esa-sceva/satcom-chunk-collection huggingface dataset:
# A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems
## 3 Proposed Model
### Improving CNN Equalizer with Skipping Branch  
We consider the simple neural network as shown in Figure 6.  
In Figure 6, the circles represent the neurons in the layers of the neural network. \(\mathbf{x}\) denotes the input signal. \(\mathbf{W}\) denotes the weight matrix. \(\mathbf{y}\) denotes the output vector of each layer. \(\mathbf{b}\) denotes the bias vector of each layer. \(\mathbf{y}\) is calculated using the following equation:  
\[\mathbf{  
As shown in Figure 8, the CNN equalizer could improve the BER performance compared with the conventional equalizer in [18] and the decision feedback equalizer (DFE) with a reliability factor in [2]. In addition, branch structures could achieve better performance than straight structures. However, the performance of Structure 4 was similar to that of the straight structure. Because Structure 4 has excessive layers (a long network) and the skip branch connects only to the beginning layer from the perspective of the long network, the weights were not updated completely. This led to Structure 4 and the straight structure achieving similar performance. In addition, we designed Structures 7 and 8 with skipped branches that skipped two and three layers, respectively. Notably, Structure 8 achieved a better performance than Structure 7. When Structure 8 skipped three layers, the parameter information was updated more easily than in Structure 7, with two layers skipped. In other words, Structure 8 reduced the vanishing gradient better than Structure 7. Therefore, Structure 8 improved the BER performance better than Structure 7. Generally, our proposed model achieved a gain of 3.5 dB at a BER of \(10^{-3}\) compared to the previous model.  
In the next simulation, we set blur = 1.85 and 10% misalignment for the HDS channel. The results are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **RMSE** \\ \hline Structure 1 & 0.2746 \\ Structure 2 & 0.2903 \\ Structure 3 & 0.2565 \\ Structure 4 & 0.2477 \\ Structure 5 & 0.2353 \\ Structure 6 & 0.2213 \\ Structure 7 & 0.2135 \\ Structure 8 & 0.2047 \\ Conventional equalizer in [18] & 0.3064 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The RMSE of nonlinear equalizer using CNN structures at 10 dB.  
Figure 8: BER performance of the CNN equalizer with 1.85 blur and 0% misalignment.  
As shown in Figure 9, the proposed model could still improve the BER performance of the HDS channel. As the misalignment increases, the 2D ISI increases. Nevertheless, by employing the proposed model that considers the nonlinear connection within the CNN, we could compensate for this effect, thereby maintaining the BER performance of the HDS channel at 0% misalignment. Our proposed model obtained a gain of 2.5 dB at a BER of \(10^{-3}\) under 10% misalignment.  
In the final simulation, the blur level was changed from 1.8 to 3 with 0% and 10% misalignments. We also set SNR = 15 dB. The results are presented in Figure 10. The proposed model achieved the best performance when the blur was less than 2.8.
## 2 Materials and Methods
### Modeling of the Three-Dimensional (3D) Chest Wall for a Pectus Excavatum Patient
#### 2.2.2 Establishment of 3D Model for PE Chest Wall  
For our study, 3D chest wall models were fabricated based on the obtained patient CT images. The models were then used to design patient-specific Nuss bars. The image processing software MIMICS version 17 from Materialise (Leuven, Belgium) was used to produce the 3D chest wall model. From  
Figure 1: Fabrication procedure for the patient-specific Nuss bar.  
the original CT images, 12 ribs, sternums, and costal cartilages were extracted in the following four steps (see Figure 2).
* Step 1: The CT file of a patient is loaded into the program (MIMICS). The loaded CT image is displayed in black and white depending on the density of each tissue. Thus, the density differences between tissues are used to select the desired body organ on the screen. The density range is specified using the \"Thresholding\" feature. The 3D surface can be obtained by selecting the rib and sternum tissues easily using the default value (bone region) of each tissue provided by the \"Region growing\" function of the program.  
Figure 2: Fabrication procedure for the 3D chest wall model for a PE patient: (**a**) Step 1; (**b**) Steps 2 and 3; (**c**) Step 4.  
* Step 2: A 3D model is created from the obtained 3D surface using the \"3D calculation\" feature. The spine or any other unnecessary tissues are removed using \"Edit mask\" or similar features because the Nuss bar is not applied to them.
* Step 3: A separate selection task must be performed to obtain the 3D surface of costal cartilage because the costal cartilage region cannot be taken with the Bone default value due to its low density compared to the rib and sternum. A 3D model is created from the 3D surface of the costal cartilage obtained through this separate process.
* Step 4: A smooth-shaped chest wall model is produced finally by modifying the 3D chest wall models of the rib, sternum, and costal cartilage using the \"Wrap\" and \"Smoothing\" features.
###### Abstract  
Nanostructures with graphene make them highly promising for nanoelectronics, memristor devices, nanosensors and electrodes for energy storage. In some devices the mechanical properties of graphene are important. Therefore, nanoindentation has been used to measure the mechanical properties of polycrystalline graphene in a nanostructure containing metal oxide and graphene. In this study the graphene was transferred, prior to the deposition of the metal oxide overlayers, to the Si/SiO\({}_{2}\) substrate were SiO\({}_{2}\) thickness was 300 nm. The atomic layer deposition (ALD) process for making a very thin film of Al\({}_{2}\)O\({}_{3}\) (thickness comparable with graphene) was applied to improve the elasticity of graphene. For the alumina film the Al(CH\({}_{3}\))\({}_{3}\) and H\({}_{2}\)O were used as the precursors. According to the micro-Raman analysis, after the Al\({}_{2}\)O\({}_{3}\) deposition process, the G-and 2D-bands of graphene slightly broadened but the overall quality did not change (D-band was mostly absent). The chosen process did not decrease the graphene quality and the improvement in elastic modulus is significant. In case the load was 10 mN, the [PERSON]'s modulus of Si/SiO\({}_{2}\)/Graphene nanostructure was 96 GPa and after 5 ALD cycles of Al\({}_{2}\)O\({}_{3}\) on graphene (Si/SiO\({}_{2}\)/Graphene/Al\({}_{2}\)O\({}_{3}\)) it increased up to 125 GPa. Our work highlights the correlation between nanoindentation and defects appearance in graphene.  
graphene; mechanical properties of thin film systems; Young's modulus +
Footnote †: journal: Applied sciences
## 3 Analysis of Shield Tunneling Parameters
### Grouting Pressure  
The correlation between the grouting pressure and the shield tunnel ring number is illustrated in Figure 6. Table 3 presents the statistical characteristics of the grouting pressure across three distinct stratum conditions.  
Based on the analysis of Figure 6 and Table 3, it is evident that the grouting pressure exhibits substantial fluctuations. Notably, a sudden decrease in the grouting pressure at ring 286 is a deliberate adjustment during shield tunneling near the highway toll station and residential area. This adjustment aims to prevent local ground uplift caused by excessive grouting pressure. Conversely, a sudden increase in the grouting pressure at ring 460 results from artificial regulation to address ground subsidence. The grouting pressure concentrates within the range of 0.3 to 0.5 MPa, ensuring compactness between the segment and surrounding rock. Inadequate grouting pressure may lead to soil instability and significant deformation, while excessively high pressure can damage the segment structure or induce local ground uplift due to slurry channeling. Based on the analysis of Figure 7, it is evident that the mean value of the grouting pressure is 0.2\(\sim\)0.25 MPa larger than that of the slurry pressure. The grouting pressure consistently exceeds the slurry pressure by approximately 0.22 MPa.  
In contrast to other strata [30; 31], the grouting pressure in volcanic ash formations experiences significant fluctuations. This is attributed to the inherently high compressibility and inhomogeneity of volcanic ash strata, resulting in complex geological conditions. Urban areas, in particular, impose stringent criteria on ground subsidence and stratum deformation, making it challenging to control the grouting pressure during the shield tunneling process. Real-time adjustments are necessary based on the specific characteristics of the stratum.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stratum Serial** & **Mean** & **Standard** & **Median** & **Minimum** & **Maximum** & **Skewness** \\
**Number** & **Value** & **Deviation** & & **Value** & **Value** & **Value** & \\ \hline I & 0.397 & 0.034 & 0.396 & 0.340 & 0.464 & 0.244 \\ II & 0.416 & 0.053 & 0.432 & 0.301 & 0.522 & \(-\)0.306 \\ III & 0.384 & 0.033 & 0.383 & 0.276 & 0.455 & \(-\)0.852 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical characteristics of different strata with the grouting pressure.  
Figure 6: The curve of the grouting pressure changing with the ring number.
## 3 The Proposed Approach
### The PSO Algorithm
#### 3.4.2 Improved PSO Algorithm  
Because the PSO algorithm needs a large particle swarm inertia factor (\(\omega\)) to traverse the entire space of solutions as much as possible in the early stage of the search, and a small \(\omega\) in the late stage of the search to make the particles approach the optimal solution and reach convergence as soon as possible, the traditional PSO algorithm uses a fixed value for the \(\omega\) in the process of searching, and the search efficiency is thus seriously affected. In this paper, an improvement rate (\(\alpha_{t}\)) is introduced to monitor the search process of the PSO algorithm so as to adjust the inertia parameter (\(\omega\)) adaptively to improve the search efficiency of the PSO algorithm, and the \(\omega\) is updated according to Equation (11):  
\[\omega_{(t+1)}=\begin{cases}\frac{\omega_{(t)}}{22}\ if\ \alpha_{t}\leq IR_{1}\\ \omega_{(t)}\ if\ IR_{1}<\alpha_{t}\leq IR_{2}\\ 2*\omega_{(t)}\ if\ \alpha_{t}>IR_{2}\end{cases} \tag{11}\]  
where \(\alpha_{t}\) is the improvement rate and is calculated via Equation (12); \(IR_{1}\) and \(IR_{2}\) are the two constant threshold values determined based on experiments:  
\[\alpha_{t}=\frac{\big{(}f_{(t)}-f_{(t-1)}\big{)}}{f_{(t-1)}} \tag{12}\]  
A flowchart of the improved PSO algorithm is shown in Figure 6.
## 3 Proposed RBAVO-DE for GS
### Converting to Binary Nature  
In the presented AVO algorithm, the positions are shown as real values. As such, they are not immediately applicable to the GS binary issue. Therefore, there is a need to convert these real position values into binary values to conform to GS's binary nature and maintain the original algorithm's structure. In this conversion, 1s represent the real values of the pertinent selected genes in the binarization vector, whereas 0s represent the real values of the unselected genes, which are not pertinent. At each iteration \(g\), the following mathematical expression can be used to convert the real position \(X_{i}^{g}\) to a binary position \((X_{i}^{g})_{bin}\):\[(X_{i}^{g})_{bin}=\left\{\begin{array}{ll}1&\text{if}\,X_{i}^{g}>\delta,\\ 0&\text{otherwise}.\end{array}\right. \tag{18}\]  
where an arbitrary threshold point within \([0,1]\) is represented by \(\delta\). According to this fundamental binary conversion approach, the binary \"\(1\)\" (selected feature) replaces its real value if \((X_{i}^{g})_{bin}\) is greater than \(\delta\). On the other hand, if \((X_{i}^{g})_{bin}\) is smaller than \(delta\), its real value is set to the binary \"\(0\)\" (a feature that was not chosen).
## 4 Design Considerations
### Determination of Duty Cycle
#### 4.1.1 Duty Cycles at Rated Load  
The maximum duty cycle at rated load, \(D_{max,rated}\), is determined by the minimum input voltage \(V_{in,min}\) and is shown as follows:  
\[D_{max,rated}=1-\frac{V_{in,min}}{I_{LED}\times\Sigma R_{F,LED}+\Sigma V_{F, LED}}=1-\frac{10.8}{0.35\times 17.14+21.6}=0.608 \tag{21}\]  
The minimum duty cycle at rated load, \(D_{min,rated}\), is determined by the maximum input voltage \(V_{in,max}\) and is shown as follows:  
\[D_{min,rated}=1-\frac{V_{in,max}}{I_{LED}\times\Sigma R_{F,LED}+\Sigma V_{F, LED}}=1-\frac{13.2}{0.35\times 17.14+21.6}=0.522 \tag{22}\]
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 2 Methodology
### Data Analysis  
The analysis method examined three primary dimensions that emerged from our review. The first one was to examine the technical implementation, and we observed several consistent architectural approaches across successful implementations. For instance, distributed architectures incorporating edge computing capabilities, as demonstrated by [16; 18], emerged as a dominant subject. The integration of AI/ML for predictive capabilities formed another crucial pattern, with significant contributions from [20; 22], while security-by-design principles, showcased in [23; 24], became increasingly prominent in recent implementations.  
The second aspect of our examination method was the applications of DT in smart grids. Based on the findings, we clustered them into three distinct categories: Asset management applications, focused primarily on predictive maintenance and real-time monitoring capabilities. System operation implementations emphasized grid optimization and energy management. Disaster response applications concentrated on system resilience and fault recovery mechanisms.  
Several key findings emerged from our analysis. Recent implementations (2022-2024) demonstrated an increased emphasis on security integration, reflecting growing concerns about cybersecurity in smart grid systems. The integration of AI/ML technologies appeared in 67% of the successful implementations, indicating their crucial role in modern Digital Twin applications. Real-time monitoring capabilities were present in almost all of the studied cases, while blockchain integration emerged as a promising trend in newer security frameworks.  
The evolution of the research focus from 2019 to 2024 revealed a clear shift from basic implementation concerns toward more sophisticated integration challenges. Early work (2019-2021) primarily addressed fundamental implementation issues, while recent research (2022-2024) increasingly focused on advanced security measures and AI integration.
## 2 Experimental Setup
### HNSW Transducers  
Three transducers were assembled. Each transducer contained sixteen AISI 302 steel particles (McMaster-Carr, Aurora, IL, USA) as schematized in Figure 3. The second particle from the top was nonferromagnetic, whereas the others were ferromagnetic. The properties of the particles were: diameter \(d\) = 19.05 mm, density \(\rho\) = 7800 kg/m\({}^{3}\), mass \(m\) = 27.8 g, modulus of elasticity \(E\) = 193 GPa, and Poisson's ratio \(\
u\) = 0.29. Each chain was held by a Delrin tube (McMaster-Carr, Aurora, IL, USA) with outer diameter \(D_{0}\) = 22.30 mm and inner diameter slightly larger than \(d\) in order to minimize the friction between the striker and the inner wall of the tube and to minimize acoustic leakage from the chain to the tube. The striker was driven by an electromagnet (made in the lab) built in our lab and powered by a (DC) power supply (B & K Precision, Melrose, MN, USA).  
Figure 3: (**a**) scheme of the solitary waves based transducers used in this study (**b**) assembly of the sensor-rod inserted in the transducers.  
The sensing system consisted of a built-in sensor-rod schematized in Figure 3b and located in lieu of the 9 th particle. The sensor-rod was made of a piezo ceramic (Steminc Piezo, Miami, AZ, USA) embedded between two half rods (McMaster-Carr, Aurora, IL, USA). The disc (Steminc Piezo, Miami, AZ, USA) was 19 mm diameter and 0.3 mm thick and insulated with Kapton tape (McMaster-Carr, Aurora, IL, USA). The rod was made from the same material as the beads, and has a mass \(m_{r}\) = 27.8 g, a height \(h_{r}\) = 13.3 mm, and a diameter \(D_{r}\) = 19.05 mm. The sensor-rod had approximately the same mass of the individual particles in order to minimize any impurity in the chain that may generate spurious HNSWs. Finally, the falling height of the striker was 5 mm.  
At the bottom of the transducers a 0.254 mm (0.1 in.) thick aluminum sheet (McMaster-Carr, Aurora, IL, USA) was glued to the plastic tube in order to prevent the free fall of the particles. A through-thickness hole was devised to allow for direct contact between the last particle of the chain and the concrete material. The transducers were driven simultaneously by a National Instruments-PXI unit running in LabVIEW (9.0, National Instruments, Austin, TX, USA, 2009), and a DC power supply and a Matrix Terminal Block (NI TB-2643) (National Instruments, Austin, TX, USA) to branch the PXI output into three switch circuits to trigger the action of the electromagnets. The lead-zirconate-titanite (PZT) sensors (Steminc Piezo, Miami, AZ, USA) were connected to the same PXI, and the signals were digitized at 400 kHz sampling frequency, _i.e._, 2.5 \(\upmu\)s sampling period.
# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 5 Results
### Evaluate the GeoLocator's Performance on Social Media Posts  
The final experiment assesses GeoLocator's capability to infer the location of a picture taken in a more complex real-world scenario. Social media posts often feature photos of the same location from various angles, accompanied by text describing the time or place, as well as some extraneous information. This complexity necessitates a higher level of synthesis from GeoLocator to accurately determine the location. The experiment comprises three sub-experiments: two involving tourist posts and one featuring a daily-life post. The results indicate that GeoLocator can infer locations down to the city level and provide detailed profiles of individuals in the posts. It accurately pinpointed the exact street or area in two out of the three posts. Furthermore, the distances between GeoLocator's estimations and the actual locations in the images were all within 100 miles.
## 5 Experimentation and Results
### Training and Results--Step 3  
Step 3 is predicting the panel-vibration-induced structure-borne noise in a cavity. The average response from the panel and cavity in Step 2 is used as the input data. These two data combine to size of 2 \(\times\) 62 and are input into the 2D CNN. The two-dimensional CNN is used to express the coupled acoustic-structure in Equation (3) as a neural network.  
The number of training/validation data was doubled to 640 samples by shifting the input data to the left or right up to 2 Hz randomly as a noise to improve the accuracy. In case of panel-cavity coupled structure-borne noise, the coupled response is affected by the frequency position of each system response peak. Therefore, in order to increase the robustness of the error of the peak position that would be included in the response of each system, the frequency of each system response peak was randomly adjusted up to 2 Hz while the target response was maintained.  
The Adam optimizer was used, and hyper parameters of 0.0001, 0.0003, and 1000 denote the learning rate, weight decay, and epoch, respectively.  
Using the proposed method, the average r-square value of the predicted structure-borne noise is 0.86. Some test data show a low r-square value, like tests no. 4 and 17 in Figure 15. They are mainly related to the low r-square value of the sub-system, such as in tests no. 4 and 17 in Figure 13, which were used as an input for Step 3.  
Figure 14: Cavity average response prediction—Step 2. Blue—target; red—predicted.
# Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
## 6 Discussion
### Performance Improvement  
The performance of the AP algorithm, as is the case with most computational procedures, is pivotal in real-world applications. The urgency for efficiency and execution speed improvements is even more pronounced when dealing with multi-robot coverage path planning, given the scale of the task and the inherent complexity associated with environment mapping and path planning. In light of this, several strategies can be considered to enhance the execution speed and overall efficiency of the AP algorithm.  
One crucial step of the AP algorithm is the calculation of the similarity matrix. Given that this phase accounts for a significant portion of the computations (approximately 40%), improving its efficiency is imperative. Given its intrinsic parallelizable nature, where the similarity between each pair of data points can be calculated independently, we could potentially harness the power of parallel computing. By distributing the calculation of similarity measures across multiple cores or nodes in a parallel computing environment, we can expedite this process markedly, thereby increasing the overall efficiency of the AP algorithm.  
On the other hand, the message passing phase of the AP algorithm, which is pivotal for its iterative structure, is more challenging to parallelize. Although in theory, each \"responsibility\" and \"availability\" message update could be computed in parallel, the iterative nature of the AP algorithm necessitates the results of each preceding iteration. Nonetheless, we could explore certain forms of \"soft\" parallelization, such as utilizing vectorized operations or parallel map functions provided by high-level languages and libraries. Even though this approach would not offer true parallelization due to each iteration still needing to await the completion of all message updates, it could still provide substantial speed improvements.  
Besides parallel computing, other potential strategies for improving the efficiency of the AP algorithm could include optimization of the algorithm's parameters or the application of hardware accelerators such as Graphics Processing Units (GPUs) [29]. Fine-tuning parameters like the damping factor or the preference value could potentially reduce the number of iterations required for convergence, thereby accelerating the execution speed. Similarly, using GPUs, which are particularly suited for parallelizable tasks, could lead to substantial reductions in computation time. However, such strategies would require careful evaluation to balance efficiency gains against the potential impact on the quality of the results.