esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Experimental Configurations  
In this section, several experiments are designed to validate the proposed approaches, which are categorized into two groups, i.e., trajectory prediction and conflict detection:  
1. Trajectory prediction: four scenarios, namely cruise, right turn, descent, and deceleration, are designed to validate the proposed trajectory prediction approach. Three baselines are applied to compare the prediction accuracy, including KDA [9], Kalman [11] and GP [22]. The GP approach serves as a type of machine learning approach, which is also applied to validate the deeper architecture in this work. The prediction accuracy is evaluated by the dynamic time warping (DTW) [38,43] between the predicted trajectory and real trajectory. The final measurement is MEDTW, which is the mean error of DTW for all test flight trajectories.
2. Conflict detection: four-hour historical data (surveillance data and flight plan) is replayed to prove the effectiveness of the conflict detection algorithm, in which three real potential conflicts occurred in this period. A total of 50 potential conflicts are also simulated to increase the number of test cases. The false alarm (FA) is applied to evaluate the algorithm performance. In this work, the FA means that how many false alarms are detected when all the real and simulated conflicts are detected. In addition, the mean prewarning time (MPWT) is also used to evaluate the timeliness of the proposed approach, which is denoted by the difference between the occurrence time and predicting time. We also conduct a baseline to validate the proposed conflict detection algorithm, which is based on a machine learning-based trajectory prediction approach.  
The replayed scenes (with conflicts) occurred on 17 November 2018, in an area control center, China. The training data were collected from 17 October 2018, to 16 November 2018, i.e., about one-month. The flights performed from 11:00 a.m. to 15:00 p.m. on 17 November 2018, are regarded as the test data to validate the proposed trajectory prediction approach, about 227 flights in this period. The predicted trajectories were further applied to detect potential flight conflicts.  
In this work, only one hidden layer is designed for each DGP model, and the number of inducing points is set to 40. The kernel function is SE. The confidence interval of the prediction results is 95%. The safety interval of two aircraft is 5 nautical miles and 150 m in the horizontal and vertical plane, respectively.
## 7 Unbalanced PSI-CA Protocol Based on Dual-Cloud Assistance
### Extensions
#### 7.7.1 PSI-CA Network  
As previously described, the small health app developer delegates PSI-CA computations to two cloud servers. In practice, a vast network of cloud servers can be built to support this delegation. The basic system description is as follows.  
* Access and Authentication of Cloud Servers: Any server can apply to become a cloud server, also known as a server assistant. These servers must undergo a series of certification processes (including hardware performance verification, security vulnerability scanning, and compliance checks) to ensure they meet security and performance standards. Servers that pass the certification but later violate regulations will be blacklisted and removed. The system maintains platform security and trust through mechanisms such as regular security scans and real-time monitoring, with any violations leading to immediate removal and further investigation of the server.
* Mechanism for Selecting Server Assistants: When needing to perform PSI-CA, small health app developers choose two cloud servers based on their performance (such as processing power, storage capacity, and network bandwidth), stability, security capabilities, and compliance with regulations, among other hard and soft factors. Cloud servers with high availability promises are preferred to minimize the risk of failures.
* Execution Mechanism for PSI-CA Operations: The PSI-CA network supports small health app developer flexibility and system scalability; small health app developers can execute PSI-CA on different large medical institutions by merely changing \(E_{pk}(a)\) and obfuscated dataset \(Y^{\prime}\), without needing to redesign the entire system. This design enhances small health app developer flexibility and the system's efficiency, reliability, and security.  
This system design not only achieves the delegation of PSI-CA computations but also introduces multiple cloud servers into the network, thereby enhancing the system's flexibility and stability. Additionally, by implementing authentication and maintaining a blacklist for cloud servers, the system can better guarantee the credibility of the cloud servers, enhancing overall security. This flexible yet secure system design provides small health app developers with more options and makes PSI-CA operations more adaptable to various practical requirements.  
In summary, under the existing framework, small health app developers can delegate computing and storage tasks to different cloud servers and perform PSI-CA operations on various large medical institutions by using different random numbers, \(E_{pk}(a)\), and obfuscated dataset \(Y^{\prime}\). This method allows small health app developers to more flexibly use multiple resource nodes and optimize task distribution, thereby further enhancing the overall performance and security of the privacy protection scheme.
## 5 IoT Service Provisioning via NDN in the Smart Campus
### NDN-Fog Node Design  
As shown in Figure 3, NDN-fog nodes implement the legacy NDN data plane, while the control plane is extended with a Service Decision Engine (SDE) that coordinates the following functionalities:  
* _Function storing module_ decides which functions should be installed in the node and which functions should be evicted. The selection is performed in a proactive way by following a popularity-based approach, similar to Reference [8]. During the network bootstrap, a first manual configuration is performed by the network administrator, who may also decide that specific functions must be installed only in specific locations, e.g., for privacy purposes. When a function is available in the node, a FIB entry is configured that binds the function name with the _AppFace_ of the node.
* _Service allocation module_ decides, after the service interest reception, if the node can execute the service and manages the signalling related to this operation.  
NDN-fog nodes can dynamically download the function code from the so-called _Function Data Base_, a storage unit located in the campus domain that contains the function catalog. A default route is installed in the FIBs of the NDN-fog nodes towards the Function Data Base; thus, functions can be retrieved by name with the standard NDN interest/data exchange. Of course, if the function code is available in the CS of an on-path node, the latter can directly satisfy the request without further forwarding it.  
Figure 3: Architecture of NDN-fog nodes.
## 4 Discussion
### Analysis of Topics by Years and Geographical Area
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{5}{c}{**Target Class**} \\ \cline{2-6}  & **COVID-19** & **Quality** & **Resource** & **Security** & **Techninnovation** \\ \hline \multirow{4}{*}{Methodology} & Data Analysis & 0.00868062 & 0.00194111 & 0.00720522 & \(-\)0.0235328 & 0.00570588 \\ \cline{2-6}  & General Approach & \(-\)0.0524088 & \(-\)0.0229678 & 0.0252449 & 0.0123701 & 0.037616 \\ \cline{2-6}  & Review and Meta-Analysis & 0.0204555 & \(-\)0.0331051 & \(-\)0.00222647 & 0.0164856 & \(-\)0.00160965 \\ \cline{2-6}  & Qualitative Research & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Quantitative Research & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Experimental Research & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Case Study & 0 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{Discipline} & Epidemiology and & \multirow{2}{*}{0.0156003} & \multirow{2}{*}{0.00635781} & \multirow{2}{*}{0.029401} & \multirow{2}{*}{\(-\)0.0134632} & \multirow{2}{*}{\(-\)0.037896} \\  & Public Health & & & & & \\ \cline{2-6}  & Healthcare Management and & \multirow{2}{*}{0.0017734} & \multirow{2}{*}{4.77045 \(\times\) 10\({}^{-05}\)} & \multirow{2}{*}{0.00216109} & \multirow{2}{*}{\(-\)0.00081557} & \multirow{2}{*}{0.00038018} \\  & Hospital Administration & & & & & \\ \cline{2-6}  & Biomedical Engineering & \multirow{2}{*}{\(-\)0.0017734} & \multirow{2}{*}{4.77045 \(\times\) 10\({}^{-05}\)} & \multirow{2}{*}{0.00216109} & \multirow{2}{*}{\(-\)0.00081557} & \multirow{2}{*}{0.00038018} \\  & and Robotics & & & & & \\ \cline{2-6}  & Artificial Intelligence and & \multirow{2}{*}{\(-\)0.00120327} & \multirow{2}{*}{\(-\)0.0589238} & \multirow{2}{*}{0.0560993} & \multirow{2}{*}{0.00686849} & \multirow{2}{*}{\(-\)0.00284068} \\  & Computer Science & & & & & \\ \cline{2-6}  & General Studies in Health & \multirow{2}{*}{\(-\)0.0989511} & \multirow{2}{*}{\(-\)0.0357179} & \multirow{2}{*}{0.330612} & \multirow{2}{*}{\(-\)0.0674638} & \multirow{2}{*}{\(-\)0.128479} \\  & and Technology & & & & & \\ \hline \multirow{4}{*}{Prediction info} & Digital Technologies & \multirow{2}{*}{\(-\)0.00654314} & \multirow{2}{*}{\(-\)0.000158302} & \multirow{2}{*}{0.004248} & \multirow{2}{*}{\(-\)0.00172286} & \multirow{2}{*}{0.00417631} \\  & and Telecommunications & & & & & \\ \cline{1-1} \cline{2-6}  & **MODEL PREDICTION** & **0.07** & **0.04** & **0.74** & **0.09** & **0.06** \\ \cline{1-1} \cline{2-6}  & **BASE VALUE** & **0.15** & **0.14** & **0.22** & **0.16** & **0.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._increased focus on TECHINNOVATION, representing 13.6% of the research by 2023. This trend reflects a growing propensity towards the adoption of innovative technological solutions, with a significant rise in interest towards TECHINNOVATION reaching 40.9% of the total in 2023. Europe displays a diversified interest across the years, with a peak for QUALITY themes, representing 12.1% in 2022, and an even distribution between QUALITY and
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 3 System Design
### Environment Configuration  
The agent performs learning by interacting with the environment. Thus, we constructed the environment for the agent to perform the learning properly, so that the UAVs with the trained neural network are able to perform the missions explained in Section 3.1. This section gives a detailed explanation about the environment configuration, including the traffic map, the state, the action, and the reward.
## 3 Results
### Effect of JGTW on CHEMOKINE Production  
ELISA was used to measure the contents of key inflammatory mediators in the pulmonary epithelial cell culture supernatant. As shown in Figure 3A,B, the concentration of eotaxin-1, eotaxin-3, and RANTES in the supernatant of IT-stimulated BEAS-2B cells were significantly higher than that in vehicle-treated cells (\(p<0.01\)). However, treatment with 125, 250, and 500 \(\upmu\)g/mL of JGTW significantly reduced the concentration of these chemokines dose dependently (\(p<0.01\)).
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 3 Case Study  
### Project Overview  
The Shenzhen intercity railway shield tunnel project is located in the northern part of Shenzhen, running in an east-west direction and primarily laid out in urban built-up areas. The project is divided into two lines, employing a dual-mode shield construction method. The maximum tunnelling speed of the shield is 80 mm/min, with a maximum thrust of 81,853 kN and a maximum cutterhead torque of 29,054 kN\(\cdot\)m, and the main drive power is 3500 kW. The left-line shield section relied on in this article is 3409 m long, with a tunnel burial depth of approximately 7.3\(\cdot\)66.0 m. The outer diameter of the tunnel is 8.8 m, and the inner diameter is 8 m. The geological profile of the section is shown in Figure 7. The  
Figure 4: Level-wise structure diagram.  
Figure 5: Leaf-wise structure diagram.  
Figure 6: PCA-PSO-LGBM model prediction process.  
shield mainly passes through slightly weathered granite, moderately weathered granite, blocky highly weathered granite, and sandy highly weathered granite.
# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
###### Abstract  
Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of -459\(\times\) and -429\(\times\) compared to the SVM scheme, while keeping constant and even slightly improving the classification performance.  
hyperspectral imaging; intraoperative imaging; brain cancer; linear unmixing; support vector machine +
Footnote †: journal: Applied  
_Applied_  
_Article_  
Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing  
Inverse  
or absorbance), acting as a fingerprint or signature used to distinguish the chemical composition of a particular pixel [8]. These spectral signatures are used together with labeling or classifying the pixels in the HS image, where the main challenges are the high dimensionality of the datasets and limited training samples. Linear unmixing is an analysis method that decomposes the continuous spectrum in each pixel of an HS image into different pure spectral signatures (called end-members) and their concentrations (called abundances) by a linear mixing model [9]. The joint estimation of end-members and their abundances in an HS image is called blind linear unmixing (BLU) analysis [10]. BLU is a type of inverse problem, where the end-members are assumed to be common in the HS image, and the abundances are estimated for each pixel. In this sense, BLU can also be applied as a classification tool for HS images based on the estimated spectral profiles of the end-members and their abundances per pixel [11; 12; 13]. Hence, the main challenge in BLU applications is to maintain high accuracy in the labeling task, while maintaining a low computational cost. On the other hand, recent approaches in HS image classification have considered machine learning and deep learning as classification tools [14; 15; 16; 17], but although the accuracy could be improved, the required computational cost has to be carefully evaluated for a prospective real-time application.
## 3 Results
### Class Separability and Feature Selection
The elements K7, K8 and K9 (descriptors of the phase delay during the scattering in a certain direction) play a minor role in the separation of classes, as phase delays happen during volume propagation. Since tundra vegetation has a relatively short stature (height), phase delays due to volume propagation are less likely. In summary, the class separability assessment indicated that the use of the full Kennaugh-Matrix is more beneficial than use of the model-based (power) Decompositions, Eigen-decompositions, or the intensities of the polarimetric channels. L-Band, followed by C-Band and X-Band showed the best separation concerning the different wavelengths. Using the PolSAR feature spaces was most beneficial for the separation of the land cover classes OL, BS, BW and WT.
## Materials and Methods
### Preparation and Particle Characterizations of ZnSO\({}_{4}\)/SP NCs  
ZnSO\({}_{4}\) and SP (3:7, \(w/w\)) were blended prior to the extrusion and they were put into the extruder at a 50 g/min rate. These mixtures were processed by a twin-screw hot-melt extruder (STS-25 HS, Hankook E.M. Ltd., Pyeongtaek, Korea) equipped with a round-shaped die (2 mm diameter). The temperature of the barrels was maintained as shown in Figure 1, and the speed of the screw was set at 200 rpm. Extrudates were cooled down to room temperature and milled by the HBL-3500S grinder (Samyang Electronics Co., Gunpo, Korea) for pulverization. The particle characteristics of dispersion of ZnSO\({}_{4}\)/SP NCs, at 10 mg/mL concentration, in distilled water (DW), pH 1.2 medium, and pH 6.8 buffer were assessed. Simulated gastric fluid (pH 1.2) was prepared by dissolving NaCl (2.0 g) and HCl (7.0 mL) in DW (1000 mL final volume). In the case of simulated intestinal fluid (pH 6.8), 0.02 mol/L KH\({}_{2}\)PO\({}_{4}\) in DW (250 mL) and 0.2 mol/L NaOH (118 mL) were mixed, and DW was added to attain a 1000 mL final volume. The mean diameter, polydispersity index, and zeta potential values of ZnSO\({}_{4}\)/SP NCs in DW, pH 1.2 medium, and pH 6.8 buffer were measured using dynamic light scattering (DLS) and laser Doppler methods (ELS-Z1000; Otsuka Electronics, Tokyo, Japan) according to the manufacturer's instructions. The morphological shapes of ZnSO\({}_{4}\)/SP NCs in DW, pH 1.2 medium, and pH 6.8 buffer were observed by transmission electron microscopy (TEM). The aliquot of dispersion was stained with 2% (\(w/v\)) phosphotungstic acid. It was placed on copper grids with films, dried for 10 min, and observed by TEM (JEM 1010; JEOL, Tokyo, Japan). The content of Zn in ZnSO\({}_{4}\)/SP NCs was measured by inductively coupled plasma-optical emission spectrometry (ICP-OES; Optima 7300 DV, PerkinElmer, Inc., Waltham, MA, USA). ZnSO\({}_{4}\)/SP NCs were dissolved in nitric acid and processed with microwave digestion.
## 2 Materials and Methods
### Bioinformatic Analysis of Rna-Seq Data  
For gene annotation, de novo assembled unigenes were compared against databases including the Phytozome, Gene Ontology (GO), UniProt, eukaryotic Ortholog Groups(LOG), Kyoto Encyclopedia of Genes and Genomes (KEGG), and The Arabidopsis Information Resource (TAIR). Unigene sequences were also mapped to IWGSC MIPS v2.2 wheat transcriptome sequences (IWGSC 2014) using BLAST with an e-value cutoff of \(1\times 10^{-10}\); gene IDs with >95% identity and <2 bp mismatch were selected [25].  
Paired-end clean reads were mapped back to de novo assembled unigenes to obtain read counts for expression analysis using Bowtie 2. Gene expression levels from read counts were normalized to Reads Per Kilobase per Million (RPKM) with the edgeR package of Bioconductor in the R software environment. Unigenes with > 500 raw read counts from at least one library were selected and the log2 value of the relative RPKM ratio was applied to determine relative expression levels. Genes with an absolute log2 value of the relative RPKM ratio > 11 (2-fold change) were defined as differentially expressed genes (DEGs). Heatmaps were generated by using the heatmap. 2 function in the gplots package in the R software environment. To analyze gene ontology (GO) enrichment and network analysis of DEGs, the lists of Arabidopsis thaliana orthologous were uploaded to ClueGO [26] plug-in of Cytoscape [27] and the hypergeometric test was applied (\(p<0.05\)).
# Predicting the Compressive Strength of Concrete Using an RBF-ANN Model
## 6 The Verification  
Additional 12 mix designs listed in [26] are used for the verification of the present RBF-ANN model. They are excluded from the database for training and testing. The predicted values are also compared with those predicted by the BP-ANN models in [26; 27]. The results are plotted in Figure 3. It should be noted that the values plotted in this figure are the values of the 28-day compressive strength of concrete whose range in the database is from 5 MPa to 95.3 MPa. The \(E_{r.m.s.}\) by using the present RBF-ANN model is 7.53 MPa. It performs a little worse than the BP-ANN model in [27] whose \(E_{r.m.s.}\) is just 4.73 MPa. As per the comparison with the BP-ANN model in [26] whose \(E_{r.m.s.}\) is 10.70 MPa, the present RBF-ANN is still better.  
In [25], the genetic algorithm was employed to train the artificial neural network which was established to predict the 28-day, 56-day, and 91-day compressive strengths of concrete admixing with fly ash or without fly ash. Blast furnace slag and superplasticizer are not admixed in their samples. Though the predicted results of [25] were not listed in their paper, the actual compressive strengths were available. So, the data were used to validate the BP-ANN model of [27] further. Here, the data in [25] are also used to validate the present RBF-ANN model. The comparisons of the predicted and the actual  
Figure 3: Comparison of the predicted values with the actual 28-day compressive strength of concrete [26] (Unit: MPa).  
28-day compressive strengths of the concrete admixed with and without fly ash are plotted in Figures 4 and 5. For those admixed with fly ash, the present RBF-ANN model has a root-mean-square error of 9.42 MPa while the BP-ANN model of [27] performs with the root-mean-square error of 10.28 MPa. Both ANN models over-estimate the compressive strength, but the results of the present model are generally closer to the actual values. For those admixed without fly ash, the root-mean-square errors are 2.65 MPa and 7.19 MPa, respectively. The present model performs much better than the model in [27].  
Figure 4: Comparison of the predicted values with the actual 28-day compressive strength of concrete in which fly ash was admixed [25] (Unit: MPa).  
Figure 5: Comparison of the predicted values with the actual 28-day compressive strength of concrete in which no fly ash was admixed [25] (Unit: MPa).  
The result is quite reasonable because the variation of particle sizes and the chemical composition in different fly ash may be the source of some uncertainties. Generally speaking, the performance of the present RBF-ANN is quite acceptable.