esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Image Analysis for Classification Challenge
#### 3.1.2 Detection of Etching Status  
Regarding this second task in the image classification challenge, determining whether a metallographic image has undergone chemical etching is necessary. The main reason is that when the image is an etched metallographic sample, the features to be extracted are completely different from those in non-etched images. Thus, in the defined workflow, this detection step is required to determine the subsequent tasks to run.  
In our work, although all tested models reached perfect classification accuracy (100%), their internal confidence levels and prediction consistency varied between them. This fact is especially relevant in practical deployment. A detailed comparative analysis reveals that MobileNetV2 not only achieved perfect accuracy but also exhibited the lowest loss (0.002) and zero false negatives in the etched class. Furthermore, it produced only 18 low-confidence predictions (i.e., probabilities below 85%), making it the most balanced and reliable model for this task.  
In contrast, ResNet50, despite also achieving 100% accuracy, showed the highest number of low-confidence predictions, that is, 41 images with less than 85% confidence, and a greater number of misclassifications within each class. Its elevated loss, specifically 0.02, suggests lower model certainty. These results possibly reflect overfitting or suboptimal feature extraction in this binary classification setting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Loss** \\ \hline Simple Dense (3 layers) & 94.83 & 0.22 \\ \hline Simple Dense (5 layers) & 97.20 & 0.08 \\ \hline ResNet18 & 78.06 & 0.56 \\ \hline VGG11 & 96.12 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Type classifier’ model-based probability estimation.  
Similarly, InceptionV3 produced a moderate number of misclassifications and low-confidence outputs, only 35, despite its high nominal accuracy. These borderline predictions imply weaker confidence calibration and reduced reliability in deployment scenarios.  
Finally, DenseNet121 offered a competitive performance, particularly in identifying etched samples with zero errors in that class and only 28 low-confidence predictions overall. Its low loss (0.004) and stable behavior make it a strong alternative.  
Given these insights, MobileNetV2 was selected as the preferred model due to its robust classification confidence, minimal prediction ambiguity, and overall consistency. A summary of the performance and evaluation results for all models is presented in Table 2.
# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
## 2 Material and Methods
### Pressure Measurements and Pressuring System  
The pressure inside the intracranial chamber, surrounding the brain phantom, represents the ICP measured in clinical settings by intraparenchymal sensors. The pressure inside the intracranial chamber was continuously measured by a needle-tip pressure transducer (CTN-0-4F, Gaeltec Devices Ltd., Edinburgh, UK) connected to an external amplification unit (NL109 Bridge Amplifier-NeuroLog System, Digitimer Ltd., Welwyn Garden City, UK). An identical sensor also measured the systemic 'arterial' pressure (ABP) at the junction between the aortic and the carotid branch.  
The extracerebral space within the intracranial chamber was filled with deionised water, whereas one of the inlets of the chamber was connected to a pressurising unit consisting of a 50 mL burette filled with ionised water. The pressure Pi (i.e., ICP) applied in the extracerebral space by the burette is given by the hydrostatic pressure exerted by the column of water, as in Equation (1).  
\[\mathrm{P_{i}}\ =\ \rho\cdot\mathrm{g}\cdot\mathrm{h} \tag{1}\]  
where \(\rho\) is the density of the fluid, g is the gravitational acceleration constant, and h is the height of the column of water  
By adding water to the column of water in the burette, the ICP was increased, whereas drainage of the water through one of the chamber inlets would allow the pressure to be decreased.
## 3 Nuclear Data
### Derived Nuclear Data  
#### 3.2.1 Random Files in the Nuclear Data Libraries  
For the calculations with TMC, the availability of the random files for every nuclide used in the MCNP geometry model is a prerequisite for the correct assessment of the nuclear data effect for the integral nuclear response. The JEFF libraries do not include any random data files for the uncertainty calculations. Alternatively, the covariance data can be used to generate such files applying the special procedure described below. The TENDL-2017 and 2019 include a large amount of the evaluated data files, more than 2800 in both libraries, including isomeric targets. Additionally, these libraries provide random files for almost all target nuclides. These files were generated by random variations of the nuclear model parameters in the TALYS code within the predefined deviations. Every random file is suitable and valid for particle transport simulations. The TENDL-2017 includes 300-600 random files for O, Si, Fe. The latest version of TENDL-2019 provides 10-300 random files, for almost all target nuclides. For instance, for the most abundant in the Eurofer steel \({}^{56}\)Fe isotope, only 10 random files are presented in the library that is not sufficient for the accurate statistical calculations. Using covariance data similar to the JEFF library, the required number of random files can be generated also for the TENDL libraries for those nuclides where random files are absent or their number is not sufficient.
## 3 Results and Discussion
### Other Discussions  
Using the above prototypes to increase distance during phone conversations will affect other factors, such as convenience and sound clarity. The use of the phone becomes less convenient, and the sound clarity becomes less clear. However, a trade-off must be made as there are no perfect products that can fulfill all aspects. The prototypes created are mainly for staying away from the radiation of the phones, which, in turn, lowers the convenience and sound clarity.  
Overall, the obtained results are consistent in that distancing is an effective method to reduce the magnetic field while using phones. The result shows that it can reduce 100% and 90% of the magnetic fields generated by a smartphone during phone conversations and the phone's standby mode, respectively.
# A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis
## 2 Materials and Methods
### Co-Expression Network Analysis--P-Wgcna and EP-Wgcna  
In this study, the classical P-WGCNA construction was implemented directly using a series of functions provided by the WGCNA package in R language, while the improved EP-WGCNA was implemented by self-coding based on the related functions in the WGCNA package. The main implementation steps of the two are as follows:  
1. Calculate the similarity matrix \(S_{ij}\). Based on the P-WGCNA method, the elements in the similarity matrix are composed of Pearson linear correlation coefficients between gene \(i\) and gene \(j\), that is, \(\left(S_{ij}\right)^{unsigned}=cor(i,j)\); and based on the EP-WGCNA method, the elements are composed of EP_dis between gene \(i\) and gene \(j\), that is, \(S_{ij}=EP\_dis(i,j)\).
2. Define the adjacency matrix \(A_{ij}=\left|S_{ij}\right|^{\beta}\), that is, perform power law operation on the similarity. In order to make the connection between genes in the network follow a scale-free distribution, select the soft threshold \(\beta\) according to the scale-free network model index \(R^{2}\); \(\beta\) is represented in code by softPower. The code implemented by the two methods in this step: #P-WGCNA Adjacency = adjacency (datExpr, power=softPower); #calculate the adjacencies, datExpr represents gene expression matrix #EP-WGCNA Adjacency = adjacency.fromSimilarity (EP_dis, power=softPower) #calculate the adjacencies based on EP_dis
3. Construct a topological overlap matrix \(TOM_{ij}\).
4. Calculate the distance matrix \(\text{disTOM}=1-TOM_{ij}\), construct a hierarchical clustering tree, and use the dynamic pruning algorithm to obtain gene modules. The minimum number of genes in the module is set to 30. Then, merge similar modules with a merge threshold of 0.2.
5. Calculate the correlation coefficient between gene module feature genes and phenotypes (denoted as ME) to identify significant modules. The P-WGCNA used Pearson correlation coefficients, and the EP-WGCNA used EP_dis [12]. The larger the ME value of a module, the more related it is to the phenotype. This study selected modules with \(|\text{ME}|>0.8\) as significant modules.
In the case of an ionic-nonionic mixture, to our knowledge, there are only a few pieces of research [42; 51]. However, there is one critical point of view on the HLD equation. In the case of a mixture of surfactants with different K coefficients, Equation (3) might not always be true. Therefore, [PERSON] et al. (2020) suggested that the HLD should be divided by the K value to avoid this issue. The new formation of HLD is called the normalized hydrophilic-lipophilic deviation (HLD\({}_{N}\)), as presented in Equation (5) [52]. For the ionic-nonionic mixture, the excess free energy term would be replaced by the interaction term, as shown in Equation (6). \[\rm{HLD_{N}=\frac{HLD}{K}} \tag{5}\]  
\[\rm{HLD_{N,mixed~{}ionic-nonionic~{} surfactant}}=\,X_{i}\,HLD_{N,i}+\,X_{n}\,HLD_{N,n}+\,\,interaction~{}term \tag{6}\]  
In this work, we investigated the characteristics of the mixed anionic-nonionic system by a phase behavior study, determined the HLD\({}_{N}\) equations for both single and mixed systems, and predicted the optimal condition in the diesel system via the HLD\({}_{N}\) equations. SDHS was selected as an anionic surfactant. The extended nonionic surfactant for this work was alkyl polyglycol ether (C\({}_{16\text{-}18}\)-(PO)\({}_{4}\)-(EO)\({}_{6}\)-OH), abbreviated as AAE(6 EO4 PO). This commercial nonionic surfactant is a low-foaming emulsifier. According to the testing guidelines of the Organization for Economic Cooperation and Development (OECD), AAE(6 EO4 PO) is identified as a substance harmful to aquatic life in the long term, but it is readily biodegradable [53]. With these intermediate structures, AAE(6 EO4 PO) was expected to improve the solubilization of both the water and the oil phases in the mixed system. The SDHS-AAE(6 EO4 PO) ratios were varied to determine the effect of the extended nonionic surfactant on the optimal condition for these factors, such as the phase transition, interfacial tension, and optimal salinity. These systems were combined with the model n-alkane oils, including n-pentane, n-heptane, n-octane, and n-decane. In this study, diesel was employed as the model oil. The HLDN with diesel oil from the experiment and prediction were compared and discussed. Moreover, as the available literature regarding the HLD and HLDN equations of the anionic-extended nonionic mixture is still limited, this work can contribute to a better understanding and growing knowledge of this field.
## 3 Routing in Satellite Networks  
One of the greatest potentials of satellite networks is the advantage of being able to integrate ground and air networks. This is especially important in rural areas, areas with insufficient infrastructure, and natural disaster areas. Integrating this potential of satellite networks with developing mobile band technologies will produce exceptional outputs at the point of access anywhere at any time.
# Recent Advances in Si-Compatible Nanostructured Photodetectors
## 3 Quantum Dot Photodetectors
Nanowires are structures where motion of carriers is limited in two of three directions. In work [47], a silicon-on-insulator substrate was used for the growth of Ge quantum dots on silicon with subsequent fabrication of a quantum wire. Ge quantum dots were synthesized by molecular beam epitaxy. Quantum wires with the length of 10 \(\upmu\)m and the width of 100 nm were fabricated by electron lithography. Figure 10 shows the cross-section of the fabricated photodetector. Figure 10: Schematic diagram of phototransistor with single Si nanowire decorated with Ge quantum dots. Figure 9: Schematic diagram of the Ge/SiGe photodetector with 10 layers of quantum dots. This study demonstrated a simple and scalable fabrication process for achieving high-performance short-wave infrared photodetectors using Ge quantum dots in a single Si nanowire on CMOS-compatible SOI platforms. Moreover, the measurement of photocurrents with varying polarization of light revealed that the device can act as a polarization-sensitive photodetector [47].
## 4 Results and Discussion
### Two-Dimensional Examples
#### 4.1.3 The Verification of Mesh Irrelevance  
The calculation results rely on the custom filter radius and mesh density, and their relationship is defined as \(r_{f}=h\eta\), where \(h\) is the mesh size as the reciprocal of the mesh density, and \(\eta\) is the length ratio parameter of the filter radius that we introduce. To maintain \(r_{f}\) unchanged, \(\eta\) changes with the variation of \(h\). After numerical verification, \(\eta\) of 2-3 is appropriate for this problem. In order to verify the correlation between the obtained results and the mesh density, the mesh densities of 10, 30 and 60 are taken to calculate the drag values corresponding to different Reynolds numbers with the same other influencing factors. Table 1 shows the drag values of the 2D problem for each Reynolds number at different mesh densities when the area fraction is 0.8. The unit of drag is N. Although the mesh density has an effect on the flatness of the edges of the generated shape, it can be known that the effect error on the final drag value is less than 1%, which verifies that the result has very little dependence on mesh density within the appropriate range.
# Study on Electromagnetic Radiation Interference Caused by Rocket Fuel
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Complexity Index** & **Classification** & **Threat Level** \\ \hline Level A & \(0\leq\sqrt{FO{2}\times TO\times SO}\leq 10\%\) & Level I & \(0\leq\sqrt{FT\times TT\times ST}\leq 10\%\) \\ Level B & \(10\%\leq\sqrt{FO{2}\times TO\times SO}\leq 30\%\) & Level II & \(10\%\leq\sqrt{FT\times TT\times ST}\leq 30\%\) \\ Level C & \(30\%\leq\sqrt{FO{2}\times TO\times SO}\leq 50\%\) & Level III & \(30\%\leq\sqrt{FT\times TT\times ST}\leq 50\%\) \\ Level D & \(50\%\leq\sqrt{FO{2}\times TO\times SO}\leq 70\%\) & Level IV & \(50\%\leq\sqrt{FT\times TT\times ST}\leq 70\%\) \\ Level E & \(70\%\leq\sqrt{FO{2}\times TO\times SO}\leq 100\%\) & Level V & \(70\%\leq\sqrt{FT\times TT\times ST}\leq 100\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification standards for electromagnetic radiation. According to the experimental results shown in Figures 5 and 6, the important data for the evaluation index of the electromagnetic radiation influence could be obtained, including the time interval [t\({}_{\text{s}}\),t\({}_{\text{E}}\)] and the frequency interval [f\({}_{\text{jmin}}\),f\({}_{\text{jmax}}\)] of the electromagnetic radiation source. Because the relevant parameters of Device A were known, the parameters of the electromagnetic radiation source and Device A were substituted into Equations (1)-(3) and (6), (9), (12) to obtain the time occupancy of TO, the spectrum occupancy of FO, the average power density spectrum of SO, the spatial threat degree of ST, the time-domain threat degree of TT, and the frequency-domain threat of FT. The above parameters could be used to calculate the electromagnetic radiation complexity index and electromagnetic radiation threat level. The classification standards for the electromagnetic radiation threat level are summarized in Table 1.  
Figure 5: Time-domain signal of electromagnetic radiation measurement experiment under the following conditions: (a) Case 1—Lox-Hydrogen; (b) Case 2—H2O4-Hydrazine; (c) Case 3—AP-HTPB-Al; (d) Case 4—Lox-Methane; (e) Case 5—Lox-Propane; (f) Case 6—Lox-Kerosene. Figure 6: Frequency-domain signal of electromagnetic radiation measurement experiment under the following conditions: (a) Case 1—Lox-Hydrogen; (b) Case 2—H2O4-Hydrazine; (c) Case 3—AP-HTPB-Al; (d) Case 4—Lox-Methane; (e) Case 5—Lox-Propane; (f) Case 6—Lox-Kerosene. radiation generated by six sets of rocket fuels are shown in Table 4. The data in Table 4 caused the electromagnetic radiation generated by the deflagration of high energetic materials to have a clear evaluation index, and the threat degree of electromagnetic radiation on electronic equipment could be classified in detail, which was helpful in terms of improving the anti-electromagnetic interference of avionics, and finally, to improve the safety of aviation missions.
## 6 Routing Protocols in EH-WSNs  
In order to effectively manage data transfer in EH-WSNs, routing protocols are necessary [102, 103]. Routing protocols in EH-WSNs must handle the particular difficulties brought forth by fluctuating network circumstances, dynamic network topologies [104], and energy limitations [105]. The main routing protocols used in EH-WSNs are examined in this section with an emphasis on their effectiveness, energy management, and security implications. EH-WSN routing protocols are developed to maximize data transfer while preserving energy and guaranteeing dependable connection. Among the primary categories of routing protocols are the following:  
* Direct Routing Protocols: Send data directly from the source to the destination without intermediate nodes.
* Hierarchical Routing Protocols: Use a tiered structure where nodes are grouped into clusters, and data are routed through cluster heads.
* Geographic Routing Protocols: Utilize the geographic location of nodes to determine the routing path.  
In addition, the energy-efficient routing protocols aim to minimize energy consumption while ensuring reliable data transmission. Key strategies include:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Physical Layer Security (PLS)** & **Traditional Encryption** \\ \hline Computational Overhead [99] & Low & High \\ \hline Security Level [100] & High in adverse Conditions & High with proper key management \\ \hline Implementation Complexity [101] & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of PLS and traditional encryption methods.  
* Energy-Aware Routing: Select routes based on the energy levels of nodes to extend the network's lifetime.
* Load Balancing: Distribute the data transmission load evenly across the network to avoid energy depletion in specific nodes.
* Sleep Scheduling: Implement sleep-wake cycles to conserve energy by putting nodes into low-power modes when not actively transmitting data. Furthermore, the multi-hop and multi-path routing protocols improve reliability and robustness in EH-WSNs by:
* Multi-hop Routing: Data are transmitted through multiple intermediate nodes before reaching the destination, which helps in overcoming long-distance transmission challenges.
* Multi-path Routing: Multiple paths are used for data transmission, providing redundancy and load balancing to improve reliability and fault tolerance.  
Security is crucial in EH-WSNs to protect against various attacks, such as eavesdropping, data tampering, and denial of service. Key security considerations include the following:  
* Data Encryption: Encrypt data during transmission to prevent unauthorized access.
* Authentication: Verify the identity of nodes to prevent malicious nodes from participating in the network.
* Secure Routing Protocols: Implement routing protocols designed to resist security threats and ensure data integrity.
## 3 Methodology
### U-Trf  
Based on the block described above, the proposed U-TFF network architecture is shown in Figure 3. It is inspired by the well known U-Net, which includes one encoder and a decoder in a U-shaped architecture. The encoder acts in a contracting path by including several TFF blocks, the output size of which is smaller than its input. Moreover, the encoder is responsible for capturing context and extracting high-level features from the input by representing them as vectors in the reduced-dimensional latent space. On the other hand, the decoder, in other words the expansive path, is the symmetric counterpart to the encoder. It consists of the same number of Time-Frequency Fusion blocks, with each block having an increased output dimension compared to its input. The purpose of the expansive path is to restore the data sequence dimension while retaining the context learned in the contracting path in order to complete the reconstruction process.  
One of the key features of the proposed network is the use of skip connections (the gray dashed arrows) between corresponding blocks in the encoder and decoder. More specifically, the input to each block of the decoder within the network is the combination of the output from the previous block and the output from the corresponding same-level  
Figure 3: Overview of the proposed U-TFF network.  
Figure 2: The architecture of the Time–Frequency Fusion block.  
block of the encoder. The process of such a combination is represented as the red \"Concat Block\" in Figure 3, where the element-wise sum between two outputs is implemented. These skip connections facilitate the direct flow of information from the encoder to the decoder in order to prevent the loss of essential information during the downsampling process in the encoder. Skip connections also mitigate the vanishing gradient problem commonly encountered during training of deep neural networks. By providing shortcut connections, gradients can flow more directly from the output to the input layers, thereby addressing the issue of diminishing gradients. This results in more stable and efficient network training.  
Because the neural network is intended for reconstruction, the loss function used for model training is defined as the 2-norm of the error (i.e., the mean square error) between the original input segment \(S_{i}\) and the reconstructed segment \(S_{i}\).  
\[L_{rec}=\|S_{i}-S_{i}\|_{2} \tag{1}\]  
Anomaly detection during the online testing phase is also based on the reconstruction error defined in Equation (1). The detector applies a static threshold to generated reconstruction errors. If the error is higher than the predefined threshold, the corresponding data point is classified as an anomaly. The value of the threshold is determined so as to be slightly larger than the maximum reconstruction error of the training dataset. Such a strategy can mitigate misclassification problems when other unobserved normal patterns produce larger reconstruction errors.