esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of Process Parameters  
#### 3.3.1 Defining Optimal Levels  
Optimal levels were indicated by selecting the maximum S/N ratio for each factor, which is chosen to maximize printability. The optimum set of the parameters is 8%, 24\({}^{\circ}\), 2 mm/s, 0.41 mm, and 0.58 mm for the alginate ratio, head temperature, extrusion speed, nozzle diameter, layer height, and layer height, respectively. In addition, as it is presented by the ranks in the response table (1, 2, and 3, respectively) (Figure 9a) and by the \(p\)-Values in the analysis of variance table (Figure 9b), the alginate ratio, nozzle diameter, and temperature have a great effect on the printability of the hydrogel. These results are also confirmed by the slope of the dashed lines in Figure 9c.  
After defining the optimum level of the extrusion speed, the best levels of the four secondary printing parameters were determined. Through this second consecutive standard analysis, the parameters were adjusted to their optimum values: 3 mm/s, 2 mm/s, 30 mm/s, and 50 mm/s for infill speed, retract speed, and travel speed, respectively. The ranks in the response table (1, 2, and 3, accordingly) (Figure 10a) and the \(p\)-Values in the analysis of variance table (Figure 10b) show that perimeter, infill, and retract speed have a significant effect on the final printing result. This is also supported by the slope of the dashed lines in Figure 10c.
# A Survey on Blockchain-Based Federated Learning
## 5 Challenges and Future Directions
### Privacy Concerns  
The public blockchain ledger allows for secure and reliable data processing, but the collected FL training data can be accessed publicly and are available for all participants to use. This might lead to issues of circumventing privacy protection mechanisms. Furthermore, the ubiquitous sensing systems in the IoT continuously collect personal and sensitive data from consumers. Placing these data into an open ledger may lead to privacy concerns. Using a private blockchain ledger can ensure data privacy by enabling encryption and allowing controlled access to the ledger. However, such private blockchain platforms will limit the accuracy of processing and execution in the FL system, thereby affecting the access to and disclosure of vast amounts of data needed for decision making and analysis.  
Most blockchain systems lack sufficiently robust privacy protection mechanisms. Therefore, BFL frameworks need to incorporate privacy protection technologies like differential privacy and homomorphic encryption to provide additional protection to the data placed on the blockchain. For instance, in the literature [89; 111], differential privacy is employed during the model extraction process by adding noise to preserve the privacy of individual data. [PERSON] et al. [111] also introduced a verifiable secret-sharing scheme for secure model aggregation. In addition, [PERSON] et al. [116] used homomorphic encryption algorithms to encrypt training data for privacy protection. For existing BFL frameworks, striking a better balance between the cost of privacy protection and training accuracy remains a crucial issue to address.
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles  
The proposed active-clamp forward inverter is controlled by SPWM. When the inverter operates at steady state, the duty ratio of the main switch \(S_{m1}\) and the auxiliary switch \(S_{a1}\) can be respectively expressed as follows [22]:  
\[d_{m1}(t)\ =\ \frac{V_{M}\sin\ \omega t}{nV_{in}}, \tag{1}\]  
\[d_{a1}(t)\ =\ 1-d_{m1}(t)\ =\ 1-\frac{V_{M}\sin\ \omega t}{nV_{in}}. \tag{2}\]  
The on-time of the main switch \(S_{m1}\) is \(d_{m1}T_{s}\), and the on-time of the auxiliary switch \(S_{a1}\) is \((1-d_{m1})\)\(T_{s}\), where \(T_{s}\) is the high-frequency switching period. Assuming that the inverter operates in continuous conduction mode (CCM), when the main switch \(S_{m1}\) is turned on and the auxiliary switch \(S_{a1}\) is turned off, the voltage \(v_{p1}\) across the transformer magnetizing inductance is equal to the input voltage \(V_{in}\) and expressed as:  
\[v_{p1}\ =\ V_{in}. \tag{3}\]  
When the main switch \(S_{m1}\) is turned off and the auxiliary switch \(S_{a1}\) is turned on, the voltage \(v_{p1}\) across the transformer magnetizing inductance is changed to be:  
\[v_{p1}\ =\ V_{in}-v_{cc1}, \tag{4}\]  
where \(v_{cc1}\) is the voltage across the clamp capacitor \(C_{c1}\). According to the volt-second balance theorem, the relationship between the input voltage \(V_{in}\) and \(v_{cc1}\) can be expressed as follows:  
\[V_{in}d_{m1}T_{s}\ =\ (v_{cc1}-V_{in})(1-d_{m1})T_{s}. \tag{5}\]  
Arranging Equation (5) can get the relationship between \(v_{cc1}\) and \(V_{in}\) as:  
\[v_{cc1}\ =\ \frac{1}{(1-d_{m1})}V_{in}. \tag{6}\]  
It can be found from Equation (6) that the transfer function of the voltage \(v_{cc1}\) is the same as that of the boost converter. When the main switch \(S_{m1}\) is turned off, since the auxiliary switch \(S_{a1}\) is turned on, the voltage \(v_{ds1}\) across the main switch \(S_{m1}\) is:  
\[v_{ds1}\ =\ v_{cc1}\ =\ \frac{1}{(1-d_{m1})}V_{in}. \tag{7}\]  
According to the state of the switching components and the direction of their currents, the operation of the active-clamp forward inverter can be divided into nine states in one high-frequency switching period. The theoretical timing diagram of each component is shown in Figure 5, and the nine states will be described in order in the following. To simplify the circuit analysis, the following assumptions are made:  
1. All components are ideal.
2. The clamp capacitor \(C_{c1}\) is much larger than the parasitic capacitor \(C_{ds1}\) of the main power switch, and the magnetizing inductor \(L_{m1}\) of the transformer is much larger than the leakage inductor \(L_{r1}\).
3. The inductor \(L_{1}\) and the capacitor \(C_{f}\) are large enough so that the output voltage \(v_{0}\) and the output current \(i_{0}\) can be regarded as constant in one switching period.
4. Because the dead time is extremely short, it can be ignored.
# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 4 Experiment and Results
### Bond Graph Algorithm (BGA)  
Compared to other algorithms, the BGA for the benchmark functions is rapid convergence and near to the global optimum. In other words, it avoids falling into local traps (the main targets are ranges 3 and 4). It is demonstrated that the performance of this model is better than PSO and GA. Figure 7 shows the convergence diagram and the effect of the BGA compared to the other two algorithms. The convergences of the two benchmark functions over time for better understanding are illustrated in Figure 7.  
Domains 3 and 4 had excellent results near the global optimum. However, the first two domains in our definition had the worst convergence, which needs to be improved (Figure 7 down). Table 4 shows the comparison of 35-dimensional results of GA and PSO and all of the models of the BGA. From this table, we can conclude that the design of the formulas and variables used is very important and influential in convergence. The BGA optimization algorithm, similar to PSO, and GA is linear with order O(n). Only one loop is used for calculation. BGA is six times more time consuming than PSO and half as time consuming as GA. For example, the average execution time for 400 iterations and 30 executions on Spheres (first bench mark) for GA, BGA, and BGA was 0.83 s, 0.42 s, and PSO 0.07 s, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Dimetion** & **Initial Range** & **Minimum** \\ \hline Sphere & 35 & \([-100,100]^{\text{D}}\) & 0 \\ SumSquares & 35 & \([-10,10]^{\text{D}}\) & 0 \\ Rosenbrock & 35 & \([-30,30]^{\text{D}}\) & 0 \\ Schwefel 2.22 & 35 & \([-10,10]^{\text{D}}\) & 0 \\ Rastrigin & 35 & \([-10,10]^{\text{D}}\) & 0 \\ Schwefel & 35 & \([-500,500]^{\text{D}}\) & \(-12\),569.50 \\ Ackley & 35 & \([-32.768,32.768]^{\text{D}}\) & 0 \\ Griewank & 35 & \([-600,600]^{\text{D}}\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the benchmark function. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Function** & **Population** & **Details** \\ \hline GA & 40 & Single point crossover (0.8), mutation rate (0.01), generation gap (0.9) \\ PSO & 40 & Inertia weight (0.6), cognitive and social components (1.8) \\ BGA & 40 & Selection model 1 to 4, an average of coefficients (1), initial element (2.5\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of the algorithm setting. Figure 7: (**Top–Left)** Convergence of GA. PSO and BGA (third model) on sphere function during 400 steps. (**Top–Right**) Convergence of GA, PSO, and BGA (third model) on Ackley function during 400 steps. (**Down–Left**) Convergence of all of BGA models (four models (domains)) on sphere function during 400 steps. (**Down–Right**) Convergence of all BGA models (four models) on Ackley function during 400 steps. (**Down–Right**) Convergence of all BGA models (four models) on Ackley function during 400 steps.
## 3 Results and Discussion
### Simulation Results of the Calibrated Model
#### 3.2.1 Validation of the Calibrated Model  
In this subsection, the hygothermal simulation results are compared against the data from both experimental campaigns. The experimental results from the first campaign were used to calibrate the model, while those from the second campaign were used to validate the model and to investigate the transition between the normal-use operation model and off-use operation mode. Figure 10 compares the experimental data and the simulation results obtained with the calibrated model for the period of the second campaign where the ventilation was performed with fresh unconditioned air. Moreover, Table 5 illustrates the CV(RMSE) values for temperatures and RH for both experimental data sets. The results show a good correlation in both parameters for the different thermal zones in the suite, with errors below 4% in all cases. This agreement is especially precise in the temperature of OR1 and OR3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**First Data Collection Period**} & \multicolumn{3}{c}{**Second Data Collection Period**} \\ \hline
**Error** & **OR1** & **OR2** & **OR3** & **OR1** & **OR2** & **OR3** \\ \hline CV(RMSE) Temperatures & 1.02\% & 2.63\% & 0.64\% & 1.12\% & 3.16\% & 0.73\% \\ CV(RMSE) RH & 3.98\% & 3.26\% & 2.54\% & 2.96\% & 3.75\% & 2.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficient of variation of the root mean square error (CV(RMSE)) of temperature and RH of the ORs during the two experimental campaigns.  
Figure 9: Temperature and RH in OR3 during the second experimental data campaign.  
On the other hand, Figures 11 and 12 display the experimental values for the indoor air temperature and the RH in OR1 compared to the simulation hygrothermal variables during an extended period where the off-use mode is combined with the normal-use mode, showing quite good agreement in both cases. The represented data are from 10:00 h on Friday 13 th September (6130 h of the year) to 2:00 h on Friday 20 th September (6290 h of the year). It is observed that the thermal model behaves appropriately by also adjusting the temperature and the RH in periods with surgical activity despite the model being calibrated only with data from off-use periods. Similar results were obtained for the other ORs of the surgical suite, although they are not represented herein to avoid repetition.  
Figure 10: Comparison between simulated and experimental air temperature and RH in the ORs during the second data campaign.
## 4 Experimental Result
### Qualitative Performance Evaluation
#### 4.2.2 Quantitative Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Baseline** \\ **(8-bit)** \\ \end{tabular} & \
\begin{tabular}{c} **Baseline** \\ **(14-bit)** \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **(TIR-MS)** \\ \end{tabular} \\ \hline S1 & 138 & 33 & 29 \\ S2 & 21 & 7 & 8 \\ S3 & 40 & 96 & 21 \\ S4 & 17 & 15 & 11 \\ S5 & 137 & 18 & 13 \\ S6 & 11 & 47 & 9 \\ S7 & 23 & 16 & 10 \\ S8 & 43 & 22 & 16 \\ \hline
**Mean** & 53.75 & 31.75 & 14.63 \\
**STDEV** & 52.82 & 28.75 & 7.190 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of tracking accuracy: central difference. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Baseline** \\ **(8-bit)** \\ \end{tabular} & \
\begin{tabular}{c} **Baseline** \\ **(14-bit)** \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **(TIR-MS)** \\ \end{tabular} \\ \hline S1 & 0.328 & 0.653 & 0.694 \\ S2 & 0.644 & 0.654 & 0.684 \\ S3 & 0.463 & 0.214 & 0.543 \\ S4 & 0.613 & 0.664 & 0.767 \\ S5 & 0.304 & 0.428 & 0.535 \\ S6 & 0.621 & 0.434 & 0.707 \\ S7 & 0.474 & 0.456 & 0.553 \\ S8 & 0.659 & 0.481 & 0.793 \\ \hline
**Mean** & 0.513 & 0.498 & 0.656 \\
**STDEV** & 0.142 & 0.155 & 0.103 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of tracking accuracy: intersection over union (IOU).
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 1 Introduction
In this work, we will neither utilize structures of the trained matrices nor structures of the input data. The vector and matrix to be multiplied may be totally arbitrary. They may, but need not, contain independent identically distributed (IID) random variables, for instance. It is not obvious that, without any specific structure in the matrix, significant computational savings are possible over state-of-the-art methods implementing matrix-vector multiplications. In this work, we will develop a theory to explain why such savings are possible and provide a practical algorithm that shows how they can be achieved. We also show that these savings are very significant for typical matrix-sizes in present day neural networks: By means of the proposed linear computation coding, the computational cost, if measured in number of additions and bit shifts, is reduced several times. A gain close to half the binary logarithm of the matrix size is very typical. Recent FPGA implementations of our algorithm [10] show that the savings counted in look-up tables are even higher than the savings counted in additions and bit shifts. In this paper, however, we are concerned with the theory and the algorithmic side of linear computation coding. We leave details on reconfigurable hardware and neural networks as topics for future work. The paper is organized as follows: In Section 2, the general concept of computation coding is introduced. A reader that is only interested in linear functions, but not in the bigger picture, may well skip this section and go directly to Section 3, where we review the state-of-the-art and define a benchmark for comparison. In Section 4, we propose our new algorithm. Sections 5 and 6 study its performance by analytic and simulative means, respectively. Section 7 discusses the trade-off between the cost and the accuracy of the computations. Section 8 summarizes our conclusions and gives an outlook for future work. Matrices are denoted by boldface upper letters, and vectors are not explicitly distinguished from scalar variables. The sets \(\mathbb{Z}\) and \(\mathbb{R}\) denote the integers and reals, respectively. The identity matrix, the all zero matrix, the all one matrix, the expectation operator, the sign function, matrix transposition, and Landau's big O-operator are denoted by \(\mathbf{I}\), \(\mathbf{0}\), \(\mathbf{1}\), \(\mathbf{E}[\cdot]\), \(\mathrm{sign}(\cdot)\), \(\mathrm{\SIUnitSymbolDegree}\), and \(\mathrm{O}(\cdot)\), respectively. Indices to constant matrices express their dimensions. The notation \(||\cdot||_{0}\) counts the number of non-zero entries of the vector- or matrix-valued argument and is referred to as the zero norm. The inner product of two vectors is denoted as \(\langle\cdot;\cdot\rangle\).
## 3 Results and Discussion
### Characterization of Cold and Hot Brew Coffee
#### 3.3.4. Phenolic Compounds in Cold and Hot Brew Coffee  
The phenolic compounds were analyzed for both cold and hot brew samples. The phenolic coffee brewed using cold brew and hot brew (French Press) methods was analyzed by LC-ESI-MS/MS using twenty-four phenolic standards, including apigenin, caffeic acid, cinnamic acid, chlorogenic acid, p-coumaric acid, 3,4-dihydroxybenzoic acid, (-)-epigallocatechin gallate, ferulic acid, galangin, gallic acid, genistein, hesperidin, 4-hydroxybenzoic acid, kaempfer, isorhamtein, luteolin, myricetin, naringenin, quercetin, rosmarinic acid, sinapic acid, rutin, syringic acid, and vanillic acid. However, all 24 phenolic compounds could not be detected in both coffee brews. This could be due to the fact that the amount of the compounds presented in the coffee brews is lower than the detection limit of the method used here.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 3 Electromagnetic Irradiation
### Microwave Irradiation  
Few-layer MoS2 were fabricated into two-dimensional nanoelectromechanical systems (NEMS) as ultralow-power, high-frequency tunable oscillators and ultrasensitive resonant transducers [152]. These devices can operate in the very high frequency band (up to \(\sim\)120 MHz). Ultra-thin MoS2 was also fabricated to MoS2 transistors operating at gigahertz frequencies [153; 154]. All these devices worked at microwave range. Therefore, it is necessary to characterize the microwave damages to MoS2 few-layers.  
It was reported that microwave can accelerate catalytic reactions [155] because of the formation of hot-spots within catalysts. However, no significant differences were detected between microwave irradiated MoS2 catalysts (size in microns) and untreated MoS2 during the sulfating reaction [155].  
[PERSON] et al. [156] irradiated MoS2 powders under a microwave (1 kW power, 2450 MHz) at 200 \({}^{\circ}\)C. It was reported that the (001) basal planes of the MoS2 crystal structure were cracked into (100) edge planes under microwave irradiation, creating additional active edge sites. The Raman intensity of microwave irradiated materials increased significantly after microwave treatments. The irradiated MoS2 could capture Hg0 efficiently because of the microwave-induced cracks.  
Figure 27: Raman intensity and linewidth (FWHM) of (**a**) mono-layer-ed (1L), (**b**) tri-layer-ed (3L), and (**c**) 5-layer-ed (5L) MoS2 nanosheets laser-treated in ambient conditions. Reprinted with permission from Reference [146]. Copyright © 2018 American Chemical Society.  
[PERSON] et al. [31] treated chemically exfoliated MoS\({}_{2}\) nanosheets in an inert nonpolar solvent of 1,2-dichlorobenzene under 2.45 GHz microwave irradiation at 130 \({}^{\circ}\)C in an inert atmosphere, with an output power of 500 W. 2H MoS\({}_{2}\) nanosheets converted to the 1T phase in minutes.
## 4 Experimental Evaluation  
### Experimental Datasets and Environment  
To verify the validity of the proposed method in geoscience, we constructed the dataset with geological reports. These geological reports include a range of professional geoscience terms covering different years, localities, and geological bodies. We extracted 1830 sentences from eight multi-factor urban geological survey reporters, taken from the website of GeoCloud ([[https://geocloud.cgs.gov.cn/](https://geocloud.cgs.gov.cn/)]([https://geocloud.cgs.gov.cn/](https://geocloud.cgs.gov.cn/)) (accessed on 25 January 2025)). Then, we constructed the dataset according to the criteria used to create the benchmark corpus in SIGHAN CWS Bakeoff, labeled them with the tag BIES (B, I, and E denote the word's beginning, inside, and ending, and S indicates a separate word).  
To further validate the performance of our method for geological words, we conducted a statistical analysis of the geological terms in the dataset. A total of 28% of the words in datasets are geological words, which are frequently used as the foundation for downstream tasks for NLP in the geoscience domain. In particular, we define complex geological words as geological words that have nested structures or a mixture of Chinese and punctuation and are longer than five characters. Complex geological words accounted for 10% of the total word count, with the majority appearing only once in the dataset. These complex geological words contain a significant amount of geological knowledge, but the word boundaries are ambiguous. Moreover, compared to the average word length of 1.84 in the publicly available MSR dataset, the average length of geological terms is twice aslong, and the average length of complex geological terms is several times longer. Longer words often introduce more challenges in terms of tokenization, as they may not be adequately represented in pre-trained models or lexicons, which can lead to lower accuracy or increased errors in segmentation.  
Due to the limited availability of labeled data in the geoscience domain, we prioritized more data for training while ensuring sufficient validation and testing. Following the approach of [PERSON] et al. [39], we chose a 6:2:2 ratio to divide the labeled data into training, development, and test sets. We use only 10% of the training set for initial teacher training to replicate low-resource settings, and the remaining training set is viewed as unlabeled data. Table 2 shows the statistics of the dataset used.  
To ensure consistency with prior research and leverage their findings, we adopted the settings from [34]. We set the batch size to 16 and the number of training epochs to 50 for the GEO training of the dataset. Additionally, to further evaluate the performance of our method, we conducted experiments with different selection thresholds and weights for the unlabeled data in the following sections, ultimately setting the threshold to 0.7 and the weight to 0.8. Our hyperparameter settings for this method are shown in Table 3.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 6 Analysis of Recent Works
### OCR Deep-Learning Works
#### 6.1.2 Transformer-Based Works [55; 56]  
[PERSON] et al. [55] built a custom dataset generated using 13 different online fonts with various styles, sizes, and noises. It includes more than 270 million words. In addition, the KHATT dataset, which features Arabic handwritten text written by 1000 participants, was also used to train the model. The model was designed to solve the problem of Arabic handwritten-text recognition and is made of two parts: the first part is CNN, and the second part is Transformer. Resnet101, with 101 layers, was used as a feature extractor in the CNN part. The Transformer part is initialized with four encoders and decoders, attention heads, and 256 hidden dimensions. The model was trained using all 12 fonts, with diacritics, and with both long sequences (more than five words) and short sequences (five or fewer), achieving a CER of 7.27%, a WER of 8.29%, and a SER of 10%.  
[PERSON] et al. [56] compared the application of two different Transformer types to the task of offline Arabic handwritten-text recognition: Transformer Transducer, which is used in speech-recognition tasks, and Transformer with cross-attention. The dataset used for training is a synthesis dataset of 500K printed Arabic images with their ground truth. Moreover, image-processing techniques, such as erosion, compression, shearing, distortion, and rotation, were employed. For testing, the KHATT dataset was used, which contains 1000 Arabic handwritten lines and, in particular, 6742 text lines from the unique text. Data-efficient Image Transformer (DeIT-base) and asafaya-BERT-base-arabic were the BERT models used for initializing the model; the first was for visual-feature extraction, and the second was for predicting the masked text. Twelve attention heads were used for the encoder, and eight were used for the decoder. The results showed that the Transformer Transducer achieved 19.76% CER and the Transformer with cross-attention achieved 18.45% CER.