esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Constructing the Knowledge Graph
#### 3.1.2 Knowledge Extraction  
The spacecraft equipment knowledge graph is built based on telemetry data, equipment data, and expert knowledge databases. Equipment data come from relational databases, telemetry data include in-orbit historical data, and expert knowledge databases are used for creating task states and dividing feature data. The anomaly event graph is constructed based on historical anomaly data and analysis data, identifying abnormal states and obtaining feature data. For structured data, the knowledge graph can be created through rule mapping or manual collation. For semi-structured and unstructured data, NLP (Natural Language Processing) methods are required for entity recognition and relationship extraction, followed by expert verification to improve quality.  
This paper employs an entity recognition method based on Bidirectional Long Short-Term Memory [44] (BiLSTM) networks and Conditional Random Fields [45] (CRFs), as shown in Figure 4.  
The embedding layer transforms text characters into vectors, which are then input into a BiLSTM network to extract semantic features from sentences, enabling the combination of forward and backward hidden states. This helps to address long-distance dependency issues in the text. The CRF inference layer is a conditional probability distribution model for handling sequence labeling in the text, receiving the output from the BiLSTM and selecting the most likely tag sequence while constraining the predicted labels.  
Before training the model, a certain number of texts need to be annotated to train the model. The BIOES sequence annotation method is used, dividing the annotated corpus into training and test sets at a ratio of 4:1. The entity annotation results of a sentence in the corpus are shown in Figure 5. During this process, model parameters are continuously adjusted to determine the optimal performance.  
For text relationship extraction, this paper adopts a Bidirectional Long Short-Term Memory (BiLSTM) model based on the self-attention mechanism, as shown in Figure 6.  
Figure 4: Entity recognition model.  
Figure 5: Entity annotation example.  
The embedding layer vectorizes the text, forming input features. The BiLSTM learns the context and shallow semantics, obtaining high-level word vectors. The self-attention layer calculates weights and learns deep global semantics, obtaining sentence global features. The output layer concatenates global and local features, calculating the relationship vector between entities.
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 4 Simulation Experiments and Result Analysis
### Prediction Results  
The ESO-BP model was used to predict the health index of engines in subsets FD001, FD002, FD003, and FD004 of C-MAPSS. In FD001, the predicted and actual health indexes of the last operating cycle for all engines in the test set were compared. Figure 10 shows slight errors in the RHI prediction of each engine's last cycle. This indicates that the proposed method can accurately predict the RHI of turbofan engines. Additionally, four engines were randomly selected from the test set to compare the predicted and actual values of the health degradation curves. Figure 11 shows the fitting ability of the degradation trend for four engines in FD001. The results indicate that ESO-BP demonstrates a high accuracy in multivariate input and univariate output prediction.  
Figure 10: Comparison of predicted and real RHI of FD001 dataset.
## 1 Introduction
### Pipelined Extraction Model of Entity and Relation  
The traditional pipelined extraction methods [5; 6; 7] treat entity and relation extractions as two separate tasks: firstly recognizing the entities [8] and then extracting the relations between them [9]. This kind of model is flexible and straightforward, yet suffers from error propagation and ignores the correlation between the two subtasks [10]. Thus, many researchers focus on building joint neural network models to extract entities and relations simultaneously. For example, ETL-Span [11] first distinguishes the entities that may be related to the target relation and then determines the corresponding tail entity and relation for each extracted entity.
###### Abstract  
Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method's high efficiency and speed.  
Keywords:lithium-ion battery; remaining useful life; transfer adaptive boosting; CART; battery management system; edge computing +
Footnote †: journal: Applied sciences
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 2 Materials and Methods
### Instrument
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CMIS** & **VIIRS *** & **MODIS ** & **MISR *** & **ASTER \(\mathbf{\sim}\)** \\ \hline Orbit & 410 km & 830 km & 705 km & 705 km & 705 km \\  & ISS orbit & Sun synch & Sun synch & Sun synch & Sun synch \\ \hline GSD & 545 m & 375 m & 1000 m & 275 m & 100 m, 30 m, 90 m \\ \hline Detector & T2 SL & HgCdTe & HgCdTe & CCD & CCD/PtSi-S/HgCdTe \\ \hline NEdT 3.75 \(\upmu\)m at 300 K & 0.05 K & 0.1 K & 0.05 K & N/A & N/A \\ \hline Optics & Body mounted & Scanning & Scanning & Body mounted & Telescope rotation/pointing/ scanning mirrors \\ \hline Cooling & 150 K & 80 K & 80 K & 278 K & 80 K \\  & & & & & SWIR and LWIR \\ \hline Observation & Day/Night & Day/Night & Day/Night & Day & Day/Night \\ \hline \hline \end{tabular}  
* [[https://ncc.nesdis.noaa.gov/documents/documentation/virs-users-guide-tech-report-142a-v1.3.pdf](https://ncc.nesdis.noaa.gov/documents/documentation/virs-users-guide-tech-report-142a-v1.3.pdf)]([https://ncc.nesdis.noaa.gov/documents/documentation/virs-users-guide-tech-report-142a-v1.3.pdf](https://ncc.nesdis.noaa.gov/documents/documentation/virs-users-guide-tech-report-142a-v1.3.pdf)), accessed on 5 January 2022, *
* [[https://modis.gsfc.nasa.gov/about/specifications.php](https://modis.gsfc.nasa.gov/about/specifications.php)]([https://modis.gsfc.nasa.gov/about/specifications.php](https://modis.gsfc.nasa.gov/about/specifications.php)), accessed on 5 January 2022.
* [[https://misip.fnasa.gov/Mission/misIrInstrument/](https://misip.fnasa.gov/Mission/misIrInstrument/)]([https://misip.fnasa.gov/Mission/misIrInstrument/](https://misip.fnasa.gov/Mission/misIrInstrument/)), accessed on 5 January 2022,
* [[http://asterweb.jplnasa.gov](http://asterweb.jplnasa.gov)]([http://asterweb.jplnasa.gov](http://asterweb.jplnasa.gov)), accessed on 5 January 2022.  
\end{table} 
Table 1: Sensor comparison. Figure 3: The CMS bands (blue shaded) area superimposed on a MODTRAN atmospheric transmission spectrum. Notice that the SWIR band (2.25 \(\upmu\)m) and MWIR bands (3.75 and 4.05 \(\upmu\)m) are in high transmittance windows. As described in Table 2, the mass of the instrument is 3 kg with an average power draw of 8 W (20 W peak power after initial turn on). The overall instrument size is \(20\times 10\times 10\) cm for the optical instrument and 9 \(\times\) 9 \(\times\) 9 cm for the electronics. The measured performance of CMIS is competitive with, yet much lower in cost and smaller in size, weight, and power (SWaP) than any of these instruments. Figure 4a presents an overview of the CMIS instrument design, showing the placement of the ceramic pallet, the filter over the FPA, lens assembly, and cryocooler. The five-zone filter is installed immediately above the FPA (Figure 4b). The system has full-frame readout of the FPA with the five regions of interest extracted, one region per filter zone. The only moving parts of CMIS are the cryocooler and calibration mechanism.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 4 MKIDs in Particle Physics
### MKIDs for Dark Matter Experiments
The paper by [PERSON] [138] proposes a calorimetric approach in which the interacting medium is a superconductor in which the dark matter particle interacts with a free electron at the Fermi energy, thus producing quasi-particles which can then be detected with the help of MKIDs. These proposed detectors are uniquely suited to detect dark matter particles whose mass is sub-MeV. Oppositely, [PERSON] et al. [139] investigates the possibility to use MKIDs coupled to scintillating materials such as sodium iodide (NaI), cesium iodide (CsI), and gallium arsenide (GaAs). Detecting scintillating photons rather than phonons can allow for measurements with significantly lower backgrounds, as there are no Compton scattering events to rule out. Clearly, this detecting mechanism is not background-free since effects such as \"afterglow\", phosphorescence induced from previous interactions, can create spurious photons in the scintillating material. But phosphorescent photons typically have a lower energy than those produced by direct scintillations. MKIDs are especially suitable for this application as they are inherently energy resolving detectors and are, for most practical purposes, effectively dark count free. Along with WIMPs, axions and dark photons are plausible dark matter candidates whose mass range extends down to fractions of one eV. An MKID based experiment has been proposed to detect axions whose masses are in the range of 0.25-2.5 eV [140; 141]. It proposes a photon-mediated detection mechanism for such elusive particles. Through the interaction of axions with optical structures, such as stacks of dielectrics, whose structure's period is of the order of the dark matter's Compton wavelength, the energy of the dark matter particles can be converted into relativistic photons that can be effectively detected [142]. Said photons are emitted in a direction perpendicular to the surface of the stack with a very small (\(10^{-3}\) degrees) aperture angle [142]. The focusing of the thus-produced photons on a detector array can be easily performed with an appropriate lens. MKIDs stand out among the possible detectors to be used for such applications thanks to their low-noise levels, the virtual absence of dark counts and high integrability [140].
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Coating of Magnetic NPs  
In order to improve the functionality, stability, and biocompatibility of MNPs, the coating process is an essential step. This is especially true for applications in biomedicine and environmental research. MNPs can be coated with a variety of different molecules, including:  
**Polymeric Coatings**: polymers, both natural and synthetic, are frequently utilized for MNP coating. Due to their biocompatibility and capacity to inhibit particle aggregation, synthetic polymers such as poly(ethylene glycol) (PEG) [76], poly(vinyl alcohol) (PVA) [77], and poly(lactic acid) (PLA) [78] are among the most preferred options.  
**Coatings made of Silica**: silica coatings are applied to MNPs in order to increase their reactivity and their potential for functionalization. The surface of an MNP consisting of maghemite or magnetite, for example, can be coated with silica. The silica shell can be further modified with a variety of functional groups by the formation of covalent bonds between organo-silane molecules and the silica shell. This approach is especially useful forattaching fluorescent dyes and/or other functional molecules to the outer surface of MNPs, which represent a particularly advantageous use [79; 80; 81].  
MNPs can be coated either during the synthesis process (in situ) with stabilizers such as surfactants or polymers, or after the synthesis process (post-synthesis) with materials such as monolayer ligands, mixtures of polymers, biomolecules, and inorganic compounds. In situ coatings are more common than post-synthesis procedures [82]. When it comes to functionalizing MNPs for specific purposes, such as medical applications, where it is important to bind biological ligands to NP' surfaces, post-synthesis procedures offer a wider range of options [83; 84; 85; 86].  
When it comes to medical diagnostics, drug delivery, or environmental cleanup, each form of coating confers particular features which align with the application that is they are specifically meant for. The functioning, biocompatibility, and stability requirements of MNPs need to be taken into consideration before deciding the coating selection.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 4 Recurrent Neural Network Models
### LSTM Working Structure  
Specialized memory cells with gating mechanisms are incorporated into LSTM networks, enabling them to store, update, and retrieve data selectively over long durations. Because of their distinct architecture, LSTM networks are better able to learn complex temporal patterns and relationships than ordinary RNNs, which helps them overcome some of their limitations. The input gate, forget gate, output gate, and cell state are the main parts of an LSTM unit. They manage information flow and maintain pertinent data throughout several time steps. LSTM networks are powerful tools for jobs requiring long-range temporal modeling and prediction because they can capture complex dependencies and context within sequential data by efficiently controlling the flow of information through the network. Figure 6a shows the internal structure of an LSTM cell. The gates of the LSTM can be mathematically expressed as follows:  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_{i}) \tag{10}\]  
\[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_{f}) \tag{11}\]  
\[g_{t}=\tanh(W_{xg}x_{t}+W_{hg}h_{t-1}+b_{g}) \tag{12}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot g_{t} \tag{13}\]  
\[o_{t}=\sigma(W_{xx}x_{t}+W_{hg}h_{t-1}+W_{co}c_{t}+b_{o}) \tag{14}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}), \tag{15}\]  
where at time step \(t\), the input is \(x_{t}\), at the \(t-1\) time step, the hidden state is expressed as \(h_{t-1}\), from the previous time step, \(c_{t-1}\) is the cell state, the input gate vector is \(i_{t}\), the forget gate vector is \(f_{t}\), the update gate vector is \(g_{t}\), the output gate vector is \(o_{t}\), \(\sigma\) is the sigmoid activation function, element-wise multiplication is represented as \(\odot\), the weight matrix is \(W\), and the bias vector is \(b\). The input layer is connected to the LSTM layer, and the data then move through a fully connected layer and a dropout layer. Lastly, a classification layer is applied to determine the class that the input data belong to. The model can efficiently process sequential data, identify pertinent features, and produce precise predictions due to its architecture.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of IRS elements & 512 \\ Number of paths of BS to IRS & 2 \\ Number of paths of IRS to user & 4 \\ Antenna spacing for transmitter & 0.5\(\lambda\) \\ IRS element spacing & 0.5\(\lambda\) \\ Transmitter antenna & 2 \\ Receiver antenna & 2 \\ OFDM subcarrier & 128 \\ Modulation & QPSK \\ Noise for channel & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 2 System Model
### Memoryless Nonlinear PA Models
#### 2.1.3 Memoryless Polynomial Model  
The memoryless model based on polynomial equations is essentially the complex baseband form of Taylor series used in the analysis of memoryless nonlinear systems. Considering that the band-pass signal after the PA only retains the signal near the central frequency, other frequency components will be filtered out. Thus, only odd-order nonlinear terms should be retained. The mathematical expression of the memoryless polynomial model is given by [56]  
\[y(t)=\sum_{k=0}^{K}a_{2k+1}|x(t)|^{2k}x(t). \tag{8}\]  
The polynomial model has wider applicability than the Saleh model and can be used to fit most memoryless RF PAs. When the model coefficients \(a_{2k+1}\) are complex-valued, the system is called a quasi-memoryless system, in which the AM-PM characteristic will change with \(|x(t)|\). When the coefficients are real numbers, the system is called a strictly memoryless system, such that the AM-PM characteristic is constant at 0, i.e., there is no phase distortion. To facilitate theoretical analysis, this paper adapts a strict memoryless PA model with a small nonlinear order (\(K=2\)). After normalizing the polynomial model, we obtain  
\[y=r_{o,\max}\bigg{(}\frac{x}{r_{\max}}\bigg{)}+c_{1}r_{o,\max}\bigg{|}\frac{x }{r_{\max}}\bigg{|}^{2}\bigg{(}\frac{x}{r_{\max}}\bigg{)}+c_{2}r_{o,\max} \bigg{|}\frac{x}{r_{\max}}\bigg{|}^{4}\bigg{(}\frac{x}{r_{\max}}\bigg{)}. \tag{9}\]  
According to the normalized limit criterion of the ideal linear model, the model coefficients can be calculated to obtain \(c_{1}=-3/16\) and \(c_{2}=1/64\). The detailed derivation process is given in Appendix A. The AM-AM characteristics of the polynomial model can then be written as  
\[g(r)=r_{o,\max}\frac{r}{r_{\max}}+c_{1}r_{o,\max}\bigg{(}\frac{r}{r_{\max}} \bigg{)}^{3}+c_{2}r_{o,\max}\bigg{(}\frac{r}{r_{\max}}\bigg{)}^{5}. \tag{10}\]Because the coefficients of the model are all real numbers, there is no AM-PM distortion, i.e., \(f(r)=0\).
## 4 Advances in Nanotechnology towards Development of Nano-Based Therapies
### AuNPs in the Targeted Treatment of Obesity and Cancer  
Since ancient times, metal and metal complexes have been used for the treatment of several health conditions, including inflammation and infections. The discovery of cisplatin (a platinum-based drug) and its subsequent approval by the FDA for the treatment of cancer in 1978 spurred research on the medical effects of other inorganic metals [18]. Gold is among the metals that have been explored to enhance the activities of anti-tumor compounds [19]. Being a noble metal that is presumably biologically inert, and given its prior therapeutic use, there was a strong belief that gold at the nanoscale would be non-toxic to healthy tissues and can, therefore, be useful as a vehicle for drug delivery. As such, in its nano-form, gold has been demonstrated to have anti-angiogenic and anti-cancer effects [20].  
The synthesis of AuNPs is based on the chemical reduction of gold aureate salt by sodium citrate, a method developed by [PERSON] in 1985 [10]. Over time this method was further modified to synthesize AuNPs with different sizes and shapes by using other chemical reducing agents such as sodium borohydride, ascorbic acid, etc. [21] The physicochemical properties of AuNPs influence their functions and applications [22]. Their large surface area-to-volume ratio facilitates the attachment of multiple biomolecules, such as targeting and therapeutic peptides [10]. In medicine, AuNPs have been used as drug delivery, therapeutic, contrast, and photothermal agents [23]. These functions have been exploited by researchers worldwide and have, over the years, become the core of BRN research expertise.
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
## 3 Experiment and Analysis
### Kinematic Test  
Figure 6 shows the positioning results of the proposed method, traditional method, and NovAtel reference trajectory through a Google map in urban roads.  
From Figure 7a-c and Table 3, it can be seen that when the number of satellites is normal, the positioning result of the proposed method has a slight advantage over the traditional method, and it shows better positioning stability and accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Longitude Error (m)** & **Latitude Error (m)** & **Height Error (m)** \\ \hline Traditional method & 2.50 & 5.08 & 2.67 \\ The proposed method & 1.63 & 2.85 & 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Root Mean Square of position error under vehicle stationary condition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **East Error (m/s)** & **North Error (m/s)** & **Up Error (m/s)** \\ \hline Traditional method & 0.87 & 1.56 & 1.16 \\ The proposed method & 0.20 & 0.73 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Root Mean Square of velocity error under vehicle stationary condition.  
Figure 6: Kinematic test positioning results in an open-sky area.
# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 4 Results and Analysis
### Time Complexity Discussion  
In Figure 10, the training time of each model is compared under the same conditions, measured in hours. On both datasets, the training time of DHAU-Net only slightly increased by 0.6 h compared to the original U-Net. Replacing the skip connection of U-Net with the DHA module does not add much time cost. Considering the improved segmentation performance of DHAU-Net on remote sensing images, the additional time cost is acceptable. Compared to the other comparison methods, the cost difference of DHAU-Net training time is controlled at approximately 1 h, with no significant increase in time cost as a whole. In general, DHAU-Net has a strong segmentation ability in urban remote sensing image buildings, and the improvement in performance is achieved without a significant trade-off in training time. The DHA module proves to be a feasible and effective method for enhancing the network segmentation ability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods\textbackslash{}EI}** & **Accuracy** & **mIoU** & **Precision** & **Recall** & **F1 Score** \\ \hline HRNet & 0.9477 & 0.8520 & 0.9116 & 0.8287 & 0.8682 \\ SegNet & 0.9569 & 0.8775 & 0.9168 & 0.8715 & 0.8936 \\ U-Net & 0.9668 & 0.9033 & 0.9503 & 0.8834 & 0.9156 \\ MANet & 0.9612 & 0.8892 & 0.9253 & 0.8848 & 0.9046 \\ MACU-Net & 0.9574 & 0.8793 & 0.9131 & 0.8786 & 0.8955 \\ AttentionUNet & 0.9501 & 0.8618 & 0.9075 & 0.8509 & 0.8782 \\ MANesU-Net & 0.9494 & 0.8579 & 0.9001 & 0.8507 & 0.8747 \\ UNet\_scAG & 0.9776 & 0.8802 & 0.9110 & 0.8823 & 0.8964 \\ U-Net + HAM-1 & 0.9548 & 0.8695 & 0.9358 & 0.8356 & 0.8829 \\ U-Net + HAM-2 & 0.9467 & 0.8488 & 0.9124 & 0.8193 & 0.8633 \\ U-Net + CBAM & 0.9684 & 0.9079 & 0.9480 & 0.8909 & 0.9185 \\ U-Net + SE & 0.9551 & 0.8710 & 0.9295 & 0.8449 & 0.8851 \\ DHAU-Net & 0.9688 & 0.9089 & 0.9519 & 0.8897 & 0.9197 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specific quantitative experimental results of DHAU-Net and comparative methods on the Inria Aerial Image Labeling Dataset.