esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The presented multimodal imaging system was successfully applied to distinguish GM and WM of ex-vivo mouse brain tissue. The brightfield images of the H&E-stained sample highlighted differences in the cellular structure of the tissue. Brightfield and darkfield images emphasized the brains morphology by complementary imaging modes. Although the information of the spectral reflectivity was exclusively based on surface characteristics, VIS spectroscopy seems to be a promising tool to receive hyperspectral data to calculate tissue absorptions, if blood is present. Molecular-specific information regarding tissue components was achieved by Raman spectroscopy. The best result regarding the distinction of GM, WM, and OCT within the global scan was achieved by an intensity peak ratio in the fingerprint region (I\({}_{1444}\)/I\({}_{1660}\)). Using this evaluation method GM, WM, as well as tissue structures like a tissue fold and a blood vessel, were highlighted within the local scan. The discrimination of the blood vessel, GM and WM was also possible with a second evaluation method (I\({}_{2857}+I_{2888}\))/I\({}_{2930}\). Additionally, this evaluation method enabled the identification of differences regarding the density of myelinated axons within WM. The created image was in good accordance with the image obtained with polarization microscopy (active WWC). For further applications, it is recommended to use microscopic-based images to get an overview and determine ROIs. A combination of the image obtained from the H&E-stained sample and the various images of the unstained sample is recommended. The spectroscopic modalities, especially the molecular specific but time-consuming Raman spectroscopy, should be applied to designated areas to create images of the tissue's molecular composition.  
Conceptualization, A.H., T.B., and M.R.; methodology, A.H., J.D., and L.S.; software, S.S. and F.W.; validation, A.H. and T.B.; investigation, A.H. and J.D; resources, J.D., L.S., and M.R.; data curation, S.S. and F.W; writing--original draft preparation, A.H. and T.B.; writing--review and editing, S.S., F.W., J.D., L.S., and M.R.; visualization, A.H. and F.W.; supervision, A.H., T.B., and M.R.; project administration, A.H.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
The article processing charge was funded by the Baden-Wurttemberg Ministry of Science, Research and Culture as part of the open-access publication fund. L.S. was supported by intramural funding provided by the Medical Faculty Mannheim of Heidelberg University and a research grant from the Hertie Foundation (medMS MyLab, P1180016).  
The C57/Bl6J mouse was purchased from Janvier labs (Le Genest-Saint-Isle, France). The mouse was housed under specific pathogen-free conditions at the animal facility Mannheim and kept on a 12 hr light/dark cycle. Food and water were provided ad libitum. The animal experimental protocol was approved by the local animal ethics committee (I-20/11).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Machine Learning Tools
Unlike PCA, which is primarily exploratory, PLS identifies latent variables that maximize the covariance between independent and dependent variables, making it particularly useful for predicting functional and compositional attributes from RVA profiles [16, 18]. The advantage of PLS is its ability to handle collinear data, extract meaningful relationships between RVA parameters and starch properties, and improve model interpretability. However, PLS models require proper validation (e.g., cross-validation or independent validation) to prevent overfitting, especially when dealing with small sample sizes. Additionally, while PLS improves the predictive accuracy, it still relies on well-defined input-output relationships and can be sensitive to noise in the dataset. In recent years, other algorithms have also been explored and reported to analyze the RVA profile such as support vector machines (SVMs), either as a classification or regression tool, artificial neural networks (ANNs), K-Nearest neighbors (KNNs), or convolutional neural networks (CNNs) [16, 17, 18, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. In the context of the RVA, ML techniques such as PCA, PLS, and PLS discriminant analysis (DA) have been used the most routinely, as demonstrated in Table 2, and appear to be the most relevant for research applications. Please note that it is beyond the objective of this review to provide detailed information about these algorithms, on which detailed information can be found elsewhere [22, 23, 24]. It is important to highlight that during the application or implementation of ML tools, not only is the selection of the algorithm important (e.g., PLS or ANN), but also, other variables such as the sampling method, the selection of samples and replicates, as well as the validation method used (e.g., cross-validation vs. independent validation) are key elements that need to be considered during the development and interpretation of the ML models.
# A High-Resolution Texture Mapping Technique for 3D Textured Model
## The Proposed Texture Mapping Method
### Mesh Parameterization and Packing
The objective in packing is to let each mesh island occupy as much space as possible, thereby maintaining the resolution of the texture as close to that of the 2D images as possible. Therefore, we consider how to efficiently arrange the mesh islands on the UV map. First, an oriented boundary box (OBB) method [41] is employed to construct a best-fit boundary box for each mesh island, as shown in Figure 4a. Using the OBB method to arrange the islands ensures that less space on the UV map is wasted compared to when using the axis aligned bounding box (AABB) method shown in Figure 4b. Next, the mesh islands are arranged together according to their OBB lengths on the UV map, as shown in Figure 4c.
## 2 Theoretical Foundation  
### Surveillance System  
Reference [5] specifies the details of surveillance systems used to detect aircraft and send detailed information related to the air traffic control system. Air traffic control is not possible without surveillance systems, especially in areas with a high density of air traffic.  
Surveillance is provided primarily by primary and secondary radars. In addition, according to [6], the \"surveillance of air traffic has several applications, which have been growing over time. Each one of them pursues its own objectives and has particular needs, especially regarding the accuracy of the data\".  
Likewise, [7] considers that \"States have several radar installations, which have a high maintenance cost and limited benefits for achieving the requirements of the current operational concepts and the goals of the different aviation system improvement modules by blocks\" (ASBU).  
In many cases, they do not fully cover the flight information region (FIR) and they have cones of silence that affect the air traffic surveillance, so the implementation of new systems with lower installation and operating costs is inevitable but also brings considerable performance improvements. This means that the only issue is deciding which one to implement.
## 4 Proposed Antenna Codebook and MAB Approach
### Proposed Nested Two-Stage MAB Approach
#### 4.2.1 Proposed Nested Two-Stage TS Algorithm
Figures 6 and 7 show the spectral efficiency against the TX signal-to-noise ratio (SNR), i.e., \(10\log_{10}\left(\frac{P}{\epsilon_{0}^{2}}\right)\), by changing the value of TX power \(P\) at zero blockage, when \(K=M=N=16\), and when \(N=36\), \(M=64\), and \(K=16\). Generally, as the value of \(P\) is increased, the spectral efficiency of all schemes is linearly increased. In addition, the spectral efficiencies in the case of \(N=36\) and \(M=64\) are slightly higher than those in the case when \(N=16\) and \(M=16\) due to the increase in beamforming gain resulting from increasing the number of antenna elements. From Figures 6 and 7 and at TX SNR = 10 dB, about 93.8% (94%), 84.9% (86.9), and 51% (49.7%) of the optimal performance are obtained by the proposed nested two-stage TS, UCB, and random selection, respectively. These values become 96.5% (96%), 94.2% (94.16%), and 81.4% (78.8%) at SNR = 100 dB, respectively. Figure 3: Spectral efficiency against the used number of PS vectors when \(M=N=K\) at 80% blockage. Figure 4: Spectral efficiency against the used number of PS vectors when \(N=36\) and \(M=64\) at zero blockage. Figure 5: Spectral efficiency against the used number of PS vectors when \(N=36\) and \(M=64\) at 80% blockage. Figure 6: Spectral efficiency against TX SNR when \(M=N=K=16\) at zero blockage. Figures 8 and 9 show the spectral efficiency convergence rate of the proposed nested two-stage MAB schemes against the time horizon \(t\) using \(K=N=M=16\) at zero and moderate blockage effect with blockage probability of 50%, respectively. Due to the effect of blockage, the spectral efficiency performance given in Figure 9 is lower than that represented in Figure 8. From these figures, it can be seen that the proposed nested two-stage TS has a faster convergence rate than the UCB-based one due to its Bayesian strategy. Interestingly, the proposed nested two-stage UCB has better convergence than the TS-based one at low values of \(t\), where the TS algorithm starts to build up the prior/posterior distributions of the achievable reward. As these distributions are constructed, the TS converges faster than UCB, as shown by these figures. At \(t=400\), about 99% (99%) and 95.12% (94.8%) of the optimal performance are obtained using the proposed nested two-stage TS and UCB-based one at zero (50%) blockage, respectively. Figures 10 and 11 show the spectral efficiency convergence rate using \(N=36\), \(M=64\), and \(K=16\) at zero and 50% blockage, respectively. By comparing Figure 8 with Figures 9 and 10 with Figure 11, it is interesting to observe that the spectral efficiency and the convergence rate performances represented by Figures 10 and 11 are better than those represented by Figures 8 and 9, respectively. This comes from the increased number of antenna elements. At \(t=400\), about 99.4% (99.1%) and 95.5% (95%) of the optimal performance

Figure 8: Spectral efficiency convergence at no blockage using \(K=N=M=16\).
## 5 Discussion
### Impact of the PCA Denoising Method  
In the proposed methodology, we employed the PCA denoising method to effectively eliminate noisy signals from the CSI data. Through experimentation, we observed that PCA leverages the correlated variations present in the CSI time series of different subcarriers, thereby effectively removing noise from the signals. This process specifically targets the elimination of uncorrelated noisy components that cannot be adequately filtered out through traditional low-pass filtering.  
General-purpose denoising methods, such as low-pass filters or median filters, unfortunately, do not perform well in handling impulse and bursty noises for two reasons. Firstly, these methods typically require much higher sampling rates than the frequency of the WiFi signal, making them less suitable for this scenario. Secondly, the noise density in CSI values is too high for traditional filters to efficiently handle [34].  
To thoroughly investigate the impact of the PCA denoising method, we conducted additional experiments. The comparative results presented in Table 7 clearly demonstrate that denoising the CSI data using PCA leads to notable improvements in the recognition performances of our proposed CNN-GRU-AttNet. Specifically, we achieved an accuracy increase of up to 1.43% for the CSI-HAR dataset and 1.00% for the StanWiFi dataset. As a result, it becomes evident that the PCA-based noise reduction plays a significant role in achieving the high recognition accuracies observed in our proposed methodology.
## 4 Engineering Application
### Application to Fault Diagnosis of Rolling Bearing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Processing Methods (Labeled Ratio)**} & \multirow{2}{*}{\(C\)} & \multirow{2}{*}{\(\sigma\) (g)} & \multicolumn{2}{c}{**Result Evaluation**} \\ \cline{3-5}  & & & \multicolumn{1}{c}{**Normalized Mutual**} & \multirow{2}{*}{**Accuracy (ACC)**} \\  & & & **Information (NMI)** & \\ \hline SSFCM-ASCA-SVM (0.3) & 39.8545 & 3.4262 & 0.8860, \(-\)0.030, 0.042 & 0.9351, \(-\)0.029, 0.024 \\ CK-means-ASCA-ID-SVDD (0.3) & 0.0983 & 72.2803 & 0.8534, \(-\)0.029, 0.027 & 0.9035, \(-\)0.020, 0.032 \\ SSFCM-ASCA-RD-SVDD (0.3) & 0.0323 & 1024.0000 & 0.8855, \(-\)0.069, 0.060 & 0.9053, \(-\)0.063, 0.054 \\ SSFCM-SCA-ID-SVDD (0.3) & 0.9560 & 29.5815 & 0.8833, \(-\)0.065, 0.033 & 0.9333, \(-\)0.056, 0.026 \\ SSFCM-ASCA-ID-SVDD (0.3) & 0.1177 & 26.7073 & 0.8868, \(-\)0.050, 0.055 & 0.9380, \(-\)0.043, 0.033 \\ \hline CK-means-ASCA-SVM (0.5) & 31.4351 & 1.2567 & 0.8883, \(-\)0.035, 0.061 & 0.9333, \(-\)0.056, 0.043 \\ SSFCM-ASCA-SVM (0.5) & 73.4361 & 0.2825 & 0.8919, \(-\)0.044, 0.071 & 0.9386, \(-\)0.044, 0.026 \\ FCM-ASCA-ID-SVDD (0.5) & 0.9892 & 1005.0437 & 0.6323, \(-\)0.044, 0.071 & 0.8421, \(-\)0.053, 0.047 \\ CK-means-ASCA-ID-SVDD (0.5) & 0.0303 & 0.0025 & 0.8877, \(-\)0.041, 0.048 & 0.9386, \(-\)0.044, 0.032 \\ SSFCM-SCA-RD-SVDD (0.5) & 0.5104 & 0.1006 & 0.8601, \(-\)0.083, 0.069 & 0.9175, \(-\)0.064, 0.053 \\ SSFCM-ASCA-RD-SVDD (0.5) & 0.2456 & 18.3967 & 0.8686, \(-\)0.043, 0.070 & 0.9240, \(-\)0.041, 0.047 \\ SSFCM-SCA-ID-SVDD (0.5) & 0.8195 & 51.0377 & 0.9102, \(-\)0.020, 0.026 & 0.9550, \(-\)0.013, 0.016 \\ SSFCM-ASCA-ID-SVDD (0.5) & 0.1523 & 0.0011 & 0.9254, \(-\)0.036, 0.021 & 0.9649, \(-\)0.029, 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fault diagnosis results with different methods. Figure 11: Boxplots of evaluation values for different methods, the x-axis tick labels correspond to: 1: semi-supervised fuzzy C-means clustering, adaptive sine cosine algorithm and support vector machine (SSFCM-ASCA-SVM) (0.3); 2: constrained-K-means, adaptive sine cosine algorithm and support vector data description with improved decision strategy (CK-means-ASCA-ID-SVDD) (0.3); 3: semi-supervised fuzzy C-means clustering, adaptive sine cosine algorithm and support vector data description with relative distance decision strategy (SSFCM-ASCA-RD-SVDD) (0.3); 4: semi-supervised fuzzy C-means clustering, sine cosine algorithm and support vector data description with improved decision strategy (SSFCM-SCA-ID-SVDD) (0.3); 5: semi-supervised fuzzy C-means clustering, adaptive sine cosine algorithm and support vector data description with improved decision strategy (SSFCM-ASCA-ID-SVDD) (0.3); 6 CK-means-ASCA-SVM (0.5); 7: SSFCM-ASCA-SVM (0.5); 8: fuzzy C-means clustering, adaptive sine cosine algorithm and support vector data description with improved decision strategy (FCM-ASCA-ID-SVDD) (0.5); 9: CK-means-ASCA-ID-SVDD (0.5); 10: SSFCM-SCA-RD-SVDD (0.5); 11: SSFCM-ASCA-RD-SVDD (0.5); 12: SSFCM-SCA-ID-SVDD (0.5); 13: SSFCM-ASCA-ID-SVDD (0.5). Figure 10: Comparison of evaluation values for different methods.
# Whisper: Programmable and Flexible Control on Industrial IoT Networks
## 4 Coordination of Whisper Nodes
### Augmenting Network Topology with Whisper Nodes  
Additional Whisper Nodes are needed in the following cases:  
**Per-node schedule reconfigurations**:  
The TSCH schedule can have a strong impact on the performance of the network. Changing slot schedules can lead to reduced latency, improved reliability, etc. As will be detailed in Section 5, Whisper modifies the TSCH schedule by injecting fake 6P commands, which allocate or deallocate slots between nodes. However, as the 6P protocol only has a local reach (no IPv6 header), additional Whisper nodes are required to also adapt the scheduling. In that case, the centralized controller will forward the request for specific slot changes to one or more whisper nodes.  
**Complex routing changes:**  
Figure 4 shows two examples where a single centralized controller cannot perform a specific routing change. In Example 1 in Figure 3(a), the policies to accomplish are Energy and Load balancing. This means that when node 6 sends a notification of low battery, its children 8 should try to switch to the next parent candidate, i.e., 9. In this case, Algorithm 3 would report that a Whisper node is required since the parent switch of node 8 could not be done remotely from the controller without undesired parent changes such as the one of 6 from node 3 towards node 4. This is because the perceived rank difference in 8 between 6 and 9 is so large that an eventual increase in the branch of 3 used for stabilizing the switch (i.e., overcome the hysteresis threshold to avoid switching back) would force 6 to change its parent to 4, and therefore change its branch (which may not be interesting in terms of load balancing). If a W node is present, it could, by using a Primitive 4, relay node 8's traffic through W towards 9 without altering the rank in the branch of 3.  
Another example in which Whisper needs a Whisper node to accomplish the given policies is shown in Figure 3(b). In this Example 2, for any reason, e.g., sensor data from 7 has to be processed in 8 for data fusion, the controller will determine that 7 should switch from 4 to 8. However, in order  
Figure 4: Example of two corner cases in which Whisper nodes are needed. Weights represent the ETX (ETX = 1 by default). (**a**) Example 1; (**b**) Example 2.  
to achieve this, Algorithm 3 will try to increase the branch of 5 with a Primitive 2, followed by a Primitive 1 towards node 7. Since the branch of node 5 has node 14 as constraint node, it will try to increase the branch of 2 as well, which is linked at the same time with branch 3 through another constraint node (i.e., node 15). However, if the algorithm tries to increase branch 3, it will realize that the original branch 4 is coupled with branch 3 through node 16 (with a ETX = 2 link). For this reason, the algorithm will notify that a Whisper node is needed. In this case, a Whisper node could force the desired switch by impersonating 6,5 and 8 using Primitive 3.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
###### Abstract  
Named data networking (NDN) is designed as a clean-slate Internet architecture to replace the current IP Internet architecture. The named data networking was proposed to offer vast advantages, especially with the advent of new content distributions in IoT, 5G and vehicular networking. However, the architecture is still facing challenges for managing content producer mobility. Despite the efforts of many researchers that curtailed the high handoff latency and signaling overhead, there are still some prominent challenges, such as non-optimal routing path, long delay for data delivery and unnecessary interest packet losses. This paper proposed a solution to minimize unnecessary interest packet losses, delay and provide data path optimization when the mobile producer relocates by using mobility update, broadcasting and best route strategies. The proposed solution is implemented, evaluated and benchmarked with an existing Kite solution. The performance analysis result revealed that our proposed Optimal Producer Mobility Support Solution (OPMSS) minimizes the number of unnecessary interest packets lost on average by 30%, and an average delay of 25% to 30%, with almost equal and acceptable signaling overhead costs. Furthermore, it provides a better data packet delivery route than the Kite solution.  
Keywords:handoff latency; mobility support; named data networking; optimal data path; producer mobility support; signaling cost +
Footnote †: journal: Applied
## 4 Random Linear Network Coding for Mobile Video Delivery
### RLNC: Where Should It Be?  
RLNC represents a flexible packet-level erasure coding sublayer that can be easily integrated at different positions within the protocol stack. RLNC flexibility rests on flexible definition of input source blocks, source block length \(K\), source/encoded packet length \(L\), coding coefficient field size \(q\), number of encoded packets \(N\) to be produced, and other RLNC properties such as sparsity, tunability and UEP. Furthermore, simple analytical expressions and bounds for packet decoding probabilities, outage probabilities and average decoding delays provide for optimized RLNC design in different scenarios, as detailed in Section 2. In the following, we discuss the suitable position for RLNC sublayer in the context of mobile video delivery. Possible opportunities for RLNC sublayer placement are divided into: (1) End-to-end solutions for RLNC residing either at application or transport layer; and (2) RLNC solutions residing within RAN protocol stack, as illustrated in Figure 4.  
Figure 3: RAN network elements supporting mobile video delivery over 4G LTE.
## 3 Proposed Ground Subsidence Prediction Method
### Extraction Element-Induced Subsidence Prediction Based on the Monte Carlo Method
The flowchart of the Monte Carlo method-based subsidence prediction for a given extraction element is shown in Figure 8, and the corresponding algorithm can be described as follows:  
1. Input the vertex coordinates of a triangular extraction element;
2. Calculate the area of the element by Equation (18);
3. Generate \(n_{A}\) random points over the domain \(D_{A}\);
4. Calculate the number of points within the domain \(D_{E}\). The random point (\(x_{i}\),\(y_{i}\),\(z_{i}\)) within the domain can be determined by \[W_{E}(\varepsilon,\eta)=\frac{W_{cm}}{r^{2}}\cdot\iint\limits_{E}e^{-\pi\frac{(x -r)^{2}+(y-y)^{2}}{r^{2}}}dxdy=\frac{W_{cm}}{r^{2}}\cdot I_{E} \tag{10}\]0 (19)
5. Estimate the extraction element-induced ground subsidence by Equation (18). \[W_{E}(\varepsilon,\eta)=\frac{W_{cm}}{r^{2}}\cdot\iint\limits_{E}e^{-\pi\frac{(x -r)^{2}+(y-y)^{2}}{r^{2}}}dxdy=\frac{W_{cm}}{r^{2}}\cdot I_{E} \tag{10}\]1 Flowchart of the extraction element-induced ground subsidence prediction using the Monte Carlo method. \[W_{E}(\varepsilon,\eta)=\frac{W_{cm}}{r^{2}}\cdot\iint\limits_{E}e^{-\pi\frac{(x -r)^{2}+(y-y)^{2}}{r^{2}}}dxdy=\frac{W_{cm}}{r^{2}}\cdot I_{E} \tag{10}\]2 The idea of the integral \(I_{E}\) estimation model based on the Monte Carlo method. Repeating the process described above produces ground subsidence predictions for each extraction element within the calculation area; the sum of each of the extraction element-induced ground subspaces is the final subsidence prediction induced by the extraction of the mining area. Note that, although the summation method is comparatively simple in terms of the subsidence calculating process and the algorithm implemented [19], the data processing results show that the calculation accuracy of the summation method is inferior to that of the Monte Carlo integration method if the number of divided triangular extraction elements is equal. In addition, in order to reach the same calculation accuracy as the Monte Carlo method, the summation method needs more intensive triangular division, which is time-consuming.