esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization Design Process and Results
### Design Variables and Optimization Objectives  
In current study, the optimization objective is to reduce the noise in the far field while the aerodynamic characteristics are maintained. The optimization variables are the flap position and the flap deflection. The flap position parameters should be included in selecting design variables. Coordinates of the flap leading point (x_flap, y_flap) and angle of the flap (angle_flap) are chosen as the design variables. Table 3 shows the design space.  
Figure 11: Sound pressure levels of far field probes.  
Figure 12: Framework of optimization process.
## 3 Proposed FFA-BiGRU Method
### Temporal Feature Learning and Emotion Prediction  
Temporal feature learning: Music audio sequences tend to be long and exhibit strong temporal dependencies. The time-dependent information in the sequences may not be captured using the spatial feature extractor alone. To further capture temporally related emotion features, we employed the BiGRU to learn the temporal features following the spatial feature learning module. The structure of the BiGRU is given in Figure 1. The BiGRU is a variant of BiLSTM with fewer parameters and higher learning efficiency. By incorporating the BiGRU layer, the model can learn to extract the temporal features in both the forward and backward directions, which helps to improve the overall performance of the model.  
The BiGRU is a bidirectional GRU that constructs two GRU layers in opposite directions. The basic GRU unit in the BiGRU is depicted in Figure 3, and the main calculation formula for the GRU is as follows:  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]), \tag{6}\]  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]), \tag{7}\]  
\[\hat{h}_{t}=\tan\hhhhhh[r_{t}\times h_{t-1},x_{t}]), \tag{8}\]  
\[h_{t}=(1-z_{t})\times h_{t-1}+z_{t}\times\hat{h}_{t}, \tag{9}\]  
where \(r_{t},z_{t},\hat{h}_{t}\), and \(h_{t}\) represent the outputs of the reset gate, the update gate, the candidate hidden state, and the GRU unit, respectively; \(W_{r},W_{z}\), and \(W_{\hat{h}}\) denote the corresponding state weight matrices, respectively; \(\sigma(\cdot)\) denotes the sigmoid function.  
For the BiGRU model, which superimposes two single-layer GRU models in opposite directions, its output is determined by the states of the two superimposed GRUs. Specifically, the forward output of the BiGRU is \(\overset{\rightarrow}{h}_{t}\), and the reverse output is \(\overset{\leftarrow}{h}_{t}\). Then, the output of the BiGRU is given as \(h_{t}=\left[\overset{\rightarrow}{h}_{t},\overset{\leftarrow}{h}_{t}\right]\).  
After the BiGRU module processes the music features, the temporal features are obtained. To fully utilize both the spatial and temporal features, the output feature maps of the FFA and those from the BiGRU are concatenated in the channel direction. The concatenated spatial-temporal features are subsequently used to predict the emotion results.  
Emotion prediction: By passing the log Mel-spectrogram into spatial and temporal feature learning modules, the spatial-temporal features are sufficiently extracted and concatenated. To predict the music emotion results, the concatenated features from the FFA and BiGRU are first flattened and then fed into two FC layers. These layers map the spatial-temporal features into the emotion space. The structure of the FC layer is given in Figure 4, where the first layer consists of 32 nodes and the second consists of 4 nodes.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Experimental Test Results
#### 3.1.4 Effect of Compaction Rate
For predictive modeling purposes, these results indicate that while cement content and curing period should remain the primary variables, incorporating compaction rate as a secondary factor might enhance model accuracy for certain specific combinations, particularly those involving high cement contents or early strength assessment.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 5 Numerical Evaluations and Discussion
### Outage Probability  
From Figure 8, it can be seen that when the vehicle approaches the BS and the RIS, the outage probability reduces. Besides, increasing the element number of the RIS can decrease the outage probability. In particular, when \(M=64^{2}\), \(128^{2}\), and \(192^{2}\), the lowest outage probabilities are \(6.05\times 10^{-3}\), \(5.70\times 10^{-3}\) and \(4.62\times 10^{-3}\), respectively. This result validates Propositions 2 and 3. Besides, increasing the number of elements could decrease the outage probability, as expected, but more elements may bring a higher computational complexity if there is no prior channel state information. Therefore, a tradeoff between enhancing system performance and reducing computational complexity is necessary when designing a practical RIS-assisted HSC system.  
Figure 8: Outage probability for one vehicle pass. \(D_{\text{MR}}(t)\) is \([-125+vt,15,20]^{\text{T}}\) m.
## 2 SIR-Based Performance Analysis
### The Moment-Generating Function  
In this subsection, we will derive the MGF for the wireless system model from Figure 1 in the presence of BX fading and \(\kappa\)-\(\mu\) CCI. The MGF is an important statistical function for each distribution with many advantages, as is analysis of sums of Random Variables (RVs). The MGF of RV defines all moments of the RV, which determines the name--moment-generating function. It is important that the MGF determines the distribution uniquely, if it exists. Consequently, two RVs have the same distribution if they have the same MGF. So, the distribution is determined after finding the MGF, especially important in the case of a complicated PDF.  
As the parameters \(\kappa_{y}\) and \(\mu\) increase, the number of elements that need to be added in order to achieve convergence to the fifth decimal increases  
and then decreases with further growth of \(L\). On the other hand, CCI parameters \(\kappa_{y}\) and \(\mu\) do not make much of an impact on the ABEP.  
After the figures, we show tables illustrating the required number of additions in the sum in expression (25) to achieve accuracy to the 5 th significant digit.  
It is visible from Table 5 that when the parameters \(\kappa_{x}\) and \(m\) increase, the number of elements that need to be added in order to achieve convergence to the fifth decimal increases, and the series does not converge so quickly.  
Figure 8: ABEP for BPSK modulation: parameters \(\kappa_{x}\) and \(m\) are changing, and constant parameters are \(\kappa_{y}=1\), \(\mu=1\), \(L=2\), \(\Omega=1\), \(s=1\).  
Figure 9: ABEP for BPSK modulation: changeable CCI parameters \(\kappa_{y}\) and \(\mu\), and number of branches \(L\); and constant are \(\kappa_{x}=1\), \(m=1\), \(\Omega=1\), \(s=1\).  
As can be seen in Table 6, as the parameters \(\kappa_{y}\) and \(\mu\) increase, the number of additions necessary to achieve faster convergence increases, and the series converges more slowly. When \(L\) is increasing, the number of elements tends to be smaller, so for \(z=-10\) dB and \(z=0\) dB, it is 8, and for \(z=10\) dB, the number of elements is 7.
# Thermodynamic Analysis and Performance Evaluation of Microjet Engines in Gas Turbine Education
## 4 Calculation Model  
The calculation model is founded on a series of mathematical relationships between the engine's thermodynamic parameters. It computes various ratios and differences between key engine parameters and performance metrics to obtain actual values necessary for the analysis. To facilitate the calculation process, constant values of heat capacity at constant pressure for air and gases can be used. However, at this small scale, using a fixed value may affect the precision of the calculations. Therefore, the current model employs polynomials for the enthalpy and entropy of air and gases, which are functions of the working temperature [31; 32]. This approach significantly reduces the calculation error, providing more accurate and realistic results. In the case of air and gases (for ideal combustion), enthalpy and entropy are thus defined:  
\[h_{a}(T_{a}),s_{a}(T_{a})^{p0}=f(T_{a})\quad h_{g.id}(T_{g}),s_{g.id}(T_{g})^{ p0}=f(T_{g}) \tag{1}\]  
where the polynomial function takes the form:  
\[f(T)=a_{1.a,g}+a_{2.a,g}\cdot T+a_{3.a,g}\cdot T^{2}+a_{4.a,g}\cdot T^{3}+a_{5.a,g}\cdot T^{4}+a_{6.g}\cdot T^{5} \tag{2}\]  
The air /gases enthalpy and entropy polynomial coefficients are presented in a previous research paper [31; 32]. In case of gases (for real combustion), the enthalpy and entropy have the next forms:  
\[h_{g}\big{(}T_{g}\big{)}=x_{id}\cdot i_{g.id}\big{(}T_{g}\big{)}+x_{a.ex}\cdot h _{a}\big{(}T_{g}\big{)} \tag{3}\]  
\[s_{g}\big{(}T_{g}\big{)}^{p0}=x_{id}\cdot s_{g.id}\big{(}T_{g}\big{)}^{p0}+x_{ a.ex}\cdot s_{a}\big{(}T_{g}\big{)}^{p0} \tag{4}\]  
where the mass participation \(x_{id},x_{a.ex}\) depending on air excess \(\alpha_{ex}\) and air-fuel ratio \(min_{L}\)  
\[x_{st}=\frac{1+min_{L}}{1+\alpha_{ex}\cdot min_{L}}\quad;\quad x_{st}=\frac{( \alpha_{ex}-1)\cdot min_{L}}{1+\alpha_{ex}\cdot min_{L}} \tag{5}\]  
The most commonly used fuel for micro jet engines is Jet-A1 kerosene. Since all micro jet engine models lack a proper oil system and rely on fuel for lubrication, the fuel must be pre-mixed with 3-5% oil. For AMT engines, Aeroshell 500 turbine oil is used for the mixture, while JetCat engines use their own JetCat turbine oil [33]. As a result, the mixture of fuel and oil will have a different lower heating value (LHV) than the pure fuel. The LHV of Jet-A1 kerosene is 42,900 kJ/kg, but according to the ET 796 instruction manual [34], the LHV of the fuel-oil mix is 42,580 kJ/kg. Additionally, the air-fuel ratio for the mixture is 14.2, compared to the standard value of 14.57. This difference in LHV and air-fuel ratio needs to be considered in performance calculations to ensure accurate results.
## 4 Discussion
### Assessment of Accuracy of MS DVSP
As a result, it can be expected that the errors of DVSP will get progressively worse. Among these three algorithms, the performance of MS-DVSP is the best. Based on the mean bias error and its corresponding standard deviation error,  
Figure 12: Comparison of displacement resolution among different sizes of subset. (**a**) Mean bias error of the red sandstone specimen; (**b**) Standard deviation error of the red sandstone specimen; (**c**) Mean bias error of the woven specimen; (**d**) Standard deviation error of the woven composite specimen. we can find that the maximum error of the red sandstone specimen is 0.022 voxels of the MS-DVSP algorithm, 0.125 voxels of M-DVSP algorithm, and 0.124 of DVSP algorithm. The maximum error of MS-DVSP is only about one sixth of those errors of the other two algorithms, DVSP and M-DVSP. In the case of the woven composite specimen, the maximum errors are as follows: 0.015 voxels of the MS-DVSP algorithm, 0.150 voxels of the M-DVSP algorithm, and 0.151 of the DVSP algorithm. Thus, the maximum error of MS-DVSP is only about one tenth of those errors of the other two algorithms. These results indicate that MS-DVSP algorithm is an effective way to increase the accuracy of the technique, and the performance of MS-DVSP also depends on the internal micro-structures of objects.
## 3 Wind Tunnel Tests
### Wind Profile  
Wind tunnel tests are conducted in an atmospheric boundary layer wind tunnel, whose testing sections are 3.0 m wide, 2.5 m tall, and 15.0 m long. The blockage ratio of the tested model is 4.9%, smaller than the critical value of 5%. The atmospheric boundary layer is simulated by setting roughness elements upstream of the testing section, as illustrated in Figure 4. The wind profile of terrain category B according to [5] is simulated in the wind tunnel test. The profiles of mean wind speed and turbulent intensity are:  
\[V_{H}=V_{0}(H/H_{0})^{\alpha},\text{ and} \tag{21}\]  
\[I_{\text{H}}=I_{10}(H/10)^{-\alpha}. \tag{22}\]  
Here, \(V_{\text{H}}\) and \(I_{\text{H}}\) refer to wind speed and turbulence intensity at height \(H\). \(V_{0}\) is the wind speed at the reference height \(H_{0}\); \(I_{10}=0.14\) is the nominal turbulence intensity at a height of 10 m for terrain category B; \(\alpha=0.15\) is the exponential wind profile index for terrain category B.  
The wind profiles in the freestream are investigated and the measured results are compared with target values including mean wind speed \(V\) and turbulence intensity \(I_{\text{u}}\), which are plotted in Figure 5a,b. It can be observed that good agreement is achieved between the target and measured values. The power spectrum of measured wind speed at roof height is plotted in Figure 5c, which follows the Von Karman spectrum.  
Figure 3: Scaled rigid model of the L-shape structure. (**a**) Dimensions, (**b**) arrangement for pressure taps.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 3 Results
### Winter Rapseed Yield Predictions  
Approaching Figure 6 from a general perspective, it is evident that there exists an average measured value across all years, with estimated values for rapeseed yield prediction closely aligning with this average. Upon closer examination, the RMSE values of 2018 and 2022, at 0.39 t-ha\({}^{-1}\) and 0.23 t-ha\({}^{-1}\), respectively, highlight that the prediction outcomes for these years deviate significantly from the mean measured value, indicating a lower level of reliability compared to the simulation results for other years. Conversely, simulation results for winter rapeseed in the years 2019, 2021, and 2023 demonstrate a remarkable level of precision, with an RMSE value below 0.1 t-ha\({}^{-1}\). These results indicate that the predicted values closely approximate the observed values, showcasing a higher level of reliability compared to other outcomes.  
Figure 5: In-situ LAI and NDVI-based on observed and projected yields for the maize crop type in the years 2018 through 2023.  
The validation data, representing actual yields in the studied area, were incorporated as the average winter rapeseed yield in the investigated region (see Table 4). Statistical analysis of the simulation results demonstrates significant convergence. However, results based on the LAI indicator exhibit higher accuracy, albeit not statistically significant. Comparative analysis conducted over the years reveals a gradual trend of predicted values  
Figure 6: In-situ LAI and NDVI-based data showing observed and projected yields for the winter rapeseed crop from 2018 to 2023.  
towards actual values. Notably, when comparing simulation results for different crops, it is observed that the model achieves optimal results for winter rapeseed.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 3 Applications
### Applications in the Petroleum Industry  
Nanoparticles are finding increasing applications in the oil and gas industry, some of the examples include drilling fluids [104], enhanced oil recovery [105] and well stimulations [106]. Nanoparticles have certain distinct characteristics, such as their small size and  
Figure 3: Energy generation approaches from (**A**) piezoelectrics actuators, (**B**) water splitting and (**C**) CO\({}_{2}\) reduction [7].  
large volume to surface area ratio when compared to those of larger particles, which results in a higher degree of reactivity. The smaller the particles, the more readily they are able to pass into the small pore spaces of the formation. Due to the aforementioned properties, the use of nanoparticles in the petroleum industry is desirable.  
A drilling fluid is a viscous mixture used to aid the drilling of boreholes. They have various functions such as removing cuttings, forcing, maintaining bore stability regulating forming pressure, etc. [107]. Different chemicals are usually employed to improve drilling fluids, such as the rheology and filtration. There are numerous features that must be considered when applying traditional additives, such as the temperature and the particulate size [107]. Thus, nanoparticles have found considerable attention for their potential benefits to surmount these issues. In a study by [PERSON] et al. aluminium, copper and magnesium oxide nanoparticles greatly enhanced the rheological properties of drilling fluid [108]. Such nanoparticles offer good potential for bore hole cleaning. Iron oxide nanoparticles were employed to improve the performance of a xanthan gum drilling fluid [109]. The addition of nanoparticles resulted in reduced friction and improved filtration properties. Various other nanoparticles have been studied to improve the lubrication, filtration, wellbore stability and other characteristics of drilling fluids. Some of these include silicon dioxide [110], titanium oxide [111] and zinc titanate [112].  
Enhanced oil recovery applications of nanoparticles have also been studied extensively in recent years. Nanoparticles are generally employed to improve the tertial oil recovery techniques such as chemical, gas and thermal injection. Various nanoparticles have been used to for these applications including aluminium oxide for reduced oil viscosity [113], silicon dioxide for improved foam stability and mobility [114] and graphene oxide for improved oil recovery [115].
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 8 Conclusions  
In this study, we introduced a range of methodologies to generate comprehensive and contrasting explanations tailored for cyber analysts engaged in CVSS scoring. To tackle the text classification problem effectively, we transformed it into a tabular format using vectorization preprocessing techniques. Subsequently, we leveraged SHAP's multi-class global importance plot to treat each word as a feature, obtaining importance values for individual words across different classes.  
Recognizing the limitation of missing multi-class local explanations, we utilized the raw Shapley values from SHAP and devised our own visual explanation. This comprehensive representation encapsulates all intermediate steps and results, facilitating a thorough understanding for analysts.  
Addressing limitations inherent in SHAP for text data classification, such as the non-continuity of importance values compared to real statistical importance, we proposed a straightforward yet effective baseline improvement. This comes in the form of a normalization function to be incorporated into the raw SHAP values of words.  
Furthermore, we introduced a simple IF rule, serving as a preliminary filter to identify straightforward cases based on exclusive words in the description. This filter streamlines subsequent work for both the model and analysts and can seamlessly integrate into a larger pipeline.  
In a departure from the prevalent trend in the literature favoring Deep Learning models, we opted for XGBoost, an ensemble ML model. This decision yielded state-of-the-art results or near equivalents, all while offering a faster, simpler, and more interpretable architecture well suited for real-world implementation.  
Looking forward, our work holds promise for enhancement through the exploration of alternative methods beyond SHAP, refinement of the proposed normalization equation, and extension of the generated IF rule by uncovering additional correlations. We believe our research paves the way for future investigations into text-specific explanation algorithms and IF rule explanations.  
To uphold principles of reproducibility and open science, we have made our dataset and code openly accessible through open-source platforms. This deliberate choice ensures that researchers can validate, build upon, and contribute to the continued advancement of the field.
### Overview  
To address the issues mentioned above, we introduce a large multi-label fine-grained emotion detection dataset. To create it, and overcome the difficulty in annotating literature, we propose to use a semi-supervised learning approach. We begin with sentences from Project Gutenberg (Section 2.1 which we preprocess and de-duplicate (Section 2.2. We create a fine-grained emotion taxonomy (Section 2.3 and leverage Natural Language Inference (abbreviated NLI, and also known as Textual Entailment) [18] for weak supervision (Section 2.4) through zero-shot text classification [19], picking the highest scored sentences for each emotion. In the next step of our approach, we train a binary classifier for each emotion based on the weak labels and use each of them to perform pseudo-labeling for all examples. In this way, from binary weakly labeled examples, we create a multi-label dataset through pseudo labeling (Section 2.5). Since we use emotion-specific binary classifiers to create the final labels of the dataset, the probability assigned to each emotion by its respective classifier can be used as a soft label or converted to a hard label with a threshold (probability \(\geq\) 0.5). We provide a detailed analysis of the dataset in Section 3, including the label distribution (Section 3.1), label correlation (Section 3.2), and label sentiment (Section 3.3). Finally, we train two supervised models, described in Section 2.6, on this dataset, with the main purpose of assessing the quality of our dataset. To evaluate our dataset and models, we begin with the typical supervised evaluation scenario against a human-labeled subset of our dataset (Section 4.2). Next, we evaluate models trained on our dataset in a zero-shot transfer learning setting by mapping the output of our classifier (Section 4.3). As our last experiment, we evaluate few-shot transfer learning based on fine-tuning the models trained on our dataset (Section 4.4). In Section 5, we discuss our results. Finally, in Section 6 we present our conclusions, where we see the biggest room for improvement, and our plans for future work.  
Our primary research objective is to explore the effectiveness of a semi-supervised approach using NLI to create a fine-grained multi-label dataset for training supervised emotion detection models specialized for literature. This approach aims to tackle the challenges presented by the complex nature of the problem and the domain, which hinder accurate annotation by crowdworkers and incur high costs for expert annotation. Our secondary research objective is to provide a dataset and associated taxonomy model that includes a set of emotion labels not present in previous work and is geared towards literature analysis.