esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Design  
The review included studies with critical-size mandibular defects. Regeneration modalities were categorized into tissue scaffolds, cell therapies, and bioactive substances. Most studies comprised a combination of these modalities (Figure 2). Most reported a combined use of scaffold with bioactive substances. Animal models included dogs, mini-pigs, non-human primates (nHPs), swine, rabbits, rats, sheep, and mice, which were used in 15, 7, 6, 2, 23, 26, 8, and 1 study, respectively (Table 2).  
The duration of follow-up (the time at which the mandibles were analyzed for bone regeneration--either using radiographic imaging of the animal or at the time of sacrifice) varied widely depending on the animal model used (Table 2). The size of the critical defect was not uniform, not even within a single animal model.  
The diagnostic modalities used for characterizing the new bone formation included micro-CT examination, histology, radiography, immunohistochemistry, real-time PCR, fluorescence microscopy, biomechanical testing, and scanning electron microscopy (Table 3). The most common diagnostic modalities used were micro-CT or other X-ray modalities (computed tomography) and histological analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Animals**} & \multirow{2}{*}{**Number of Studies** **(\%)**} & \multicolumn{2}{c}{**Critical-Sized Defect** **(mm)**} & \multirow{2}{*}{**Follow Up (Weeks)**} & \multirow{2}{*}{**References**} \\  & & **Mean (SD)** & & \\ \hline Rats & 26 (29.5) & 4.76 (0.3) & 10.1 (5.8) & [24,30,39,41,49,54,56,57,80,83,87,91,95,99,104] \\ \hline Rabbits & 23 (26.1) & 17.2 (5.3) & 12.4 (4.1) & [22,23,25,29,32,33,44,59,69,71,75,76,79,85,98,100,100,100,100] \\ \hline Dogs & 15 (17) & 25.3 (7.8) & 27.1 (24.5) & [31,37,38,46,67,78,81,82,86,88,90,96,97,106,107] \\ \hline Sheep & 8 (9.1) & 34.3 (4.6) & 22.1 (16.3) & [47,64,72,74,84,89,94] \\ \hline Mini-pigs & 7 (8) & 33.5 (6.9) & 34.6 (12.9) & [34,35,48,63,92,93,109] \\ \hline nHPs * & 6 (6.8) & 29.1 (12.2) & 43.2 (34.5) & [45,55,66,68,70,103] \\ \hline Swine & 2 (2.3) & 24.3 (5.6) & 10.0 (4.4) & [36,43] \\ \hline Mice & 1 (1.1) & 10.0 & 4.0 & [42] \\ \hline \hline \end{tabular}  
* nHPs—non-human primates.  
\end{table}
Table 2: Overview of the animal models.  
Figure 2: Diagram indicating the number of studies that investigated each component of the regenerative triad (bioactive molecules, scaffolds, and cells).
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 5 Security Analysis
### Descriptive Security Analysis
#### 5.2.4 Mutual Authentication  
During authenticating, \(S\) verifies \(SP\) by checking the correctness of \(V_{sp}\). For \(V_{sp}=h(WS_{s}||TS_{sp}||DS_{sp}||SSK_{sp})\), where \(SSK_{sp}=x_{sp}\cdot B_{sp}=k_{sp}x_{sp}\cdot A_{s}\), \(V_{sp}\) cannot be figured out without long-term secrets \(k_{sp}\) of \(SP\). Similarly, \(SP\) verifies \(S\) by checking \(V_{s}\).
## 3 Proposed Signal Processing Algorithm
### Fine Tuning
Especially, when _SNR_ of the fringe scans is high enough, the true zero order fringe peak \(p_{t}\) is ideally within the half sample of the discrete zero order fringe peak \(p_{d}\). So, for the above case \(J(M)\) is calculated only for the range of \(\left[-\frac{N_{sub}/\hat{f}_{S}}{2}\right]\leq M\leq\left[\frac{N_{sub}/\hat{ f}_{S}}{2}\right]\) for faster signal processing and Equation 24 can be expressed as shown Equation 25 where \(\left[\frac{N_{sub}/\hat{f}_{S}}{2}\right]\) is the closest integer number of \(\frac{N_{sub}/\hat{f}_{S}}{2}\). \[\exp\left\{-\left[\frac{2\pi\left(n-n_{d}-M\Delta n\right)/\hat{f}_{S}}{\pi \hat{L}_{C,eff}/\lambda}\right]^{2}\right\}\cos\left\{\frac{2\pi\left(n-n_{d} -M\Delta n\right)}{\hat{f}_{S}}\right\} \tag{25}\]  
Additionally it must be emphasized that the principle of fine tuning in Equation (24) and Equation (25) can be used for matched filtering in order to obtain the best similarity between the signal inside the one fringe of cross-correlation (signal between any peak \(i(p_{i})\) and \(i(p_{i+1})\)) and the matched filter  
\(i_{M}\left(n\right)\) and to maximize the _SNR_ improvement at peaks by matching the phase between the discrete sample \(i\)-th order fringe peak \(i(p_{i})\) and the very first value of the matched fil
# A Novel Fractional Fourier Transform-Based ASK-OFDM System for Underwater Acoustic Communications
## 3 Fractional Fourier Transform (FrFT)  
The theory of FrFT was first introduced in mathematics at the late 1980s and early 1990s [27]. It is considered as a comprehensive standard of the Fourier transform and explicated as an alternation of the rotation angle in the time-frequency analysis [28]. The conventional Fourier transform is a rotation  
Figure 1: Spectrum of an OFDM signal with \(N\) subcarriers. Subcarriers of \(M\)-ary QAM OFDM or M-ary PSK-OFDM with spacing of \(1/T\).  
Figure 2: Spectrum of an OFDM signal with \(N\) subcarriers. Subcarriers of \(M\)-ary ASK-OFDM with spacing of \(1/2T\).  
of time axis at a defined angle of \(\pi/2\) with respect to the frequency axis; however, the FrFT allows the time axis of the signal to be rotated at any fractional angle (\(\alpha\)) with respect to the frequency axis, that is establishing new time and fractional domain axes (\(\alpha\) domain). Fractional Fourier transform is confined to the application of chirp signals only, which are linear frequency modulation (LFM) signals. The FrFT of signal \(x(t)\) as defined in [24; 29; 30]:  
\[X_{m}=\{F^{m}\left[x\left(t\right)\right]\}\left(u\right)=\int\limits_{-\infty }^{\infty}x(t)H_{m}(t,u)dt \tag{4}\]  
where \(F^{m}\) is the FrFT operator, \(m\) denotes a real integer and \(H_{m}(t,u)\) is the FrFT kernel, which is given as [24; 29; 30]:  
\[H_{m}\left(t,u\right)=\left\{\begin{array}{c}\sqrt{\frac{\left(1-j\c  
where \(d\) represents the distance between the transmitter and receiver, \(a(f)\) is the absorption coefficient and \(s\) denotes the spreading factor. The absorption coefficient \(a(f)\) is dependent on frequency of propagation, temperature, and pressure. The spreading factor is based on the geometry of propagation. For a practical UWA channel, the spreading factor \(s\) is a hybrid of spherical and cylindrical geometric spreading, with a value of \(s=1.5\)[1].
## 4 Discussion  
### Validation of Causes of Vertical Deformation of Bridge Superstructure  
To validate the cause of bridge vertical deformation, the deformation along with the variation of the track geometry due to temperature change were calculated using the commercial finite element analysis program _Abaqus_. The structural analysis model used in the calculation is shown in Figure 9. The rail and arch members were modeled with frame elements, the wall with plate elements, and spring elements were applied to the support of each arch rib.  
The input temperature for each measuring position of the bridge deck slab and main arch was selected as the average of temperatures measured from 0 to 6 o'clock (see Table 5).  
Figure 10 compares the rail-level differences between summer and winter from the structural analysis and measurements. Since the absolute vertical displacement of the rail depends on its initial level, the differences between the rail levels of summer and winter were examined for the comparison. Although there were considerable discrepancies between the calculated and measured rail-level differences, the change patterns are exactly the same as the measured patterns. Therefore, it  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Track**} & \multirow{2}{*}{**Locations**} & \multicolumn{2}{c}{**Input Temperatures (\({}^{\circ}\)C) 1**} \\ \cline{3-4}  & & **Summer** & **Winter** \\ \hline \multirow{4}{*}{North bound} & Mid-arch & +28.5 & \(-1.8\) \\ \cline{2-4}  & Side arch & +27.8 & \(-2.0\) \\ \cline{2-4}  & Deck slab (on mid-arch) & +29.9 & \(-3.6\) \\ \cline{2-4}  & Deck slab (on side-arch) & +25.8 & \(-3.1\) \\ \hline \multirow{4}{*}{South bound} & Mid-arch & +29.8 & +2.4 \\ \cline{2-4}  & Side arch & +29.3 & +2.2 \\ \cline{2-4}  & Deck slab (on mid-arch) & +26.9 & \(-0.2\) \\ \cline{2-4}  & Deck slab (on side-arch) & +31.0 & +0.1 \\ \hline \hline \end{tabular} \({}^{1}\) Selected as the average of temperatures measured from 0 to 6 o’clock.  
\end{table}
Table 5: Input temperatures for finite element analysis.  
Figure 9: Finite element mesh of test bridge: (**a**) south-bound; (**b**) north-bound.  
could be confirmed that the cause of the seasonal change of vertical deformation was the temperature fluctuation of the arch members. The difference between the analysis and measurement results seems to be attributed to the difference of boundary conditions such as spring stiffness as well as the difference between the actual temperatures and the input temperatures chosen as the average temperatures of the members.
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 1 Introduction
The main contributions of this paper are summarized below:  
1. We developed a temporal feature extraction module based on the Transformer-based encoder structure and self-attention mechanism. This module employs a sliding window to segment historical data into temporal steps, enabling the parallel processing of multiple subspaces and learning the correlations of single-differenced pseudoranges across consecutive time intervals. 2. We developed a spatial feature extraction module combining a 1D CNN with multi-scale convolutional kernels and the Transformer encoder structure. This module not only captures global spatial attributes but also efficiently extracts multi-scale, locally sensitive information while maintaining a low network depth. 3. We developed a novel positioning framework for long tunnels named the Enconv1d model by integrating the temporal and spatial feature extraction modules. To evaluate its performance, we conducted real-world tests in a 4.6 km long highway tunnel. The results demonstrate that the model achieves sufficient positioning accuracy to meet the requirements of tunnel environments.
## 4 Background Study
### Literature Review
By training an ensemble of models, including CNNs and LSTMs, they boosted diagnostic performance reaching an F1-score of 0.82 and an AUC of 0.93. The study shows how combining real and synthetic data can enhance accuracy and generalization. However, it was limited by a small number of real Alzheimer's cases and lacked testing on outside datasets. Future work could benefit from more diverse and long-term data to better capture disease progression. \begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Technique/Method** & **Model** & **Results** \\ \hline \multirow{3}{*}{[21]} & \multirow{3}{*}{Network Diffusion Model} & Network eigenmode & Strong correlation between predicted and actual atrophy maps; eigenmodes \\  & & diffusion model & accurately classified AD/bvFTD; ROC \\  & & AUC higher than PCA \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{3D CycleGAN + LM3 IL} & Two-stage: PET synthesis & AD vs. HC—Accuracy: 92.5\%, \\  & & (3D-cGAN) + classification & Sensitivity: 89.94\%, Specificity: 94.53\%; \\  & & (LM3 IL) & PSNR: 24.49 \(\pm\) 3.46 \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{WGAN-GP + L1 loss (MRI slice reconstruction)} & WGAN-GP-based & \multirow{3}{*}{AUC: 0.780 (CDR 0.5), 0.833 (CDR 1),} \\  & & unsupervised & 0.917 (CDR 2) \\ \cline{1-1}  & & reconstruction + anomaly & \multirow{3}{*}{AUC: 0.780 (CDR 0.5), 0.833 (CDR 1),} \\  & & detection using L2 loss & \\ \hline \multirow{3}{*}{[25]} & \multirow{3}{*}{GANDALF: GAN with discriminator-adaptive loss for MRI-to-PET synthesis and AD classification} & \multirow{3}{*}{GAN + Classifier} & Binary (AD/CN): 85.2\% Acc3-class: \\  & & & 78.7\% Acc, F2: 0.69, Precc: 0.83, Rec: \\ \cline{1-1}  & & & 0.664-class: 37.0\% Acc \\ \hline \multirow{3}{*}{[26]} & DCGAN to generate PET images for NC, MCI, and AD & DCGAN & PSNR: 32.83, SSIM: 77.48, CNN \\ \cline{1-1}  & & & classification accuracy improved to \\ \cline{1-1}  & & AD & 71.45\% with synthetic data \\ \hline \multirow{3}{*}{[27]} & Bidirectional GAN with & \multirow{3}{*}{Bidirectional GAN} & PSNR: 27.36, SSIM: 0.88; AD vs. CN \\ \cline{1-1}  & & ResNet-34 encoder, & \multirow{3}{*}{classification accuracy: 87.82\% with synthetic PET} \\ \cline{1-1}  & PatchGAN discriminator & & \\ \hline \multirow{3}{*}{[28]} & DCGAN for PET synthesis & \multirow{3}{*}{DCGAN + DenseNet classifier for AD vs. NC} & \multirow{3}{*}{Accuracy improved from 67\% to 74\%;} \\ \cline{1-1}  & & & MMD: 1.78, SSIM: 0.53 \\ \cline{1-1} \cline{2-4} \end{tabular}
\end{table} 
Table 3: Performance metrics of GAN-based methods and Diffusion Models used in the reviewed literature.
# Stomatal and Non-Stomatal Turbulent Deposition Flux of Ozone to a Managed Peatland
## 3 Results and Discussion
### \(O_{3}\)-Flux Partitioning Uncertainties  
Generally speaking, the partitioning of \(F_{O_{3}}\) into \(F_{sto}\) and \(F_{nsto}\) works well. Nevertheless, there are several sources of uncertainty that have not yet been reported in the literature. When using the Penman-Monteith approach to calculate the stomatal conductance (or stomatal resistance) for H\({}_{2}\)O (ep. 9), the molecular diffusivities of H\({}_{2}\)O and O\({}_{3}\), of \(D_{H_{2}O}\) and \(D_{O_{3}}\), are employed. The values of this ratio as employed by various authors ([PERSON] _et al._, 2015; [PERSON] _et al._, 2015) vary between 1.67 and 1.51, which translates to a variation of \(F_{sto}\) by about 10%.  
Figure 6: Top panels: Relationships between \(v_{d,nsto}\) (y-axes) and \(\mathbf{u^{*}}\), Rg, rH, Tair, \(\chi_{O_{3}}\) and VPD shown as grey full circles. Purple triangles are means \(\pm\) standard deviations; light blue squares are medians with 25 th and 75 th percentiles. Bottom panel: Relative importance analysis of the drivers of \(v_{d,nsto}\); bars indicate the percentage that each variable contributes to the total R\({}^{2}\) of all variables.  
The exclusion of direct evaporation from plant surfaces and open water surfaces is stated, and [PERSON] et al. [PERSON] et al. (2011) proposed a method to account for this issue by using the relationship between _GPP_ and stomatal conductance under conditions with relative humidity below 60% (Equation (11)). However, the effect of soil evaporation can not be corrected for with this method. Most likely, soil evaporation can only be excluded if additional soil evaporation measurements are performed.  
Finally, additional uncertainty originates from the _GPP_ data which were used when applying the correction according to Equation (11). In our case, we separated the net ecosystem flux (NEE) of CO\({}_{2}\) into _GPP_ and ecosystem respiration (\(R_{eco}\)) by estimating the daytime \(R_{eco}\) from light response curves [47]. This yields a _GPP_ of 0 umol m\({}^{-2}\) s\({}^{-1}\) during the night. When using nighttime \(F_{\text{CO}_{2}}\) data instead and extrapolating these values to the daytime through a temperature relationship [46], the nocturnal _GPP_ is not necessarily zero. Rather, it fluctuates around 0 and could eventually cause negative stomatal conductance during the night. Additionally, the daytime _GPP_ values as calculated from these two methods would differ from each other as well.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 5 Results
### Performance Analysis  
The performance of the proposed protocol was evaluated using metrics, including latency, scalability, cryptographic overhead, and resource utilization, to assess its effectiveness in real-world scenarios.
# Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning
## 3 Controller Design
### SAC Algorithm  
The SAC algorithm was proposed by [PERSON] (1999), which is characterized by introducing entropy regularization into the policy. The algorithm considers maximizing the sum of expected rewards and the policy entropy, which improves the explorable ability of the policy. Thus, it can converge to the optimal value more efficiently. The optimal policy \(\pi*\) can be expressed as follows:  
\[\pi*=\underset{\pi}{argmax}\mathbb{E}_{(s_{t},a_{t})\sim p_{\pi}}[\sum_{t}R(s_ {t},a_{t})+\alpha H(\pi(\cdot|s_{t}))] \tag{4}\]  
The SAC is a reinforcement learning algorithm based on the actor-critic structure. The actor is the policy function to generate actions that the agent uses to interact with the environment, represented by \(\pi_{\phi}(a_{t}|s_{t})\). The policy update equation can be obtained by minimizing the KL divergence:  
\[J_{\pi}(\phi)=\mathbb{E}_{s_{t}\sim\mathcal{D},a_{t}\sim\pi_{\phi}}[log\pi_{ \phi}(a_{t}|s_{t})-\frac{1}{\alpha}Q_{\theta}(s_{t},a_{t})+logZ(s_{t})] \tag{5}\]  
The critic is the value function that evaluates actions output by the actor, which guides the updating of the policy and is represented by \(Q_{\theta}(s_{t},a_{t})\). The TD error method is used to update the value function of critic:  
\[J_{Q}(\theta)=\mathbb{E}_{(s_{t},a_{t};s_{t+1})\sim\mathcal{D},a_{t+1}\sim\pi_ {\phi}}[\frac{1}{2}(Q_{\theta}(s_{t},a_{t})-(r(s_{t},a_{t})+\gamma(Q_{\theta}(s _{t+1},a_{t+1})-\alpha\text{log}(\pi_{\phi}(a_{t+1}|s_{t+1}))))^{2}] \tag{6}\]  
As an off-policy algorithm, the SAC also stores the experience samples \((s_{t},a_{t},s_{t+1},R(s_{t},a_{t}))\) collected by the agent in the previous episodes in the replay buffer. During training, these samples are randomly sampled to update the neural networks, which improves the sample efficiency of reinforcement learning.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 4 Optimization Process
### Use of Regularization Minimizing Norms
To overcome this difficulty and reconstruct small and large structures, the Hubert regularization term is formulated as follows:  
\[R_{Hubert}(\mu)=\int\psi(\mu(\mathbf{r}))\;d\mathbf{r}, \tag{22}\]  
\[\psi(\mu)= \begin{cases}|\
abla\mu|^{2}/2&\text{if }|\
abla\mu|\leq\sigma\\ \sigma|\
abla\mu|-\sigma^{2}/2&\text{otherwise}\end{cases} , \tag{23}\]  
where \(\sigma\) is the parameter for switching \(\Psi\), which is automatically adjusted during each iteration. In the flat homogeneous part with a small gradient of the reconstructed image, \(\Psi\) functioned similarly to the Tikhonov regularization to smooth the image while preserving the discontinuities with a large gradient. The numerical simulations demonstrated that the changes in \(\mu_{a}\) and \(\mu^{\prime}_{s}\) of the small area in a large flat part was reconstructed more clearly by Hubert regularization than by TV regularization. The Hubert regularization method was extended to include prior information on the edges of regions in an object imaged using different imaging modalities [119].
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 6 Emulation Results and Analysis  
To conduct a more realistic performance evaluation, we carried out series of emulations. The emulation environment was created with an ONOS SDN controller [50] and a Mininet emulation tool [51]. The performance of SP was compared with that of: (i) naive placement; (ii) general placement (GP); and (iii) Stoer-Wagner algorithm based placement (SWP) [34]. Naive placement and GP are explained in Section 5. SWP is a network-aware VM placement approach that uses a Stoer-Wagner minimum cut algorithm [52] for grouping the highly dependent VMs. SWP would be the most similar work to the proposed method because it uses a traffic weight between the VMs for clustering the VMs as done in the proposed SP.
## 3 Materials and Methods
### Data Cleaning Process  
The integrity of the input data significantly influences the accuracy of the predictive model. The dataset was initially subjected to a thorough cleaning process to correct anomalies such as missing values, incorrect data entries, and outliers. Key steps included:  
* Conversion and Cleaning: The raw data, initially in Excel format, was converted to a CSV format to standardize the data input process for use in Python 4.6. This step was crucial as it ensured compatibility with the Pandas library for subsequent manipulations.
* Error Checking and Noise Reduction: The dataset was meticulously checked for errors such as misspellings, incorrect punctuation, and inconsistent spacing which could lead to inaccuracies in the model. Irrelevant data points such as unnecessary identifiers were removed to streamline the dataset and focus the model on relevant features.
* Normalization and Encoding: Numerical normalization and categorical data encoding were performed to standardize the scales and transform categorial variables, such as grades and nationality, into a format suitable for machine learning analysis.  
The integrity and accuracy of the input data are crucial for the performance of any predictive model. In our study, the data cleansing process involved standardizing the data format, correcting data entry errors, and handling missing values, which significantly impacted the reliability of our model's predictions. Each step in the data cleansing process was carefully designed to minimize the introduction of bias and error into the predictive modeling process. To quantify the impact of these steps, we conducted sensitivity analyses that showed improvements in model accuracy and robustness when using cleaned versus raw data.  
We have provided a detailed explanation for the data preprocessing steps as shown below.  
1. Feature Selection. Relevant features were selected, including academic performance, demographic information, and course engagement metrics. These features were used to predict student dropout. The random forest algorithm was relied upon for its ability to implicitly handle feature selection by identifying the most influential factors during model training.
2. Handling of Missing Data. Missing data were addressed through a data cleaning process, which included filling in gaps where information was incomplete. The exact methods for handling missing data (e.g., mean imputation, interpolation, or removal of incomplete records) were also discussed.
3. Normalization and Encoding. Numerical normalization and categorical encoding were applied to standardize data for machine learning. Numerical normalization was used to scale features such as academic grades to ensure they were on comparable scales, though the exact normalization technique (e.g., min-max scaling or z-score normalization) was not specified. Categorical data, such as student demographics and course names, were encoded into numerical values to be processed by the random forest model.