esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Datasets
#### 3.1.2 BACH Dataset  
The BACH dataset, introduced by ICIAR 2018 (International Conference on Image Analysis and Recognition), is designed for breast cancer histopathological image classification. It consists of 400 high-resolution (2048 \(\times\) 1536 pixels) H&E-stained images. The images are equally distributed across four tissue categories: Normal, Benign, In situ carcinoma, and Invasive carcinoma, with 100 images per class, all annotated by expert pathologists. To accommodate different levels of classification tasks, this study establishes both four-class and two-class classification settings. In the two-class classification task, the Normal and Benign categories are merged into the Benign class, while In situ carcinoma and Invasive carcinoma are combined into the Malignant class, simulating a typical clinical scenario of benign versus malignant decision making in breast cancer screening.
###### Abstract  
The tremendous development of both optical wireless communications (OWC) and implantable medical devices (IMDs) has recently enabled the establishment of transdermal optical wireless (TOW) links that utilize light waves to transfer information inside the living body to the outside world and conversely. Indeed, numerous emerging medical applications such as cortical recording and telemetry with cochlear implants require extremely high data rates along with low power consumption that only this new technology could accommodate. Thus, in this paper, a typical TOW link is investigated in terms of outage capacity which is a critical performance metric that has so far not been evaluated for such wireless systems in the open technical literature. More precisely, an outage capacity analysis is performed considering both skin-induced attenuation and stochastic spatial jitter, i.e., pointing error effects. Analytical expressions and results for the outage capacity are derived for a variety of skin channel conditions along with varying stochastic pointing errors which demonstrate the feasibility of this cross-field cooperation. Lastly, the corresponding simulation outcomes further validate our suggestions.  
optical wireless communications (OWC); transdermal optical wireless (TOW) links; implantable medical devices (IMDs); outage capacity; pointing errors +
Footnote †: journal: _Photonics_
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 5 Packet Format  
A packet is composed of a header and a payload. There are seven header fields, namely S-ID, D-ID, Packet Type, Seq#, ST_ID, Middle Node, and Last Node. The total length of the packet header is 37 bits. The packet header format is shown in Figure 12. The first two fields are the source node ID (S-ID) and the destination node ID (D-ID). The D-ID will be FF for broadcast communication. The packet type field helps the receiving node to identify the type of the packet. Total length of the packet type field is eight bits. Seven bits out of eight bits are used for the unique code for each packet type (shown in Table 1) and the last bit (LSB) is used to indicate the ring topology. LSB \"1\" indicates that the topology is the ring topology. In case of multiple transmissions of the same packet, such as the RR packet, the three-bit sequence number (Seq. #) is used to identify duplicate packets. The string identification (ST_ID) is represented by six bits. With the Middle Node field, the gateway informs its neighbors, in the RR packet, whether they are side nodes or intermediate nodes. The Last Node field is used by the last node in a string when it sends a CF_RR or a CFRR_BACK packet. The list of packet names, their description, and code is given in Table 1.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 4 A Novel Self-Organizing Approach to Drone-Based MANETs
### Drone Movement  
Drone movement is determined by a force \(f\) seemingly _pushing_ (or _pulling_) the drone in a specific direction (\(f\) is given by the separate forces acting in the \(x\) and \(y\) direction, respectively). In our case, the force \(f\) is the weighted sum (see Section 4.1.4) of three separate forces (see Sections 4.1.1-4.1.3). These three forces are (a) the _attraction_ a node experiences towards its clients, (b) the _repulsion_ it experiences from other nodes (other than its parent) and (c) the node's _desire_ to converge towards a given (as a parameter) optimal distance to its parent node (i.e., either _attractive_ or _repulsive_), respectively.  
The calculation of these contributing forces uses (a) \(r_{i,j}\) the distance between the entities \(i\) and \(j\) (calculated as Euclidean distance, cf. Equation (4)) as well as \(dx_{i,j},dy_{i,j}\), the delta between their \(x\) and \(y\) position. The _direction_ of this delta (i.e., its sign) will determine the direction of the resulting force (see the use of \(r\) and \(dx,dy\) in Equations (5)-(7)).  
\(r_{i,j}\), the Euclidean distance between the entities \(i\) and \(j\), is calculated as usual:  
\[r_{i,j}=\sqrt{dx_{i,j}^{2}+dy_{i,j}^{2}}\text{with}\begin{aligned}  dx_{i,j}&=x_{i}-x_{j}\\ dy_{i,j}&=y_{i}-y_{j}\end{aligned} \tag{4}\]  
Please note that while \(r_{i,j}=r_{j,i}\), this symmetry does not hold for \(dx\) or \(dy\) (meaning that \(dx_{i,j}\
eq dx_{i,j}\)) as either of these is directional, i.e., will always differ in their sign when reversed (\(dx_{i,j}=-dx_{i,j}\)). This comes back in Equations (5)-(7) where it determines the resulting direction of the force, i.e., whether the force is _attractive_ or _repulsive_.
## 3 Monitoring and Detection Technology of SF\({}_{6}\) and Its Decomposition Components
### Raman Spectroscopy (RS)  
Raman spectroscopy is one of the spectroscopic techniques that can be used for gas sensing. Raman spectroscopy uses a single-wavelength laser that enables simultaneous analyses of most gases. However, the Raman scattering cross-section is relatively small, and the Raman signal provided by the gas is very small [48; 49]. Fiber-reinforced Raman spectroscopy (FERS) improves the Raman signal of the gas. The gas fills the core of the hollow optical fiber, which allows the optical fiber to be used in a gas battery so that the Raman signal is greatly improved [50; 51]. Cavity-enhanced Raman spectroscopy (CERS) can also improve Raman signals, with increased laser power based on optical interference at its core. Multiple-pass cavity, Fabry-Perot cavity, laser cavity, and microcavity are several commonly used, strong enhancement techniques that can improve the Raman scattering intensity and LOD of Raman spectral gas sensors [52].  
Cavity-enhanced Raman spectroscopy (CERS) has high sensitivity, selectivity, and accuracy of results and high availability for a variety of gas measurements. [PERSON] et al. [10] designed a dissolved gas analysis (DGA) method using a CERS device (Figure 5a) based on a 110 kV power transformer for equipment diagnosis. SF\({}_{6}\) was used as the internal standard gas. CERS equipment extracts and analyzes dissolved gases. Figure 5b,c shows the Raman spectrum of CO and CO\({}_{2}\) at 0.5 kPa. The limit of detection (LOD) values for CO (2142 cm\({}^{-1}\)) and CO\({}_{2}\) (1388 cm\({}^{-1}\)) were 1.22 Pa and 0.54 Pa, respectively. Finally, the ppmv level sensing was achieved by N\({}_{2}\), O\({}_{2}\), CO\({}_{2}\), CO, and H\({}_{2}\) at a total pressure of 1 bar. If the exposure time is more than 10 min, subppm level LOD can also be achieved. The Raman signal obtained by CERS has been shown to have an excellent linear relationship with gas partial pressure, laser power, and exposure time.  
Based on density functional theory, experts built a simulated Raman spectrogram, designed a gas sample pool, and set up an experimental platform to study the pretreatment method and spectral signal enhancement technology of Raman spectrogram detection of characteristic components of sulfur hexafluoride gas decomposition and made modifications. The field detection and application research of the characteristic components of sulfur hexafluoride gas decomposition based on Raman spectroscopy were carried out and put into production after repeated use, improvement, and verification.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 4 Results
### Effect of Adjacent Wall Deflection  
The effect of spatial relationships between adjacent monitoring points is demonstrated by comparing the performance of the ST-GAT neural network under different monitoring point combinations. The results evaluated using MAE are presented in Figure 13. The result shows a clear variance in MAE for different monitoring point combinations. For example, combinations like (4,6), (3,6), and (3,4,6) tend to have higher MAE values, especially for wall deflection point 6, which consistently shows the highest errors across several combinations. The largest MAE values are observed for wall deflection 6 across multiple combinations, indicating that predicting this monitoring point is more challenging. This may be due to its proximity to the maximum deflection point of the inclinometer and the fact that the support is installed last in this area, leading to the greatest impact. However, when multiple monitoring points are used together, such as in the combination (2,3,4,6), the prediction error for deflection 6 improves significantly. This demonstrated that utilizing the spatial relationships between multiple monitoring points can lead to better performance for all deflection points.  
Figure 12: Prediction results of ST-GAT neural network.
## 4 Experiment and Analysis  
### Dataset Selection  
Figure 4 shows the long time-series variation diagrams of the seven indices over the four vegetation types. For all four vegetation types, the general trend of NMRI is consistent with that of NDVI, and it shows obvious annual cycle variability with one peak. GPP and LAI have similar variation trends and are more consistent with NMRI. For NDWI and NDII, the annual variation cycle is obvious but different from that of NMRI with two peaks in each cycle. Then, we analyzed the correlation between NMRI and other VWC-related indices among the 329 sites during the 10 years, as shown in a statistical bar chart featuring the number of sites in different ranges of correlation coefficients (R) in a 10 year range and the statistical distribution box charts of the R among the 329 sites of each year (Figure 5). The distribution of the R in each year is about the same. For NDVI, the R of most of the sites is between 0.2 and 0.6. For NDWI and NDII, their correlation with NMRI is much lower than that of NDVI, with most of the sites concentrated in the range of 0 to 0.4. When it comes to GPP and LAI, the results are clearly different. The correlation between GPP (LAI) and NMRI is very high with R of most of the sites concentrated on 0.6-0.9 (0.5-0.8). However, R values between precipitation and NMRI are relatively low, between \(-0.4\) and 0.4, and the distribution of positive and negative values is symmetrical.  
Figure 4: The long time-series variation diagrams of the seven indices over the four vegetation types: **(a)** Savannas; **(b)** cropland; **(c)** shrubland; **(d)** grassland.  
Figure 5: The bar charts and box charts of the correlation coefficients (R) between the six indices and NMRI: (**a1–f1**) bar charts of the correlation coefficients between the indices and NMRI in the different regions during the 10 years; (**a2–f2**) statistical distribution box charts of the correlation coefficients of each year and the total of the 10 years from 2007 to 2016.  
Based on the above analysis, the conclusion can be drawn that the overall correlation between NMRI of GPP and LAI is the highest, NDVI is the second, NDWI and NDII are smaller still, and precipitation is the lowest. Then, to reduce data redundancy, we analyzed the correlation between the six VWC-related indices during the 10 years (Figure 6). It indicates that the correlation between GPP and LAI is particularly high, in that the R can reach 0.9. Finally, the ultimate fusion input datasets are formed by NDVI, GPP, and precipitation, along longitude, latitude, and date, considering requirements in Section 3.1. The longitude, latitude, and date were added to introduce temporal and spatial information. The NDWI and NDII were removed owing to their low correlation with NMRI. The meteorological factor precipitation was retained for the change of precipitation directly causes the change of soil moisture, which may have a lag effect on the growth of vegetation and the variation of VWC.
###### Abstract  
Electrical resistivity tomography (ERT) is a well-known geophysical method applied to geological, hydrogeological and geoenvironment research. To date, 2D ERT is still used much more than 3D ERT, thanks to its greater immediacy, survey speed and lower complexity in processing and inversion. However, the assumption of two-dimensionality of the underground structures can mean that the effects of 3D structures on the 2D ERT can sometimes lead to gross errors in interpretation. This work aims to evaluate these effects by testing synthetic and experimental models. Numerical simulations are performed starting from different resistivity models, and from the results, 2D data sets are derived to study and quantify the effects of 2D inversion on 3D structures. Tests simulating prismatic resistive blocks with a vertical square section are presented. Prisms extend orthogonally to the survey line. Depending on their length, they range from a minimum equal to the length of the section (cubic resistive block) to infinity (2D prism). On these models, 2D and 3D electrical resistivity tomography (ERT) surveys are simulated. The results show that resistive blocks with a limited extension orthogonal to the profile are not effectively resolved by 2D ERT. Additionally, resistivity values obtained from a 2D ERT inversion on a 3D resistive prism are underestimated more than those obtained on the corresponding 2D prism when compared with the true value. This underestimation increases with the three-dimensional characteristics. Furthermore, resistive blocks located near the survey line but not crossed by it create artifacts that can lead to erroneous interpretations. A field test performed on a calcarenite quarry, of which the three-dimensional geophysical model was reconstructed, confirmed the results obtained by the synthetic tests, highlighting that the effects of three-dimensionality can lead to the identification of artifacts in the vertical section or produce strong errors in the estimation of depth and size, thus causing misleading statements.  
Applied geophysics; electrical resistivity tomography; 3D structures; artifacts; cavity; inversion Article
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Print Quality Problems
#### 3.3.4 Sparse Top Fill  
The top layers of the print are not completely solid. There are gaps and holes between the extrusion paths for the solid layers. This could be due to not having sufficient solid layers on top of the hollow infill, the infill percentage is set too low, and/or having under-extrusion. Solutions include the following:  
1. Ensuring that the top solid layers are at least 5 mm thick. If each layer height is 0.25 mm, then two layers are required; for 0.1 mm, five layers are required.
2. The extruded solid layer may droop or sag if there are no supports. Increase the infill percentage to create a better foundation for the top solid layers.
###### Abstract  
The usage of Visible Light Communications (VLC) technology in automotive applications is very promising. Nevertheless, in outdoor conditions, the performances of existing VLC systems are strongly affected by the sun or other sources of light. In such situations, the strong parasitic light can saturate the photosensitive element and block data communication. To address the issue, this article analyzes the usage of an adaptive logarithmic transimpedance circuit as an alternative to the classical linear transimpedance circuit. The simulation and experimental evaluation demonstrate benefits of the proposed technique, as it significantly expands the communication distance and optical noise functionality range of the VLC systems and reduces the possibility of photoelectron saturation. As a result, this approach might enable outdoor VLC sensors to work in strong sun conditions, the experimental results confirming its validity not only in the laboratory but also in outdoor conditions. A reliable 50 m communication distance is reported for outdoor sunny conditions using a standard power traffic light VLC emitter and a PIN photodiode VLC sensor.  
Keywords:noise; logarithmic transimpedance circuit; inter-vehicle communications; visible light communications +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Experiment Setup
#### 3.3.4 Sampling Rate of the Audio Signals  
Sampling rate has a direct effect on the quality of the recorded audio, on storage requirements, and on computational resources needed. It was found that almost 34.4% of the articles did not mention their audio sampling rate, while 18.0% and 9.8% mentioned using 44.1 kHz and 22.05 kHz, respectively. As various studies have emphasized that acoustic events of interest lie typically between 100-1000 Hz [69; 110], some studies relied on downsampling from 22.05 kHz to 4 kHz [31; 46; 55], or even 3 kHz [89], to reduce storage requirements while still keeping useful information for analysis.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 3 Structural-Missing Tensor Completion
### ADMM for Solving the Hankel Tensor Completion Problem
Subsequently, at the \((w+1)\)-th iteration, the variables \(\boldsymbol{\hat{\mathcal{H}}},\boldsymbol{\mathcal{Y}}_{j}\) and \(\boldsymbol{\mathcal{G}}_{j}\) can be, respectively, updated as  
\[\begin{split}\boldsymbol{\mathcal{Y}}_{j}^{(w+1)}& =\text{argmin}_{\boldsymbol{\mathcal{Y}}_{j}}\ \mathcal{L}\Big{(}\boldsymbol{\hat{\mathcal{H}}}^{(w)},\boldsymbol{ \mathcal{Y}}_{j},\boldsymbol{\mathcal{G}}_{j}^{(w)}\Big{)},\\ \boldsymbol{\hat{\mathcal{H}}}^  
Using the principle of gradient descent to solve (13), we can obtain the closed-form solution to \(\mathbf{\mathcal{Y}}_{j}^{(w+1)}\) and \(\mathbf{\mathcal{H}}^{(w+1)}\), i.e.,  
\[\mathbf{\mathcal{Y}}_{j}^{(w+1)}=\text{fold}_{(j)}\Big{(}\text{Y}_{ \frac{\alpha_{j}}{\rho}}\Big{(}\big{[}\mathbf{\mathcal{H}}^{(w)}\big{]}_{j}+\frac{1 }{\rho}\big{[}\mathbf{\mathcal{G}}_{j}^{(w+1)}\big{]}_{j}\Big{)}\Big{)},\] \[\text{P}_{\Omega}\Big{(}\mathbf{\mathcal{H}}^{(w+1)}\Big{)}=\text{P}_ {\Omega}\Big{(}\frac{1}{6}\Big{(}\sum_{j=1}^{6}\mathbf{\mathcal{Y}}_{j}^{(w+1)}- \frac{1}{\rho}\mathbf{\mathcal{G}}_{j}^{(w)}\Big{)}\Big{)}, \tag{14}\] \[\text{P}_{\bar{\Omega}}\Big{(}\mathbf{\mathcal{H}}^{(w+1)}\Big{)}= \text{P}_{\bar{\Omega}}(\mathbf{\mathcal{H}}),\]  
where  
\[\text{Y}_{\frac{\alpha_{j}}{\rho}}(\mathbf{U})=\mathbf{F}_{l}\mathbf{\Sigma}_{U}^{\big{(} \frac{\alpha_{j}}{\rho}\big{)}}\mathbf{W}_{U} \tag{15}\]  
denotes the shrinkage singular value decomposition of a matrix \(\mathbf{U}\in\mathbb{C}^{U_{1}\times U_{2}}\), \(\mathbf{F}_{l}\), \(\mathbf{W}_{U}\) represent the left singular matrix and the right singular matrix, respectively, and  
\[\mathbf{\Sigma}_{U}^{\big{(}\frac{\alpha_{j}}{\rho}\big{)}}=\text{diag}\Big{(} \max\big{(}\eta_{l}-\frac{\alpha_{j}}{\rho},0\big{)}\Big{)}. \tag{16}\]  
Here, \(\eta_{l}\) denotes the singular value of \(\mathbf{U}\), \(l\in\big{\{}1,2,\cdots,\min(U_{1},U_{2})\big{\}}\), \(\min(\cdot)\) and \(\max(\cdot)\), respectively, denote the minimum and maximum value, and \(\text{diag}(\cdot)\) forms a diagonal matrix from its arguments. By repeating the above updating procedure, we can finally obtain the optimized variable, i.e., the completed Hankel tensor \(\mathbf{\mathcal{\hat{H}}}\). In particular, the ADMM for solving the Hankel tensor completion problem converges when the relative error of the completed Hankel tensor between two iterations is less than a threshold \(\xi>0\), that is,  
\[\frac{\|\mathbf{\mathcal{\hat{H}}}^{(w+1)}-\mathbf{\mathcal{\hat{H}}}^{(w)}\|_{\text{ F}}}{\|\mathbf{\mathcal{\hat{H}}}^{(w)}\|_{\text{F}}}\leqslant\xi. \tag{17}\]  
Note that, the ADMM for solving the general tensor nuclear norm optimization problem is proven to be globally convergent. The detailed proof is provided in [43].
# Effect of Cu, Cr, S Doped TiO\({}_{2}\) for Transparent Plastic Bar Reinforced Concrete
## 5 Conclusions  
In this study, in order to increase the NOx removal efficiency from visible light, Cu, Cr, and S were mixed with NP-400 TiO\({}_{2}\) for ultraviolet reaction, then calcined and powdered, and through the NO removal rate test, the possibility of utilizing the visible photocatalyst of TPBRC was analyzed and evaluated.  
1. As the photocatalyst mixing rate increased, the slump flow of UHPC gradually decreased. It is believed that an increase in the amount of mixed photocatalyst fineness induces a decrease in slump flow. As a result of the J-Ring test, all test specimens showed a pass rate of less than 25 mm, and the L-Box test had a pass rate of 0.8 or higher, so it was judged that good material dispersion during the pouring process was possible.
2. Compressive strength is 100 MPa or more, and the flexural test has confirmed the test results of 7.0 to 8.4 MPa, and it is judged that there will be no major difficulties in manufacturing interior and exterior materials in the future.
3. SEM and EDS analysis of TiO\({}_{2}\) in powder form after calcination at 1000 \({}^{\circ}\)C for 24 h showed that TiO\({}_{2}\) doped with Cu had the best microstructure and EDS peak value. In addition, as a result of the XRD test, anatase peaks were confirmed for Cu, Cr, and S.
4. As a result of confirming the TiO\({}_{2}\) energy band gap in the UV-visible absorption spectrum test, TiO\({}_{2}\) doped with Cu has a strong absorption of 0.8 up to 700 nm, and the inherent band gaps have also decreased from 3.3 to 2.8 eV. It was confirmed that the TiO\({}_{2}\) test specimen doped with Cr and S had a sharp drop in absorption from 400 nm.
5. The specimen mixed with TiO\({}_{2}\) doped with Cu showed a removal rate of about 15.8% for 5 h in the NO removal test, showing the best results among the specimens. When applied to TPBRC using this method, visible light is transmitted through the inside of the test specimen. Considering the photocatalytic effect on the other side of the specimen, it is believed that about 25.3% of NO removal efficiency can be secured.
6. Although the test results for the NOx removal test could not be derived due to the lack of the test method for the TPBRC test body, it confirmed the possibility that harmful substances in the indoor space can be improved due to photocatalytic reaction under visible light when considering the light transmittance and the exposed area on the concrete surface.  
[PERSON]-I.K. conceived and designed the experiments; [PERSON] and [PERSON]-I.K. performed the experiments; [PERSON] and [PERSON]-I.K. analyzed the data; [PERSON]-[PERSON] and [PERSON]-I.K. wrote the paper. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Land, Infrastructure and Transport Construction Technology Research Project, grant number 20 SCIP-B146255-03.  
The authors declare no conflict of interest.