smooth cases (glass curtain walls on Street 1 and Street 4-FB), relatively rough cases (decorative structures on Street 3-FB), and very fluctuated cases (windows and balconies on Street 5 and rectangular structures on Street 3-FA). To control the sound absorption by building facades, the materials of building facades in all streets were similar and reflective and mainly made of glass and concrete. Moreover, there were no trees or wooden surfaces, which may lead to significant sound absorption.  
All five streets were pedestrian shopping streets, and the measurements were carried out late at night (12:00-3:00 a.m.). The experimental zone was defined by the central 50 m of each street without a junction, while the length of each street was greater than 100 m. Therefore, the measurements were carried out when there were no pedestrians or vehicles nearby to avoid the direct influences of traffic noise.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Multi-Chaotic Public Key Cryptographic Algorithm Based on Chebyshev Polynomials  
In [27], [PERSON] proposes a specific design case, where the key point of the algorithm lies in both \(f_{1}(x)\) and \(f_{2}(x)\) being linear transformations and satisfying \(f_{1}\circ f_{2}=f_{2}\circ f_{1}\). In fact, \(f_{1}(x)\) and \(f_{2}(x)\) do not need to be strictly linear transformations, so the commutative property becomes particularly important. The semi-group property of Chebyshev polynomials happens to satisfy this property.  
To avoid attacks based on the 2-norm as presented by [PERSON] et al. [53], [PERSON] et al. propose a multi-chaos public key algorithm based on Chebyshev polynomials (CMPKC). The key exchange step of this algorithm is like [PERSON]'s multi-chaos public key algorithm, as shown in Figure 7 for \(\widetilde{m}=2\).  
It is easy to see that the basic ideas of these two algorithms are consistent, with the difference being that the former involves [PERSON] and [PERSON] publicly negotiating \(\widetilde{m}\) linear functions \(f_{1}(x),f_{2}(x),\cdots,f_{\widetilde{m}}(x)\) that satisfy the commutative property, while the latter involves [PERSON] and [PERSON] publicly negotiating \(\widetilde{m}\) choice functions \(f_{1}(x),f_{2}(x),\cdots,f_{\widetilde{m}}(x)\) as Chebyshev polynomials with mutually prime degrees. Since Chebyshev polynomials have the semi-group property, they still satisfy the requirements of a public key encryption scheme. At this point, \(x_{i}=h(x_{i-1},\ k_{i})\).  
Figure 6: The proposed key exchange protocol using CCS [27].  
\[h_{\widetilde{m}}(x,\;k)=\begin{cases}f_{1}(x)(modN)&if\;k=1,\\ f_{2}(x)(modN)&if\;k=2,\\ \vdots\\ f_{\widetilde{m}}(x)(modN)&if\;k=\widetilde{m}.\end{cases} \tag{14}\]
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 5 Sensing Technologies
### Catalytic  
Catalytic gas sensors are used to detect combustible gases in environments with concentrations of at least 15% of O\({}_{2}\). These sensors perform the detection by measuring the variation on the internal resistance, which occurs when combustible gases are present in the ambient atmosphere. The process of detecting gas on this type of sensor consists on the chemical reaction between the catalytic element and the combustible gases on the environment, generating an elevation on the temperature on the sensor, which causes the variation on its resistance [126; 127; 128].  
These sensors have been reported to display fast response times, although they are not accurate in determining the concentration of the gas; it only detects combustible gases and respond with the change on its resistance [126; 127; 128]. These sensors can be deployed on environments with humidity and temperature variations, without losing its capacity of detecting the target gases.  
The lifetime of these sensors will depend on the concentration of the target gas in the environment in which it has been deployed, being operative for 10 years in normal conditions; the presence of corrosive gases and exposure of the sensor to the target gases in concentration levels above the recommended can limit the lifetime to months of operation [126; 127; 128].  
Catalytic gas sensors are composed of two semiconductor elements: a passive element, used as reference, and one active element that reacts with the target gas, generating an elevation in the temperature and, thus, generating a variation in the internal resistance. The reaction between the target gas and the catalytic element is responsible for the consumption of these kind of gas sensor, and the exposure of catalytic gas sensors to concentrations above the upper flammable limit will deteriorate the catalytic element, therefore, the sensor will lose its capacity to detect the target gas, providing invalid results [126; 127; 128]. Figure 4 shows the concept of the catalytic gas sensors. The target gas burns the detector material, elevating the temperature, and thus, the internal resistance, allowing measuring the target gas' concentration.  
Figure 4: Representation of catalytic gas sensor. The target gas burns the detector material, raising the temperature and its internal resistance, allowing the measurement of the concentration.
# Heavy Metal Ion Detection Platforms Based on a Glutathione Probe: A Mini Review
## 3 Heavy Metal Ion Detection Platforms Using Glutathione
### HMI Detection Combined with Electrochemical Techniques
#### 3.2.1 Field Effect Transistor-Based Platforms
The response for Pb\({}^{2+}\) was at least 12 times larger than those of the control subjects, demonstrating a good selectivity for Pb\({}^{2+}\) detection. A rapid response of 1-2 s and an overall assay time of \(\sim\)10 s were achieved, which was fast compared with other existing HMI detection techniques. The LOD of this platform was below 1 ppb, and the approximate linear operational range was 5-20 ppb, which was around the limit amount (15 ppb) of lead that the U.S. EPA imposes as a restriction for drinking water. As an added bonus, this work produced a compact platform utilizing a preprogrammed microcontroller.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 5 Conclusions and Future Work  
This study seamlessly integrates RIS technology into multi-user downlink OFDMA communication systems, optimizing RIS reflection coefficients, OFDMA frequency allocations, and power distributions. Guided by stringent QoS requirements and transmission power constraints, as well as unit modulus constraint on the RIS phase shifts, the efficiency-focused methodology systematically address these multifaceted optimizations. These strategies redefine the original problem, deriving computationally accessible lower bounds on energy efficiency while striking a harmonious balance between performance enhancement and real-world feasibility. At its core, the AO algorithm, featuring the MDSA, SQP, and PSO approaches, effectively addresses complex challenges in energy-efficient wireless networks. Robust numerical results confirm substantial energy efficiency improvements compared to non-RIS scenarios. Our current work focuses on the simplicity of the SISO system, avoiding the complexities introduced by MISO and MIMO systems. For future research, we propose the exploration of the application of multiple antennas at both transmitter and receiver ends, anticipating a deeper understanding and a broader scope of findings.  
Conceptualization, S.B. and M.A.O.; methodology, S.B.; software, S.B.; validation, S.B., M.A.O., M.B. and D.M.; formal analysis, S.B.; investigation, S.B.; resources, S.B.; data Creation, S.B.; writing--original draft preparation, S.B.; writing--review and editing, S.B., M.A.O., M.B. and D.M; visualization, S.B.; supervision, M.A.O. and M.B.; project administration, S.B.; funding acquisition, S.B., M.A.O. and M.B. All authors have read and agreed to the published version of the manuscript.  
This study received no external funding.  
**Data Availability Statement:** Please contact the corresponding author at [EMAIL_ADDRESS].  
**Conflicts of Interest:** The authors declare no conflicts of interest.