esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\(p\)-values are based on the Friedman test within groups (\(p>0.05\)). Figure 3: Photographs of a specimen in each group at baseline and at the end of the 6 th month. (G0) Group 0: Control group; (G1) Group 1: ECPR was inserted into the cavities; (G2) Group 2: Bio-C Repair was inserted into the cavities; (G3) Group 3: NeoPUTY was inserted into the cavities; (G4) Group 4: DBA application and ECPR placement; (G5) Group 5: DBA application and Bio-C Repair was placed; (G6) Group 6: DBA application and then NeoPUTY was placed; (G7) Group 7: Nd-YAG laser application and ECPR placement; (G8) Group 8: Nd-YAG laser application and Bio-C Repair placement; (G9) Group 9: Nd-YAG laser application and NeoPUTY placement. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & **T0–T7** & **T0–T30** & **T0–T90** & **T0–T180** & \(p\uparrow\) \\ \hline \multirow{2}{*}{**Group 8**} & **Mean \(\pm\) SD** & \(2.4\pm 1.12\) & \(1.74\pm 1.31\) & \(2.63\pm 1.26\) & \(1.72\pm 0.97\) & \\ \cline{2-6}  & **Median (IQR)** & 2.34 (1.47–3.04) & 1.59 (0.62–3.16) & 2.35 (1.72–3.61) & 1.87 (0.65–2.68) & \\ \hline \multirow{2}{*}{**Group 9**} & **Mean \(\pm\) SD** & 3.1 \(\pm\) 1.59 & 2.63 \(\pm\) 1.13 & 2.45 \(\pm\) 1.58 & 1.62 \(\pm\) 1.32 & \\ \cline{2-6}  & **Median (IQR)** & 2.83 (1.72–4.58) & 2.56 (1.71–3.25) & 1.59 (1.13–4.38) & 1.09 (0.64–2.23) & \\ \hline \multirow{2}{*}{} & \(p\downarrow\) & 0.352 & 0.377 & 0.598 & 0.073 & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 2: Change in \(\Delta\)E values at different time periods. All mean values remained below the clinically perceptible threshold (\(\Delta E_{00}=3.5\)), with no statistically significant differences over time. Error bars represent standard deviations.
# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 3 Results
### Trends in Flux Estimates
To put this carbon uptake into perspective, we compared it to the carbon exchange inferred from CT during the GP (from day 126 to 275) and the whole year in this region. The averaged biospheric CO\({}_{2}\) flux during the GP during 2003 to 2016 over EC was \(-99.8\pm 30.6\) g C m\({}^{-2}\) yr\({}^{-1}\) (uncertainty is 1 standard deviation of inter-annual variability). Annual fossil fuel CO\({}_{2}\) emission for the same period was \(328.48\pm 91.38\) g C m\({}^{-2}\) yr\({}^{-1}\) (uncertainty is 1 standard deviation of inter-annual variability). The satellite-observed total carbon uptake increases from 2003 to 2015 accounted for 6.4% to 10.3% of the mean biosphere flux during the GP, approaching the percentage increase in the vegetation indices of 8.4% to 10.3% from 2003 to 2015. This uptake could offset 1.9% to 3.1% of annual fossil carbon emissions over that region.
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 2 System Model
### SINR Estimation
This channel model extends readily to a situation where the interferer is a higher rank transmission. As rank-1 interference is the worst [17], we concentrate on it in the following discussion.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 3 Channel Estimation Schemes
### MMSE Estimation Using a Virtual Pilot Subcarrier Scheme  
The MMSE is a technique that designates arbitrary virtual pilot subcarriers in frequency-domain-received symbols and uses them to enhance the accuracy of channel estimation. The virtual pilots are preliminarily designated data subcarriers, which are uniformly allocated to maintain the correlation with the subcarriers, i.e., the data and comb pilot subcarriers. The MMSE scheme employs virtual pilots to obtain channel covariance matrices and performs frequency-domain channel estimation using them.  
In the MMSE scheme, the initial channel estimates of the m-th OFDM symbol obtained by (3) are first converted into column as  
\[\begin{split}\hat{\mathbf{H}}_{m}=[&\hat{H}(m,-26),\hat{H}(m,-25),\hat{H}(m,k)\\ &\cdots\ \hat{H}(m,26)]^{T},\qquad k\in S_{d},\ S_{p}\end{split} \tag{9}\]  
where \(S_{d}\) and \(S_{p}\) represents the index set of data and comb pilot subcarriers mentioned Section 2.1, respectively.  
The channel estimates of the comb and virtual pilots are then represented in vector notation  
\[\begin{split}\mathbf{P}_{m}=&[\hat{H}(m,-21),\hat {H}(m,-7),\hat{H}(m,k)\\ &\cdots\ \hat{H}(m,21)]^{T},\qquad k\in S_{p},\ S_{cp}\end{split} \tag{10}\]where \(S_{pp}\) denotes index set of virtual pilots. A total of virtual pilot can be changed as 4, 12, or 48 in an OFDM symbol. The last channel estimate vector is then obtained by  
\[\mathbf{H}_{MMSE}^{m}=\mathbf{R}_{HP}^{m}\left(\mathbf{R}_{PP}^{m}+\sigma_{m}^{2 }\mathbf{I}\right)^{-1}\mathbf{P}_{m}, \tag{11}\]  
where \(\mathbf{R}_{HP}^{m}\) and \(\mathbf{R}_{PP}^{m}\) represent the cross-correlation and auto-correlation matrix, respectively. These matrices are expressed as  
\[\mathbf{R}_{HP}^{m}=\frac{1}{m}\sum_{j=1}^{m}\mathbf{\hat{H}}_{j}\mathbf{P}_{j }^{H} \tag{12}\]  
\[\mathbf{R}_{PP}^{m}=\frac{1}{m}\sum_{j=1}^{m}\mathbf{P}_{j}\mathbf{P}_{j}^{H}, \tag{13}\]  
where \(\mathbf{I}\) denotes the unit matrix of \(N_{p}\times N_{p}\). \(\sigma_{m}^{2}\) is the average noise power of the \(m\)th received symbol, and \(N_{p}\) is the total number of comb and virtual pilots in each OFDM symbol.  
The MMSE scheme uses these correlation matrices to enhance the channel estimation performance. In addition, unlike conventional MMSE, prior knowledge of a channel, i.e., the root mean square (RMS) delay spread and doppler frequency, are not required, and the complexity of the proposed scheme can hence be reduced. This scheme performs well in certain cases, but it still cannot provide outstanding performance compared to STA in low-SNR regions.
## 2 Related Work
### Improvement of Big Data Processing Platforms for Edge Devices  
Some previous studies have evaluated data processing platforms under different application scenarios and proposed design changes to off-the-shelf software platforms to cater to the requirements of applications in edge/fog computing layers. In [30], the authors proposed a serverless edge platform based on the OpenWhisk serverless platform to address the challenges of real-time and data-intensive applications in fog/edge computing. The proposed platform comprised additional components for latency-sensitive computation offloading, stateful partitions, and real-time edge coordination. The platform was evaluated in terms of its resource utilization footprint, latency overhead, throughput, and scalability under different application scenarios. The results show that the serverless architecture reduced the burden of infrastructure management, allowed greater functionality to be deployed on fog nodes with limited resources, and fulfilled the requirements of different application scenarios and the heterogeneous deployment of fog nodes. To optimize stream processing in the edge/fog environment, the authors of [31] proposed Annis, a stream processing framework that considers computational and network resources at the edge. It extended the Storm framework in terms of stream processing operator allocation and placement for stream queries. Compared to the default operator scheduler in Apache Storm, it performed better in terms of end-to-end latency and overall throughput. These works targeted only individual data processing paradigms, such as batch processing, stream processing, or function processing. Our work differs by investigating big data processing platforms for all three of these computing paradigms, namely batch processing, stream processing, and function processing, based on various applications across the computing layers.
## 3 Models to Predict Slant Link Attenuation  
Apart from a formulation of the rain attenuation scheme, existing rain fade models for Earth-space links can be grouped into five categories. These include the empirical, physical, statistical, fade slope, and learning-based models.  
* Empirical models: The model depends on experimental data findings rather than mathematically describable input-output relationships.
* Physical models: In the physical model, there exists some physical resemblance between the formulated rain attenuation model and the physical structure of rain.
* Statistical models: This type of model is built on the long-term data of rain attenuation, rainfall rate, and related atmospheric parameter statistical analysis.
* Fade slope models: In the fade slope model, a change in rain attenuation is determined from the fluctuations of measured experimental rain attenuation over time. These results can subsequently be used to forecast the attenuation of rain.
* Learning-based models: The learning-based rain attenuation is new in the domain of knowledge. Long-term rain attenuation and huge datasets of related parameters are  
Figure 2: Illustration of two-layer structured rain attenuation model for the slant link.  
used as the input to a learning network (i.e., artificial neural network) to train, and later this trained network (e.g., optimized weights) can be used to predict rain attenuation.  
A complete taxonomy for the slant link rain attenuation models is presented in Figure 3. In addition, a typical illustration of an Earth-space link with related geometric parameters is shown in Figure 2.
## 2 Materials and Methods
### Mathematical Model of System Coupling
#### 2.3.1 System Coupling Matrix  
Coupling matrices indicate the interactions in vectorial meaning from system S1 to the system S2 (S1 outputs connected to S2 inputs). By its elements, the coupling matrix expresses all couplings between the two subsystems.  
For two systems S1 and S2 coupled in series, the coupling matrix (Kei), o (output), and i (input) will be an (m x n) sized matrix, with the following ordination:  
\[\frac{columns}{lines}=\frac{o}{i}=\frac{q}{p}=\frac{n}{m}, \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Flow Type** & **Symbol** \\ \hline Information flow (IF) & \\ Energy flow (EF) & \\ Semi-finished product flow (SF) & \\ Finished parts/flow parts (FF) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flow symbols.  
Binary matrix. The matrix elements (\(e_{ij}\)) are defined binary variables, according to Equation (3):  
\[e_{pq}=e_{ij}:B=\{0,1\}\to B, \tag{3}\]  
These elements describe the coupling states according to the following rules:  
1. If there are connections (couplings), then \(e_{pq}=1_{2}\rightarrow\forall p=\overline{1,m}\) and \(\forall q=\overline{1,n}\)
2. If there are no connections (couplings), then \(e_{pq}=0_{2}\rightarrow\forall p=\overline{1,m}\) and \(\forall q=\overline{1,n}\)
3. The rules were also established: * \(e_{pq}=1\) when the connection between \(p\to q\) is defined; * \(e_{pq}=0\) when the connection \(p\to q\) is undefined.  
The resulting Equations (4) is as follows:  
\[\begin{array}{c}y(1)q=x(2)p\Rightarrow e_{pq}=1\,,\ \forall p=\overline{1,m} \text{ and }\forall q=\overline{1,n},\\ y(1)q\
eq x(2)p\Rightarrow e_{pq}=0,\end{array} \tag{4}\]  
For a system of (r) order, consisting of (N) subsystems of (r + 1) orders, the maximum number of links (couplings) will be N(N - 1). Because many couplings are identical and repeated, in reality, the number of coupling is small. The structure of a system S of (r) order is given by the type and number of component subsystems as well as by the type and number of the relations existing between them.  
Between two coupled subsystems (ordinates 1 and 2), the linking relationship will be as follows:  
\[\begin{array}{c}\overline{X}(2)=\overline{K}_{12}\cdot\overline{Y}(1)\\ \overline{Y}(1)=\overline{T}_{k12}\cdot\overline{X}(2)\end{array} \tag{5}\]  
Writing coupling matrix can be made directly from the decomposable structural diagram or by separating each subsystem, therefore, by drawing component block diagrams. The following symbols are used for subsystems and for input (i) or output flows (s) (Table 2).  
To write the coupling matrices, the block diagrams of the components were drawn using the decomposition methodology of the large system into subsystems. For the flexible manufacturing system analyzed, the result was 25 coupling matrices (K\({}_{\text{ci}}\)), making up the structure matrix (K\({}_{\text{STR}}\)).
## 5 Cased Study
### Wavelet Basis Function  
In wavelet denoising, the choice of wavelet basis function is directly related to the effect of signal denoising. To compare the performance difference between GNSS and  
Figure 11: GNSS data: (**a**) original data, (**b**) pre-processing data.  
Figure 10: The sky plot of the visible satellites.  
precise leveling, signal-to-noise ratio (SNR) and root mean square error (RMSE) are used in the study as below:  
\[\text{SNR}=10\text{lg}\left[\frac{\sum\limits_{n}x^{2}(n)}{\sum\limits_{n}[x(n) -x^{\prime}(n)]^{2}}\right];\quad\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{n}[ x(n)-x^{\prime}(n)]}. \tag{6}\]  
The SNR generally means the dimensionless ratio of the signal power to the noise power contained in the recording, and RMSE is a frequently applied measure of the differences between the denoising signal with original signal. Table 1 compares the SNR and RMSE of the wavelet basis functions of five different symmlets clusters. As shown in Table 1, the SNR of the wavelet basis function sym2 is the highest and the RMSE is the lowest with the condition of the hardening threshold. It can be considered that this wavelet basis function is suitable for eliminating the multipath error in GNSS signal.  
Figure 12 shows the comparison between the GNSS signal denoised by sym2 wavelet basis function and the results of manual measurement by precise leveling. It can be seen that after the SWT noise reduction, the difference sequence of multipath model extracted from GNSS settlement accumulation curve is basically on the zero level, which indicates that such a method can play a more effective role in extracting the systematic (repetitive) section of multipath errors.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
###### Abstract  
Various technologies have been evaluated as alternatives to conventional heating for pasteurization and sterilization foods. Ohmic heating of food products, achieved by passage of an alternating current through food, has emerged as a potential technology with comparable performance and several advantages. Ohmic heating works faster and consumes less energy compared to conventional heating. Key characteristics of ohmic heating are homogeneity of heating, shorter heating time, low energy consumption, and improved product quality and food safety. Energy consumption of ohmic heating was measured as 4.6-5.3 times lower than traditional heating. Many food processes, including pasteurization, roasting, boiling, cooking, drying, sterilization, peeling, microbiological inhibition, and recovery of polyphenol and antioxidants have employed ohmic heating. Herein, we review the theoretical basis for ohmic treatment of food and the interaction of ohmic technology with food ingredients. Recent work in the last seven years on the effect of ohmic heating on food sensory properties, bioactive compound levels, microbial inactivation, and physico-chemical changes are summarized as a convenient reference for researchers and food scientists and engineers.  
ohmic; microbial inactivation; bioactive compounds; PME; bacteria +
Footnote †: journal: Applied Sciences
## 2 Theoretical Framework
### Defect Formation and Relaxation Volumes
The expression for computing formation volume from DFT calculations is often given as [23; 54]:  
\[\Delta\Omega^{f}(X,\,q)=\Delta\Omega^{r}(X,\,q)\pm\Omega_{0} \tag{5}\]  
where,  
\[\Delta\Omega^{r}=\Omega(defect)-\Omega(bulk) \tag{6}\]  
Here, \(\Delta\Omega^{r}\) is the relaxation volume, and \(\Omega\)(_defect_) and \(\Omega\)(_bulk_) are the volumes of the supercell with a defect \(X\) in charge state \(q\), and the perfect bulk supercell, respectively, both relaxed to zero stress. The \(+\) and \(-\) signs in Equation (5) denote vacancy and interstitial defects, respectively. The defect relaxation volume can be understood as the change in volume due to relaxation of the crystal lattice around the defect site, or the direct volume change in the system upon introducing the defect in a constant pressure calculation. By contrast, the formation volume also accounts for the reference volume (or the choice of reservoir) associated with the origin or placement of the species comprising the defect, \(\Omega_{0}\). The relaxation and formation volume are actually tensor quantities, e.g., \(\Delta\Omega^{r}_{ij}\) is given as [23]:  
\[\Delta\Omega^{r}_{ij}=\Omega_{bulk}\big{[}\varepsilon_{ij}\big{]} \tag{7}\]  
where \(\varepsilon_{ij}\) is the strain tensor (strain between supercells computed from DFT calculations) due to the defect. This definition holds for any anisotropy, shape or size of the supercell. The magnitude of the volume change is then given as, \(\Delta\Omega^{r}=\) 1/3 \(\times\) tr(\(\Delta\Omega^{r}_{ij}\)), and is consistent with that computed using Equation (6). This description of the relaxation volume accounts for changes in cell shape upon introduction of the defect. It is important to note that the relaxation volume is a finite converged value even for very large simulation cell size. Finally, the elastic dipole tensor \(G_{ij}\) is related to \(\Delta\Omega^{r}_{ij}\) (Equation (1)) and thus, in order to describe the response of charged defects to strain, it is imperative to be able to accurately determine the relaxation volumes.
## 1 Introduction
### Our Contributions  
To overcome the above-mentioned limitations, we propose a novel three-party PPDT inference protocol that achieves secure, structure-hiding, and communication-efficient decision tree predictions over encrypted data and models. The key innovations of our work are as follows:  
* Homomorphic matrix multiplication-based inference: Departing from polynomial approximation and linear path evaluation methods [10], we introduce homomorphic matrix multiplication as the primary operation for encrypted path computation. This novel application supports structured and scalable evaluation of encrypted inputs.
* Leaf node pruning during inference: We propose leaf node pruning at inference time, a novel runtime optimization that reduces the number of nodes involved in computation, significantly improving performance. Unlike traditional model pruning during training, this technique operates during encrypted inference.
* Structure-hiding inference protocol: The decision tree structure--including internal nodes and branching conditions--is fully hidden from both the client and the server. By ensuring that all path computations are homomorphically encrypted, the protocol mitigates leakage risks present in prior PPDT methods.
* Semi-interactive three-party architecture: Our protocol requires only one round of interactive communication between the data holder (client) and the outsourced server. No interaction is required from the model holder during inference. This design enables low-latency, real-world deployment scenarios.  
To achieve these properties, we adopt the efficient integer comparison protocol by [PERSON] et al. [17] and the homomorphic matrix multiplication techniques from [18; 19] for encrypted path evaluation. By integrating homomorphic matrix multiplication, inference-time leaf node pruning, and tree structure hiding into a semi-interactive three-party framework, our method enables efficient, secure, and practical decision tree inference in untrusted environments. We evaluated the proposed PPDT protocol on standard UCI datasets [20] to demonstrate its performance and practicality.  
The algorithm design is detailed in Section 3, the experimental results are presented in Section 4, and the conclusions are provided in Section 5.
# Targeting Antigen-Presenting Cells in Multiple Sclerosis Treatment
## 4 MS-Approved Drugs Targeting Immunocompetent Cells
### Targeting the APC Activity by Peptide Vaccines  
Induction of tolerance to myelin antigens may be also obtained by administration of the antigen by non-conventional route. Numerous trials investigated the effect of myelin antigens administered orally, intravenously, subcutaneously, or intranodally. Therapy with myelin peptides depends on their uptake without strong activation of APCs, which induce tolerance to the antigen. Trials utilizing the immune peptides administration were summarized in Table 2. Common disadvantages of peptide administration are the necessity of repetitive administration in high doses and the fast clearance of injected peptide [122; 139; 140; 141].  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Used Peptide** & **Form** & \begin{tabular}{c} **MS Clinical** \\ **Form** \\ \end{tabular} & **Administration** & **Safety** & **Result** & **References** \\ \hline Bovine myelin & Encapsulated & RR & Orally & safe & \begin{tabular}{c} No benefits for \\ multiple sclerosis \\ course \\ \end{tabular} & [122; 142] \\ \hline MBP\({}_{82-98}\) & Peptide vaccine & SP & \begin{tabular}{c} Intravenously \\ Every 6 month \\ for 2 years \\ \end{tabular} & safe & Non-effective & [122; 143] \\ \hline MPB\({}_{30-44}\) & & & \
\begin{tabular}{c} Intranodally \\ and \\ subcutaneously \\ Biweekly for 16 \\ weeks \\ \end{tabular} & safe &
\begin{tabular}{c} Decrease in \\ number of new or \\ persisting lesions \\ (MRI) \\ \end{tabular} & [122; 144; 145] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clinical trial results for myelin antigen use as a therapeutic options in multiple sclerosis. Trials without any beneficial effect are labeled with black shade; trials with strong side effects are labeled with red shade; trials with positive results are labeled in green.  
Figure 1: Clinical studies conducted on tolerogenic dendritic cells.  
This process may be facilitated by the incorporation of selected peptides into the liposomes modified to elevate their uptake by DCs via specific surface receptors. For example, immunodominant MBP peptides are incorporated into mannosylated liposomes, which increase their uptake by APCs via the CD206 receptor. This approach enhances immune tolerance for the CNS antigens [151; 152].  
Another option is nanoparticles loaded with self-antigen, with or without a bioactive payload, which have been developed to induce immune tolerance [153; 154; 155; 156; 157]. It has been shown that biodegradable particles loaded with myelin-specific antigen, either alone or with immunomodulators, ameliorate EAE [158; 159]. Particles around 500 nm in size are phagocytosed by APCs [160; 161; 162]. Poly (lactide-co-glycolide) (PLGA) and poly (DL-lactide) (PLA) particles have been mostly investigated in antigen-specific treatment for MS and have shown the capability to modulate immune cells for EAE disease amelioration [157; 159; 163].