esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Path Generation Module
#### 4.4.2 Sequential Outcome Decoder  
Currently, most papers on solving pathfinding problems using attention mechanisms solely employ reinforcement learning for training [34]. The paper utilizes a hybrid strategy of reinforcement learning and pseudo-label learning to address complex path-planning problems. If the same dataset is used and the same encoder is utilized, it may be theoretically expected that, if solved correctly, the sequences returned from different decoders should be consistent. Furthermore, when dealing with large datasets, it becomes difficult to locate the associated accurate labels. Therefore, we considered utilizing the forecasts from one decoder as pseudo-labels for another decoder in order to oversee its training. This module utilizes two decoders, namely a label generation decoder and a sequential outcome decoder, to decode the encoder embedding, which is shown in Figure 4. The main functions of pseudo-label are as follows:  
* Cross-Decoder Supervision : Pseudo-labels from Decoder 1 supervise Decoder 2 via cross-entropy loss.
* Dynamic Thresholding: Low-confidence edges (\(S_{ij}<\tau\)) are masked to prevent noise propagation.  
To implement this idea, initially, concatenating node embeddings h and graph embeddings \(h_{g}\) yields a novel embedding, \(h_{cg}\), encompassing both intricate node-specific details and inter-nodal relationship information.  
\[h_{cg}=Concat(h,h_{g}) \tag{32}\]  
Then, utilizing the output sequence from the previously label generation decoder for 0-1 encoding, where the presence of an edge denotes 1 and absence denotes 0, yields the prediction result \(P_{cg}\). Simultaneously, concatenating the resultant \(h_{cg}\) as input to the sequential outcome decoder, followed by a multi-layer perceptron operation, \(\overline{h}_{cg}\) undergoes a softmax operation, deriving the probability of edge presence for the nodes \(S_{cg}\).  
\[\overline{h}_{cg}=MLP(h_{cg}) \tag{33}\]  
\[S_{cg}=\text{softmax}\big{(}MLP\big{(}\overline{h}_{cg}\big{)}\big{)} \tag{34}\]  
By amalgamating node-level and graph-level information, the performance enhancement of the classification decoder concurrently aids in elevating the predictive encoder, consequently yielding superior results.
## 2 PFC Converters Power Components Design Analysis and Selection
### The Boost Inductor Design and Manufacturing
#### 2.1.1 Conventional PFC Boost Inductor
The inductor number of turns (N) to obtain the required inductance value (Lb) can be calculated as:  
\[\text{N}=\sqrt{\frac{\text{L}_{\text{b}}}{2\text{ A}_{\text{L}_{\text{min}}}}} \tag{7}\]  
where A\({}_{\text{L}_{\text{min}}}\) is the minimum inductance factor for each core (A\({}_{\text{L}_{\text{min}}}\) = 74.52 nH/T2). The copper loss of the winding wire can be calculated on maximum value of the rms input current (I\({}_{\text{in}\_{\text{rms}}}\)) as given  
\[\text{P}_{\text{L}\_{\text{D}\_{\text{Loss}}}}=\text{I}_{\text{in}\_{\text{rms }}}{}^{2}\times\text{DCR} \tag{8}\]  
\[\text{I}_{\text{in}\_{\text{rms}}}=\frac{\text{P}_{\text{out}}}{\text{\eta }\times\text{V}_{\text{in}\_{\text{min}}}\times\text{PF}} \tag{9}\]  
where DCR is the inductor wire DC resistance and PF is the designed converter power factor. The inductor wire which is selected based on the inductor maximum peak current, and should meet the requirements that it should be thick so that the DCR is small and it should be possible to form the required number of turns around the core. In this work, the conventional CCM PFC inductor was manufactured with 60 turns of the copper wire with 1.15 mm diameter size around the 2-stacked Kool Mu 77083A7 toroids cores. The manufactured inductor DCR value was experimentally measured, and it was about 0.087 \(\Omega\). Figure 4: Boost inductor core selection for the conventional and the interleaved PFC topologies.
## 1 Introduction
### Csg  
The CSG approach was developed to optimize bundle routing in delay-tolerant networking using a machine learning approach by leveraging the cognitive networking support offered by HDTN. A cognitive network controller (CNC) continuously updates the synapse strengths (weight) of the neurons in the spiking neural network using reinforcement learning. The number of vertices of the SNN is \(V=3n+1\), and the number of edges is \(W=n(n+2)\), where \(n\) is the number of outbounds at a satellite node. The SNN has a core neuron for each outbound link. Each core neuron is connected to one excitatory and one inhibitory neuron. All core neurons are connected to an inhibitory neuron using post-synaptic connections [25]. The synapse weights are updated using a reward, which is mapped to the optimization goal function and updated based on the performance of the selected outbound link. The neurons emit spikes when their membrane potential reaches a threshold. The outbound link associated with the neuron that emits a spike at the earliest will be selected. Further details can be found in the literature [25; 26]. CSG uses forward and backward reports to collect metrics used for helping the CNC to make dynamic routing decisions.
## 2 Materials and Methods
### Sentinel-1 SAR Interferometry
#### 2.3.3 A-DInSAR Analysis with SNAP and StaMPS Integration  
The A-DInSAR analysis based on the PS-InSAR technique was performed using the acquired Sentinel-1 free dataset. The PS-InSAR technique has emerged as a matured geodetic monitoring tool characterized by millimeter accuracy. The processing is split into two independent workflows [47; 48; 49] and presented in Figure 6: (1) single master DInSAR processing through SNAP, and (2) the PS-InSAR processing through StaMPS.  
In SNAP processing, the master image was selected using the InSAR Stack Overview tool. The selected master image was approximately in the barycenter of the dataset considering both the temporal and spatial baselines to ensure high coherence in all interferometric pairs generated over the whole stack. Image splitting and orbit file correction were applied, and slave images were coregistered to the geometry of the master image. Interferograms were generated and topographic phase was removed using SRTM DEM 1 arcsec data. Then, debursting was applied to both the original coregistered images and interferograms. Finally, the processed data was exported in a format that is compatible with StaMPS readers. The snap2 stamps scripts were used to automate the process except for the master image selection [50]. It is worth noting that interferograms need to be checked before proceeding to the next part to avoid processing errors during the PS-InSAR analysis.  
The second part is StaMPS processing. The first step is the preparation of the SNAP exports into StaMPS using the _mt_prep_snap_ script included in the distribution. After running the script, Matlab was launched, and StaMPS processing was subsequently carried out from Step 1 (Load data) through Step 8 (Atmospheric filtering). Additionally, to mitigate the tropospheric phase screens, the _aps_linear_ approach was used as part of the integrated Toolbox for Reducing Atmospheric InSAR Noise  
Figure 6: Advanced differential interferometric synthetic aperture radar (A-DInSAR) (based on Permanent Scatterer InSAR (PS-InSAR) technique) processing workflow through integration of SNAP and StaMPS. The workflow was implemented for tunneling-induced ground deformation monitoring in Site C and rainfall-induced landslide analysis in Site D.  
(TRAIN) [43]. Finally, the displacement time series and mean annual velocity were exported for post analysis and visualization in StaMPS-Visualizer [51] and QGIS.
## 2 State of the Art
### Additional Features of Industry 5.0
#### 2.6.3 Hyper Customization  
Industry 4.0 aimed to connect machines, create intelligent supply chains, promote smart product production, and separate manpower from automated industries, among others. However, Industry 4.0 has struggled to keep up with the growing demand for customization, whereas Industry 5.0 does so through hyper customization. Hyper customization is a personalized marketing strategy that employs cutting-edge technologies such as AI, Machine Learning (ML), cognitive systems, and computer vision to analyze real-time data and provide more tailored products, services, and content to each customer. Manufacturers can customize products in bulk thanks to the integration of human intelligence and robots. Many variants of the functional material are shared with other personnel in order to customize the product with different variants for customers to choose from. Industry 4.0 aimed for mass customization with minimal cost and maximum accuracy, whereas Industry 5.0 aimed for large-scale production with minimal waste and maximum efficiency. The collaboration of operators and robots, as well as cognitive systems, allows  
Figure 2: Core values of Industry 5.0 [44].  
industries to coordinate manufacturing processes in order to meet customer needs and market changes. The transition to an agile manufacturing process and supply chain is the first step in hyper personalization. Human intervention, a production team, and customer preferences are also required. Hyper customization's applicability is also heavily reliant on the cost-effectiveness of the developed products [61].
# Implementing CAD API Automated Processes in Engineering Design: A Case Study Approach
## 4 Case Studies
### Case Study 1: Automated Folder and Archiving Structure for Design Documentation  
Goal  
The objective in the first case study was to make data archiving related to reverse engineering tasks simpler by automating common folder hierarchies and index files. This is a mandatory step toward attaining traceability, consistency, and proper project-linked documentation organization, particularly for components such as those in this case in a turbocharger.  
Process  
Using the user interface designed for this case (illustrated in Figure 3), engineers begin by entering basic part-related information into a form. Upon clicking the \"Next\" button, the system automatically generates a predefined folder hierarchy tailored to the selected project type. This includes directories for part analysis, scan data, and reverse engineering outputs. A notification confirms the folder creation path, while additional prompts inform the user of archiving options. To support accessibility and future data analysis, an Excel index file is generated (Figure 5), which contains hyperlinks to all relevant subdirectories and files. The automation logic ensures uniformity in folder naming and structure, eliminating variation between users and minimizing manual input errors. The specific directory naming structure and file system logic are detailed in Appendix A.5.  
The automation of folder generation and data archiving significantly improved documentation efficiency by removing the need for manual folder setup. It ensured that all project data followed a consistent structure, enhancing file retrieval and version control. The use of an automatically generated Excel spreadsheet as a centralized index allowed users to manage documentation through a single interface, with embedded hyperlinks enabling rapid navigation to any stored file. Apart from structural organization, the Excel index also offers potential as a statistical dashboard, supporting future data-driven evaluations of part characteristics or project metrics.
## 5 Conclusions  
In this paper, the design of a proposed RRC with harmonic suppression and size reduction is described and presented by using an RBF-ANN. An RBF network is used in this design to predict the desired parameters of the proposed RRC. The performance of the proposed coupler is analyzed using a full-wave electromagnetic simulator, and the simulation results are presented and discussed. Finally, the RRC is fabricated using a Rogers 5880 and measured using a vector network analyzer (VNA). The experimental results demonstrate that the proposed coupler exhibits excellent performance characteristics, including a high isolation of 19 dB, low insertion loss of 0.2 dB, a size reduction of more than 82%, and the suppression of the 2 nd to 5 th harmonics. The proposed design is highly compact, making it suitable for integration into various microwave circuits. The experimental results confirm the effectiveness of the proposed design, which can be used as a fundamental building block for many microwave circuits.  
**Author Contributions:** Conceptualization, S.R. ([PERSON]), S.I.Y. and S.R. ([PERSON]); Formal analysis, S.I.Y. and M.A.C.; Methodology, S.I.Y. and Y.Y.G.; Software M.A., Y.Y.G. and S.R. ([PERSON]); Validation, M.A.C. and Y.Y.G.; Writing--original draft, S.R. ([PERSON]), S.I.Y. and S.R. ([PERSON]); Writing--review & editing, S.I.Y. and M.A.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** All the material conducted in the study is mentioned in article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Simulation Results
### Impact of CP  
The CP used in OFDM systems can mitigate the ISI caused by optical multipath channels, but it degrades transmission efficiency and costs time and energy. As in the conditions of with-CP and CP-removal DCO-OFDM, the BER of the proposed scheme over LOS and NLOS \(U_{1,5}\) are compared in Figure 9. In addition, as for the CP-removal NLOS case, the other four competitive approaches, in terms of the traditional ZF equalization with 32 comb-pilots, the Volterra equalizer based on recursive prediction error method (RPEM), the basic DNN with one hidden layer, and the AE based on two DNN, are also implemented for convenient analysis and comparison.  
As for the other four competitive approaches, they suffer serious ISI from the diffuse optical channel in the case of CP removal, leading to severe BER performance degradation. However, for the LOS channel, the BER of the proposed scheme with CP is almost the same as the one without CP. As for NLOS \(U_{1,5}\), the BER is slightly decreased when the CP is omitted, but they still have high similarity. In general, compared with the traditional ZF and the Volterra equalizer, the proposed scheme can work well in resolving ISI even under the CP removal condition from the BER investigation of LOS and NLOS. Furthermore, it can still provide an excellent equalization performance, which indicates that the characteristics of IM/DD channel have been learned in the training stage by the proposed scheme. This mainly benefits from the fact that the DNN has a powerful self-study ability. Moreover, we can also infer that the selected model has good adaptability and generalization ability when the conditions of online deployment do not exactly agree with that of the training stage.
## 3 Results and Discussion  
For each measurement of water content changes, the coefficient \(n\) of the model (6) was selected; therefore, the relative model error did not exceed 5%. Figures 1-3 show the exemplary models of the changes in water content (6) during the drying process of leek, for selected drying temperatures and three thicknesses of slices, with an empirically selected coefficient \(n\).  
Model (6), presenting the changes in water content, describes the results of the empirical measurements of water content, from the initial to the critical value, i.e., up to approximately 1-1.5 kg H\({}_{2}\)O/kg d.m. (with a relative error of less than 5%).  
Figure 1: Model of water content changes in leak slices 4 mm thick, during drying at temperature 60 \({}^{\circ}\)C, when \(n=0.85\).  
Figure 2: Model of water content changes in leak slices 8 mm thick, during drying at temperature 50 \({}^{\circ}\)C, when \(n=1.25\).  
The exponent \(n\) in Equation (3) allows us to determine the relationship between the change in the area of the dried material and the change in its volume. If every single particle of dried material would shrink like a perfect isotropic body, then \(n=1\), consequently \(N=3\). Any other values of the coefficient \(n\) determined by fitting the theoretical model (6) to the measurement results indicate that the dried particles change their shape and their surface ceases to be smooth. The corrugation of the surface means that the surface shrinkage is smaller than the volume shrinkage, resulting in the coefficient \(n\
eq 1\). This was the case for the leeks, where selected values of the coefficient \(n\) were different from 1, both smaller and larger.
# Degradation of Hydraulic System due to Wear Particles or Medium Test Dust
## 4 Discussion
### Comparison of Directional 4/3 Valves Tested with Wear Particles and Test Dust  
When comparing the final measurements of internal leakage between the two valves, one tested with wear particles and the other with test dust, the following differences occur. The valve tested with wear particles had a leakage of 0.09 \(\pm\) 0.01 L/min at port A in the zero position, while the valve tested with dust (also at port A) had a leakage of 0.12 \(\pm\) 0.01 L/min in the same position at 200 bar. Figure 18 shows the data for ports A and B. It can be seen that the valve tested with wear particles and an oil cleanliness of 22/21/19 completed 11.27 million cycles, while the valve tested with MTD and an oil cleanliness of 21/21/21 completed only 1581 cycles. This is a small amount of leakage, which is common with worn valves in both cases, but the differences are still notable. The volumetric efficiency of the valve tested with wear particles was 0.998 at the beginning of the test and decreased to 0.990, and the volumetric efficiency of the valve tested with test dust was 0.995 and decreased to 0.986. The volumetric efficiency of the valve tested with wear particles decreased by 0.008 and that of the valve tested with test dust decreased by 0.009. The difference in the reduction in volumetric efficiency is much smaller (less than 1%) compared to the pumps. The elements that come into contact with the dust, and are susceptible to wear, are the control spool, made of tool steel, and the valve housing made  
Figure 17: Comparison of volumetric efficiencies of hydraulic pump at different operating pressure points (0 bar, 100 bar, 150 bar, and 200 bar), tested with test dust (far left) and wear particles.  
of gray cast iron. The mentioned tribological pair is much more wear-resistant than the gear pump housing, which is made of aluminum alloy. Another reason for the difference in wear is that the gear pump has a higher contact pressure between the surfaces of the elements than the directional 4/3 valve.  
The endurance test of the hydraulic directional 4/3 valve revealed a minor internal leakage, which can be seen in Figure 18, when tested with wear particles and when tested with test dust. It should be pointed out that the valve tested with wear particles was less worn than the one tested with test dust, even though it was in operation for 624 h longer. It can be seen from Figure 12 that the leakage measurements are scattered. One possible explanation for such variation could be the position of the control spool with respect to rotation. If the housing and control spool are in a position where both worn surfaces are in contact, increased leakage may occur.
# Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives
## 3 Results
### Description of Studies  
It was found from the literature search that a few research groups have been working on utilizing PA imaging for breast cancer detection for the last two decades. The performance of their PA imaging systems for breast cancer detection has been evaluated progressively, beginning with the study on breast tissue-mimicking phantoms. Once the phantom studies were successful, the research groups further proceeded to perform ex vivo as well as in vivo studies with animals. Eventually, some of the research groups performed clinical studies with human patients and reported their observations comparing their results with that of other clinically approved medical imaging modalities like X-ray mammogram, US  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Electronic** & \multicolumn{2}{c}{**Web of Science [55]**} & \multicolumn{2}{c}{**PubMed [56]**} & \multicolumn{2}{c}{**IEEE [57]**} & \multicolumn{2}{c}{**Nature [58]**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Remarks**} \\ \cline{2-2} \cline{6-10}
**Database** & **Phrase1** & **Phrase2** & **Phrase1** & **Phrase2** & **Phrase1** & **Phrase2** & **Phrase1** & **Phrase2** & \\ \hline
**Number of Articles** & 424 & 128 & 190 & 32 & 38 & 9 & 103 & 30 & 924 &
\begin{tabular}{c} Initial Search \\ \end{tabular} \\ \hline
**PA/OA for other** **cancers** & 79 & 30 & 23 & 1 & 7 & 2 & 20 & 5 & 167 & \\ \hline
**PA/OA imaging** **systems** & 62 & 38 & 10 & 2 & 4 & 2 & 16 & 8 & 142 &
\begin{tabular}{c} 431 \\ Articles \\ \end{tabular} \\ \hline
**BC with other** **imaging system** & 24 & 2 & 5 & 0 & 2 & 0 & 5 & 0 & 38 & Excluded \\ \hline
**Others** & 19 & 7 & 1 & 0 & 2 & 0 & 45 & 10 & 84 & \\ \hline
**Remaining** & \multicolumn{6}{c}{Articles from initial search (924)—Articles excluded (431)} & \multicolumn{6}{c}{493} \\
**Articles considered** **for this review** & \multicolumn{6}{c}{Remaining articles (493)—(Duplicates)—(Articles involving studies not having ethics committee/institutional board approval)} & \multicolumn{6}{c}{66} \\ \hline
**Breast Imaging** & 47,514 & 42,036 & 16,907 & 18,740 & 125,197 & \\ \hline PA—Photoacoustic, OA—Optoacoustic, Phrase1—PA imaging for breast cancer, Phrase2—OA imaging for breast cancer. & & & & & \\ \end{tabular}
\end{table}
Table 2: Literature search statistics.  
imaging, and MRI. Prior to proceeding in vivo studies with human patients, some groups performed ex vivo studies with freshly excised breast tissue specimens obtained after mastectomy. This review paper tries to provide a comprehensive account of the chronological, as well as the technological progress of the research related to PA imaging for the early detection of breast cancer.