esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Therapeutic Management of EGPA
### Anti-IL-5-Targeted Therapies
#### 3.2.3. Benralizumab  
Efficacy of benralizumab in EGPA is mainly supported by case reports [41]. In addition, a small open-label pilot study including 10 EGPA patients suggested that benralizumab was effective in reducing both the daily dose of oral steroids and the exacerbation rate during treatment [42]. Importantly, 8 of 10 subjects reached a minimum oral steroid dose of <5 mg daily, with 5 (50%) able to stop oral glucocorticoids after 40 weeks of treatment [42]. Intriguingly, benralizumab has been reported to be effective even in EGPA patients failing previous treatment with mepolizumab or reslizumab. In 2 case studies (16 EGPA patients overall), including 6 patients failing previous treatment with mepolizumab and 1 patient failing both mepolizumab and reslizumab, benralizumab (30 mg s.c. every 4 weeks for the first 3 injections, then 30 mg s.c. every 8 weeks) significantly reduced oral glucocorticoid doses and improved both BVAS and ACT scores at the completion of the studies [43; 44]. Complete depletion of peripheral eosinophils was seen in most of the patients, consistent with the drug's mechanism of action (Figure 3b). Currently, a phase three trial comparing benralizumab with mepolizumab in relapsing or refractory EGPA is ongoing (MANDARA trial, NCT04157348) [45].
## 3 Field Measurement
### Rail Accelerance  
The rail accelerance was measured for the purpose of verifying the accuracy of the Timoshenko-beam track model and the SEM. An accelerometer was placed on the mid-span railroad, and then a hammer was used to impact the same position. The direct vertical accelerometer of the mid-span rail was obtained by processing the signals of the accelerometer and the hammer. The field measurement is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Track component** & **Parameter** & **Symbol** & **Value** \\ \hline \multirow{8}{*}{CHN60 rail} & Elastic modulus (GPa) & \(E\) & 210 \\  & Cross-section area (m\({}^{2}\)) & \(A\) & \(7.745\times 10^{-3}\) \\  & Mass density (kg/m\({}^{3}\)) & \(\rho\) & 7850 \\  & Shear correction factor & \(\kappa\) & 0.5329 \\  & Area moment of inertia (m\({}^{4}\)) & \(I\) & \(3.217\times 10^{-5}\) \\  & Damping loss factor & \(\eta_{\text{r}}\) & 0.05 \\  & Shear elastic modulus (GPa) & \(G\) & 80.77 \\ \hline \multirow{2}{*}{Fastener} & Vertical stiffness (kN/mm) & \(k_{\text{v}}\) & 37 \\  & Damping loss factor & \(\eta_{\text{f}}\) & 0.25 \\ \cline{1-1}  & Spacing (m) & \(a\) & 0.625 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of models.  
The operating frequency range, the nominal sensitivity, and the measurement range of the accelerometer were 1-15,000 Hz, 5 mV/g, and 1000 g, respectively. As distinct hammers have different applicable frequency ranges, a heavy hammer with a nylon head was used to acquire the low-frequency measurement result, while a light hammer with an aluminum head was used to acquire the middle- and high-frequency measurement result. The lower boundary of the applicable frequency range depends on the coherent coefficient of the rail accelerance. It should exceed 0.8 within the applicable frequency range [21]. The upper boundary of the applicable frequency range should meet the requirement that the decrease of the amplitude spectrum of the impact force is less than 10 dB when the amplitude at low frequencies is taken as the reference [22]. Based on these rules, the applicable frequency ranges of the heavy and light hammers were 25-600 Hz and 90-3900 Hz, respectively. The sampling frequency of the acquisition instrument was 3200 Hz when the heavy hammer was used, and it was changed to 25,600 Hz when the light hammer was used. Besides, the measurement ranges of the heavy and light hammers were 125 kN and 50 kN, respectively.  
To eliminate the measurement errors induced by the noises, five effective impacts were required in the measurement, and the average of the results was taken as the final result. The final measurement result of the rail accelerance is comprised of two parts: the result obtained by the heavy hammer for the frequency range of 30-90 Hz and the result obtained by the light hammer for the frequency range of 90-2000 Hz.
# Ventilation Systems and Their Impact on Nanoparticle Concentrations in Office Buildings
## 3 Results  
### Concentrations of Nanoparticles  
The concentrations of NPs varied quite largely between the different buildings (Figure 3). The highest concentration was found in a newly completed building, especially during the heating season, just a few weeks after opening. The maximum values reached around 30,000 particles per cm\({}^{3}\). High concentrations were also found in the two buildings closest to a busy street; however, this trend did not appear in the nonheating season (Table 2). The lowest concentrations were found in a building near a park. In some of the buildings, the concentrations were higher during the heating season, whereas in other buildings, they were higher during the nonheating season. During the heating season, the daily NP concentrations tended to be larger (Figure 3), whereas during the nonheating season, there were several cases with higher NP concentrations during nighttime (Figure 4).  
Figure 3: Mean, 25 th, and 75 th percentile and min and max values for the NP number concentrations in twelve office buildings during daytime (from 6 a.m. until 6 p.m.) and nighttime (from 6 p.m. until 6 a.m.) during a one-week measuring period in the heating season.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Daytime 1**} & \multicolumn{4}{c}{**Nighttime 2**} \\ \cline{2-7}
**Buildings** & \multicolumn{2}{c}{**Concentration**} & \multicolumn{2}{c}{**Concentration**} & \multicolumn{2}{c}{**(Particles per cm3)**} & \multicolumn{2}{c}{**Size**} \\
**1-12** & \multicolumn{2}{c}{**(Particles per cm3)**} & \multicolumn{2}{c}{**Size**} & \multicolumn{2}{c}{**(Particles per cm3)**} & \multicolumn{2}{c}{**Size**} & \multicolumn{2}{c}{**from Busy**} \\ \cline{2-7}  & **\textless{}100 nm** & \(\leq\)**461 nm** & \multicolumn{2}{c}{**(nm)**} & \multicolumn{2}{c}{**\textless{}100 nm**} & \(\leq\)**461 nm** & \multicolumn{2}{c}{**(nm)**} \\ \hline \multicolumn{7}{c}{**Heating season**} \\ \hline
1 & 2980 & 3224 & 44 & 3054 & 3336 & 49 & 850 \\
2 & 366 & 458 & 73 & 144 & 178 & 66 & 790 \\
3 & 725 & 867 & 59 & 853 & 970 & 58 & 700  
Subsequently, we also calculated the concentration of NPs smaller than 100 nm, also called ultrafine particles (Table 2). In the offices with the highest number concentrations, at least 80-90% of the particles were <100 nm. During the nonheating season, the fraction of particles <100 nm was relatively smaller in comparison with the heating season. The average size of the NPs varied from 37 to 105 nm, being relatively smaller during the heating season (Table 2). Average temperature and average relative humidity during the measurements are presented in Table 1, Appendix A.
# Fused Gromov-Wasserstein Distance for Structured Objects
## 3 Mathematical Properties of \(Fgw\)
### Topology of the Structured Object Space  
The \(FGW\) distance has the following properties:  
**Theorem 1** (Metric properties).: _Let \(p,q\geq 1\), \(\alpha\in]0,1[\) and \(\mu,\
u\in\mathbb{S}_{pq}(\Omega)\). The functional \(\pi\to E_{p,q,\alpha}(\pi)\) always achieves an infimum \(\pi^{*}\) in \(\Pi(\mu,\
u)\) s.t. \(d_{FGW,\alpha,p,q}(\mu,\
u)=E_{p,q,\alpha}(\pi^{*})<+\infty\). Moreover:_  
* \(d_{FGW,\alpha,p,q}\) _is symmetric and, for_ \(q=1\)_, satisfies the triangle inequality. For_ \(q\geq 2\)_, the triangular inequality is relaxed by a factor_ \(2^{q-1}\)_._
* _For_ \(\alpha\in]0,1[\)_,_ \(d_{FGW,\alpha,p,q}(\mu,\
u)=0\) _if an only if there exists a bijective function_ \(f=(f_{1},f_{2}):supp(\mu)\to supp(\
u)\) _such that:_ \[f\#\mu=\
u\] (12) \[\forall(x,a)\in supp(\mu)\,f_{2}(x,a)=a\] (13) \[\forall(x,a),(x^{\prime},a^{\prime})\in supp(\mu)^{2},\ d_{X}(x,x^{\prime})=d _{Y}(f_{1}(x,a),f_{1}(x^{\prime},a^{\prime}))\] (14)
* _If_ \((\mu,\
u)\) _are generalized labeled graphs then_ \(d_{FGW,\alpha,p,q}(\mu,\
u)=0\) _if and only if_ \((X\times\Omega,d_{X},\mu)\) _and_ \((Y\times\Omega,d_{Y},\
u)\) _are strongly isomorphic._  
Proof of this theorem can be found in Section 7.2. The identity of indiscernibles is the most delicate part to prove and it is based on using the Gromov-Wasserstein distance between the spaces \(X\times\Omega\) and \(Y\times\Omega\). The previous theorem states that \(FGW\) is a distance over the space of generalized labeled graphs endowed with the strong isomorphism as equivalence relation defined in Definition 7. More generally, for any structured objects the equivalence relation is given by (12)-(14). Informally, invariants of the \(FGW\) are structured objects that have both the same structure and the same features in the same place. Despite the fact that \(q=1\) leads to a proper metric, we will further see in Section 4.1 that the case \(q=2\) can be computed more efficiently using a separability trick from [26].  
Theorem 1 allows a wide set of applications for \(FGW\), such as \(k\)-nearest-neighbors, distance-substitution kernels, pseudo-Euclidean embeddings, or representative-set methods [27; 28; 29]. Arguably, such a distance allows for a better interpretation than to end-to-end learning machines, such as neural networks, because the \(\pi\) matrix exhibits the relationships between the elements of the objects in a pairwise comparison.  
Figure 8: Difference on transportation maps between \(FGW\), \(GW\), and \(W\) distances on synthetic trees. **(Left)** the \(W\) distance between the features is null since feature information are the same, **(Middle)**\(FGW\) with \(\alpha\in]0,1[\) is different from zero and discriminate the two structured objects, and **(Right)**\(GW\) between the two isometric structures is null.
# Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
###### Abstract  
Seismic data are considered crucial sources of data that help identify the litho-fluid facies distributions in reservoir rocks. However, different facies mostly have similar responses to seismic attributes. In addition, seismic anisotropy negatively affects the facies predictors extracted from seismic data. Accordingly, this study aims at estimating zero-offset acoustic and shear impedances based on partial-stack inversion by two methods: statistical modeling and a multilayer feed-forward neural network (MLFN). The resulting impedance volumes are compared to those obtained from isotropic simultaneous inversion by using impedance logs. The best impedance volumes are applied to [PERSON]'s anisotropy equations to solve for the anisotropy parameters Epsilon and Delta. Finally, the shear and acoustic impedances are transformed into elastic properties from which the facies and fluid distributions are predicted by using the logistic regression and decision tree algorithms. The results obtained from the MLFN show better matching with the impedance and facies logs compared to those obtained from isotropic inversion and statistical modeling.  
Keywords:anisotropy; rock physics; inversion; parameter estimation +
Footnote †: journal: Applied Sciences
# Cascade or Direct Speech Translation? A Case Study +
## 7 Targeted Evaluations of Cascade and Advanced Direct Models
### Error Propagation
With models trained on in-domain data only, for EU-ES (Figure 3a), only Q3 featured a result under the average difference; in all other quartiles, the difference was larger than average, although the differences on both Q2 and Q1 were lower than with Q4. In ES-EU (Figure 3b), the tendencies were opposite the expectation, with the cascade model performing markedly better than average compared to the direct model on data associated with a higher WER, i.e., Q2 and Q1. The results for models trained on additional data were more in line with the assumed impact of data associated with higher WER scores. For EU-ES for instance (Figure 3c), the positive difference in favour of the cascade model against the direct model was lesser on Q1 and higher on Q4, with Q1 and Q3 in line with the average difference. For ES-EU (Figure 3d), the direct model scored higher than average compared to the cascade model, with a consistent tendency as the WER scores increased from Q4 to Q1. The results for this evaluation were thus not uniform, with opposite tendencies for the two pairs of systems depending on their having been trained only on in-domain data or also on additional data. In this case as well, additional references and manual assessments of the audio files in terms of difficulty could help determine the actual tendencies in terms of error propagation. A manual examination of non-literal transcriptions in the test sets would also support a more precise evaluation of WER-related differences between models. These tasks were however beyond the scope of this study and are left for future work.
## Appendix B Parametric Double S-Shaped Functions
### Logistic Functions  
The Parametric Double Logistic Function (PD_Logi_F) is composed of 2 Logistic functions, each characterized by its own set of parameters:  
\[f(x) =p_{0}+f_{1}(x)+f_{2}(x), \tag{117}\] \[f_{1}(x) =p_{1}/\left(1.0+\exp(-(x-p_{2})\;p_{3})\right),\] (118) \[f_{2}(x) =p_{4}/\left(1.0+\exp(-(x-p_{5})\;p_{6})\right), \tag{119}\]  
where  
* \(f(x)\) is the value of the Parametric Double Logistic Function at argument \(x\), returned by the IDL function pd_logi_f,
* \(f_{1}(x)\) is the value of the first Parametric Logistic Function at argument \(x\), using parameters \(p_{1}\) to \(p_{3}\) and provided as the output positional parameter plf1, and
* \(f_{2}(x)\) is the value of the second Parametric Logistic Function at argument \(x\), using parameters \(p_{4}\) to \(p_{6}\) and provided as the output positional parameter plf2. The 7 parameters have the following meanings:
* \(p_{0}=\) Base value of \(f(x)\), i.e., asymptotic value when \(x\to-\infty\).
* \(p_{1}=\) Amplitude of \(f_{1}(x)\) (positive for an increase, negative for a decrease), independently from the base value.
* \(p_{2}=\) Phase shift along the \(x\) axis for the inflection point of \(f_{1}(x)\): a positive (negative) value shifts the function to the right (left) of \(x=0\).
* \(p_{3}=\) Slope of \(f_{1}(x)\) at the inflection point.  
\(p_{4}=\) Amplitude of \(f_{2}(x)\) (positive for an increase, negative for a decrease), independently from the base value. \(p_{5}=\) Phase shift along the \(x\) axis for the inflection point of \(f_{2}(x)\): a positive (negative) value shifts the function to the right (left) of \(x=0\). \(p_{6}=\) Slope of \(f_{2}(x)\) at the inflection point. The partial derivatives of the PD_Logi_F model with respect to the 7 parameters are as follows: \[\partial f(x)/\partial p_{i} =\partial p_{0}/\partial p_{i}+\partial f_{1}(x)/\partial p_{i}+ \partial f_{2}(x)/\partial p_{i},\] (100) \[\partial p_{0}/\partial p_{i} =\left\{\begin{array}{ll}1.0&\quad\text{if }i=0\\ 0.0&\quad\text{if }i>0\end{array}\right.,\] (101) \[\partial f_{1}(x)/\partial p_{1} =1.0/(1.0+\exp(-(x-p_{2})\,p_{3})),\] (102) \[\partial f_{1}(x)/\partial p_{2} =-(p_{1}\,\exp(-(x-p_{2})\,p_{3}))/(1.0+\exp(-(x-p_{2})\,p_{3}))^ {2},\] (103) \[\partial f_{1}(x)/\partial p_{3} =-(p_{1}\,\exp(-(x-p_{2})))/(1.0+\exp(-(x-p_{2})\,p_{3}))^{2},\] (104) \[\partial f_{2}(x)/\partial p_{4} =1.0/(1.0+\exp(-(x-p_{5})\,p_{6})),\] (105) \[\partial f_{2}(x)/\partial p_{5} =-(p_{4}\,\exp(-(x-p_{5})\,p_{6}))/(1.0+\exp(-(x-p_{5})\,p_{6}))^ {2},\] (106) \[\partial f_{2}(x)/\partial p_{6} =-(p_{4}\,\exp(-(x-p_{5})))/(1.0+\exp(-(x-p_{5})\,p_{6}))^{2},\] (107)  
and all other partial derivatives (e.g., \(\partial f_{1}(x)/\partial p_{0},\partial f_{1}(x)/\partial p_{5}\) or \(\partial f_{2}(x)/\partial p_{3}\)) are null.
# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 2 Method  
### Network Architecture  
Our baseline architecture is YOLOv8, which is widely adopted in object detection tasks due to its balance between accuracy and inference speed. The Dark-YOLO detection network is composed of three main modules: the Backbone, the Neck, and the Detection Head. The Backbone consists of an adaptive image enhancement module (SCI), multiple convolutional modules (Conv), a dynamic convolutional feature localization module (PSM), and a Cross-Spatial Pyramid Pooling module (CSPPF). The SCI module is responsible for enhancing the quality of low-light images, restoring image details, and enriching the feature information of the images. The PSM module utilizes partial convolution to dynamically extract image features, combined with a parameter-free attention mechanism to enhance the localization ability of the detection targets, thereby enabling the model to focus on the critical parts of the image. The CSPPF module employs a cross-use of max pooling layers and average pooling layers, where max pooling extracts the most salient features from the feature maps, and average pooling retains the overall local information, helping to mitigate the problem of local information loss in low-light images. In the Neck section, a combination of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) is used to enhance the model's multi-scale feature representation capabilities. The Neck also introduces the Dimension Reciprocal Attention Module (D-RAMiT), which mixes attention across both spatial and channel dimensions, addressing the issue of the model focusing only on either local or global features. SimAM in PSM applies pixel-level focused attention to key features. D-RAMiT models the global-local relationships through bidirectional spatial-channel attention. Together, they enhance the context-awareness ability in low-light scenarios. SimAM is used for prioritizing local features, while D-RAMiT is used for global context modeling, forming a multi-attention framework. The Detection Head is composed of several convolutional layers and activation functions. The convolutional layers are used to process the feature maps output from the Neck to extract more advanced features,while the activation functions introduce non-linearity to improve the feature representation capability. The overall structure of the Dark-YOLO network is illustrated in Figure 1.
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 3 Bi-Dual Inference for Detecting Emotions  
**Definition 1**.: _(structural duality): Structural duality refers to the relationship between two machine learning tasks where one task maps from space \(X\) to space \(Y\), while the other task maps from space \(Y\) to space \(X\). This concept highlights the reciprocal nature of the two tasks and their interconnectedness._  
**Definition 2**.: _(primal and dual models): When two tasks exhibit duality, we refer to the task mapping from space \(X\) to space \(Y\) as the primal task or forward task, and its corresponding model as the primal model or forward model, denoted as \(f\). Similarly, the task mapping from space \(Y\) to space \(X\) is referred to as the dual task or backward task, with its corresponding model denoted as the dual model or backward model, denoted as \(g\)._  
In this section, we present the mathematical formulation of the Bi-dual Inference framework. Specifically, sentiment classification and sentence generation are considered as dual forms of artificial intelligence tasks. Sentiment classification aims to classify the polarity of a given natural language sentence, while the dual task focuses on automatically generating sentences with specific emotional polarity categories. Let us consider two domains, denoted as \(X\) and \(Y\). In this paper, \(X\) represents sentence generation, and \(Y\) represents polarity labeling. We define \(D_{x}\) as the collection of training data from \(X\) to \(Y\), and \(D_{y}\) as the collection of training data from \(Y\) to \(X\). It should be noted that \(D_{x}\) is a subset of \(X\), and \(D_{y}\) is a subset of \(Y\). Our objective is to learn two agents, \(f:X\to Y\) and \(g:Y\to X\), which represent the models for the primal task and dual task respectively. We introduce the mapping \(\Delta x(x,x^{\prime})\) from \(X\times X\) to \(\mathbb{R}\), which represents the dual reconstruction error between \(x\) and \(x^{\prime}\), also known as the feedback signal. Here, \(x\) and \(x^{\prime}\) are elements of \(X\). Similarly, we have the mapping \(\Delta y(y,y^{\prime})\) that is analogous to \(\Delta x(x,x^{\prime})\), where \(y\) and \(y^{\prime}\) are elements of \(Y\).
## 3 Experiment and Evaluation Criteria  
The proposed framework integrates two different advanced ML methodologies: NLP (i.e., SMILES-based BERT model) and GNN (i.e., RGCN), as shown in Figure 2. The SMILES-based BERT model, renowned for its proficiency in processing molecular structures, was used to encode molecular information from SMILES strings (i.e., input embeddings or feature values) for antimalarialal drugs and three targets (i.e., PfAcAS, F/GGPPS, and PMAGL). This encoding of SMILES strings facilitated the representation of drugs and three potential targets in a manner conducive to subsequent graph-based analysis as shown in Figure 2. BERT was first introduced in this study specifically for the prediction of antimalarial drugs as a tool for extracting and generating features (i.e., Bert-based features) from drugs and targets SMILES strings as shown in Figure 2. To further validate the contribution and effectiveness of Bert-based features, we compared them with features generated by Mordred (i.e., Mordred-based features) [55], the most widely used tool for feature extraction in this area of antimalarialal drugs prediction [2, 54].  
Thereafter, the GNN model, namely RGCN, was deployed to handle graph-structured data, encapsulating the relationships between antimalarial drugs and potential targets found in Plasmodium, as shown in Figure 1. Input nodes' features used during implementing and training RGCN were of course ones computed by using the DeepChem adopted Bert SmilesTokenizer module and further compared to the ones calculated by a well-known descriptor calculator known as Mordred. Training in RGCN involved iterative processes to discern and capture intricate drug-target interactions embedded within the multi-relational data. An overview of the architecture and training strategy of the proposed RGCN model is summarized in Table 4.  
To evaluate the predictive capabilities of the integrated models, rigorous evaluation methodologies were used. The proportion of the dataset used in this study for model training and evaluation is summarized in Table 6.  
The datasets were partitioned into training, validation, and test sets to facilitate model training and subsequent assessment. Cross-validation techniques, such as 10-fold cross-validation, ensured robustness and mitigated biases in the model evaluation process. Evaluation metrics, including accuracy, sensitivity, specificity, Matthew's correlation coefficient (MCC), area under receiver operating characteristics (AUROC), and area under the precision-recall curve (AUPRC), were used to quantify the predictive performance of the model. Comparative analyses against baseline models and existing approaches to antimalarial drug discovery served as benchmarks to gauge the efficacy and superiority of the proposed frameworks.