esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Efficiently Enhancing the Data and Procedure Organization in DSSs, CMSs, and ISs  
The methodologies and techniques presented in this study are highly valuable in realistic situations, as they allow efficient resource allocation through clear thematic categorization. Moreover, they enhance decision making by providing well-categorized, relevant information that is crucial for time-sensitive missions. Additionally, operational awareness is achieved by continuously integrating input data with relevant categorization. It must be stressed the technique facilitates systematic data management, allowing the system to categorize and prioritize data for efficient handling and retrieval from the storage system. Furthermore, the DSS's design supports scalability and adaptability to various mission types and sizes, and its robust information-processing capabilities enable it to handle a wide range of data sources, ensuring comprehensive situational awareness. Nonetheless, the DSS ensures accurate documentation, proper formatting, and easy accessibility of data input. Notably, the detailed documentation and organization of data input in a DSS are vital components for supporting any operational procedure/process and coordinating any mission effectively. Moreover, the system is designed to improve data accuracy and reliability by sourcing information from appropriate channels.  
We emphasize that the integration of complex georeferenced data within the IS is crucial for layering various datasets effectively. This is further enhanced by the DSS, which supports missions efficiently and flexibly by linking operational procedures with actionable information required by the actors involved. This system distributes input information in a manner that meets the vital requirements of these actors, ensuring that real-time updates are provided as the mission evolves. Consequently, this approach establishes the system's robustness, characterized by its ability to adapt to varying circumstances while upholding the mission's objectives. The system's adaptability is also evident in its capacity to handle sudden changes and empower missions to manage multi-themed tasks. This includes the distribution of real-time geospatial data to relevant actors, a critical feature for dynamic mission environments. Additionally, the system is designed to efficiently manage and update mission-critical data cores by combining the five methodologies described previously. This comprehensive framework supports a wide range of operational procedures, thereby enabling the mission to tackle various unforeseen scenarios effectively. Summarizing, the application of these methodologies and frameworks in mission scenarios underscores their practical utility in complex, realistic scenarios and missions. They facilitate the efficient and flexible management of diverse and dynamic data, which is vital for coordinating complex tasks in changing environments.
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
###### Abstract  
In this paper, a novel state of health (SOH) estimation method based on partial charge voltage and current data is proposed. The extraction of feature variables, which are energy signal, the Ah-throughput, and the charge duration, is discussed and analyzed. The support vector machine (SVM) with radial basis function (RBF) as kernel function is applied for the SOH estimation. The predictive performance of the SOH by the SVM are performed with full and partial charging data. Experiment results show that the addressed approach enables estimating the SOH accurately for practical application.  
capacity degradation; charge voltage; state of health (SOH); support vector machine (SVM) +
Footnote †: journal: Applied Sciences
### Research Questions and Hypotheses  
This study expands our knowledge in the fine-root phenology of two late-successful (beech and oak) and one pioneer (birch) tree species. All three species have fine-roots that are colonized by ectomycorrhizae, known for their nitrogen, phosphorus, and water uptake ability [13; 52; 23]. Nevertheless, we expect differences in the fine-root lifespan, turnover rate, and phenology among species. For example, based on the difference in the resource strategy (i.e., acquisitive or conservative) and related root traits, the fine-root lifespan and turnover rate of birch is expected to be smaller and greater than those of beech and oak, respectively [40].  
Because of the use of different methods and the measuring of roots at different depths, estimations of the fine-root lifespan and turnover rate vary widely in the literature [53; 54; 55; 56; 57; 58; 59; 60; 61]. Beech, oak, and birch were reported to have a mean fine-root lifespan of approximately 424 days, 358 days, and 935 days, respectively [50; 62; 63; 64; 65]. The fine-root turnover rate would then correspond for beech, oak, and birch to approximately 0.86 year\({}^{-1}\), 1.02 year\({}^{-1}\), and 0.39 year\({}^{-1}\), respectively. These estimates are opposite to the expectation that fine-roots of late-successful species live longer than those of pioneer species (with the literature reporting only a few estimations for oak and birch) [62; 66; 67; 64; 66]. Therefore, we aimed to clarify this confusion. However, one should note that these estimations of the fine-root lifespan and turnover rate in the literature might also have been affected by differences in the locations (e.g., the soil, the latitude, or the elevation) of the experiments and therefore might not solely represent species-specific differences.  
In conclusion, this study addressed three questions: (I) How do the below- and aboveground phenology of deciduous trees relate to each other? (II) Are there differences in the fine-root lifespan, turnover rate, and phenology among beech, oak, and birch?(III) Are there differences in the fine-root lifespan and turnover rate among roots with different diameters or those born in different seasons?
# Prediction System for Prostate Cancer Recurrence Using Machine Learning
## 3 Results
### BCR Prediction Model Analysis  
In order to analyze the statistical performance of the BCR predictions, we evaluated the AUC, accuracy, sensitivity, specificity, and MCC of each analysis method. The analytical data was divided randomly, with 80% of the data allocated to the training dataset and 20% allocated to the test dataset. We trained the model using the training data, and then we evaluated the performance of the model using the test data.  
According to the result of analysis (Table 2), the highest AUC value for the three year predictions was 0.8419, which was associated with the GBC model. For the five year predictions, the highest AUC value (0.8071) was associated with the Ridged Regression model. The highest MCC values for the three year and five year predictions were associated with the Random Forest (ntrees = 80) model (0.4621) and the GBC model (0.4836), respectively. The Cox proportional hazard model showed good results in terms of accuracy and specificity but performed poorly in terms of sensitivity, and so was not used as a predictive model. In terms of overall performance across the five analysis criteria (AUC, accuracy, sensitivity, specificity, MCC), the GBC model had the highest average value and showed excellent performance. Therefore, we applied this model to develop the BCR prediction system. To develop prediction models for three and five years after prostate cancer surgery using the GBC model, we developed the BCR prediction system using Python.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **BCR (1207 Patients)** & **Non-BCR (3907 Patients)** \\ \hline Age & Average & 65 & 65 \\ \hline Initial PSA (ng/mL) & Average & 17.58 & 9.74 \\ \hline  & 2–4 & 0 & 5 \\  & 5 & 3 & 19 \\ Gleason score (sum) & 6 & 111 & 994 \\  & 7 & 719 & 2576 \\  & 8–10 & 374 & 313 \\ \hline  & T1 & 399 & 1753 \\ Clinical T stage & T2 & 467 & 1416 \\  & T3 & 276 & 677 \\  & T4 & 65 & 61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patient Characteristics and Distribution.
## 2 Fundamental Knowledge
### Gradient Centralization (GC)  
Gradient descent is of great importance to effectively and efficiently train a DCNN, which can directly affect the model's convergence speed and prediction accuracy. When the model becomes larger, the gradient descent becomes more difficult, causing issues like gradient vanishing, gradient exploding, or nonconvergence to affect the accuracy. To deal with this, GC [26] was presented to make the gradient descent smoother and to normalize the gradient before backward propagation, as described in Equation (1). GC operates directly on gradients by centralizing each column of convolutional layers and fully connected layers to have zero mean; the gradient is then subtracted from its mean value and sent back to continue the training of the model, as illustrated in Figure 7. Hence, the abnormal data will not affect the training process.  
\[\varnothing_{GC}(\
abla_{w_{i}}\mathcal{L})=\
abla_{w_{i}}\mathcal{L}-\mu \
abla_{w_{i}}\mathcal{L} \tag{1}\]  
where \(W_{i}\) denotes the weight matrix of CL or FCL. The gradient \(\
abla_{w_{i}}\mathcal{L}\) is obtained through backward propagation of \(\mathcal{L}\). After the calculation of mean value \(\mu\), average \(\mu\
abla_{w_{i}}\mathcal{L}\) can be realized, and \(\varnothing_{GC}(\
abla_{w_{i}}\mathcal{L})\) is gained through GC.
## 1 Introduction
### Ert--State-of-the-Art  
ERT is a versatile geophysical method used extensively for subsurface investigations, particularly in detecting lithological boundaries, water-filled cavities, and saturation zones. Complementing other methods like GPR, ERT has demonstrated its efficacy across diverse geological settings. For instance, in urban areas such as Zaouit Ech Cheikh (Morocco) and the Cheria basin (Algeria), ERT has been instrumental in delineating underground karsts and artificial cavities, mitigating risks to building stability and urban subsidence [16,17]. Its adaptability extends to mapping complex subsurface systems, including the Sierra de Atapuerca endocast in Spain [18] and the Komberek karst in Slovakia [19]. Recent studies underscore ERT's capacity to detect underground anomalies, such as cavities beneath construction sites, by integrating statistical analyses with field data. For example, its application in landfill environments has highlighted its ability to map leachate zones through resistivity and chargeability contrasts, offering critical insights into environmental hazards [19]. Additionally, ERT has been effectively used to locate subsurface voids, as demonstrated in projects where high-resolution data were essential for assessing risks to infrastructure [20]. These capabilities position ERT as a vital tool in monitoring highway pavement subsidence, providing detailed resistivity profiles to identify underground cavities that pose significant threats to road stability. Despite challenges such as reduced resolution in deep or conductive overburden, its integration with satellite and GPR data addresses these limitations.
# NAS-CRE: Neural Architecture Search for Context-Based Relation Extraction
## 4 Experiments
### Comparison Models  
To verify the effectiveness of our proposed model, we used the CRE method in [10] as the baseline. We set the parameters according to the original text when reproducing the baseline to ensure a fair comparison. The baseline models are as follows:  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Training** & **Validation** & **Held** \\ \hline Relation triple & 284,295 & 113,852 & 287,902 \\ Relation & 578,199 & 190,160 & 600,804 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset statistics, where relation triple denotes the number of \(\{entity_{1},relation,entity_{2}\}\).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Dropout\_rate & 0.5 \\ Batch\_size & 128 \\ Max\_sentence\_len & 36 \\ Learning\_rate & 0.001 \\ RNN\_units & 256 \\ Decay & 0.0001 \\ Position\_embedding & 3 \\ Windows\_size & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter statistics.  
**LSTM-baseline**[10] does not consider the impact of other entities of the sentence on the target entity. Instead, GloVe is used as an encoder, and LSTM is adopted to obtain the characteristics of the sentence.  
**ContextSum**[10] considers the impact of other entities in the sentence on the target entity. Relationships between entities are represented using weighted sums. It is implemented using GloVe and LSTM.  
**ContextWeighted**[10] considers the impact of other entities of the sentence on the target entity. Other relations in the sentence are spliced with attention scores during target entity extraction.  
**NAS-CRE** is our proposed method and uses the XLNet pretraining model as an encoder. In addition, the RNN structure is automatically optimized to better consider the relationships of other entities in the sentence.
# A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching
###### Abstract  
This paper presents a novel approach to designing a CMOS inverter using the Mayfly Optimization Algorithm (MA). The MA is utilized in this paper to obtain symmetrical switching of the inverter, which is crucial in many digital electronic circuits. The MA method is found to have a fast convergence rate compared to other optimization methods, such as the Symbiotic Organisms Search (SOS), Particle Swarm Optimization (PSO), and Differential Evolution (DE). A total of eight different sets of design parameters and criteria were analyzed in Case I, and the results confirmed compatibility between the MA and Spice techniques. The maximum discrepancy in fall time across all design sets was found to be 2.075711 ns. In Case II, the objective was to create a symmetrical inverter with identical fall and rise times. The difference in fall and rise times was minimized based on Spice simulations, with the maximum difference measuring 0.9784731 ns. In Case III, the CMOS inverter was designed to achieve symmetrical fall and rise times as well as propagation delays. The Spice simulation results demonstrated that symmetry had been successfully achieved, with the minimum difference measuring 0.312893 ns and the maximum difference measuring 1.076540 ns. These Spice simulation results are consistent with the MA results. The results conclude that the MA is a reliable and simple optimization technique and can be used in similar electronic topologies.  
Mayfly Optimization Algorithm (MA); CMOS inverter; cost function; rise time; fall time; Spice
## 2 Wheel-Track Coupling Model
### Numerical Model
#### 2.2.2. Rail Submodel  
The equation of the ballasted track motion is as follows:  
\[\left[\begin{array}{ccc}M_{rr}&0&0\\ 0&M_{\mathrm{ss}}&0\\ 0&0&M_{bb}\end{array}\right]\left[\begin{array}{ccc}\ddot{\delta}_{r}\\ \ddot{\delta}_{s}\\ \ddot{\delta}_{b}\end{array}\right]+\left[\begin{array}{ccc}C_{rr}&C_{sr}&C _{br}\\ C_{rs}&C_{ss}&C_{bs}\\ C_{rb}&C_{sb}&C_{bb}\end{array}\right]\left[\begin{array}{ccc}\dot{\delta}_ {r}\\ \dot{\delta}_{s}\\ \dot{\delta}_{b}\end{array}\right]+\left[\begin{array}{ccc}K_{rr}&K_{sr}&K _{br}\\ K_{rs}&K_{ss}&K_{bs}\\ K_{rb}&K_{sb}&K_{bb}\end{array}\right]\left[\begin{array}{ccc}\dot{\delta}_ {r}\\ \dot{\delta}_{s}\\ \dot{\delta}_{b}\end{array}\right]=\left[\begin{array}{ccc}F_{r}-F_{contact }\\ F_{s}\\ F_{b}\end{array}\right] \tag{17}\]  
where \(M\), \(C\) and \(K\) denote the mass, damping and stiffness matrices of the track, respectively. \(F\) denotes the load vectors. \(\delta\) is the displacement vectors of each track components. The subscripts \(r\), \(s\) and \(b\) correspond to the rail, sleeper and ballast each.  
For the ballasted railway track structure, considering the modelling accuracy and efficiency, the rail is simulated as a continuous Timoshenko beam with dynamic response frequencies up to 3000 Hz. In this study, the cut-off frequency is chosen as 3000 Hz because if the vibration propagation waves in the rail is higher, the rail profile's local deformation becomes the major vibration mode, which will be inaccurately simulated by the beam  
Figure 3: Schematic diagram of coupled wheel–track model.  
hypothesis [30]. In other words, only when the frequency range is not higher than 3000 Hz, the rail profile local deformation can be ignored, and thus the model established based on the beam assumption can be used. In the first modelling step of the finite element analysis, the rail is simulated using the standard 60 kg/m rail section and the mesh size is 0.3 m per element. To eliminate the boundary effect on the numerical calculation accuracy, the rail is considered to be 60 m long and fixed at both ends, and the wheel is dropped at the centre of the rail. When the rail model is defined, the related information such as the geometry, material properties, mass, damping and stiffness matrices are extracted through a substructure analysis. By performing a modal analysis, the mode shapes and vectors are obtained and later loaded to serve as the foundation for the dynamic analysis based on the modal superposition method. In the substructure analysis, the Guyan method is adopted, and all the nodes are employed as the master nodes. In the modal analysis, the block Lancoz method is adopted to obtain the modal solution, and a total of 800 eigen-frequencies (up to 3000 Hz) are included in the rail model.
## 4 Materials and Methods
### De novo Transcriptome Assembly and Unigene Functional Annotation  
To ensure the accuracy of de novo assembly and subsequent analyses, the raw reads and low quality reads (above 50% of bases with Q-value \(\leq\) 20), ambiguous reads, adaptor sequences, and duplication sequences were removed before assembly. Clean reads were assembled into contigs using Trinity software [39]. All transcripts were analyzed on the BGISEQ-500 platform [40]. The assembled transcripts were extended and clustered using the TGICL software [41]. The assembled transcripts were processed for further functional annotation and classification analysis.  
Unigene functional annotation was achieved by mapping unigenes to five databases (NT, NR, KOG, KEGG, and SwissProt) using the software BLAST (version 2.2.23, E-value \(\leq\) 1e-5) [42]. Morever, unigenes were mapped to metabolic and secondary metabolic pathways using MAPMAN (version 3.6.0) [43]. GO functional annotation was performed using Blast2 GO (version 2.5.0, default parameters) [44] with NR annotations and Pfam annotations were performed using Hmmscan [45].
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 4 Attitude Point-to-Point Generator
#### 4.2.2 APPG-Step 3.  
Following the scheme in Figure 5, and similarly to step 1, the angular velocity and acceleration leading the satellite to the final conditions are found starting from a steady state and accelerating until the desired angular velocity and acceleration. Boundary constraints are imposed only on angular velocity and acceleration  
\[\begin{cases}\omega_{i}(0)=0;\ \omega_{i}(t_{f_{3}})=\omega_{i_{f}},\\ \dot{\omega}_{i}(0)=0;\ \dot{\omega}_{i}(t_{f_{3}})=\dot{\omega}_{i_{f}},\quad i=x,y,z,\end{cases} \tag{40}\]  
where \(t_{f_{3}}\) is the guidance law time of the third step, and \(\omega_{i_{f}}\) and \(\dot{\omega}_{i_{f}}\) are the final condition of each \(i^{th}\) component of the angular velocity and acceleration, respectively. The free parameter is \(t_{f_{3}}\), similar to \(t_{f_{1}}\) in the previous step. Therefore, \(t_{\omega_{max_{3}}}\) and \(t_{\omega_{max_{3}}}\) are given as a function of the final time \(t_{f_{3}}\) as well as the physical constraints, i.e.,  
\[\begin{cases}|\omega_{i}(t=t_{\omega_{max_{3}}})|\leq\omega_{max}\\ |\dot{\omega}_{i}(t=t_{\omega_{max_{3}}})|\leq\dot{\omega}_{max},\quad i=x,y,z.\end{cases} \tag{41}\]  
Figure 7: Representation of the symmetrical positions where \(|\dot{\omega}|\) gets extreme values.  
As a consequence, the polynomial trajectories that approximate \(\omega_{i}(t)\) and \(\dot{\omega}_{i}(t)\) in this step are fully defined, i.e., the boundary conditions and the physical constraints are satisfied. The quaternion trajectory can be evaluated by integrating backward the differential equation of quaternion kinematics, i.e., the initial attitude of this guidance law (\(\mathbf{q}_{f_{2}}\)) can be evaluated starting from the desired one (\(\mathbf{q}_{d}\)) by integrating the differential equation from \(t_{f_{3}}\) to 0.
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 5 Experiments
### Experimental Settings  
#### 5.2.1 Comparison Methods  
To comprehensively evaluate the comparative performance of our proposed method, we selected the following five models as baselines.  
* **IONE**[38]: A semi-supervised method based on network structures, in which users' relations are represented as input/output context vectors.
* **REGAL**[13]: An unsupervised network alignment method that incorporates both network structures and attributes. In this experiment, TF-IDF is utilized to represent node attributes.
* **ABNE**[25]: A graph attention-based UA model, which exploits the network structure by modeling the contribution probabilities in users' relationships.
* **TADW**[50]: An attributed network embedding model. In our experiment, an MLP is employed to map source embeddings to target space for the UA task.
* **PALE**[22]: A supervised method that adopts structural regularities of networks for user alignment. Herein, LINE [51] is taken as a substitute network embedding method for PALE.
* **CENALP**[52]: An embedding model that jointly performs link prediction and network alignment using the skip-gram framework. The weight parameter \(c\) is fine-tuned from 0.1 to 0.9 with a step size 0.1 for optimal results.
* **Grad-Align**[53]: A semi-supervised model that gradually predicts aligned node pairs by node consistency and prior information. The gradual matching function is set to a base-2 exponential function.
* **Grad-Align+**[54]: An extension of Grad-Align that use attribute augmentation for UA.  
**Variants of JARUA**: In order to study the effectiveness of the model components of JARUA, we also examine three variants of JARUA, including:  
* **JARUA-a**, which ignores the components of structure embedding. Attributes are adopted as the only features to perform the UA task.
* **JARUA-s**, which takes only network structural features for user alignment, ignoring attribute features.
* **JARUA-v**, which directly combines the attribute embedding component and structure embedding component to align users without iterative training.
* **JARUA-t**, which replaces the multi-granularity attribute embedding module of JARUA with TF-IDF vectors to represent user attributes.  
All comparison models described above can be divided into three experimental control groups by the features they adopted.  
Figure 3: The degree distributions of our experimental data sets.  
1. Structure based: IONE, ABNE, PALE, and JARUA-s.
2. Attribute based: JARUA-a.
3. Combination of structure and attributes: REGAL, TADW, CENALP, Grad-Align, Grad-Align+, JARUA-v, JARUA-t, and JARUA.