esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application and Results
### Neutron and Gamma Convolutional Neural Network Model Construction  
This platform is based on the lightweight CNN architecture of LeNet-5, which includes three convolutional layers, two pooling layers, and two fully connected layers, primarily used for handwritten digit recognition. In order to further reduce the model's complexity, the model only performs one layer of fully connected computation, which significantly reduces computational and hardware resources; adopts a small convolutional kernel of 1 \(\times\) 3, which enhances the ability of feature information extraction and reduces the amount of computation; and increases the padding operation, which retains the edge information and maintains the output size. In order to reduce the hyperparameter search space and the number of convolutional layer output channels, the activation function and pooling operation are no longer traversed, and the model architecture in Figure 11 is directly adopted.  
The hyperparameter optimal search for the backpropagation algorithm, learning rate, batch data size, and dropout rate is performed using Optuna architecture, and the search range is shown in Table 1.  
Based on the results of the above figure, after 500 trials, the classification accuracy basically no longer increases, and under the condition of higher classification accuracy, the batchsize is mainly distributed between 64 and 128, and the optimization algorithm focuses on Adam. Therefore, upon setting the batchsize range to 64 and 128 and fixing the optimization algorithm relative to Adam for a new round of 500 iterations, the optimal accuracy results were as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Search Categories** & **Search Scope** \\ \hline Algorithmic space & Adam, RMSprop, SGD \\ Range of learning rates & 0.0001–0.1 \\ batchsize & 64,128,256,512 \\ Dropout range & 0–0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter search range table.  
Figure 11: CNN network architecture diagram.  
Figure 10: Three types of test dataset.  
Based on the last optimization state, the batchsize is set to 128, the learning rate is 0.0026, the dropout rate is 0.1197, and 1500 iterations of training are performed. The training accuracy, loss function value, and validation set accuracy curves are shown in Figure 12.  
Using the trained convolutional neural network, the forward inference of the test waveforms was carried out, which included the full waveform data, the rising-edge data, the falling-edge data, and the overall test set. The evaluation results are shown in Table 3.  
Comprehensive results of the test accuracy are shown in the above table. The training accuracy of the trained convolutional neural network on the rising edge of the waveform, the falling edge, and the full waveform data can reach more than 97%.
# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 3 Harmony Search Applications in China
### Engineering
#### 3.1.3 Civil  
In Ref. [17], an algorithm combined with enhanced Deb law was used to refine truss systems using distinct architecture variables with a Subspace Harmony Search (SHS). The SHS also implemented a local search approach with a dynamically accessible alternative to boost operations. During the optimization phase, the proposed enhanced Deb rule filters unnecessary limit infringement evaluations and significantly reduces the number of analyses necessary for structural optimization relative to traditional approaches. The results illustrate the robust search ability of SHS. Empirical findings demonstrate that the current SHS has a strong search capability.
# Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph
## 3 Methodology
### Personalized Travel Recommendation Algorithm Based on Knowledge Graph  
The traditional recommendation algorithm has the advantages of a simple algorithm, easy to understand and implement, but there are problems of the sparseness of scoring matrix data and cold start. Aiming at the traditional recommendation of algorithms ignoring the text semantic information in similarity calculation, they do not consider or consider less the attributes of users or tourist attractions, and only rely on users' historical behavior. In this paper, the KG-CF (Knowledge Graph Collaborative Filtering) recommendation algorithm is proposed [24; 25; 26]. Firstly, the algorithm uses rich semantic information of the  
Figure 4: Recommended flowchart.  
Knowledge Graph to alleviate the sparseness of the data, embeds the tourist attractions information into the low-dimensional vector space, and expresses it with a distributed vector to obtain a tourist attractions similarity matrix. Secondly, the algorithm constructs a user interest model and the user-attraction rating matrix. Then, the algorithm combines the similarity weight with the attractions-attraction similarity matrix to calculate, obtains the attraction prediction score, and generates a recommendation list. The specific recommendation steps are as follows (as shown in Figure 3): first, analyze the user's behavior data, construct the user-attraction scoring matrix, map the attractions in the matrix to the entities in the Knowledge Graph, use the B-TransD model to learn to represent the entities and attributes in the Knowledge Graph, obtain a set of low-dimensional dense real-valued vectors to represent the entities \(I_{i}=(e_{1i},e_{2i},...,e_{ni})^{T}\) and the entities with similarity when facing different relationships, use the Euclidean distance formula to calculate the distance between entities, and reduce their value range to \((0,1]\). The calculation formula for the similarity between entities is as follows:
## 2 CCD-Based Fine Tracking Optical System
### The Composited Velocity Closed-Loop Built by MEMS Accelerometers and CCD  
As we know, since the whole DSA is the product of each loop, it will continue to improve if we add a velocity loop to the structure. Fortunately, without gyroscopes, we can also acquire the velocity through the acceleration integral, but meanwhile the integral of the low-frequency acceleration noise will lead to drift of the velocity, which is harmful to the system's stability. Although the common low-frequency cut-off or attenuation algorithm could eliminate the drift, it would also filter out useful signals, which seriously affects the system's performance, so based on the acceleration integral, we propose to use the CCD signal to correct the velocity. The structure of TLC with DOB method is described in Figure 4.  
Figure 3: The structure of the ADLC with DOB. \(\widetilde{G}_{a}\) is the approximate acceleration model of the FTOS. \(C_{f}\) is the DOB controller.  
The DSA of TLC with DOB is showed as follows:  
\[\begin{array}{rcl}E_{TLC-DOB}=\frac{\theta}{\theta_{d}}&=\frac{1-C_{a}\tilde{G}_ {a}C_{f}}{1+C_{a}G_{a}+C_{c}G_{a}G_{a}\frac{1}{c_{f}}+C_{p}C_{c}G_{a}G_{a}\frac{ 1}{c_{f}}+C_{a}(G_{a}-\tilde{G}_{a})C_{f}}\\ &\qquad\qquad\qquad\approx\frac{1}{1+C_{a}G_{a}}\cdot\frac{1}{1+C_{p}\frac{1}{c }}\cdot\frac{1-C_{a}\tilde{G}_{a}C_{f}}{1+C_{p}\frac{1}{c}}\end{array} \tag{5}\]  
It's easy to see that \(E_{TLC-DOB}\) is smaller than \(E_{ADLC-DOB}\), which means the added velocity loop would enhance the DSA of the system. However, due to the time delay of velocity loop brought by CCD's correction, the actual improvement of DSA would only distribute at low frequency.  
In FTOS, the CCD's time delay is fixed and about 0.02 s (two frames whose sampling rate is 100 Hz). Take 1 Hz into consideration, the phase lag brought by time delay is \((1/100)*2\pi=\pi/50\), which could be ignored. Although the bad effect on DSA by time delay becomes bigger as the frequency rises, but since the DOB has greatly improved the medium-frequency DSA and the high-frequency DSA is mainly determined by passive DSA relying on mechanical design, the last whole DSA is satisfied. Actually we choose to sacrifice a part of medium-frequency performance for the low-frequency DSA. The detailed procedures to get the composited velocity will be presented in Section 4.
## 3 Results  
### Datasets  
To evaluate the performance of ERGCN, we selected six representative datasets widely used in previous works for the entity prediction task on TKGs. They are YAGO [27], WIKI [21], ICEWS14 [11], ICEWS15 [11], ICEWS18 [28] and GDELT [29]. YAGO and WIKI include temporal facts extracted from open-source datasets. The series of ICEWS are event based datasets from the Integrated Crisis Early Warning System. GDELT is from the Global Database of Events, Language and Tone. The statistical details of all datasets are shown in Table 1.
## 5 Numerical Simulation Results Analysis
### Response Characteristics of Roadway-Anchored Surrounding Rock
#### 5.2.1 Response Characteristics of Rock at the Anchorage End
The vertical displacement of the roof increases linearly at a rate of 0.5 mm/s, with the direction oriented towards the roadway. (3)a sudden reduction prior to entering a stable vibration phase. The displacement of the roof bolt (cable) shows a rapid increase during the initial phase; subsequently, both the displacement and axial force exhibit vibrations at the same frequency. Stage II (0.12\(-\)0.23 s): This phase is characterized by a combination of wave actions. Prior to 0.19 s, the P-wave is predominant, and both the axial force and displacement at the measurement point display a synchronous frequency vibration pattern. Following a duration of 0.19 s, a significant increase in the frequency of axial force fluctuations induced by S-waves is observed. During this interval, the displacement recorded at each measurement point demonstrates minimal vibration, indicating that the roadway support system remains intact and undamaged at this stage. Stage III (0.23\(-\)0.7 s): During the initial interval characterized by high-frequency, low-energy S-wave action, the axial force applied to the side bolt undergoes a rapid reduction to 90 kN due to the influence of the high-frequency shear force. Concurrently, the increase in displacement remains negligible. The second interval of high-frequency and low-energy S-wave action leads to a reduction in the axial force of the left-side bolt to 77 kN, accompanied by a 23% decrease in the pre-tightening force. This observation suggests that the left-side bolt experiences shear failure due to the influence of low-frequency and high-energy S-waves. During this phase, the axial force of the roof anchor bolt demonstrated two notable  
Figure 20: Dynamic curve of axial force-displacement of surrounding rock: (**a**) left-side anchor bolt; (**b**) roof anchor bolt; (**c**) roof anchor cable. increases, ascending from 99 kN to 104 kN, while the horizontal displacement increased from 2.1 mm to 2.9 mm. The transmission of the vibrational wave to the measurement point of the roof anchor cable occurs after it passes through the 4-5 coal seam, leading to a gradual attenuation of the S-wave energy. As a result, the impact on the roof anchor cable is minimal. The variation in axial force remains limited, with an overall displacement increase recorded at 2 mm. In the initial phase, the vibration wave generates a corresponding vibration within the roadway support system, thereby aiding in the restoration of stability. Throughout this period, the roadway support system maintains its operational functionality. Following the third stage, alterations in high-frequency vibrations and axial forces are observed in both the bolt and anchor cable. Notably, a reduction in the axial force of the side bolt is recorded, which coincides with an increase in the linear displacement of the roof support structure. It can be concluded that the anchorage performance of roadway anchorage systems deteriorates as a result of the influence of low-frequency, high-energy S-waves.
# Tailored Magnetic Linear Birefringence in Wedge-Shaped Co Nanocluster Assemblies
## 3 Results
### AFM Topography  
Mean diameter and aggregation performance of cobalt nanoparticles in our PLD setup were investigated by AFM topography. First, an estimation of their mean cluster diameter, D, was accomplished at some point of a wedged Co nanocluster assembly, where its thickness was \(\sim\)2 nm (according to shown results in Figure 2). Resulting AFM scan (\(4\times 4\,\upmu\)m\({}^{2}\)) shows a disordered spread of cluster assemblies (Figure 3a). Their mean D can be estimated from an statistical analysis of island height, which has been included as Figure 3b. For this calculation, z-values (or heights) referred to the baseline, i.e., the initial signal value at the AFM photodetector or, equivalently, its ground level during scanning. After this relative mode of topography analysis, the resulting histogram shows a clear maximum at around 1.5 nm, with this value corresponding with the height of most of  
Figure 2: (**a**) The density plot outlines the expected thickness gradient profile of our sample in off-axis geometry. Blue line AB corresponds to the selected direction for measuring the sample thickness. (**b**) 3D graph simulates the surface of the Co assembly along AB direction. Color bar scale is in nm. (**c**) Thickness profile measured along AB direction. Thickness resolution was \(\leq\)1 nm with our profilometer (see Supplementary Material for details).  
the assemblies. As we assume these assemblies are formed mostly by one-cluster-thick layer, the mean assembly height must be equal to the diameter D of individual clusters. The measured value of cluster diameter D is quite smaller than that reported about Co assemblies grown by different techniques, e.g., molecular beam epitaxial or plasma-gas condensation [18; 25; 26]. Not surprisingly, the collimated beam in PLD provides a huge energy density at the Co target surface and evaporates a distribution of very fine particles. Due to high kinetic energy and the vacuum environment, these fine particles arrive at the substrate without a significant rate of aggregation, enabling us to deposit small Co nanoclusters [24]. Figure 3c,d concern aggregation and growth of cluster assemblies for two locations on the wedge assembly with different thicknesses, and selected on initial stages of the formation of thickness gradient (approximately 2 and 5 nm). At a reduced scanning scale of \(1\times 1\) um\({}^{2}\), those AFM images give us an idea of the growth mechanism for such terraces at higher thickness. In the case of \(t\)\(\sim\)2 nm, only a few wide assemblies stand out with a height about 2 nm or higher, and most film is hardly seen as a set of interconnected assemblies. However, as \(t\) increases, different assemblies with increasing area and height can be clearly observed in Figure 3d. As the aggregation process of nanoclusters is more intense, growing islands are progressively wider in the substrate plane, and with augmenting height due to the stacking of additional terraces.
## 2 Methodology  
### Description of the Plant  
In this study, a real operated binary geothermal power plant (GPP; Sinem GPP), shown schematically in Figure 1, was chosen to perform the analysis of the advanced exergoeconomic (AdvExEcon) effects. The power plant is in Germencik/Aydin province in Turkey and can be operated at low temperatures (between 90 \({}^{\circ}\)C and 150 \({}^{\circ}\)C) with 24 MW installed power. As seen in Figure 1, due to the use of low-temperature geothermal fluid in the plant, a geothermal fluid (brine) and a refrigerant fluid are used, and it is, as such, called \"binary\". The most important difference between the binary cycle and other cycles is that it can generate electricity at poor temperatures. However, a double pressure cycle is used to reduce thermodynamic losses caused by geothermal fluid in the evaporators. Therefore, binary GPPs have been designed in which two interconnected organic Rankine cycles (ORCs) (double pressure) are fed from a single geothermal fluid. Furthermore, turbines in two interconnected ORCs are connected to the generator through a single shaft. In addition to these, an air-cooled condenser was used in the GPP. The mentioned features of the Sinem GPP plotted in Figure 1 make it a complex system. Hence, for such a system and its components, the cost is a key issue. Actual operational data, i.e., temperatures, pressures, and volumetric flow rates of the Sinem GPP, were collected by the Central Supervisory Control and Data Acquisition (SCADA) program in 2020. In this context, average data was used throughout 2020 for the advanced exergoeconomic (AdvExEcon) analysis.  
to cool the n-pentane in both condensers (CONs 1 and 2). According to the uncertainty analysis method defined by [PERSON] [27], total uncertainties for the data collected, such as pressure, temperature, and volumetric flow rate, are 2.04% (kPa), 0.88% (K), and 3.25% (m\({}^{3}\)/s), respectively. Thus, the total uncertainty for exergy efficiency is 3.27%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Steam, \(j\)** & **Fluid Type** & \(T_{j}\) (\({}^{\circ}\)C) & \(P_{j}\) (kPa) & \(\dot{m_{j}}\) (kg/s) & \(\dot{E_{j}}\) (kW) \\ \hline
0 & Dead state & 18 & 101.325 & - & - \\ \hline
1 & Brine & 164 & 1040 & 445 & 52,693 \\ \hline
1\({}^{\prime}\) & Brine—steam & 165 & 1040 & 5.83 & 699 \\ \hline
1\({}^{\prime}\) & NCG & 165 & 1040 & 2.50 & 380 \\ \hline  
\end{tabular}
\end{table}
Table 1: The thermodynamics variables at different steam points for the Sinem GPP.  
Figure 1: Schematic diagram of the Sinem GPP in Turkey.
## 3 Results and Discussion
### Characterization of La-EPRC Particles for Static Adsorption of Phosphorus Removal
#### 3.2.4 Effect of Solution pH on Adsorption Effect  
The correlation between the initial pH of the solution and removal efficiency is graphically represented in Figure 14. Upon observation of the figures, it becomes evident that the removal efficiency of phosphorus by La-EPRC particles exhibits a pattern of initially increasing, reaching a peak at pH = 6, and a subsequent decline. Specifically, within the pH range of 4 to 10, the removal rate of phosphorus by the adsorbent exceeds 90%, underscoring the optimal effectiveness of phosphorus adsorption by La-EPRC particles in weakly acidic, neutral, and weakly alkaline aqueous environments. Notably, phosphorus removal rate demonstrates relative resilience to changes in solution acidity and alkalinity within this range.  
In weakly acidic and neutral water environments, the predominant phosphate form is H\({}_{2}\)PO\({}_{4}^{-}\), known for its robust affinity with La\({}^{3+}\). Consequently, phosphorus removal efficiency is notably high. In weakly alkaline water conditions (pH = 10), the binding between  
Figure 14: Initial pH versus removal rate curve.  
Figure 13: La-EPRC dosage versus adsorption curve.  
La\({}^{3+}\) and phosphate weakens, leading to the formation of calcium phosphate through the reaction between Ca\({}^{2+}\) on the adsorbent's surface and OH\({}^{-}\), ultimately resulting in removal in the form of hydroxyapatite crystals. The efficacy of this reaction is maximized at pH = 10.  
At pH values below 4, phosphate removal efficiency diminishes noticeably due to the prevalence of H\({}_{3}\)PO\({}_{4}\), which lacks effective binding with La\({}^{3+}\) and exhibits low adsorption capacity. In extremely acidic conditions (pH = 2), surface material detachment occurs, resulting in slight turbidity in the solution. La\({}^{3+}\) may also desorb during this process, leading to a compromised final phosphorus removal effect.  
Conversely, at pH values exceeding 10, a significant decline in material adsorption efficiency is observed. This is attributed to the elevated concentration of OH\({}^{-}\), leading to competition with phosphate adsorption. The high concentration of OH\({}^{-}\) in the solution hinders phosphate adsorption, as the binding affinity of phosphate dominated by HPO\({}_{4}^{-}\) with the material's surface is weaker than that of H\({}_{2}\)PO\({}_{4}^{-}\). Additionally, Ca\({}^{2+}\) on the material's surface is completely bound, further diminishing adsorption effectiveness.
## 3 Proposed Methods  
The research methodology proposed in this work implements black-hole attack detection along with a design for protection using routing by the Secured Optimal Path-Routing (SOPR) protocol in WBANs. Figure 2 depicts the protection technique and also identifies the minimal path between the source and sink. Here, we presume the source and sink are not malicious. The proposed technique gives suggestions for identifying malicious routes using the routing protocol SOPR, interrelating with the Balanced Energy-Efficient and Reliable (BEER) algorithm, which offers enhanced energy. The SOPR protocol is incorporated with encryption along with routing.  
Protection from a black-hole attack is performed in three phases. Initially, the network is noted by employing the sensor nodes. In stage 2, the functions of the proposed algorithm  
Figure 2: Black-hole attack.  
are operated through the evaluation of a minimal path between the source and sink using the proposed SOPR algorithm. The forwarding rate or trust value of every node through every route is used to detect the black-hole attack on the network. Therefore, every route has been taken into account for the SOPR state in this proposed design. The final stage in SOPR, along with encryption using a one-time pad, is incorporated for the evaluation of the maximum possibility for security in routing. Because of this mechanism, a route based on trust has been acquired by the hash function generation of this encryption technique. The one-time pad is used for the encryption and decryption process when selecting a secured path for transmitting the data.  
All the data have been captured, including all the malicious nodes in the WBAN, by the black-hole node. Therefore, the fake responses have been forwarded through the path, which either blocks or drops the packet rather than forwarding true information along the active path. This scenario is presented in Figure 3. Due to this constraint, the source cannot communicate through the sink node. Although the source sensor node requires communication with the sink node, every node in the network receives the request message. The malicious node (E) replies fake REPR to the source node with a lower hop count number that could reach the sink node. Thus, a black-hole attack is executed within the network.  
The proposed technique for protecting the network from attack is discussed below.