esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The other two predictors stem from the kriging with an external drift (KED) model (e.g., [39]), which, provided it is calibrated within a local neighbourhood (_i.e._ KED-LN), similarly captures both spatial effects; whilst KED within a global neighbourhood (_i.e._, KED-GN) only caters for stationary relationships, as with MLR [37, 40]. Although this particular model comparison is not novel in the wider literature (e.g., see [37, 38]), its use in this study's context is. Thus, a key study aim is to determine to what degree we need to account for our observed exploratory findings in order to provide the most accurate water depth predictions. Are the spatial non-stationarity and spatial autocorrelation effects, equally important (_i.e._, prefer a KED-LN or GWRK fit)? Alternatively, is one effect significantly more important than the other (_i.e._, choose GWR in preference to KED-GN or visa-versa)? The MLR fit is not expected to perform well, and simply acts as a benchmark predictor, which the other four models would default to, if spatial non-stationarity and spatial autocorrelation effects were entirely absent in the data. Thus, our study objectives can be summarized as follows:  
* Evaluate the overall suitability of RapidEye satellite data for deriving coastal bathymetry in a representative C-shaped bay environment, through application of a blue/green band ratio and statistical models using ground calibration points. * Determine the most suitable prediction model for the data analysed using a non-spatial, multivariate model _versus_ four spatial alternatives, each catering for a variety of spatial effects. * Demonstrate the value in comparing a range of predictors, carefully chosen via a suitable exploratory analysis. * Discuss the spatial patterns of the best model's predictions in relation to the bay's physical characteristics.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 4 Simulation Results and Discussion
### Comparative Study of Related Works  
In this section, the performance of previous reported wireless signal classification algorithms at specific values of signal-to-noise ratio (SNR) are summarized and compared with NSVM. The analytical analysis performed in this section is given in Table 4, which shows that the proposed scheme NSVM achieves higher performance than its counterpart in both low and high SNR scenarios.  
Figure 11: Performance of NSVM with different Phase Offsets.  
Figure 12: The Performance of NSVM with Different Timing Offsets.  
Figure 13: The Performance of NSVM with different Doppler Shifts relative to Velocity.
## 4 Discussion
### Limitations and Agenda for Future Research  
Despite the intriguing findings of this study, several limitations must be acknowledged. The absence of in-person supervision hindered our ability to correct participants' techniques and tailor exercise intensity on an individual basis. Furthermore, the reliance on self-reported evaluation questionnaires may introduce social bias in responses and interpretations. The lack of a control group significantly restricts data interpretation and highlights the need for future studies to include such a group. Additionally, factors such as sample size, intervention duration, and exercise program intensity may have contributed to the observed lack of significant findings. Small sample sizes and short intervention periods often reduce statistical power and may not provide sufficient opportunities for meaningful changes in overall quality of life. While mean scores showed improvement, the lack of statistical significance may reflect these methodological limitations, as well as the participants' already high baseline health status. Future research should consider employing more efficient scientific designs, such as a crossover design, especially if recruitment of potential participants proves challenging. The absence of a control group further complicates the attribution of improvements solely to the interventions. Finally, the small sample size limits the generalizability of the results; a larger sample could yield more robust findings and enhance the external validity of the study.  
The authors acknowledge the limitation of data from more than 50% of the participants initially considered for the study. This includes participants who had an adherence rate lower than the initially defined 75% and the inability to reassess the parameters for 14 participants. For exploratory reasons, the authors included data from 13 participants with an adherence rate below 75% (ranging between 60% and 70%) along with the 19 participants who met the initial criteria, and the results were similar to those reported in Table 1. In fact, including these 13 participants did not alter the significance of the various quality of life indicators. Therefore, for transparency, the authors presented the data from the 19 participants who had an adherence rate above 75% and attended both evaluation sessions. Additionally, for exploratory purposes, the authors analyzed data from the 19 participants with the inclusion of the 14 participants who had an adherence rate above 75% but failed the re-evaluation. Again, the results were similar to those reported in the Section 3, with the inclusion of these participants not altering the significance of the quality-of-life indicators. In fact, some indicators showed a greater effect size compared to the results with only 19 participants. Thus, despite the study's limitation regarding sample size, the results are considered relevant for future studies. It is also worth noting that other studies have faced difficulties in obtaining a sufficient sample size but have demonstrated interesting results [41; 42; 43].
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 1 Introduction
To address this issue, a Markov chain is introduced for error correction, thereby improving prediction accuracy. While this strategy significantly enhances model performance, it also substantially increases the demand for computing resources. These methods have shown excellent prediction capabilities in experiments, indicating that extracting typical features from battery capacity decay data and establishing a mapping relationship between features and SOH are crucial for data-driven battery SOH prediction. Compared with single-feature extraction, the use of multiple-feature sets and feature combinations of different scales has been proven to be a key technology to improve model performance [27, 28]. However, for batteries with different electrochemical reactions, charge and discharge curves, and ambient temperatures, the generalization ability and scope of application of existing models still need to be further verified and optimized. To this end, this paper proposes an innovative method based on multidimensional features and a transformer-LSTM fusion model. This method first extracts multiple features from the battery charge and discharge cycle data, including voltage, energy, and temperature curve time domain, frequency domain, and time dimension features. Subsequently, the local outlier factor (LOF) algorithm is used to identify and remove outliers in the data, and data smoothing is performed through linear interpolation and a Savitzky-Golay filter, thereby effectively improving the data quality. Next, the features most relevant to SOH are selected through correlation analysis as model input. In order to further improve the accuracy and stability of SOH prediction, we constructed a transformer-LSTM fusion model, combining the self-attention mechanism of transformer and the long-term dependency capture capability of LSTM. To verify the effectiveness of the model, we used 124 battery datasets generated under 72 different fast-charging conditions for training and testing. The experimental results indicate that the proposed model exhibits superior performance in SOH prediction and demonstrates strong generalization capability. The SOH prediction process is shown in Figure 1.  
The main contributions of this paper are summarized as follows. 1. A feature extraction method based on battery charge and discharge cycle data is proposed to extract the time domain, frequency domain, and time dimension characteristics of voltage, energy, and temperature curves. 2. Combining an LOF algorithm, Savitzky-Golay filter, and Pearson correlation analysis, the effectiveness of the feature dataset is further improved. 3. A transformer-LSTM fusion model is built, which uses the self-attention mechanism of transformer and the long-term dependency capture capability of LSTM to improve the accuracy and stability of SOH prediction. 4. Experimental results verify that the proposed method has significant SOH prediction performance, showing strong reliability and good generalizability.
## 3 Results
### Trends of Climate Factors and Vegetation
#### 3.3.2 Trends in Vegetation  
Figure 6a,b, respectively, show the spatial patterns of the LAIH trend values and significance over Mainland China between 1999 and 2018. The LAIH shows increasing trends across more than 90% of China. The area ratio of significant (\(p<0.05\)) increasing trends over China is more than 70%. The most intense trends (>0.01 yr\({}^{-1}\)) are mainly distributed in Central and Southern China. On average, both the actual LAI and LAIH driven by human activities over China significantly increase, at 0.015 and 0.014 (\(p<0.001\)) during 1999-2018 (Figure 6c). Anthropogenic activities have dominant effects on vegetation restoration. Compared to the positive trends of the actual LAI, the temporal trends of the LAIH change from negative to positive in the year 2010 (Figure 6c). Among the four sub-regions, SubT shows the highest increase rates of 0.026 yr\({}^{-1}\) and 0.024 yr\({}^{-1}\) for the LAI and LAIH; TP has the lowest increase rates of 0.0032 yr\({}^{-1}\) and 0.0026 yr\({}^{-1}\) for the LAI and LAIH, respectively (Figure 6d).  
From the perspective of the spatial distribution of the LAIH trend (Figure 6a), there are significantly positive LAI trends in the west of TMon and northwest of SubT due to the implementation of the \"Grain for Green Program\". The evident positive LAIH trend in the southwest of TCon is attributed to the implementation of the \"Rocky Desertification Integrated Management\" scheme. The \"Three-North Forest Shelterbelt Program\" is an important factor causing positive changes in the LAIH in the north of TMon.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of AP Locations
#### 4.2.3 Performance of JMART  
Figure 10 shows the error bounds under different locations of the fourth AP by using the JMART for localization. Obviously, the error bounds achieved by the JMART are the lowest compared with the ones by the metrics of single and double measurements.  
Figure 8: Error bounds under different locations of the fourth AP by the metrics of (**a**) RSS, (**b**) TOF and (**c**) AOA.  
Figure 9: Error bounds under different locations of the fourth AP by the metrics of (**a**) AOA/RSS, (**b**) TOF/AOA and (**c**) RSS/TOF.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 2 Identifying the Distinctions between S-MPC and AR-RNN-LSTM
### Calculational Effort  
S-MPC can require significant computational effort, especially for large-scale systems. S-MPC necessitates the solution of an optimization problem in each time step, which can be computationally costly. Moreover, a significant disadvantage of S-MPC is the need to solve an optimization problem online, which can be complex and involve many optimization variables. Consequently, controller models for S-MPC are commonly simplified at the expense of optimality, and gains in optimization solver efficiency are highly desired. Moreover, switching logic and prediction must be performed at each control step. Nonetheless, several techniques have been developed to reduce the computational effort required for S-MPC, such as online optimization and ML techniques that update the optimization problem as the system evolves.  
The computational effort required for training and utilizing AR-LSTM can be substantial, especially for large-scale problems with many time steps and/or parameters. BPTT is the primary computational bottleneck, because it is required to compute the gradients of the loss function with respect to the network parameters. Considering that the computational complexity of BPTT scales linearly with the number of time steps, training AR-LSTM on lengthy sequences can be computationally expensive. In addition, the number of network parameters can contribute to the computational complexity, as larger networks require more computation to update weights during training and to make predictions during inference. Several techniques have been developed to ease these computational challenges, including mini-batch training, which involves updating the weights based on a subset of the training data at each iteration, and gradient clipping, which involves capping the magnitude of gradients to prevent gradients from exploding during training [69].
# Binaural Auralization of Room Acoustics with a Highly Scalable Wave-Based Acoustics Simulation
## 3 Scalability of Wave-Based Room-Acoustics Solver on an HPC Cloud Environment  
This section presents the parallel performance of the massively parallel wave-based room-acoustics solver for an HPC cloud environment using two practical room-acoustics problems (Problem 1 and Problem 2) having different problem sizes and geometrical complexities as described subsequently. As the HPC cloud environment, we use Amazon Web Services HPC6a.48 xlarge instance (AMD EPYC 7003, 96 CPU, and 384 GBytes per node) and Intel Fortran compiler ver. 2021. The speedup of computation is examined up to the use of 64 nodes with 6144 CPU cores.
## 5 Results and Discussion
### Outdoor Experiment Results  
Since the contact detection performance of the trajectory-based outdoor GPS contact tracing varies, depending on \(R_{max}\) and \(S_{max}\), the experiment was conducted by changing \(R_{max}\) and \(S_{max}\). The performance of casual contact detection is shown in the graph in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline ANN & 83.06 & 69.00 & 83.06 & 75.38 \\ \hline DT & 84.61 & 84.52 & 84.61 & 84.56 \\ \hline SVM & 85.22 & 83.91 & 85.22 & 84.34 \\ \hline kNN & 85.01 & 84.44 & 85.01 & 84.69 \\ \hline \end{tabular}
\end{table}
Table 2: Instantaneous co-location recognition performance of machine learning models trained to find casual contacts.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Precision** & **Recall** & **Accuracy** & **F1 Score** \\ \hline
96.68 & 78.82 & 88.41 & 86.84 \\ \hline \end{tabular}
\end{table}
Table 3: Performance of space-based indoor Wi-Fi contact tracing.  
To evaluate exposure detection performance, we set the combination with the best accuracy (94%), \(R_{max}\) at 30 m, and \(S_{max}\) at 0.5, considering the trade-off between \(R_{max}\) and \(S_{max}\). The performance of the exposure detection is shown in the graph of Figure 8.  
We need to obtain the appropriate threshold \(O_{threshold}\) to determine the close contact (see Figure 4). If the precision is low, excessive contact is detected, and if the recall is low, the actual contact may not be detected. Considering this, we set \(O_{threshold}\) to 7 to achieve a balance between the two metrics. With this setting, precision showed 96% and recall showed 96%, as shown in Table 4.
# Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande +
## 2 Materials and Methods
### The Common-View Time-Transfer Technique  
The output of the receiver is in a standard format called CGGTTS files [13], which contains all information about visible satellites, applied delays, as well as the time differences to monitor in 0.1 ns, which are computed from fits between each GNSS data point (each 13 min). The time differences given by the GNSS receiver do not directly give UTC time tags for events. The use of the standard \"Common view technique\" of time transfer (the software used is available here [14]) is therefore needed. In each country or region of the world, there is an official time-keeping lab in charge of the local UTC, which is SYRTE in France. This lab can provide CGGTTS files every day, obtained with the UTC signal, as an input to their receiver. One can use an algorithm that selects the satellites that were seen at both sites at the same time so that it cancels out the GPS time and obtains time differences between a specific system (here, it is our test set up) and the local UTC. Global UTC is made of all local UTCs with different coefficients and the correspondence is published every month, as shown in [15].
###### Abstract  
This work analyzes the technical and economic viability of using coarse recycled aggregates from crushed concrete in shotcrete, as a primary lining support in tunnels. Four incorporation ratios of coarse natural aggregate (CNA) with coarse recycled concrete aggregates from concrete (CRCA) were studied: 0%, 20%, 50% and 100%. The mechanical properties of the dry-mix shotcrete were obtained in an independent experimental campaign. Initially, the technical viability of CRCA shotcrete was validated for deep rock tunnels, based on the convergence-confinement method. Two cases were studied to determine the equivalent thickness for each combination of replacement ratio using CRCA shotcrete: (i) similar stiffness and (ii) similar yield stress. Subsequently, an economic assessment was performed. The stiffness criterion increased the thickness below 10% in both the 20% and 50% replacement ratios, which shows their technical viability with very marginal cost increase (<5%). On the other hand, the maximum pressure criteria required higher increments, close to 30% in the 50% replacement ratio. A full replacement was proven to be impracticable in both analyses.  
shotcrete; deep tunnels; convergence-confinement method; coarse recycled concrete aggregate; dry-mix process +
Footnote †: journal: Applied Sciences
# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 1 Introduction
Although aerial photos, LiDAR, and high-resolution satellites provide high-quality data, they have relatively restricted spatial and temporal coverage, high costs, and variations in light conditions and angles of view. These aspects restrict the use of such imagery for assessing patterns of forest damage and recovery [47]. Previous studies have successfully related windthrow tree-mortality recorded in forest inventories to changes in reflectance by using Spectral Mixture Analysis (SMA) [27] of Landsat images [13; 14; 16]. These studies show a consistent correlation between field and remote sensing data. Recent advances in computational techniques using Machine Learning have enabled the automatic detection of windthrows in forests outside the Amazon using high spatial resolution satellite imagery [48; 49; 50]. These techniques could enhance future research results using high spatial resolution imagery [51; 52; 53]. Although optical satellites with high spatial resolution (pixel size \(\leq\)10 m) have become available for the Amazon in the last decade, they have not yet been applied for mapping windthrows and quantifying associated tree mortality. Therefore, how windthrow tree mortality estimates obtained with high-resolution satellites will be related to ground truth (i.e., precision and accuracy) in dense tropical forests remains unexplored. Here, we combined optical remote sensing with forest inventory data across a disturbance gradient created by a single windthrow event from 2015 to investigate how spatial resolution affects the accuracy and precision of tree mortality estimates in the Central Amazon. Our study provides a framework for future research that aims to assess the severity of natural disturbances through reliable estimates of tree mortality. We addressed the following questions: (i) How does spatial resolution affect estimates of windthrow tree-mortality? (ii) Which sensor (i.e., Landsat 8, Sentinel 2, and WorldView 2) produces the most reliable estimates of tree mortality across an extent gradient of windthrow severities?
# Trends and Technological Challenges of 3D Bioprinting in Cultured Meat: Technological Prospection
## 4 Discussion
### Biofabrication of Cultured Meat
#### 4.3.2 Cells  
The main challenge in tissue culture for meat production is the obtention of an ideal cell source since a large number of homogeneous initiator cells is essential to promoting cell proliferation and differentiation effectively [61]. The process of cell proliferation and expansion is directly related to the industrial scalability of cultured meat production [62]. Currently, some of the most studied cell lines used in cultured meat production are satellite cells [63, 64, 65], pluripotent stem cells [66], mesenchymal stem cells (MSCs) [67], and dedifferentiated fat cells (DFAT) [68].  
Since all the cell lines used in cultured meat are adherent, it is necessary to have an appropriate matrix serving as a support (scaffold) in order that the cells can successfully grow and differentiate [69]. Hydrogels are widely used as a cellular matrix in bioinks, and they can be adapted to mimic or replace native tissue due to their high water content, mechanical properties, and 3D network structure [70, 71]. Bioinks must have specific properties for tissue engineering, such as biodegradability, biocompatibility, printability, high mechanical integrity, chemical stability, non-immunogenicity, non-toxicity, and insolubility in the cell culture medium [72]. These bioinks are made by combining cells and structural-based biomaterials to biofabricate tissue-like constructs [73, 74].
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## 2 Quantum Hacking in \(QKD\) Systems
To conclude this section, we emphasize that the \(IRFS\) attack works successfully on widely-used \(QKD\) protocols, namely \(BB84\), \(SARG04\), Differential Phase Shift (\(DPSK\)), Coherent One Way (\(COW\)), Ekert [9] and the _decoy state_ method, as pointed out in [13; 20]. The attack exhibits an extra 3 dB loss because of the basis mismatch between [PERSON] and [PERSON]. This is easily compensated in practice by [PERSON], since she may use better detector efficiencies and surpass loss in the channel. Blinding attacks over detectors have been demonstrated in two commercially available \(QKD\) systems [11]. It has been reported that [PERSON] obtains the entire secret key while she remains undetected by the legitimate parties [12]. Finally, we remark that control detector attacks with active basis selection cause the gain from [PERSON] to [PERSON] be reduced by a half compared to the gain from [PERSON] to [PERSON] (see Figure 2 and Table 10 of Appendix A), according to the following rules [13]:  
1. _For [PERSON]'s basis choice matching [PERSON]'s, the detector clicks deterministically;_
2. _For [PERSON]'s basis choice not matching [PERSON]'s, the faked state is not detected._