esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Control Strategy Design
### ECMS Problem Formulation
#### 3.3.3 Engine Operating Range  
The other issue related to drivability is the engine's frequent on/off and frequent mode switches between SI and HCCI. These phenomena are mainly a result of the fact that the ECMS only optimizes the fuel economy by minimizing the cost function of the equivalent mass flow rate at each instant without considering the engine's operational condition switches within a period of time. To solve this problem, a hysteresis of engine operation is set to reduce the condition switch.  
The whole logic of engine operating range determination is shown in Figure 7. In detail, for SI combustion mode operation, a minimum engine-on time (such as 5 s) is defined; once the engine starts, it cannot be shut down until this requirement is satisfied. For SI/HCCI dual-combustion-mode operation, both the minimum SI operation time and the minimum HCCI operation time are defined (both are 5 s in this study); the engine is not allowed to switch modes unless it has stayed in the current mode for longer than 5 s. Based on this strategy, the engine operating range can be determined; that is, the values of \(p_{g\mathrm{max}}\) and \(p_{g\mathrm{min}}\) in Equation (20) are determined. If there is no restriction in the next moment, then the engine can operate within 0 kW-120 kW; 0 kW means the engine is off. If the engine must operate in the HCCI mode, then the operating range is between 5 kW and 20 kW; although the maximum power in the HCCI mode is 22 kW, in order to switch to the SI mode on the engine's optimal operation curve smoothly, the maximum operating point is chosen to be 20 kW at 1800 rpm. If the engine must operate in the SI mode, then the operating range is between 5 kW and 120 kW; below 20 kW, the engine should operate along the optimal BSFC curve in the SI mode instead of the HCCI mode, and the minimum  
Figure 6: Map of NVH constraints on the engine–generator set’s output power.  
power is chosen to be 5 kW instead of 0 kW. This is because the lowest NVH-constrained power is 5 kW and there is no reason for the engine to operate below this value.
## 3 Methodology
### Model Architectures and Training
#### 3.4.1 FFNN Model  
The FFNN was designed specifically for multiclass classification. The selection of its architecture and other hyperparameters was determined based on literature insights and our trial-and-error experiments, particularly focusing on the model's architecture, optimizer choice, learning rate and rho values. Its architecture consists of an input layer with 512 units, aligned with the feature vectors generated by the USE4 model. USE4, a pretrained embedding model, converts textual data into fixed-size feature vectors, encapsulating semantic nuances, enabling the FFNN to discern data intricacies without directly interfacing with the raw text. Following the input layer, the FFNN incorporates a hidden layer of 256 units using ReLU activation. The output layer, with its 15 units, corresponds to the distinctjob categories and utilizes softmax activation. For configuring the model, we employed the tf.keras.Model function, selecting Adadelta as the optimizer with a learning rate of 0.15 and a rho of 0.95. The model compilation leveraged'sparse_categorical_crossentropy' for the loss and 'accuracy' as the primary metric. The training spanned 40 epochs, using a dataset partitioned into training and validation subsets. The test subset size was set at 20% of the training dataset.
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
###### Abstract  
Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models--a custom CNN, an Edge Impulse CNN, and a custom LSTM--on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.  
TinyML; lung disease classification; early detection 2024/16/3918/11/0391
## 3 Results
### Metal Transport into Leaves of Tobacco of Different Ages.  
Tobacco has the ability to accumulate metals and transport them from the roots into the shoots. The contents of metals in soil and tobacco leaves were analyzed. Here \"accumulation\" means the concentration of a metal in the plant is higher than that in the soil. Figure 3 shows that tobacco had an accumulation effect for Cd, while it had only an uptake effect for Pb, Cu, and Zn. The soils treated with HA received a dramatic decrease in the Cd, Pb, Cu, and Zn contents in the tobacco leaves (\(p<0.05\), Figure 3), and with the HA dosage increasing, the uptake of metals in the tobacco leaves decreased. It should be noted that in the HA14.8 treatment, the Cd contents in the upper, middle, and lower parts of the leaves were reduced by 32%, 32%, and 33%, and those of Pb were reduced by 50%, 31%, and 25%, respectively. Additionally, the Cu contents were reduced by 15%, 25%, and 29%, and the Zn contents were reduced by 9%, 9%, and 9%, respectively. That is to say, the inhibit effect of Cd and Pb is much stronger than that of Cu and Zn.  
The distribution of metals in the tobacco leaves followed certain rules. For Cd, the contents in the leaves exclusively followed the order of lower leaves \(>\) upper leaves \(>\) middle leaves; for Cu, upper leaves \(>\) middle leaves \(>\) lower leaves; for Zn, middle leaves \(>\) lower leaves \(>\) upper leaves. However, the Pb distribution was irregular.  
Figure 2: The chemical fractions of Cd (A), Pb (B), Cu (C), and Zn (D) in soil after harvest of tobacco. Data are means \(\pm\) SD (\(n=4\)) and bars represent standard errors. Lowercase letters represent significant differences at the 0.05 level between different treatments at the same fraction.
## Literature Review
### Research Overview  
Two-sided matching theory has high theoretical and practical value; the gist of the G-S deferred acceptance algorithm is to make the two sides unwilling to break the match, in order to maintain its stability. The one-to-one, many-to-one, and many-to-many stable matching models have wide applications in the real world. In the heterosexual marriage matching model, both the men and the women have preferences for each other and the match between a man and a woman is the choice of these two people of opposite sexes based on their preferences. In the college admission matching model, the applicants have preferences for colleges and the colleges have preferences for applicants and may also have preferences for applicant sets. In the worker-firm matching model, the firms have preferences for workers as well as for worker sets and vice versa.  
Compared with the existing research, the contribution of this paper is mainly reflected in the following: Firstly, the theory of two-sided matching is applied to the field of coal-power transaction, which expands the research field of stable allocation and market design theory, optimizes the matching path of the coal-power transaction, and reflects the interest demands of the coal mining and power generation companies. Secondly, based on current situation of the coal-power transaction conflict, this paper puts forward the concept of pairwise stable matching for the coal-power transaction creatively. The anti-dismantling of the transaction relations makes it impossible for both the coal mining and power generation companies to easily change the existing pairwise stable matching relationship. This situation then lays a sturdy foundation for the coal mining and power generation companies to establish medium- and long-term stable cooperation. Thirdly, this paper creatively designs the stable matching algorithm and mechanism for coal-power transaction considering the trading volume. It is a complement and perfection of the existing coal-power trading platform in transaction mechanism and trading function.  
On this basis, this paper builds on the two-sided matching theory and further expands its application. It employs the deferred acceptance algorithm that was proposed by [PERSON] and [PERSON] and applies two-sided matching theory to coal trading between coal mining and power generation companies. Considering the uniqueness of the market transactions that involve these companies, this paper proposes a deferred acceptance algorithm that takes into consideration the trading quantity in order to establish stable matching between the coal mining and power generation companies. It is expected that the proposed algorithm will help coal mining and power generation companies to find satisfactory trading partners,improve the current trading mechanism, complement the matching functions of existing coal-trading centers, and help to stabilize the medium- and long-term trading relations between coal mining and power generation companies.
# MITIBOT: An Intelligent Mitigation Technique for IoT Botnets
## 4 Results and Discussion
### Comparative Analysis  
This section focuses on the evaluation of performance parameters such as accuracy, precision, recall, F1-score, and latency. All of these parameters are evaluated by using a confusion matrix that measures distinct values like true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs). These parameters can be represented mathematically as in Equations (9)-(12), respectively.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
\[\text{F1}-\text{score}=\frac{2*(\text{Precision}*\text{Recall})}{\text{ Precision}+\text{Recall}} \tag{11}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TotalPredictions}} \tag{12}\]  
Through the comparison of all of the parameters, the proposed work is compared some past works conducted by several researchers for the classification and mitigation of IoT botnets.  
Table 1 shows the comparative analysis of the proposed model with existing models. The results show better performance with ensemble learning classifiers as compared to the other classifiers, such as KNN, CNN, RF, DT, and ANN. All performance parameters show improved results with the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Classifier** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline [PERSON] et al. [30] & KNN & Anubis & 0.9808 & - & - & - \\ \hline [PERSON] et al. [31] & CNN & IoTPOT & 0.9400 & - & - & - \\ \hline [PERSON] et al. [32] & Multi-level Deep NN & BIG & 0.973 & 0.96 & 0.93 & 0.940 \\ \hline [PERSON] et al. [21] & KNN, RF, DT & N-BaIoT & 0.9995 & 0.9993 & 0.9977 & 0.9977 \\ \hline [PERSON] et al. [29] & ANN & NSL-KDD & 0.9986 & 0.652 & 1.000 & 0.955 \\ \hline \multirow{3}{*}{Proposed Model} & Ensemble Learning (Adaboost + XGBoost + Random Forest) & Simulated & & & & \\  & and Random Clustering & \multicolumn{1}{c}{} & & & & \\ \cline{1-1}  & \multicolumn{1}{c}{} & & & & & \\ \end{tabular}
\end{table}
Table 1: Comparative analysis of the proposed model.  
Figure 7: Packet loss for normal and botnet traffic.
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 3 Results
### Synthetic Data
the portion before the P-wave arrival to zero, leading to a relatively lower SNR of the denoised results. The correlation coefficient serves as a metric to quantify the similarity between the denoised signal and the original noise-free signal. As presented in Table 2, after denoising, the proposed method yields signals that exhibit a higher correlation with the original noise-free signals compared to the other two methods. The mean absolute error evaluates the degree of distortion inflicted upon the effective signal during the denoising process. As illustrated in Table 3, the proposed method incurs the least signal distortion compared to the other two methods. Tables 1-3 present the denoising results for the test set, which contains 9476 signals. We excluded signals with an SNR lower than \(-1\) dB or higher than 7 dB from the test set, and the tables report the statistics from the remaining 3211 signals. To figure out the computational costs of different models, we compared the floating-point operations (FLOPs) and a number of parameters of DeepDenoiser, ARDU, and the proposed method. As shown in Table 4, DeepDenoiser requires the least FLOPs and parameters, and the proposed method has slightly lower FLOPs and parameters than ARDU. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal SNR (dB)** & **DeepDenoiser** & **ARDU** & **Ours** \\ \hline \(-1\) & 2.568 & 4.933 & 6.945 \\ \(0\) & 4.086 & 7.593 & 9.384 \\ \(1\) & 10.802 & 11.650 & 14.471 \\ \(2\) & 12.643 & 13.001 & 14.229 \\ \(3\) & 14.847 & 15.480 & 15.502 \\ \(4\) & 19.111 & 20.444 & 16.081 \\ \(5\) & 21.425 & 21.065 & 16.021 \\ \(6\) & 21.934 & 22.351 & 15.857 \\ \(7\) & 23.350 & 23.015 & 16.173 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of SNR after noise reduction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal SNR (dB)** & **DeepDenoiser** & **ARDU** & **Ours** \\ \hline \(-1\) & 0.387 & 0.391 & 0.499 \\ \(0\) & 0.453 & 0.466 & 0.558 \\ \(1\) & 0.703 & 0.709 & 0.772 \\ \(2\) & 0.793 & 0.797 & 0.841 \\ \(3\) & 0.870 & 0.873 & 0.914 \\ \(4\) & 0.910 & 0.910 & 0.954 \\ \(5\) & 0.932 & 0.935 & 0.978 \\ \(6\) & 0.938 & 0.957 & 0.976 \\ \(7\) & 0 Considering that ARDU is an improvement over DeepDenoiser, incorporating dilated convolutions and residual dense blocks, simply increasing the parameters of DeepDenoiser to match the proposed method does not necessarily achieve better denoising performance. It can be considered that with the same number of parameters, the proposed method would achieve the best results.
# Assessing the Impact of Varied Dark Chocolate Concentrations on Enamel and Dentine Microhardness
## 3 Results  
### Enamel Microhardness  
Figure 2 depicts a bar chart illustrating microhardness average mean values for enamel both initially and after immersing specimens in various concentrations of dark chocolate. The study groups include G1 (control group), exposed to artificial saliva, which exhibited a mean difference ranging from 236.09 to 243.34. In comparison, G2 (70% dark chocolate), G3 (85% dark chocolate), and G4 (100% dark chocolate) displayed average mean differences spanning from 235.09 to 296.81, 235.53 to 299.70, and 235.95 to 345.10, respectively.  
Table 1 displays the mean and standard deviation values, along with the standard deviation difference and the difference of mean denoted as DF. The findings indicate a noteworthy, gradual increase in the average microhardness values for enamel, specifically 61.72 \(\pm\) 22.729, 64.17 \(\pm\) 23.397, and 109.15 \(\pm\) 34.625, corresponding to the escalating concentrations of dark chocolate (70%, 85%, and 100%). Significantly discernible differences were observed between the initial and final phases within each group, with a _p_-value < 0.05 indicating statistical significance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Enamel Microhardness Mean Value and SD**} \\ \cline{2-9} \multicolumn{1}{c}{\multirow{2}{*}{**Group**}} & \multicolumn{2}{c}{**Initial Microhardness**} & \multicolumn{2}{c}{**Final Microhardness**} & \\ \cline{2-9}  & **N** & **Mean** & **SD** & **Mean** & **SD** & **DF** & **SD** & _p_**-Value** \\ \hline Artificial saliva (control group) & 10 & 236.09 & 0.533 & 243.34 & 5.290 & 7.25 & 5.389 & 0.002 \\ \hline
75\% Dark chocolate & 10 & 235.09 & 1.298 & 296.81 & 22.253 & 61.72 & 22.729 & \textless{}0.001 \\ \hline
85\% Dark chocolate & 10 & 235.53 & 1.349 & 299.70 & 23.118 & 64.17 & 23.397 & \textless{}0.001 \\ \hline
100\% Dark chocolate & 10 & 235.95 & 0.875 & 345.10 & 34.681 & 109.15 & 34.625 & \textless{}0.001 \\ \hline \hline \end{tabular}  
N = number of samples in each group, SD = standard deviation, DF = difference of mean.  
\end{table}
Table 1: Enamel microhardness comparison between initial and final phases in each group.  
Figure 2: Represents an error bar chart, showcasing the standard deviation and mean values of enamel microhardness at both the initial and final stages.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup
### Indicator Extraction
#### 4.4.3 Eye Tracking Data  
The device's sampling rate is 60 Hz. It can output information such as timestamps, blinks, eyelid opening sizes, pupil diameters, and gaze coordinates. Over a time interval of more than 100 ms, if the Euclidean distance between gaze coordinates at adjacent timestamps is less than 50, it is considered one fixation. Since there is no browsing behavior in this experiment, eye movements during non-fixation periods are considered saccades. The saccade amplitude is the Euclidean distance between the centers of two fixation points.  
The video screen from the rear camera is divided into area of interest A (AOI A), area of interest B (AOI B), and non-interest area, as shown in Figure 3, to obtain eye movement indicators within specific areas of interest. Eye movement indicators are calculated in 10 s windows. Table 3 shows the definitions of each indicator.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 3 Spoofing Detection Method  
This section introduces the EDDM-MSC-MMP method, which was used to detect spoofing. The performance of the MSC-MMP in edge detection for eye diagrams was analyzed, and the methods of identifying spoofing signals and calculating thresholds were discussed.
# A Review of the Potential Climate Change Impacts and Adaptation Options for European Viticulture
## 5 Climate Change Adaptation Strategies
### Long-Term Adaptation Strategies
#### Changes in Training Systems  
The joint effect of growing temperatures with abiotic and biotic stresses projected for the upcoming decades will require changes in the current/traditional training systems, which may also be undertaken by optimizing canopy management practices [211]. Changes in training systems may target the following objectives:  
1. Delay of the maturation period (see also Section 5.1.1). To avoid grapes ripening under high-temperature conditions, training systems that delay ripening may be beneficial under future climate conditions. Minimal pruning systems in their various forms (i.e., semi-minimal pruned hedge) can delay maturity and bunch rot formation [212].
2. Lower sugar accumulation. To achieve reduced sugar content of grapes and thus reduced alcohol content, the leaf area to fruit weight ratio can be manipulated. Higher crop load per unit leaf area may, however, cause undesired lowering of wine quality, whereas a reduction of leaf area to fruit weight ratio by limiting the canopy height may cause the opposite [168].
3. Reduced radiation in the cluster-zone. Modifications of canopy geometry, such as row orientation, will influence light interception and wind velocity, and may also provide high adaptive potential [213; 214]. For example, higher canopies, as well as closer distances between rows, may increase shadowing effects and, hence, decrease sunburn risk. However, both changes might lead to conflicts of objectives, such as higher water consumption and potential earlier maturation (high canopies) concomitant to unfavourable microclimatic conditions in the cluster-zone, fostering bunch rot (narrow rows) [215]. Vineyard orientation should be optimized whenever possible, as it may significantly change radiation interception [213; 214; 216].
4. Higher water use efficiency. Regions under extreme dryness should promote higher water use efficiency [183] by adopting training systems that promote shorter trunks, such as the Gobelet [211] or Guyot systems [3].  
Changes in the training systems can also be combined with changes in planting density and/or in trunk height, as they are inter-dependent. In general, higher planting densities lead to enhanced root competition, lower vegetative growth, deeper root penetration, and larger available soil volume, which can be useful in temperate humid climates. If canopy geometry remains the same, higher density at similar row distances reduces water stress [14]. However, when row distance changes concomitant to density, water deficit may be exacerbated [14].
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 2 Preliminaries  
**Notation.**_Let \(\mathbb{R}\) and \(\mathbb{Z}\) represent the set of real numbers and integers, respectively. For a positive integer \(q\), define the set \(\mathbb{Z}_{q}\) as:_  
\[\mathbb{Z}_{q}=\Big{\{}-\Big{\lfloor}\frac{q}{2}\Big{\rfloor},-\Big{\lfloor} \frac{q}{2}\Big{\rfloor}+1,\ldots,q-\Big{\lfloor}\frac{q}{2}\Big{\rfloor}-1 \Big{\}}.\]  
_When \(q\) is not divisible by 2, the floor function \(\lfloor x\rfloor\) is used to ensure proper rounding during modular reductions. Modulo allowing negative values to be equivalent to positive values, we use the negative sign to denote half of the integer field. This symmetric representation provides a balanced interval for modular arithmetic, which is particularly beneficial in lattice-based cryptographic schemes. We use \(\mathbf{a}\gets D\) to represent drawing a sample \(\mathbf{a}\) from the distribution \(D\). For a finite set \(S\), \(U(S)\) denotes the uniform distribution over \(S\), and \(\mathbf{a}\gets S\) indicates that the sample \(\mathbf{a}\) is drawn according to the uniform distribution \(U(S)\). The notation \([\mathbf{A}\mid\mathbf{B}]\) is used to indicate the concatenation of matrices \(\mathbf{A}\) and \(\mathbf{B}\). The Euclidean norm of a vector \(\mathbf{A}\) is denoted as \(\|\mathbf{A}\|=\sqrt{\sum_{i}A_{i}^{2}}\), and \(\sigma_{1}(\mathbf{R})\) represents the largest singular value of matrix \(\mathbf{R}\). For a vector \(\mathbf{a}=(a_{1},a_{2},\ldots,a_{n})\in\mathbb{Z}_{q}^{n}\), \(a_{i}\) denotes the \(i\)-th component of the vector. For a matrix \(\mathbf{A}\in\mathbb{Z}_{q}^{n\times m}\), \(\mathbf{A}[i,j]\) refers to the element in the \(i\)-th row and \(j\)-th column._