esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Developing algorithms for analyzing hyperspectral images as an intraoperative tool for margin assessment during breast-conserving surgery requires a dataset with reliable histopathologic labels. The feasibility of using tissue slices hyperspectral dataset with a high correlation with histopathology for developing an algorithm for analyzing the images from the surface of lumpectomy specimens was investigated. We presented a method to acquire hyperspectral images from the lumpectomy surface with a high correlation with histopathology. The tissue slices dataset was compared with the dataset obtained on lumpectomy specimen and the wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices were used to develop a tissue classification algorithm. Spectral differences were observed between tissue slices and lumpectomy datasets due to differences in the sample thickness between both datasets; wavelengths with a high penetration depth were able to penetrate through the thinner tissue slices, affecting the captured signal. By using only wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices, the adipose tissue could be discriminated from other tissue types, but differentiating malignant from connective tissue was more challenging.  
Keywords:hyperspectral imaging; surgical margin assessment; diffuse reflectance; penetration depth; tissue classification; breast surgery +
Footnote †: journal: Medical Imaging
## 3 Results
### Automatic Category Classification
#### 3.2.3 Model Testing and Evaluation  
First, this study compared two word-embedding methods, Word2 vec and One-hot encoding, in the CNN model. The comparison results in Table 5 summarize the performance of these two methods.  
The results in Table 5 indicate that the overall accuracy of the model is expected to be around 82%. Upon careful examination, the following findings were observed:  
1. Word2 vec achieves an average accuracy of 84%, showing only a slight difference compared to the One-hot encoding model. In construction accident texts, the lack of complete contextual information often leads to sparse text features. However, with Word2 vec training, it captures various types of relationships between words and provides more contextual information for similar words. Therefore, we expect the Word2 vec model to better handle such texts.
2. From the perspective of the F1 score, the One-hot encoding model performs similarly to Word2 vec in the \"f  
As shown in Figure 5, the CNN model performs well in categories such as \" object strikes\", \"collapse of objects\", and \"falling\". In terms of mechanical injury, KNN is less effective, and the difference in accuracy between CNN and the other two machine learning models is so small as to be negligible. However, in the case of \"electrocution\", the performance of the CNN model is not as good as the other three shallow machine learning models. Due to the loss of control over the body during electrocution accidents, it is more prone to cause fall accidents. When extracting features, the CNN model may extract features related to fall accidents, leading to confusion between electrocution accidents and falling accidents, thus affecting its performance in electrocution accident classification. In summary, the CNN model demonstrates higher accuracy in classifying construction accident narrative texts compared to the other three shallow machine learning models.  
Figure 6 illustrates the comparison results of different machine learning models in terms of accuracy. The CNN model achieved an overall accuracy of 84%, the NB model achieved an overall accuracy of 82%, the SVM model achieved an overall accuracy of 75%, and the KNN model achieved an overall accuracy of 70%. It is evident from the data that the CNN model has higher accuracy compared to the other three shallow machine learning models, making it more suitable for construction accident text classification tasks.
## 3 Results and Discussion
### Link Budget Simulation
#### 3.7.1 Link Budget Sample Calculation  
The calculation in this section uses a spreading factor of eight and a code rate of 4/5 to determine the required transmit power and the received power at the receiver end. The results of the simulation indicate that the required successful link margin is 4.3 dB at a minimum elevation of 20\({}^{\circ}\). These results indicate that the link is operating within the specified power budget, and the communication is expected to be successful. Figure 19 below highlights the link budget simulation with the left-hand-side showing the unacceptable link budget. The yellow is a moderate link budget whilst the green is the successful link margin.
## 3 Materials and Methods  
### PS-InSAR Principle  
The traditional differential InSAR (D-InSAR) method is greatly affected by various phase noises, including decorrelation and atmospheric disturbance. The PS-InSAR technique can overcome these problems by selecting point-like targets (also referred to as persistent scatterers) that can maintain high coherence over a long period [19; 20]. First, a master image is selected; the other images are then used as the slave images to generate differential interferograms. Next, the persistent scatterers are selected according to the stability of the phase information in the time series. Following spatiotemporal filtering, phase unwrapping, phase noise estimation, etc., the displacements are finally calculated [21]. The processing flow of PS-InSAR technology is shown in Figure 2. The calculation of time-series deformation on persistent scatterers greatly improves the accuracy of InSAR results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Satellite**} & **Orbit** & **Azimuth** & **Incidence** & **Number of** & \\  & & **Direction** & **Angle (\({}^{\circ}\))** & **Angle (\({}^{\circ}\))** & **SAR Images** & **Data Period** \\ \hline
1 & Sentinel-1A & ascending & 347.57 & 34.16 & 99 & 12/02/2018–27/05/2021 \\
2 & Sentinel-1A & descending & 192.40 & 34.25 & 91 & 22/03/2018–17/05/2021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Synthetic aperture radar (SAR) image-specific data parameters.  
Figure 2: Processing flowchart of PS-InSAR technology.  
First, one image was selected as the master image by optimizing the spatiotemporal baseline and the Doppler centroid difference distribution. Second, other slave images were co-registered with the master image to generate \(\mathrm{n}-1\) interferograms [22]. The phase at a pixel in the \(\mathrm{j^{th}}\) interferogram generated (\(1\leq\mathrm{j}\leq\mathrm{n}-1\)) can be expressed as:  
\[\varphi=\varphi_{atm}+\varphi_{topo}+\varphi_{flat}+\varphi_{defo}+\varphi_{noise} \tag{1}\]  
In Equation (1) above, the interference phase \(\varphi\) comprises multiple components, including the atmospheric noise phase \(\varphi_{atm}\), the topographic relief phase \(\varphi_{topo}\), the flat earth phase \(\varphi_{flat}\), the phase change caused by two observation targets moving along the line-of-sight (LOS) \(\varphi_{defo}\), and the interference phase attributed to various noise components \(\varphi_{noise}\). Then, PS candidates were extracted by setting a reasonable amplitude deviation threshold. An adjacent PS point phase difference model was established to obtain surface deformation information. Nonlinear deformation and the atmospheric delay phase were removed through high-pass filtering in the time domain and low-pass filtering in the space domain [23; 24; 25]. Finally, the time-series displacement of the study area was obtained.
## 3 Methodology
### Subsampling and Weighting of Data  
The LOS and 3D data as shown in Figures 2 and 4 consist of million data points. As the displacement field is varying smoothly, we can subsample the data with a factor of 4 and a quadtree algorithm to obtain reasonable number of data points and good spatial representation of the LOS and 3D displacements [34]. The numbers of remaining data points for four LOS deformation fields are 1261, 959, 1552, 1184, respectively. Meanwhile the numbers of remaining data points for EW, NS, and U-D directions are 1012, 1124, and 869, respectively. Despite dramatic reduction in quantity, this down-sampled observations well retain the major features of the original interferograms.  
We weight the data based on standard deviation of their deformation data [35]. The standard deviation can be calculated by Equation (2),  
\[\sigma=\sqrt{\left(\frac{\Sigma_{N}(u(i,j)-m_{u}(i,j))^{2}}{N-1}\right)} \tag{2}\]  
where \(u(i,j)\) is the observed displacement of one point, \(m_{u}(i,j)\) is the mean value of displacements of all points, \(N\) is the number of points. After calculation, the weight factors for four subsampled LOS data sets are 0.31, 0.24, 0.2, and 0.25; the weight factors for EW, NS, and U-D directions are 0.45, 0.15, 0.4.
# Study of Implantation Defects in CVD Graphene by Optical and Electrical Methods
## Materials and Methods  
The Chemical Vapor Deposition (CVD) of graphene on 6H-SiC (0001) substrates was used for implantation experiments. The procedure of growth was performed along the lines described previously [15]. After the graphene growth, the samples were annealed in hydrogen at temperature 1000\({}^{0}\) C for 1 hour to separate graphene layer from the SiC surface. As the result of this process it was found by electrical measurements that the graphene layer was p-type.  
The samples were annealed in vacuum in 10\({}^{-3}\) Pa at a temperature of 300 \({}^{\circ}\)C for 30 minutes before ion implantation process to remove the residual moisture from the graphene surface. Two ions He\({}^{+}\) and N\({}^{+}\) at energy of 100 keV were implanted. This ion energy causes the range of ions in the target to be much larger than the thickness of the graphene layer, therefore graphene doping by implanted ions is very unlikely. Various ion fluencies were applied ranging from 10\({}^{12}\) cm\({}^{-2}\) up to 10\({}^{16}\) m\({}^{-2}\) for He\({}^{+}\) ions and from 10\({}^{11}\) cm\({}^{-2}\) up to 4 \(\times\) 10\({}^{14}\) cm\({}^{-2}\) for N\({}^{+}\) ions. The whole sample surface was irradiated and ion fluence was increased step by step in subsequent processes. All ion implantation processes were performed at room temperature and no post implantation annealing of investigated samples was applied. The Hall mobility and carrier concentration and Raman spectra were measured after each step of ion irradiation.  
Raman spectroscopy was performed at room temperature with [PERSON] in Via Raman Microscope using 532 nm wavelength from Nd: YAG laser. The laser spot was focused on the sample using X 100 objective and numerical aperture NA = 0.9 in the backscattering geometry. The size of the laser spot was about 0.5 \(\upmu\)m with an average power below 0.5 mW.  
The electrical measurements were performed by van der Pauw method [16].
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 4 Performance Evaluation
### Federated Reinforcement Learning
#### 4.1.1 FRL Implementation  
For performance evaluations, we constructed the RL model of the proposed system utilizing PyTorch library [35] referring to the work in [36]. Table 2 shows the hyper parameters used in the PPO algorithm. Using the RL model, we additionally implemented the FL, referring to the work in [37], to build our proposed FRL system. We built the system on Ubuntu 20.04 using the desktop equipped with AMD Ryzen(tm) 7 5800X and 32 GB RAM. We trained the learning model by utilizing NVIDIA's compute unified device architecture (CUDA) on NVIDIA GeForce RTX 3070 8 GB GDDR6 PCI Express 4.0 graphic card for faster learning. We constructed traffic map, shown in Figure 3, referring to 2D Gaussian grid map introduced in [38].
## 4 Secure FSO Link Experimental Demonstration  
Now that a secure FSO link has been demonstrated within a simulation, it can be moved to an FSO link prototype using optics and transceiver hardware. The aim is to replicate the parameters of the simulation in real life and compare the results of the simulation and the physical FSO link. The encryption should hold up both in a simulation and a real environment for it to be considered secure.
## 2 Materials and Methods
### Procedure  
Prior to data collection, the participants underwent a brief medical history screening to verify eligibility and physical examination measurements were recorded for body anthropometrics. Five 30 s trials of six different balance conditions were completed with each participant as shown in Table 1. The eyes open, feet together (EOFT) condition was completed first and considered to be the most stable configuration and a baseline condition to compare data from other balance conditions. The participants progressed through increasingly unstable balance conditions. Between each trial, a 30 s break was allowed, with a longer 2 min break between each balance condition. Conditions were completed in the order listed in the table for all participants.  
The self-reported foot dominance, defined as the foot one would kick a ball with, was used for each participant to satisfy the description of relevant conditions. Balance tasks were performed barefoot with the arms positioned with the index finger pointed towards the shoulder on the same side of the body, the elbows pulled in, and knees extended (Figure 1). Participants were asked to stand upright and maintain even weight distribution between feet.  
The participant was asked to hold the quiet standing position for the full thirty seconds without stepping out of position. If significant foot movement occurred, trials were repeated so that data used for analysis originated from trials with only minor bodily movements induced to maintain balance.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Order** & **Balance Condition** & **Description** \\ \hline
1 & EOFT & Eyes open, feet together (most stable, baseline) \\
2 & ECFT & Eyes closed, feet together \\
3 & EOTanDB & Eyes open, feet tandem, dominant foot in back \\
4 & ECTanDB & Eyes closed, feet tandem, dominant foot in back \\
5 & EOTanDF & Eyes open, feet tandem, dominant foot in front \\
6 & ECTanDF & Eyes closed, feet tandem, dominant foot in front \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quiet standing balance conditions.
## 2 Isolated Single Tree Radio Channel Characterization
### Experimental Measurements
#### 2.4.1 Path Loss
OLoS zone: it occurs for a distance from the tree beyond 1.6 m for 2 m height, and beyond 1.8 m for 3 m case. In this zone the path loss admits a linear regression according to the floating intercept (FI) model Equation (2) applied in [15; 20]: \[PL(d)=\alpha+10\cdot\beta\cdot\log_{10}(d)\] (2) where \(d\) is the distance, \(\beta\) the line slope, and \(\alpha\) the floating-intercept in dB. The slopes \(\beta\) are \(-0.818\) for 2 m and \(-0.7047\) for 3 m, and the fit errors are 1.053 and 0.2681 dB, respectively. A linear regression was tried with smaller fit errors than the FI model. As noticed in Figure 8a,b, the division into two propagation zones is more remarkable for the experimental data. The values of the parameters \(\alpha\), \(\beta\), \(P_{0}\) and \(n\) obtained for each zone and height are summarized in Table 3. The RMS error was used as an estimation of the error of the models. Figure 8: Comparison of path loss curves fitting for an isolated oak tree: (**a**) experimental results and (**b**) simulation-based results.
# Calculation of Guided Wave Dispersion Characteristics Using a Three-Transducer Measurement System
## 7 Conclusions  
This paper has presented a new methodology for creating dispersion curves based on measuring the phase difference and time lag between two pulses acquired at different distances. Firstly, the theoretical basis of the method has been presented where the phase velocity has been related to the phase at the pulse centre for systems of limited dispersion; then, a signal processing methodology to extract the phase shift and time lag between two signals and subsequently calculate the phase and group velocities has been outlined. Three different tests were performed to validate the formulation and methodology; using synthesized signals, signals from an FE model and experimental signals. In the three cases the performance achieved agrees well with the theoretical values.  
There are some limitations to the proposed method for the extraction of the phase shift to calculate the dispersion curves. Highly dispersive modes, like high-order modes, are more challenging to evaluate; to mitigate this issue, much shorter spacing distances between transducers should be established to minimize the mode-shape distortion. Overlapping between wavepackets limits the method's applicability, since it distorts the apparent phase of the mode being analyzed. Hence, relatively large samples are required to avoid overlapping between wavepackets and edge echoes. The size of the sample can be smaller at higher frequencies. The velocity-frequency spectrum that can be evaluated using this method will be determined by the excitation mechanism used to generate the guided waves in the solid media. With appropriate experimental design, errors of less than 1% are obtained.  
For future work, the proposed technique will be applied to more complex structures, such as composite plates where the elastic constants are not easy to obtain. In this case, by studying the phase and group velocities as a function of propagation direction, the full angular dependency of the dispersion curves will be obtained.  
[PERSON] conducted the modeling, analysis, experiments and drafted the manuscript. [PERSON] drafted the theory and helped edit the manuscript. [PERSON] helped edit the manuscript.  
This research was funded by the European Commission under the Marie Curie project grant number FP7-PEOPLE-2012 ITN 309395 \"MARE-WINT\".  
The authors declare no conflict of interest.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 4 Aggregation-Based Compressed Sensing
### Data Aggregations
#### 4.3.3 Data Reconstruction at the Border Router  
The compressed data [31] from various aggregators are reconstructed at the border router using Equation (4).  
\[\mathcal{R}\text{D}_{\text{n}}=\text{argmin}\ \parallel\text{D}_{\text{n}} \parallel\ \text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\  
* 4. AGG_B replies DAO to AGG_A;
* 5. AGG_B sends DIO to AGG_C;
* 6. AGG_C replies DIO to AGG_B;
* 7. Completing all the ICMPV6 messages, DODAG root nodes know all the prefix information of the route towards the leaf nodes.  
Expected transmission count [32] is a metric to identify the path quality given by Equation (5). It is calculated from forward data delivery (FDD) to reverse data delivery (RDD).  
\[\text{ETX }(\text{Agg}(\text{N}_{\text{i}}))=\frac{1}{(\text{FDD}*\text{RDD})} \tag{5}\]  
Power Factor: The power factor of the aggregator node is derived from residual energy. The power factor is calculated from Equation (6).  
\[\text{P}(\text{Agg}_{\text{i}})=\frac{(\text{Initial\_power}-\text{Consumed\_power} )}{\text{Initial\_power}} \tag{6}\]  
Objective Function: The objective function acts as the main point to select the parent for the DODAG formation and optimize the route towards the ROOT node. The proposed metric combines ETX and the power factor of the node to enhance the efficiency of the RPL protocol. The objective functions used by the proposed work are as follows:  
* ETX objective function.
* Power factor.  
Rank Calculation: The rank calculates parent rank and rank increase. The rank increase is calculated from step and MinHopRankIncrease, where the MinHopRankIncrease default value is 256 [33]. The step value is calculated from the objective function, and it returns the scalar value. The rank calculation formula is denoted in Equations (7) and (8). Parent selection using rank metric is explained in Figure 3.  
\[\text{Rank }(\text{Agg}_{\text{i}})=\text{Rank}(\text{PN})+\text{Rank\_Inc} \tag{7}\]  
\[\text{Rank\_Inc}=\text{ETX }(\text{Agg}(\text{N}_{\text{i}}))+\text{P}( \text{Agg}_{\text{i}})+\text{Min\_hop\_rank\_increase} \tag{8}\]  
Figure 2: DODAG formation process.
## 5 Calibration of STMD Using Numerical Optimization
### Analysis of the Optimized STMD at Its Performance Limit
#### 5.4.3 Analysis of the Force-Displacement Characteristics at Crucial Frequencies  
Figure 11a,b provide a comparison of the force-displacement characteristics between the optimized STMD and the sub-optimal STMD at two specific frequencies. These frequencies correspond to the left and right peaks of the STMD response, as indicated by the red dotted lines in Figures 9 and 10.  
The force plots in Figure 11 are supplemented with values for relevant parameters: \(k_{a}\) (the optimal controlled stiffness of the ATMD, also used in force calculation for the sub-optimal STMD), \(k_{eq}\) (the resultant equivalent stiffness after force clipping achieved for the sub-optimal STMD), \(k_{sa}\) (the stiffness used in force calculation for the optimized STMD), and \(k_{eq-opt}\) (introduced here to denote the equivalent stiffness resulting from the semi-active force of the optimized STMD, calculated using (45) but for \(k_{sa}\) and \(c_{sa}\)). These parameters clearly demonstrate that the resultant equivalent stiffness \(k_{eq-opt}\) for the optimized STMD is approximately equal to the optimal stiffness \(k_{a}\) of the ATMD, for both positive and negative stiffness \(k_{a}\).  
For a better illustration of this effect, Figure 11a,b include plots of the linear stiffness force \(k_{a}x_{d}\) of the optimal ATMD, as well as the equivalent linear stiffness forces \(k_{eq}x_{d}\) and \(k_{eq-opt}x_{d}\) for both the sub-optimal and the optimized STMD, respectively. Additionally, a comparison of the optimal stiffness \(k_{a}\), with the resulting equivalent stiffnesses \(k_{eq}\) and \(k_{eq-opt}\), is presented as a function of frequency in Figure 11c. Based on these results, it can be concluded that the presented calibration method for the STMD enables precise control of both positive and negative stiffness. Around crucial frequencies of both peaks, the resulting equivalent stiffness for the optimized STMD is closely aligned with the optimal stiffness of the reference ATMD with the same mass ratio and \(K\) parameter. However, it should be emphasized that as the value of \(K\) increases, the absolute value of the optimal stiffness \(k_{a}\) also increases (at a given frequency), while the demanded viscous damping \(c_{a}\) decreases. According to the previous analysis, increasing \(k_{a}\) leads to a higher degree of clipping. This, in turn, results in greater cycle energy dissipation, as well as increased the resultant equivalent viscous damping. As a result, it is not possible to satisfy both the  
Figure 10: Characterization of the control force of the optimized STMD: (**a**) Maximum control force versus normalized frequency for the optimized and sub-optimal STMDs and ATMD, all with \(\mu=1\%\) and \(K=3\), and for TMD with \(\mu=1\%\) and 3%; (**b**) force clipping coefficient for the optimized and sub-optimal STMDs.  
demands for increased stiffness and decreased damping simultaneously. This leads to an inherent performance limit of the STMD.