esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Because of changes in technology, a continuous evolution is occurring in industries such as medical services, industrial manufacturing, and comfort services. In these industries, technological advances must be monitored to support decisions or actions that could benefit the processes supporting the industry. In medical services, researchers are developing teleoperated robots, prostheses, and smart monitoring systems to benefit services such as medical interventions, medical studies, and medical care.  
In public health institutions (PHIs) in some countries, the excessive number of hospitalized patients cared for by the nursing staff represents a problem [1]. The nurses invest substantial amounts of time updating information about each patient's health by measuring values such as body temperature, heart rate, and glucose levels.  
Nurses play an important role in caring for the sick, because they are in direct contact with the patient when taking vital signs. Often, nurses and doctors record information about their patients' progress by filling out reports by hand. A smart monitoring system could measure the patients' progress in real time, providing continuous feedback on the improvement in the health of patients who have undergone treatment. Additionally, nurses and doctors could save valuable response time with real-time notifications. Integrating intelligent monitoring systems could improve care by providing the nurses with efficient documentation and timely access to information [2]. The remote monitoring of vital signs could be useful in overcrowded PHIs, especially for the continuous monitoring of patients in the emergency department or of unattended patients when there is concern that the condition of a patient in the waiting area could deteriorate suddenly without being observed [3]. WSNs are being used to provide feedback during the monitoring process. WSNs can be used to support intelligent decisions, and moreover, these networks can provide data from many process points, providing an overall view of conditions [4]. However, if an image is better than a thousand words, handwritten reports produced by nurses and physicians could be replaced by an MAR environment used as a tool to facilitate the management of the patient information obtained from a WSN.  
In this paper, a real-time smart multi-level tool for remote patient monitoring (SMTRPM) is proposed that integrates two cutting-edge technologies, WSN and MAR, to support nurses and physicians in PHIs. The SMTRPM uses WiFi technology to transmit data, reports, and alarms to a nurse server or mobile device in wireless mode. Moreover, smart nodes evaluate the patient's condition through data obtained from biometric sensors (body temperature and heart rate). Finally, virtual files implemented as an Android application complement handwritten nursing reports. The rest of this paper is organized as follows. Section 2 presents related research. In Section 3, a description of the novel SMTRPM is presented. Test results are presented in Section 4. Finally, Section 5 concludes the paper.
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### Method
#### 2.2.5 The MASK Network Module for Obtaining Binary Masks  
The mask branch network module is illustrated as part C in Figure 3. It adopts a fully convolutional network (FCN) architecture for semantic segmentation. Notably, the softmax function is deliberately omitted in this module to prevent the high-scoring categories from suppressing low-scoring ones. This design avoids the potentially detrimental coupling relationship where all categories compete against each other due to the constraint that their prediction probabilities must sum to 1. Instead of using softmax, the predicted category from the classification branch network is directly employed to extract the corresponding mask, thereby eliminating inter-category competition. Such a decoupling strategy helps improve detection accuracy. Each ROI mask contains four categories corresponding to cold fronts, warm fronts, stationary fronts, and occluded fronts in frontal analysis.  
It should be noted that during model training, both category prediction and mask generation are performed simultaneously. During prediction, after obtaining the frontal category results for grid points, these results are then fed into the mask network to generate the corresponding binary masks.
# Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
## 4 Experiments and Discussion
### Case Study  
To further analyze the role of each linguistic feature in our task, two samples are selected and visualized by attention weights on each word, and they are expressed through Figure 7. As shown in the figure, each row means the visualization of the representation obtained by the single branch GCN, and each column denotes the visualization of each word presented by the four branch GCN. From Figure 7, we can conclude two observations related to the core idea of the proposed model. First, it is obvious that the attention of each branch of the GCN is attracted by the different words in the sentence. For example, in Figure 7b, the key word in branch \(F^{G}_{\text{B}+sdt}\) is \"is\", while \"is\" is the last word in sorted attention sequence from \(F^{G}_{\text{B}+psc}\), and \(F^{G}_{\text{B}+psc}\) gives a heavy attention weight to \"this\". The same conclusion can be summarized from \(F^{G}_{\text{B}+tbd}\) and \(F^{G}_{\text{B}+tpd}\). Second, we find that if one branch misses the specific word, such as \"is\" in \(F^{G}_{\text{B}+psc}\) and \(F^{G}_{\text{B}+tpd}\) in Figure 7b, another branch of the GCN would provide a higher attention weight on this word, such as \(F^{G}_{\text{B}+sdt}\) and \(F^{G}_{\text{B}+tbd}\). Furthermore, this phenomenon directly corresponds to the core of feature integration. In addition, Figure 7a also provides the same information to us about the attention distributions via a four branch GCN. For this case, our proposed MBGCN completes its work of fusing various branch features to enhance the textual representation and finally improve the performance of the ASTE task.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Versions**} & \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Restaurant14**} & \multicolumn{3}{c}{**Laptop14**} & \multicolumn{3}{c}{**Restaurant15**} & \multicolumn{3}{c}{**Restaurant16**} \\ \cline{3-13}  & & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{\(\mathcal{V}_{1}\)} & MBGCN & 72.89 & 71.79 & **72.33** & 57.30 & 57.62 & 57.46 & 60.76 & 58.42 & **59.57** & 71.68 & 69.22 & **70.43** \\  & \(w/o\) Structure bias & 70.38 & 72.99 & 71.66 & 60.86 & 54.50 & **57.50** & 56.76 & 60.45 & 58.55 & 64.53 & 73.04 & 68.52 \\ \hline \multirow{2}{*}{\(\mathcal{V}_{2}\)} & MBGCN & 67.92 & 75.18 & **71.37** & 59.96 & 57.86 & **58.89** & 62.25 & 63.92 & **63.07** & 63.76 & 71.35 & 67.34 \\  & \(w/o\) Structure bias & 69.49 & 70.19 & 69.84 & 59.77 & 57.67 & 58.70 & 58.88 & 60.83 & 59.84 & 65.36 & 71.74 & **68.40** \\ \hline \hline \end{tabular} Note: The “w/o” denotes the abbreviation for without.  
\end{table}
Table 5: The contribution of adapter to MBGCN for ASTE task on \(\mathcal{V}_{1}\) and \(\mathcal{V}_{2}\).
## 2 Wind Power, Photovoltaic Power and Load Power Forecasting Model
### Stacking Ensemble Learning Model
#### 2.2.1 GBDT  
GBDT is a boosting algorithm. Specifically, the residuals from prior decision trees are fed into succeeding ones, which are then combined progressively for the final forecasting results. Since GBDT makes good use of multiple weak learners in cascade, it has decent accuracy. But at the same time, due to the cascade relationship between weak learners, GBDT is difficult to train data in parallel. The calculation procedure of GBDT is as follows:  
1. Given a sample set \(\left\{(x_{i},y_{i})|x_{i}\in\mathbb{R}^{n},y_{i}\in\mathbb{R}\right\}_{i}^{N}\), initialize the weak learner as follows: \[f_{0}(x)=\underset{c}{\text{argmin}}\sum_{i=1}^{N}\mathcal{L}(y_{i},c)\] (5) where \(\mathcal{L}(\cdot)\) is the loss function.
2. Calculate the negative gradient, namely the residual, which is defined as \[r_{i}^{m}=-\bigg{[}\frac{\partial\mathcal{L}(y_{i},f(x_{i}))}{\partial f(x_{ i})}\bigg{]}_{f(x)=f_{m-1}(x)},i=1,2,\ldots,N;m=1,2,\ldots,M\] (6) where \(m\) denotes the number of iterations and \(M\) denotes the number of decision trees.
3. Take the residuals obtained in step 2 as the new sample labels, and take the dataset \(\left\{(x_{i},r_{i}^{m})|\,x_{i}\in\mathbb{R}^{m},r_{i}^{m}\in\mathbb{R} \right\}_{i}^{N}\) as the training data for the next decision tree, where \(\mathbb{R}_{j}^{m}\) is the \(j\)th leaf node region of the \(m\)th decision tree. Then the value of the \(j\)th leaf node region of the \(m\)th decision tree is as follows: \[\mathcal{R}_{j}^{m}=\underset{\mathcal{R}}{\text{argmin}}\sum_{x_{i}\in \mathbb{R}_{j}^{m}}\mathcal{L}(y_{i},f_{m-1}(x_{i})+\mathcal{R}),m=1,2,\ldots, M;j=1,2,\ldots,J\] (7) where \(J\) is the number of leaf node.
4. Update the strong learner as follows: \[f_{m}(x)=f_{m-1}(x)+\sum_{j=1}^{J}\mathcal{R}_{j}^{m}I(x_{i}\in\mathbb{R}_{j} ^{m})\] (8) where \(I(\cdot)\) represents the indicator function.
5. Obtain the final learner \(f(x_{i})\) as follows: \[f(x_{i})=f_{M}(x_{i})=f_{0}(x_{i})+\sum_{m=1}^{M}\sum_{j=1}^{J}\mathcal{R}_{j }^{m}I(x_{i}\in\mathbb{R}_{j}^{m})\] (9)
## 2 Material and Methods
### Molecular Dynamics Simulation  
MDSs were performed using GROMACS 2023.1 [63] with the CHARMM36 force field to investigate the stability and interaction dynamics of the top-ranked ginseng ligands complexed with ADGRG3. The top five ginsenosides (Rg3, Rk3, F5, Rg7, and F1) as well as two best control ligands--dexamethasone and HCY (from the 7D77 PDB structure)--were selected for the simulations. The preparation of the ligand and protein structures involved converting the docked ligand files to.mol2 format and generating a.pdb file for the protein. Hydrogen atoms were added to the ligand, and ligand topology files were generated using SwissParam 2023 (accessed on 3 Jun 2023). These ligand parameters were incorporated into the simulation topology, ensuring compatibility with the protein structure.  
The ADGRG3-ligand complex was centered in a dodecahedral simulation box with a buffer distance of 1.0 nm from the edges and was solvated with SPC/E water molecules. Neutralizing ions were added to the system, maintaining physiological ionic strength (0.15 M NaCl). After system setup, energy minimization was conducted to remove steric clashes, followed by two equilibration phases as follows: an NVT (constant number of particles, volume, and temperature) equilibration for 100 ps to stabilize temperature, and an NPT (constant number of particles, pressure, and temperature) equilibration for another 100 ps to stabilize pressure. The production MD simulation was then carried out for 100 ns under NPT conditions, with trajectory data collected for post-simulation analysis.  
Following the MD simulations, the stability and interaction patterns of each complex were analyzed. The root mean square deviation (RMSD) was calculated to assess the overall stability of the protein-ligand complex, while the root mean square fluctuation (RMSF) provided insights into the flexibility of individual residues in the protein. The radius of gyration (Rg) was analyzed to evaluate the compactness of the protein structure, and hydrogen bond analysis was performed to quantify the interactions between the protein and ligand over the course of the simulation.  
For the analysis of binding free energy (\(\Delta\)G_bind) within the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) framework, the g_mmpbsa v1.6 package was utilized as per the established protocols [64]. The MM-PBSA method offers a detailed quantitative assessment of the interactions between proteins and their ligands. The formula used to calculate the binding free energy is expressed as follows: \(\Delta\)G bind = G complex \(-\) (G protein + G ligand). In this equation, \(\Delta\)G bind represents the net binding energy of the complex. G complex denotes the energy of the protein-ligand complex, while G protein and G ligand refer to the energies of the isolated protein and ligand, respectively.
# RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation
## 4 Experiments
### Data Set and Experimental Settings  
We choose the data set of Sina News, which contains 121,157 documents between 1 March and 31 August 2015. These documents are different in length and cover various categories, including politics, economy, sports, entertainment, etc. After preprocessing, 127,128 named entities and 3,230,441 entity pairs are extracted from the whole corpus.  
We perform a comparative experiment among Word2 Vec [32], GloVe [9] and RGloVe. Word2 Vec is a very popular model based on neural networks to train word vectors. For Word2 Vec, we choose the model of CBOW with 25 iterations for relationship type of 300 dimentions. For GloVe and RGloVe, we train the models using AdaGrad with initial learning rate of 0.05. We run 100 iterations for entity vectors of 300 dimensions. For global vectors, we set \(x_{cutoff}\) = 100 and \(\alpha\)=3/4. Each model generates two sets of word vectors \(W\) and \(\widetilde{W}\), which are equivalent. The final results of our entity vectors are decided by the sum \(W+\widetilde{W}\).
## 2 Materials and Methods
### EAT Inflammation and Angiogenesis  
Immunohistochemistry (IHC) was performed according to standard protocols on formalin-fixed, paraffin-embedded sections. IHC examination focused on inflammation and angiogenesis assessed by using CD68 and CD34 markers (3). The endogenous peroxidase of deparaffinized sections was neutralized by hydrogen peroxide 3% for 10 min. Antigen unmasking was performed by incubating the slides in a pH 9 buffer at 98 \({}^{\circ}\)C for 45 min. For immunohistochemical detection, antibodies to CD68 (monoclonal antibodies Clone PG-M1; RTU, DAKO, Glostrup, Denmark, catalog code no. IS609) and antibodies to CD34 (monoclonal antibodies Class II, Clone QBEnd 10, RTU, DAKO, Glostrup, Denmark, catalog code no. IS632) were applied for 30 min followed by the streptavidin-horseradish peroxidase conjugate for 15 min. Peroxidase was developed using the DAB working solution (DAKO, Glostrup, Denmark) and washed in deionized water. Sections were counterstained using hematoxylin Gill II (Sigma, Sigma-Aldrich, Darmstadt, Germany). The negative control was obtained by the replacement of the primary antibody with PBS.  
Assessment of immunohistochemistry stained sections was made by the presence of positive brown staining; CD 34, as cell surface glycoprotein, gives brown membranous staining, allowing todetermine microvessel density, while CD68, as glycoprotein expressed predominantly on the lysosomal membrane, gives brown cytoplasmic staining.  
Morphometry was performed by the assessment of the macrophages and microvessel density. Immunohistochemistry, we analyzed five histological fields at 200\(\times\) magnification for each case (one field corresponded to a manually traced area reported to 100,000 \(\upmu\)m\({}^{2}\)/0.1 mm\({}^{2}\)). The results were expressed as percentages or mean values of the number of positive cells or vessels related to the studied area.  
The semi-quantitative evaluation was made by using a manually-driven evaluation method.
## 2 Materials and Methods
### Micro-FT-IR Spectroscopy  
The microspectroscopy FT-IR measurements (\(\mu\)-FT-IR) were performed at the Chemical and Life Sciences branch of the SISSI beamline at Elettra Sincrotrone, (Trieste, Italy), using published methods [52].  
Two small flakes of wood were collected from both the ancient wooden samples (#1 and #2). The flakes were pressed using a diamond compression cell (Diamond EX press by S.T. Japan, clear aperture 1.6 mm) to flatten them to a thickness suitable for FT-IR measurements in transmission mode. After this procedure, the diamond compression cell was opened, and the infrared images were collected on the exposed face of the compression cell by a Vis-IR Bruker Hyperion 3000 microscope coupled with the Vertex 70v interferometer in the mid-infrared range (Mid-IR) with a focal plane array (FPA) detector. The spectral range was between 3950 and 900 cm\({}^{-1}\), collecting 256 scans at 4 cm\({}^{-1}\). The same procedures were also applied for the _Acacia_ standard wood sample (#3). An FPA mosaic of 6 tiles (2 \(\times\) 3) was acquired on sample #1, while 9 tiles (3 \(\times\) 3) were collected for sample #2. Considering 4096 spectra for each tile, a total amount of 61,440 spectra were acquired on the two samples. A single FPA image was collected for the standard wood sample.  
Soil samples (#4, #5, and #6) were also analysed to evaluate their mineral composition, with the same condition described above.  
The \(\mu\)-FT-IR data set was analysed by OPUS 7.5 software. FPA maps were firstly compensated for atmospheric contribution (water vapour and CO\({}_{2}\)) and baselined. The spectra were also pre-processed by the Quasar open-source software ([[https://quasar.codes](https://quasar.codes)]([https://quasar.codes](https://quasar.codes)), accessed on 10 January 2020). All the empty pixels (absorbance < 0.01) were removed. Averaged spectra were extracted from the mosaic of each wood sample and compared with the wood standard. Then, the integral areas of the bands in the 1180-930 cm\({}^{-1}\), 1530-1480 cm\({}^{-1}\), and 1800-1530 cm\({}^{-1}\) ranges were calculated for both ancient wood samples (#1 and #2) and the standard one (#3) in order to visualise the distribution of cellulose, lignin, and deterioration products, respectively. Statistical calculations were performed on the processed data (by Origin Pro 2020 9.7 software, Origin Lab, Ltd., Northampton, MA, USA). The obtained values were normalised against the wood standard sample. To compare the ancient wood samples with the standard one, the scales of the FPA false colour maps were calibrated from blue (low intensity) to red (high intensity) to the highest mean value of integration recorded (usually from the standard sample, with the exception of the band due to the deterioration products where the highest value was reached by the sample #1).
## 2 Basic Knowledge
### Artificial Electric Field Algorithm
Starting from the second generation, the iterative formula of the charge \(Q_{i}^{t}\) is as follows:  
Figure 1:1  
Figure 1:2  
Among them, \(q_{i}^{t}\) represents the charge of the \(i\)-th particle at the \(t\)-th iteration; \(Q_{i}^{t}\) is the normalization of \(q_{i}^{t}\); \(N\) is the number of particles; \(fit_{i}^{t}\) is the fitness value of the \(i\)-th individual at the \(t\)-th iteration; \(best(t)\) and \(worst(t)\) represent the optimal fitness value and the worst fitness value at the \(t\)-th iteration, respectively (the same is true in Algorithm 1). The position of the algorithm at each iteration is determined by the greedy strategy:  
\[P_{i}^{t+1}=\left\{ \begin{array}{ll}P_{i}^{t}&whenfit\big{(}P_{i}^{t}\big{)}< fit\Big{(}X_{i}^{t+1}\Big{)}\\ X_{i}^{t+1}&whenfit\big{(}P_{i}^{t}\big{)}\geq fit\Big{(}X_{i}^{t+1}\Big{)} \end{array} \right. \tag{11}\]  
Equation (11) represents the selection method of the position of the individual in the next generation. If the fitness of the updated individual is worse than that of the previous generation, the position of the individual will not change; otherwise, the updated individual is used as the next generation. To sum up, the pseudocode of the artificial electric field algorithm is described as follows. ```
Initialization;  Randomly initialization \(\big{(}X_{1}^{t},X_{2}^{t},\ldots,X_{N}^{t}\big{)}\) of population size \(N\) in the search range \([X_{min},X_{max}]\);  Initialize the velocity to 0;  Evaluate the fitness values \(\big{(}fit_{1}^{t},fit_{2}^{t},\ldots,fit_{N}^{t}\big{)}\) of agent \(X\);  Set iteration \(t=1\);  Reproduction and Updating  while stopping criterion is not satisfied do  Calculate \(K^{t},best(t)\) and \(worst(t)\)  for \(i=1\) to \(N\) do  Evaluate the fitness values \(fit_{i}^{t}\)  Calculate the total force in each direction \(F_{i}^{t}\)  Calculate the acceleration \(a_{i}^{t}\) \(V_{i}^{t+1}=rand\times V_{i}^{t}+a_{i}^{t}\) \(X_{i}^{t+1}=X_{i}^{t}+V_{i}^{t+1}\) end for end while
```  
**Algorithm 1** Pseudocode of the artificial electric field algorithm (AEFA). Figure 2: The iterative curve of the Coulomb constant.
## 3 Design of Physical Layer Security Communication System Using Proposed LPD Signal
### Wireless-Tap Channel Model  
In this work, the typical wire-tap channel models consisting of [PERSON], [PERSON], and [PERSON] are considered. The secure transmission model is shown in Figure 4, and details of the transmission protocol are presented as follows:  
Figure 3: Block diagram of receiver.  
1. [PERSON] sends a transmission request signal to [PERSON], followed by pilot signals in the same frequency that [PERSON] is going to use for secure data transmission.
2. [PERSON] estimates the sampling clock offset and CSI via the pilot signals.
3. According to the sampling clock offset between [PERSON] and [PERSON], [PERSON] shifts the central frequencies of sub-band signals to ensure that they are aligned at [PERSON]'s side after sampling.
4. As the CSI of the channel from [PERSON] to [PERSON] has become known, the CSI from [PERSON] to [PERSON] can also be informed according to the channel reciprocal principle. Then, a precoding scheme is employed to enhance both capacity and security.
5. [PERSON] securely transmits the modified LPD signal to [PERSON].  
Details of the sampling clock compensation and precoding scheme for security enhancement are presented in this section. These two methods exploit the physical properties of the sampling clock and channel characteristics between [PERSON] and [PERSON], respectively. For simplification, the sampling phase offset is considered to be \(\Delta=0\) in what follows unless stated otherwise.