esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### RNA Isolation and RT-qPCR  
Total RNA was isolated from the RAW 264.7 cells treated with LLLT and RANKL using an RNeasy mini kit (Qiagen Inc., Valencia, CA, USA) and RNA (2 \(\mu\)g) was reverse-transcribed using a RevertAid First-Strand Synthesis System kit (Thermo Fisher Scientific, Pittsburgh, PA, USA) to synthesize cDNA. The above manufacturer's protocols and methods have been described previously [36]. The gene expression was quantified by RT-qPCR using a SYBR Green kit (Applied Biosystems, Warrington, UK) on the ABI 7500 Real-Time PCR Detection System (Applied Biosystems, Foster City, CA, USA). The primers used in this experiment are as follows; NFATC, forward: 5'-CCAGCTTTCCAGTCCCTTCC-3' and reverse: 5'-AGGTGACACTAGGGCACACAC-3'; TRAF, forward: 5'-ATATGACACACCACCACCCT-3' and reverse: 5'-GCCAAGCAGTTCTGTTGG-3'; TRAP, forward: 5'-CTACCTGGTG ACATGCCA-3' and reverse: 5'-GCACATAGCCCCACAC GCTTC-3'; cathepsin K, forward: 5'-TACCATATGGGGCAGG-3' and reverse: 5'-TTCAGGGCTTCTGTTCCCC-3'; calcium receptor, forward: 5'-TAGTTAGGTCCTCGGCT-3' and reverse: 5'-AGTACTCTCCTCCTTGGT-3'; GAPDH, forward: 5'-CCGACTCTTGTGCAGT-3' and reverse: 5'-TCAATGAAGGGTCGTTGA-3'. Gene expression was calculated using the comparative Ct method and quantified with the housekeeping gene GAPDH to calculate Ct values for all samples.
## 2 Background
### Blockchain and IPFS
#### 2.2.3 Integration with Blockchain  
The integration of blockchain and IPFS provides a robust mechanism for secure data storage and verification. Blockchain guarantees the immutability and timestamping of the data, while IPFS provides efficient and decentralized storage. Specifically, blockchains can store IPFS content addresses within smart contracts or transaction data, allowing for off-chain decentralized storage of large files. By storing only the hash on the blockchain and the actual file on IPFS, blockchain scalability issues [15] are mitigated, as only the critical data are stored on-chain [16].  
By combining these technologies, we can achieve a reliable and tamper-proof system for message accountability and integrity in MQTT. In our approach, the broker publishes the root of the Merkle hash tree (MHT) on the blockchain and the MHT itself on IPFS. This ensures that both publishers and subscribers can verify the integrity of the exchanged messages, and both the publisher and the broker cannot repudiate the messages they have sent.
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
## 3 Machine Learning HRTF Restoration
### Model Enhancement
#### 3.3.7 Bigger Model  
Considering the current results and the limited number of labelled data, training a bigger model is against normal machine learning practices. However, to demonstrate the potential capability of the proposed method and insight for future research, a slightly deeper model was also trained with extra data. The goal was to minimise the training and validation error as much as possible, neglecting the trade-off in test datasets' results.  
To balance out the model size and training time, only the convolution neural network was changed. An extra convolution layer and transposed convolution layer pair was added in the convolution model (Figure 11).  
As this model only focuses on the test and validation results, dropout and weight decay regularisation methods are lifted, which defeats the purpose of using a bigger neural network. The model was trained with 633,000 HRTFs with a batch size of 32 for training, similarly to Section 3.3.6, as bigger models usually work better with more data.  
Compared to the baseline model, the training time of each epoch from the bigger model (Model I) increased from 6 min to 26 min. The model was trained with 500 epochs and the results are shown in Table 7. As expected, the model provided huge improvements in training and validation, but not much of an improvement in the SADIE II test data. On the other hand, it provided the best performance for the Bernschutz data. Comparing the results of the smaller model, baseline model and the bigger model, it seems like the bigger model has better performance with the Bernschutz data which indicates it generalises better across different measurement methods.
# Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review
## 8 Conclusions  
The analysis of the available literature on microwave planar resonant sensors for glucose concentration detection reveals intense scientific activity during the last decade, even more noticeable during the last few years. Whereas the fundamentals of these sensors are based on general dielectric properties measurement techniques with resonant methods, the unique features of the targeted input variable and final application make their contextualized, in-depth study worthwhile. This work provides an updated review of the most significant attempts published within this approach, sorted by the sensing parameter. To ease the comparison and the general understanding of the different proposals, a common relative sensitivity has been proposed and applied to most of the references involved, which enables even the comparison between sensors belonging to different categories.  
The general discussion points to some elements, such as SRR, interdigital capacitors or electric \(LC\) resonant designs, that have been commonly used to attain interesting sensitivities. Indeed, the reported sensitivities show optimism in some cases, for all of the sensing parameters. These sensitivities can be even further enhanced by means of recently developed active circuitry or machine-learning techniques. The selectivity, however, remains a challenging issue still requiring further clarification and development. In this sense, the acquisition of redundant information could be of interest for future multisensorsystems aiming at more robust, more selective measurements. Considering the sensitivities reported and the integration capabilities, planar resonant sensors could play a noticeable role in such systems. Therefore, the selection of the proper approaches from the early design stages becomes essential, and thus the availability of clear comparison tools seems convenient. This, in addition to the interest in obtaining complementary information from the measurements, becomes the main motivation to provide a classification of the sensors according to sensing parameter whilst considering a general relative sensitivity to glucose.  
The work by [PERSON] was supported by SAD Region Bretagne, No-Needle Project.  
Not applicable.  
The authors declare no conflict of interest.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 2 Related Work
### Metrics for the Assessment of Models' Performance  
In classification tasks, evaluating performance and interpreting confusion matrices correctly is critical, yet there remains no universal agreement on an optimal metric to be universally adopted in machine learning. Traditionally, accuracy and \(F_{1}\) scores derived from confusion matrices have been popular choices for evaluating binary classifications.  
However, these metrics often present over-optimistically inflated results, particularly in scenarios involving imbalanced datasets. [PERSON] et al. [19] explored the dynamics between the optimal \(F_{1}\) score and the decision-making threshold that achieves it, revealing insightful correlations that aid in refining performance measurements. Concurrently, [PERSON] et al. [20] conducted comparative analyses between the MCC, \(F_{1}\) score, and accuracy. Their findings suggested that the MCC provides a more reliable assessment of model performance, offering distinct advantages over the \(F_{1}\) score and accuracy, especially in binary classification evaluations. Furthermore, [PERSON] et al. [21] undertook a systematic review of how class imbalance affects various classification performance metrics. They concluded that while Geometric Mean or Bookmaker Informedness serve well under conditions where classification successes are the primary concern, the MCC emerges as the superior metric when both classification successes and errors need equal consideration. These studies collectively highlight the need for a more discerning selection of performance metrics in binary classification tasks, particularly in light of the challenges posed by dataset imbalances. Such insights are invaluable for developing more accurate and equitable machine learning models.
## 3 Results and Discussion
### Effect of the Applied Voltage
The maximum of the initial slope is reached for \(\mathrm{V_{rms}}=15\,\mathrm{V}\) and \(\alpha=0=160^{\circ}\). Furthermore, it clearly appears that the temperature rise due to the Joule heating has a quadratic variation versus the applied voltage. This result is expected due to the source term in the heat equation. By increasing the angle \(\theta\), the electrode surface increases causing an increase in the electric field and subsequently the temperature. For example, the electrical field for V\({}_{\text{rms}}\) = 15 V increases from 4 MV/m for \(\theta\) = \(\alpha\) = 40\({}^{\circ}\) to 6.2 MV/m for \(\theta\) = \(\alpha\) = 160\({}^{\circ}\). Thus, there is an increase in Joule heating. For microfluidic biosensors under AC electric field, the Joule heating could provoke an important temperature rise. The exceeding of the safe limit temperature can damage the thermally labile biological samples (say about 3 K) [23]. For this reason, it is necessary to work with a voltage lower than 15 V.  
To avoid exceeding the limit temperature of the biological samples, we work at V\({}_{\text{rms}}\) = 15 V. For this type of biosensor, the optimum efficiency is achieved for a larger electrode angle and high applied voltage. Figure 8 depicts the temporal evolution of average surface CRP complex concentration without and with electrothermal effect for u\({}_{\text{ave}}\) = 100 \(\mu\)m/s. Three different AC voltages, 5, 10, and 15 V, were tested in this study. We noticed a significant improvement in reaction rate, especially for the larger applied voltage. For example, the increase rate of initial slope for the association phase was about 5% when the voltage V\({}_{\text{rms}}\) increased from 0 to 5 V and 105% when the voltage V\({}_{\text{rms}}\) increased from 10 to 15 V (Figure 9). Figure 8: Transient evolution of CRP complex concentration during the association and dissociation phases for several applied voltage V\({}_{\text{rms}}\) from 0 to 15 V and for u\({}_{\text{ave}}\) = 100 \(\mu\)m/s. The average inlet velocity is fixed at 100 \(\mu\)m/s. Figure 7: Surface plot of temperature rise versus the applied voltage V\({}_{\text{rms}}\) and the electrode angle \(\alpha\) = 0. The average inlet velocity is fixed at 100 \(\mu\)m/s. In Table 6, we calculated the temperature rise for different applied voltages. For V\({}_{\rm rms}\) = 15 V, \(\Delta\)T = 3.0 K represents the limit value of the biological samples. Indeed, the maximum temperature rise obtained for the first model of the biosensor for \(\alpha=\theta=160^{\circ}\) is \(\Delta\)T = 2.7 K under the same conditions of applied voltage and inlet velocity.
## 4 Results and Discussion
### Comparison of ETR and DL
Completely in line with our findings, these findings also suggest prioritizing ETR for sales prediction [34], potentially bypassing extensive analyses with alternative supervised machine learning algorithms or avoiding black-box models, like LSTM. Tree ensembles inherently offer a more interpretable structure. This transparency can be invaluable in industries where understanding the rationale behind predictions is crucial for decision making. In contrast, deep learning models, often dubbed \"black boxes\", can be challenging to interpret, although techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are bridging this gap [49; 50]. A previous study has applied LSTM in the context of smart buildings and smart grids and compared it to traditional machine learning approaches, such as random forests or support vector machines. The results indicate that LSTM performs well in electric load prediction, mainly due to its capability to include missing values to conserve data continuity [51]. It would be interesting to apply ETR to their dataset, allowing for a comparison of performance for this type of environment. Also, it is stated that there is no one-size-fits-all approach, and LSTM might have performed better for one power profile and may be worse for other ones. This is also reflected in our results, where the differences in performance between ETR and LSTM were quite significant in the product category of fresh meat, while only minor in the context of fruits. This suggests that although ETR was found to outperform LSTM in all three product groups, the differences strongly vary depending on data, i.e., product demand behavior. Hence, a generalization in terms of the \"best\" approach cannot be made, which also represents a limitation of our paper. Regarding retail demand forecasting in general, especially for perishable products, such as fresh meat and fruits, the number of papers comparing tree-based and deep learning models is low. A previous study by [PERSON] et al. (2021) has compared SARIMA and LSTM. It was found that both models yielded satisfactory to commendable outcomes [5]. Typically, LSTM excelled in forecasting products with consistent demand, whereas SARIMA was superior for products exhibiting seasonal trends. For enhanced forecast accuracy at the store level, hybrid strategies are recommended, integrating both SARIMA(X) and LSTM for analogous, pre-grouped store clusters. It would be interesting to build on these previous results and compare them with ETR models. All in all, and in line with our findings, previous research confirms the high performance of tree-based approaches, stating that such models show high potential in various fields and have a high ability to deliver accurate predictions [52].
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### SDR Quality Assessment Metrics  
We have selected 12 SDR metrics commonly used in academic research, standardization or industry. There are six achromatic or color-blind metrics (PSNR, SSIM, MS-SSIM, FSIM, PSNR-HVS-M and PSNR-HMA) and six metrics including chrominance information (\(\overline{\Delta\text{E}}\), \(\overline{\Delta\text{E}^{S}}\), SSIMc, CSSIM, FSIMc, PSNR-HMAC). Table 1 summarizes the principle of each metrics. More detailed information about these metrics can be found in a supplementary materials.
## 3 Results
### Simulation Analysis  
We compared the performance of scCGImpute with eight other methods using a simulation of the dataset generated by splatter, consisting of 1000 genes and 300 cells. The details of the parameters are in the methods. We referred to the data before dropout as reference data and the data after dropout as raw data. We used this simulated data to compare the clustering performance of scCGImpute and other methods. Since there are only 1000 genes in the simulation, we used spectral clustering to cluster them directly. _ARI_, _NMI_, and purity were selected to evaluate clustering performance, and the results are shown in Figure 3. It can be found that scCGImpute has more accurate clustering results than other methods for all three indexes on this simulated data. As the dropout rate increases, scCGImpute can still maintain good clustering performance. Even at a dropout rate of 80%, scCGImpute can still have a good clustering effect. The next closest methods are SAVER and MAGIC, while the performance of other methods shows a significant reduction.  
We also calculated the Pearson correlations for reference data with raw data and scCGImpute imputation on the cell level and on the gene level and presented the results in a box diagram. The correlation between scCGImpute imputation and the raw data with reference data was calculated for each dropout rate, as shown in Figure 4. Although there was no significant change in the cell correlation, scCGImpute showed a significant improvement in the gene correlation compared with raw data. While it was not significant at 0.5 dropout.mid, the median value was still higher than the raw data.
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 5 Results and Discussion
### Effects of Impulsive Noise  
It was observed in Section 5.3 that in a network of 50 nodes, application size must not exceed 64 bytes to guarantee at least 99% packet delivery. Using the same network configuration here, we investigate the effect of impulsive noise on the HEMS application performance using an application size ranging from 16-128 bytes. By varying the number of appliances on the HEMS network, we highlight the impacts of background and impulsive noise on the one-way delay. The delay here is the time taken to deliver a request from the controller to the node. By simulating different packet sizes, we cover both monitoring and control types of traffic. The result is presented in Figure 11.  
Although it is generally known that impulsive noise degrades PLC system performance, two key observations can be derived from Figure 11: (i) impulsive noise degrades the latency performance of the HEMS, regardless of the application size; (ii) larger application data are more susceptible to degradation, for example in the case of the 128-byte and 16-byte data sizes, the degradation in latency is as high as 32.759 ms and 1.8977 ms, respectively, in terms of additional delay caused by impulsive noise. These occur because as impulsive noise degrades the power line channel, more transmit power is needed to guarantee consistent error-free transmission. In the absence of higher transmit power, the price to pay is higher latency.
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
## 5 Discussion  
Based on the theoretical analysis and simulation results provided above, the discussion between MPC based method and PSO method are given as follows:  
Figure 11: Acceleration and velocity performances. (**a**) longitudinal velocity; (**b**) longitudinal acceleration; (**c**) lateral velocity; (**d**) lateral acceleration; (**e**) angular velocity; (**f**) angular acceleration.  
Figure 12: Computing efficiency comparison. (**a**) computing time; (**b**) box plot of computing time.  
* 1 Comparing the simulations, it is obvious that dynamic-based methods proposed in this paper perform better than kinematic model based methods. In the kinematic model based methods, it is difficult to measure the side slip directly. The majority of research works are trying to design observers to predict its side slip. However, this kind of method can only get an approximate estimate. Thus, it is not feasible to use a kinematic model based controller to completely eliminate the error due to side slip. Both MPC-SQP and PSO methods are proposed based on dynamic models and controlled by drive forces. In the dynamic model, the lateral forces can be obtained easily using force sensors. This kind of method gives a practical way to avoid the side slip estimation and achieve precise tracking along curved paths.
* 2 Both MPC-SQP and PSO methods are proposed partly or completely based on optimizations. As is compared in the simulation, the PSO controller achieves obtaining the solution with a stable computing time. The algorithm can optimize the quality of solutions and the calculation times at the same time, which guarantees the feasibility and capability of the controller. The MPC-SQP controller may encounter the calculation timeout in the simulation results. This is because the SQP solver spends more time when calculating a complex Hessian matrix. Thus, it is necessary to set a sufficient sampling time when applying the MPC-SQP method.
* 3 The MPC-SQP method gives a stability proof of the control system while the PSO method has difficulty with the mathematical proof due to the limitation of intelligent optimization. The stability analysis of MPC-SQP method provides a possibility to analyze the stable range of its control parameters and margin of errors.
* 4 Both control algorithms are reduced to constrained optimization problems with six input variables. PSO is a global algorithm that performs better with searching for a global optimal solution while SQP is a reliable solver but may be held in local optimal solutions. Thus, for each method, it is important to define a proper search range. In this paper, the boundaries are calculated based on the vector \(s\), which improves the efficiency and stability of the optimization process.
## 4 Analyzing Historical Data and Future Forecast  
In this section, the XGBoost model is first used for understanding the impact and importance of each factor on the sales from 2015 to 2024, and then the predictions are performed using the Prophet and SARIMAX models.  
In addition, to better understand the influence of each factor on EV sales, we first examine the parameter estimates derived from all three models. Table 4 summarizes the estimated coefficients and feature importances based on historical data from 2015 to 2024. This parameter comparison helps interpret how each model prioritizes explanatory variables before forecasting future outcomes or examining interaction effects.  
According to Table 4, Charger Density is the strongest predictor across all models: it has the largest positive effect in SARIMAX and the highest importance in XGBoost. Policy and GDP also show consistent effects across methods. The negative sign for chargers in Prophet may reflect temporal shifts in influence or multicollinearity, but its magnitude still shows significance. Taken together, these findings reinforce the strategic importance of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **SARIMAX Coefficient** & **XGBoost Importance (\%)** & **Prophet Coefficient** \\ \hline Charger Density Index & +33.54 & 58.96 & -9.34 \\ \hline Year (time) & — & 37.65 & — \\ \hline GDP Growth (\%) & +2.97 & 2.95 & +0.39 \\ \hline Battery Price (USD/kWh) & -0.24 & 0.29 & +0.21 \\ \hline Policy Dummy & -139.25 & 0.16 & -8.57 \\ \hline AR(1) (lagged sales) & +0.17 & — & — \\ \hline Residual variance \(\sigma^{2}\) & 692.18 & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative Parameter Estimates by Feature.  
charging infrastructure as the most consistent and powerful predictor of EV uptake in the UK market during 2015-2024.