esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Thermogravimetry and Differential Scanning Calorimetry Analysis  
Many authors have used thermogravimetry (TGA) and differential scanning calorimetry (DSC) analysis in order to define incipient combustion processes of biomass [14; 38; 39] and coals [40]. In this study, the temperature of combustion induction and maximum weight loss, which is related to the reactivity of the sample [29], were obtained from performing thermogravimetry analysis (TGA). TGA allows the measurement of the weight loss of a sample, while constant heating from 30 \({}^{\circ}\)C until 800 \({}^{\circ}\)C with a ratio of 5 K/min is applied. In the last 10 min of the test, the sample remained at 800 \({}^{\circ}\)C. The test is carried out in an air atmosphere, however if a more precise determination of the initial oxidation of the sample is wanted, TGA must be carried out using oxygen flow. In this case, instead of the temperature of maximum weight loss, a new parameter will be obtained: The so-called characteristic temperature (T\({}_{\text{char}}\)). In both cases, TGA allows the study of oxidation reactions at high temperatures. The present work used both (air and oxygen flow) when performing TGA.  
The exothermic processes can be defined by using the differential scanning calorimetry analysis (DSC). During the test, two crucibles were heated: One filled with the sample and one empty. The crucibles were heated with a constant ratio of 20 K/min from 30 \({}^{\circ}\)C until 550 \({}^{\circ}\)C. As the temperatures of the sample and the reference crucibles were measured, it is possible to define the exothermic and endothermic processes as the heatflow is recorded. When a sudden change of slope of the heatflow curve takes place, it defines the temperature at which the exothermic reaction accelerates. Both analyses, TGA and DSC, were carried out using a Mettler Toledo TG-50 instrument.  
The data obtained from these analyses allowed the calculation of the apparent activation energy using [PERSON]'s equation (1) [41], where the first order of kinetics is applied to the rate of weight loss:  
\[\ln\left(-\frac{1}{w}\cdot\frac{dw}{dt}\right)=\ln\ A\cdot\frac{E_{a}}{RT} \tag{1}\]  
where:  
\(w\) is the weight of unburnt sample (g)  
\(dw/dt\) is the instantaneous rate of weight loss (g\(\cdot\)s\({}^{-1}\))  
\(A\) is the frequency factor (s\({}^{-1}\))  
\(E_{a}\) is the apparent activation energy (J\(\cdot\)mol\({}^{-1}\))  
\(R\) is the universal gas constant (8.314 J\(\cdot\)mol\({}^{-1}\) K\({}^{-1}\))  
\(T\) is the absolute temperature (K)
## 2 Materials and Methods
### Identification of Differentially Expressed Unigenes  
Clean reads were mapped to the assembled unique genes by Bowtie2 (v2.2.5) [38]. Gene expression profiles were analyzed by RSEM [39] and expressed by fragments per kilo base of transcript per million mapped reads (FPKM) values. PossionDis [40] was utilized to analyze differentially expressed genes (DEGs), and unigenes with Log2 fold change \(\geq\)1 or \(\leq\)\(-\)1 and false discovery rate (FDR) \(<\) 0.001 were considered to be DEGs. KEGG pathway enrichment analysis was carried out to further understand the annotated DEGs.
## 3 Primary Resonance Analysis
### Stability  
In order to investigate the stability of the fixed points, Equation (19) serves as the basis for the Jacobian matrix evaluation [33] as  
\[D_{\mathbf{q}}\Gamma=\begin{bmatrix}-\zeta&\frac{1}{2\Omega}(\sigma A+H_{i})\\ -\frac{1}{2\Omega\Lambda}\left(\sigma+\frac{\partial H_{i}}{\partial A}\right) &-\zeta-\frac{2\mu}{\pi\Omega\Lambda}\end{bmatrix} \tag{28}\]  
where the characteristic equation is  
\[\lambda^{2}-\text{tr}\big{(}D_{\mathbf{q}}\Gamma\big{)}\lambda+\text{det} \big{(}D_{\mathbf{q}}\Gamma\big{)}=0 \tag{29}\]  
Since \(\text{tr}\big{(}D_{\mathbf{q}}\Gamma\big{)}<0\), all the eigenvalues have a negative real part; thus, Hopf bifurcations cannot occur. Nevertheless, a static bifurcation (i.e., saddle nodes), as per its definition, can occur if at least one eigenvalue is purely imaginary. Thus, the stability of fixed points can be investigated by finding the values for which \(\text{det}\big{(}D_{\mathbf{q}}\Gamma\big{)}\) is null:  
\[\Omega^{4}+\bigg{(}\frac{H_{i}}{A}+\frac{\partial H_{i}}{\partial A}+4\zeta^{ 2}-2\bigg{)}\Omega^{2}+\frac{8\zeta\mu}{\pi A}\Omega+\bigg{(}1-\frac{H_{i}}{A }\bigg{)}\bigg{(}1-\frac{\partial H_{i}}{\partial A}\bigg{)}=0 \tag{30}\]  
with \(H_{i}\) evaluated according to Equations (21) and (22).  
From the analysis of Equation (30), the role played by the dry friction on the stability is marginal and strictly related to the value of the viscous damping ratio. That is, as \(\zeta\to 0\), a variation in the dry friction force does not affect the stability boundary.  
For the sake of clarity, the derivative of \(H_{2}\) with respect to the oscillation amplitude \(A\) is reported as follows:  
\[\frac{\partial H_{2}}{\partial A}=\frac{1}{24\pi p}\bigg{(}\frac{16\gamma}{A^ {3}}u_{cr}^{7}+\frac{24\beta-20\gamma A^{2}}{A^{3}}u_{cr}^{5}-\frac{64(1-a)+ 2q}{A^{3}}u_{cr}^{3}+6\frac{q}{A}\ u_{cr}-3\ p\ q(\pi-2\ \psi_{0})\bigg{)} \tag{31}\]  
where  
\[p=\sqrt{1-\frac{u_{cr}^{2}}{A^{2}}},\ \ q=-8+8\alpha+18A^{2}\beta+25A^{4} \gamma,\,\psi_{0}=\arccos\Big{(}\frac{u_{cr}}{A}\Big{)} \tag{32}\]
## 5 Limitations and Future Work  
Although our proposed transfer learning method effectively reduces calibration time, a major limitation of our approach is that it requires a small number of training samples  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Subjects**} & \multicolumn{5}{c}{**C**} \\ \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Repetitions**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **10** & **15** \\ \hline Proposed method & 58.1 & 77.4 & 90.3 & 100 & 100 & 100 & 100 \\ SVM [37] & 64.5 & 71.0 & 83.9 & 96.8 & 100 & 100 & 100 \\ LDA [31] & 54.8 & 35.4 & 61.2 & 80.6 & 87.1 & 100 & 100 \\ LSDA + LDA [31] & 61.3 & 41.9 & 64.5 & 87.1 & 90.3 & 100 & 100 \\ MMC + LDA [31] & 58.0 & 45.1 & 67.7 & 77.4 & 90.3 & 100 & 100 \\ sBLDA [38] & - & - & - & - & 90.3 & 100 & 100 \\ gsBLDA [38] & - & - & - & - & 93.5 & 100 & 100 \\ LASSO [38] & - & - & - & - & 96.7 & 100 & 100 \\ gLASSO [38] & - & - & - & - & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Character recognition accuracy (%) for subject “C” from BCI competition II dataset II using different previous studies and our proposed method in four repetitions.The evaluation involved using different methods from previous studies and our proposed technique.  
from target subjects to train the model. This limitation can restrict the applicability of our approach in real-world settings, where it is often challenging to obtain a sufficient number of training samples from target subjects. In this direction, future work could investigate alternative methods, such as developing a zero-training transfer learning approach that can reduce or eliminate the reliance on target subject data to train the model.  
Another potential area for future research is the use of synthetic data generation techniques to enhance the training dataset. These techniques could be used to augment the existing target data or to simulate new data that can improve the model's performance. Such an approach could help overcome the limitation of the small number of target subject data samples.  
It is also essential to note that not all source samples may be suitable for the target data, which can lead to negative transfer. Therefore, future research should focus on developing effective methods for selecting appropriate source samples for the transfer learning algorithm. An efficient source sample selection strategy can significantly improve the performance of transfer learning and is an important research area to explore.  
Overall, the proposed research has highlighted some significant limitations and potential areas for future research in transfer learning for BCI systems. Addressing these limitations can help improve the effectiveness and applicability of transfer learning approaches in P300-based BCI systems, ultimately improving the quality of life for people with disabilities.
## 3 Types of Underwater Communication Systems
### Underwater Wired Communication
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Types of Cables** & \begin{tabular}{c} **Communication** \\ **Range** \\ \end{tabular} & **Data Rate** & **Advantages** & **Disadvantages** \\ \hline Coaxial & \begin{tabular}{c} Short to medium \\ distances (up to a \\ few kilometres) \\ \end{tabular} & \begin{tabular}{c} Moderate data \\ rates (up to several \\ Mbps) \\ \end{tabular} & \begin{tabular}{c} Reliable, \\ cost-effective \\ \end{tabular} & \begin{tabular}{c} Susceptible to \\ corrosion and \\ mechanical \\ damage \\ \end{tabular} \\ \hline Optical Fibre & \begin{tabular}{c} Long distances \\ (tens to hundreds) \\ of kilometres) \\ \end{tabular} & \begin{tabular}{c} High data rates \\ (Gbps) \\ \end{tabular} & \begin{tabular}{c} Low attenuation, \\ high bandwidth \\ \end{tabular} & \begin{tabular}{c} Sensitive to \\ bending and \\ tension \\ \end{tabular} \\ \hline Hybrid & Long distances & High data rates & \begin{tabular}{c} Combined power \\ and data \\ transmission \\ \end{tabular} & \begin{tabular}{c} Complex design \\ and installation \\ \end{tabular} \\ \hline EMI & \begin{tabular}{c} Short distances (a \\ few meters) \\ \end{tabular} & \begin{tabular}{c} Low data rates \\ \end{tabular} & \
\begin{tabular}{c} Robust against \\ corrosion and \\ mechanical \\ damage \\ \end{tabular} &
\begin{tabular}{c} Limited range, \\ susceptible to \\ interference \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Underwater wired communication systems [45; 55; 69; 45]. In the oil industry, umbilical cables are widely used for subsea power and control systems, specifically designed to endure the harsh conditions of the sea [49]. These subsea power and control systems are installed on the seabed, with umbilical cords connecting subsea equipment to surface facilities, providing fast, reliable, and robust underwater cabled communication [49]. Like airborne systems, underwater cables are widely used for various purposes, including communications, power transmission, and distribution to islands, oilfield platforms, and underwater infrastructure [78]. However, unlike airborne cables, underwater cables require more specific design considerations for protection and shape to withstand the marine environment, including factors such as pressure, temperature, ocean currents, salinity, and water ingress [55].
## 4 Discussion
First, the spectral acquisition process must be adapted for high-throughput settings, where fruit motion, positioning errors, and vibration introduce variability. Second, ambient environmental conditions--such as light drift or temperature fluctuations--may reduce spectral fidelity, necessitating real-time correction strategies. Third, integration with complementary modalities like imaging or hyperspectral data fusion could improve detection robustness, especially for ambiguous or borderline bruising cases. The study also acknowledges the limitations of its controlled experimental design. Bruise simulation was conducted under lab conditions with fixed energy inputs, which may not capture the variability of commercial postharvest logistics. The dataset, while balanced, remains relatively small and homogeneous. Future studies should incorporate larger, multi-seasonal datasets and test robustness under real packinghouse conditions. In addition, incorporating objective physiological measures such as firmness or water loss could improve both class labeling accuracy and model interpretability. Finally, although the current approach relies on traditional machine learning models, future research may explore deep learning architectures--such as CNNs or autoencoders [49]--for automated feature extraction. These models have shown promise in spectroscopy-related tasks, particularly for capturing nonlinear patterns and enhancing robustness in real-time classification scenarios. In summary, the proposed framework offers a scalable, interpretable, and high-performing solution for cumulative bruise detection in prunes [20]. While further validation is required, it holds strong potential for deployment in automated, nondestructive fruit grading systems.
# On the Performance of Interleavers for Quantum Turbo Codes
## 3 Results and Discussions
The memory requirements for the JPL interleaver are also lower than for the random and \(S\)-random ones, as the permutation can be defined by parameters \(N\), \(k_{1}\) and the primes \(p_{q}\), while its performance is better than that of random interleavers but worse than that of \(S\)-random ones. Welch-Costas interleavers do have less memory requirements than JPL interleavers, but their performance is far worse in the error floor region. Table 3 summarizes the memory requirements for each of the interleavers. Note that both random and \(S\)-random interleavers require a memory consumption that increases linearly with \(N\), while the memory requirements for the JPL and Welch-Costas interleavers are constant \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Spread (S)** & **Dispersion (\(\gamma\))** \\ \hline Random [17] & 1 & \(\approx\)0.81 \\ \hline \(S\)-random & 25 & 0.8136 \\ \hline Welch-Costas & 1 & 1 \\ \hline JPL & 16 & 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spread and dispersion parameters. Regarding the overall complexity of the decoding algorithm we have corroborated that it is practically the same for all interleavers. As explained in Reference [17], the spread parameter is related to the number of distinct low weight error patterns that are possible to appear in the decoding, while the dispersion is related to the multiplicities of those existing low weight errors. Consequently, high spread is desired to avoid those harmful error sequences, while a dispersion parameter that is close to that of a random interleaver (\(\gamma\approx 0.81\)) will mean that those harmful errors will happen less regularly. This is why in classical turbo coding it is known that to achieve good error correction performance in the error floor region, an interleaver must have a high spread parameter and a dispersion parameter similar to the dispersion of random permutations [17]. The results presented in Figure 2 show that such rationale also applies to the QTC scenario.
## 2 Materials and Methods
### Instrumental Set-Up  
The analyses were performed using a HPLC-DAD Agilent 1100 Series system (Agilent Technologies, Waldbronn, Germany), constituted by vacuum degasser (G1322A), quaternary pump (G1311A), autosampler (G1313A), thermostated column compartment (G1316A), and a diode array detector (G1315B). The data acquisition and instrumental control were performed by the software LC3D Chemistation (versions Ver. A. 10.02, Agilent Technologies, Germany). The separation of the analytes was performed on a Kinetex C18 column (150 \(\times\) 4.6 mm, 2.6 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\); Phenomenex, Torrance, CA, USA). The mobile phase consisted of an isocratic mixture of MeOH and 20 mM acetate buffer (pH 4.75 at a mixing ratio of 30:70 (\(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/\(\mathrm{\SIUnitSymbolMicro}\))) with a flow rate of 0.5 mL min\({}^{-1}\). The column temperature was maintained constant at 50 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)C. The injection volume was 20 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)L, with a draw speed of 200 \(\mathrm{\SIUnitSymbolMicro}\)L. min\({}^{-1}\). The detector was set at 300 nm. For identification purposed, the retention time and peak purity were compared with the UV/vis spectral reference data of each compound. For extraction efficiency calculation, peak areas obtained from each assay were compared with the peak areas of standard controls used for spiking. For quantification purposes on real matrices, calibration plots from the SAM were used.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
###### Abstract  
The development of state estimators for local electrical energy supply systems is inevitable as the role of the system's become more important, especially with the recent increased interest in direct current (DC) microgrids. Proper control and monitoring requires a state estimator that can adapt to the new technologies for DC microgrids. This paper mainly deals with the DC microgrid state estimation (SE) using a modified long short-term memory (LSTM) network, which until recently has been applied only in forecasting studies. The modified LSTM network for the proposed state estimator adopted a specifically weighted least square (WLS)-based loss function for training. To demonstrate the performance of the proposed state estimator, a comparison study was done with other SE methods included in this paper. The results showed that the proposed state estimator had high accuracy in estimating the states of DC microgrids. Other than the enhanced accuracy, the deep-learning-based state estimator also provided faster computation speeds than the conventional state estimator.  
DC microgrids; deep learning; long short-term memory; loss function modification; state estimation +
Footnote †: journal: Applied  
Article
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
## 2 Envelope Tracking PA Linearization
### EGMP Coefficients Extraction Process
The process is inspired in the DDPG algorithm, which is a popular algorithm for reinforcement learning [20]. The SED-GMP model can be described as the critic that estimates the action value, the EGMP model as the actor or policy that generates the action (supply envelope), and the Jacobian of the EGMP model as the policy gradient. In a reinforcement learning architecture, exploration noise is added to the actions to evaluate and explore the actions that are not generated by the actor. However, in the ET PA system, injecting random noise into the supply envelope is not recommended. On one hand, because the envelope random noise can modulate the output of the ET PA and degrade the overall linearity; and on the other hand, because the slew-rate of the random noise is not under control, and thus the slew-rate limitation would not be met. Therefore, in this paper, multiple shaping functions with different configurations are defined in order to perform the supply envelope exploration. For example, several SR reduced envelopes can be generated by considering different number of SR reduction steps in (4) (e.g., \(\mathbb{N}=\) 30, 60, 90), so that the ET PA can experience the same baseband input with different supply envelopes, instead of adding noise to the output of the PA. In step 2 of Algorithm 1, \(\Gamma_{1}\cdots\Gamma_{k}\) define \(k\) different shaping functions and generate \(k\) different envelopes \(\boldsymbol{Env}_{gen,1}\cdots\boldsymbol{Env}_{gen,k}\) in step 5. Since the generation of SED-GMP matrix depends on the input envelope, in step 7 and 15, the SED-GMP matrix \(\boldsymbol{U}_{1}\cdots\boldsymbol{U}_{k}\) are generated separately and concatenated row-wise to \(\boldsymbol{U}\). In steps 10 and 18, for the LM estimation of \(\boldsymbol{\alpha}^{\prime}\), only \(\boldsymbol{U}_{1}\) is used to optimize the EGMP model, targeting the first envelope generation function \(\Gamma_{1}\). From the ET PA behavioral modeling point of view, the exploration process with different supply envelopes enriches the statistics of the training data and helps to create a precise SED-GMP model, which has a positive impact on the optimization process of the EGMP model. As it will be shown in the experimental results section (Section 3), thanks to the exploration process, it is possible to convergence to a global optimum, while without exploration, the training process can diverge after several iterations.
# Analyzing Important Elements for Improving the Safety of Motorways
## 2 Literature Review
### Traffic Accident Prediction Model Using Explainable AI  
The advent of AI has significantly enhanced predictive performance compared to traditional statistical models. However, this advancement has also raised concerns regarding the interpretability of AI models [23]. It is essential not only to achieve high performance but also to understand how a model arrives at its estimates of traffic accident frequency and the rationale behind these estimates. To interpret a model, users must be able to comprehend the process by which the model's algorithm arrives at specific estimates. Deep learning models, often termed \"black box\" models, possess complex internal structures that are challenging to interpret [24], rendering interpretability a significant weakness of these models. Addressing this issue, [PERSON] and [PERSON] [25] introduced SHAP, a concept that quantifies the impact or importance of each input variable, such as AADT, on the model's estimates. By quantifying the direction and magnitude of the influence of specific input variables, SHAP helps alleviate the interpretability challenges inherent in deep learning models.  
[PERSON] and [PERSON] [26] crafted a model utilizing traffic accident data from Nebraska, USA, alongside a DNN to estimate injury severity resulting from traffic accidents. The developed DNN exhibited notably superior performance in terms of accuracy, precision, and recall when compared to the baseline logistic regression model. However, it encountered the drawback of high computational costs. To mitigate the interpretability challenge, the researchers integrated SHAP, enabling the interpretation of prediction outcomes for individual observations. This facilitated a deeper analysis of the factors influencing injury severity stemming from traffic accidents.  
[PERSON] et al. [27] devised four models elucidating the correlation between traffic accident frequency and severity with factors such as road geometry, weather conditions, and speed, utilizing suburban highway data from Texas, USA. Among the four model types--Random Forest, AdaBoost, XGBoost, and DNN--the XGBoost model demonstrated the most impressive performance. Leveraging SHAP, they pinpointed the contribution of each influencing factor to the model's estimations. The outcomes underscored the substantial impact of weather fluctuations and speed distribution on traffic accident frequency.
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 3 Analysis and Verification  
### Verification  
A simulation balancing circuit model was built in Saber to verify the function of the proposed balancing circuit and the duty cycle of the ZVS of the implemented MOSFETs analyzed in Section 2. The circuit parameters are selected as shown in Table 1; the voltages of the high-energy cells involved in balancing are set to 3.92 V and the voltages of the low-energy cells are set to 3.72 V.