esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Key Variables in the Analytical Approximation Model
### Integration Time, Slant Range, and Approximate QPE  
For a given SAR configuration and true anomaly, the integration time is used to calculate QPE, while the slant range contributes both to the integration time and QPE. In our proposed analytical approximation model for QPE, both the accurate and approximate integration time and slant range are considered. The former ones are applied to generate the approximate QPE with true anomaly as the independent variable, and the latter ones are merged into the maximum of the QPE calculation.  
In the accurate calculation, the slant ranges are obtained by solving a quadratic equation:  
\[\frac{\left(\rho\hat{a}_{a,ECI,x}+R_{sat,ECI,x}\right)^{2}+\left(\rho\hat{a}_{ a,ECI,y}+R_{sat,ECI,y}\right)^{2}}{E_{a}^{2}}+\frac{\left(\rho\hat{a}_{a,ECI,z}+R_{ sat,ECI,z}\right)^{2}}{E_{b}^{2}}=1 \tag{41}\]  
where \(E_{a}=\) 6,378,137.000 m and \(E_{b}=\) 6,356,752.314 m are the semi-major axis and semi-minor axis of the ellipsoid reference in WGS 84, respectively. Then, the integration time is  
\[T_{int}=\frac{\lambda}{L_{a}}\frac{\left|\mathbf{R}_{st}\right|}{\left| \mathbf{V}_{st}\right|}\frac{\left|\mathbf{R}_{s}\right|}{\left|\mathbf{R}_{ t}\right|} \tag{42}\]  
In the approximate maximum QPE calculation, we use a slant range that is based on the circular Earth with a radius of \(E_{mean}=\left(2E_{a}+E_{b}\right)/3\) and  
\[\rho_{mean}=E_{mean}\frac{\sin\left[\theta_{L}+\pi-\arcsin\left(a\sin\theta _{L}/E_{mean}\right)\right]}{\sin\theta_{L}} \tag{43}\]  
With the satellite's velocity at apogee only instead of \(\left|\mathbf{V}_{st}\right|\), together with \(a\) instead of \(\left|\mathbf{R}_{s}\right|\), we get the mean integration time.  
\[T_{int,mean}=\frac{a\lambda\rho_{mean}}{E_{mean}L_{a}}\sqrt{\frac{a\left(1+ e\right)}{\mu\left(1-e\right)}} \tag{44}\]  
Thus, we have the approximate expected value and standard deviation of QPE and its maximum expression:  
\[\mathbb{E}\left[\text{QPE}\right] =\pi\,\mathbb{E}\left[\Delta f_{r,a}\right]\left(\frac{T_{int}}{ 2}\right)^{2} \tag{45}\] \[\sigma\left[\text{QPE}\right] =\pi\sqrt{\sigma^{2}\left[\Delta f_{r,r}\right]+\sigma^{2}\left[ \Delta f_{r,a}\right]}\left(\frac{T_{int}}{2}\right)^{2}\] \[=\sqrt{\sigma^{2}\left[\text{QPE}_{v}\right]+\sigma^{2}\left[ \text{QPE}_{a}\right]}\] (46) \[\sigma\left[\text{QPE}\right]_{max} =\pi\sqrt{\sigma^{2}\left[\Delta f_{r,v}\right]_{max}+\sigma^{2} \left[\Delta f_{r,a}\right]_{max}}\left(\frac{T_{int,mean}}{2}\right)^{2} \tag{47}\]  
where \(\sigma\left[\text{QPE}_{v}\right]\) and \(\sigma\left[\text{QPE}_{a}\right]\) are the standard deviations of QPE introduced by the velocity vector term and acceleration vector term, respectively.
# Action of Antimicrobial Peptides against Bacterial Biofilms
## 2 Antimicrobial Peptides
### Mechanism of Action against Biofilms
\begin{table}
\begin{tabular}{l l l l l l} \hline
**AMPs** & **Sources** & **Amino Acids Sequence** & **Microorganismss** & **Proposed Mechanism of Action** & **Ref** \\ \hline LL-37 & Human & LLGDFFRKSKEKIGKFKRIVQRIKDFLENLVPRTES & _Pseudomonas aeruginosa_ & Reduces swimming and swarming utilities, promotes twitching motility, downregulates \\ \hline
1037 & Denovo & KGFRRIVRIV & _Pseudomonas aeruginosa_ & the genes repaired for biofilm formation and influenza QS system \\ \hline
1018 & Denovo & VRILVVWRIWRR & _Pseudomonas aeruginosa_ & Decrease intracellular (p) PpGpp & [14] \\ \hline Escalentina-1a (1-21) & Denovo & GIFSKLACKKIKNLLISGLKG & _Pseudomonas aeruginosa_ & Disrupts cell membrane & [82] \\ \hline Nisin A & Denovo & MSTKDFNELDUSVSKKDSGASPR & _Stphylococcus aureus_ & Depolarizes cell membrane & [78] \\ \hline latction Q & Denovo & MAGFLKVQUALAKYSGKAVQMAWANGKKILDWNAGOALDR & _Stphylococcus aureus_ & Depolarizes cell membrane & [78] \\ \hline Nukacin SK-1 & Denovo & KK-KSCVIPTVSKHCHMSFQFFVTCC & _Stphylococcus aureus_ & Depolarizes cell membrane & [78] \\ \hline RN35-17P22-36 & Denovo & RPFTRAQWFAQHQSHFTPIAMRANNYWR & _Pseudomonas aeruginosa_ & Depolarizes and permobilize cell & [79] \\ \hline St (1-16) & Denovo & ALWKTLKKVLAAAK & _Pseudomonas aeruginosa_ & Disintegrates and release membrane lipids & [90] \\ \hline PI & _Callibibarra_ & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & _Escherichia coli,_ & _Escherichia coli,_ & \\ \cline{3-4}  & _vicin_ & & _Gierchelbacter humanini_ & & \\ \hline L-K6L9 & Denovo & LKLKLKLKLKLKLKLKLKL & _Pseudomonas aeruginosa_ & Depolarizes biofilms matrix & [52] \\ \hline PSIGm-3 & Fish & FHHHFRGVHARGSKCRTLTG & _Pseudomonas aeruginosa_ & Depolarizes cDNA & [91] \\ \hline PI & Tick & PARKARAATAATAATAATAATA & _Stroglococcus mutants_ & Interferes and degrade EPS matrix & [87] \\ \hline Hepcdin 20 & Human & ICIFCCCCCHSHCHCCCCKT & _Stphylococcus epidermidis_ & Acts polysaccharide intercellular adhesin & [88] \\ \hline Nal-P1-113 & Denovo & AKR-Nal-Nal-GYKKRF-Nal- & _Porphyomonas gingivalis_ & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline Human p- & & & & \\ defensin 3 & Human & GINITLQKYYCRVRGCRCAVLSCLPKEEQGKSCRKRK & _Stphylococcus epidermidis_ & Targets scaA, icaD and icaR genes & [98] \\ \hline DJK-5 & Denovo & VQWRAIRVRIVR & _Pseudomonas aeruginosa_ & Suppress spIoT promoter activity & [96] \\ \hline \end{tabular}
\end{table} 
Table 1: Representative AMPs and their anti-biofilm mechanism of action
# Mesostructural Model for the Fatigue Analysis of Open-Cell Metal Foams
## 5 Conclusions  
This research proposes a mesostructural model to assess the fatigue behavior of open-cell metal foams subjected to cyclic loading conditions. The proposed model aims to evaluate the fatigue life of the foam by considering the previous load history and the analogy of progressive collapse.  
The proposed model comprises a finite element model, a fatigue analysis model, an equivalent number of cycles model, and a failure criterion model. The finite element model is based on linear ligaments forming a three-dimensional structure, recalculating the stress in each ligament remaining after removing the ligaments that have reached their respective fatigue life. The fatigue analysis model is based on a probabilistic approach and corresponds to the Weibull fatigue model. The equivalent number of cycles model is obtained from the Weibull model, recording the previous history of the loads applied to the material. The failure criterion model is based on the fact that a material's mechanical properties deteriorate over time.  
The validation of the proposed model considers experimental campaign data of open-cell metal foam subjected to a fully reversed strain-controlled cycle with a strain amplitude of 0.003. For a probability of failure of 0.5, the fatigue life is close to 3600, which is the predicted fatigue life according to the proposed model. Therefore, the proposed model allows us to predict the fatigue life of the studied foam for a specific strain amplitude.  
Conceptualization, H.P. and A.S.; methodology, H.P. and A.S.; software, H.P.; validation, H.P., A.S., J.G. ([PERSON]), J.G. ([PERSON]) and P.M.; formal analysis, H.P.; investigation, H.P. and A.S.; resources, A.P.; data curation, H.P.; writing--original draft preparation, H.P. and A.S.; writing--review and editing, H.P., A.S., J.G. ([PERSON]) and P.M., H.A.G.; visualization, H.P.; supervision, H.P.; project administration, H.P.; funding acquisition, A.P. All authors have read and agreed to the published version of the manuscript.  
[PERSON] is funded by VINCI-DI:039.463/2024.  
Figure 6: E-N curve for the studied metallic foam.
## 4 Security Analysis
### Informal analysis
#### 4.2.6 Man-in-the-Middle Attacks  
In a man-in-the-middle attack, attackers can eavesdrop and impersonate a communications entity to communicate with the UAV or GCS for the purposes of messagetampering and information left. If attackers modify messages \(M1(ID_{U_{i}},P_{U_{i}},Cert_{U_{i}},t1)\), \(M2(P_{G},Cert_{G},t2)\) and \(M3(ID_{U_{i}},P_{U_{i}},Cert_{U_{i}},t3)\), but lack information about internal credentials such as \(Cert_{U_{i}}\) and \(Cert_{G}\), as well as the randomness of \(S_{U_{i}}\), \(S_{G}\) and timestamps, attackers cannot complete the man-in-the-middle attack.
## 3 Methods
### Segmenter  
The proposed segmenter is based on the U-net architecture and primarily consists of the encoder down-sampling path, the decoder up-sampling path, a hybrid multi-scale attention module, and an attention module based on skip connections. These connections correspond to the respective layers between the two paths, as illustrated in Figure 1. The detailed description of the segmenter is as follows:
## 2 Theoretical Background  
### First-Order MSR System Model  
The MSR model has periodicity and multiple potential barriers and potential wells. It can effectively avoid output saturation by independently adjusting system parameters. The first-order MSR system model is expressed as follows [39; 40; 41]  
\[\frac{dx}{dt}=-U^{\prime}(x)+s(t)+n(t) \tag{1}\]  
where \(s(t)\) is the input signal, \(n(t)\) represents the Gaussian white noise, \(x(t)\) is the final output, and \(U(x)\) is the periodic potential function. The specific expression of \(U(x)\) is given as follows  
\[U(x)=-a\cos(bx) \tag{2}\]  
where \(a\) and \(b\) are positive real numbers. Let \(a=b=1\); \(U(x)\) is shown in Figure 1. The periodic potential function has several potential barriers and potential wells can be found from it.  
The following Formula (3) can replace the Equation (1)  
\[\frac{dx}{dt}=-ab\sin(bx)+s(t)+n(t) \tag{3}\]  
The Runge-Kutta method is used in Equation (3), and the specific calculation process is given as follows:  
\[\left\{\begin{array}{l}k_{1}=h(-ab\sin(bx_{n})+s_{n}+n_{n})\\ k_{2}=h(-ab\sin(b(x_{n}+\frac{1}{2}k_{1}))+s_{n}+n_{n})\\ k_{3}=h(-ab\sin(b(x_{n}+\frac{1}{2}k_{2}))+s_{n+1}+n_{n+1})\\ k_{4}=h(-ab\sin(b(x_{n}+k_{3}))+s_{n+1}+n_{n+1})\\ x_{n+1}=x_{n}+\frac{1}{6}(k_{1}+2k_{2}+2k_{3}+k_{4})\end{array}\right. \tag{4}\]  
In Formula (4) \(s_{n}\) and \(n_{n}\) represent the input signal and noise, respectively. \(x_{n}\) is the output signal and \(h\) is the iterative step of the numerical calculation.
# Adaptive Feature Fusion for Small Object Detection
## 2 Materials and Methods
### Materials
#### 2.1.2 Feature Fusion  
In the network, shallow features generally lack abstract semantic information and rich geometric details, while deep layers are just the opposite of shallow layers. In order to make the features have both deep fine-grained features and shallow high-resolutionspatial position information, the study is mainly from the perspective of feature fusion. Common fusion technologies mainly include feature splicing, feature summation, and the multiplication of corresponding elements. Although these fusion technologies can bring rich feature information between different stages, their processing of feature information is indistinguishable. That is, the convolution in the forward propagation treats all features equally. In fact, this will bring a lot of negative information, which will cause challenges to small object detection. Therefore, it is particularly important to selectively fuse features. [PERSON] et al. [32] improved the RCNN and FPN structures, designed and integrated a global context network and a pyramid local context network, extracted context information globally and locally, and introduced a spatially aware attention module to guide the network to focus on more informative regions and generate more appropriate image features. [PERSON] [33] proposed a multi-level feature pyramid network (MLFPN) to build a more efficient feature pyramid to detect objects of different scales, using the decoding layer of the U-shaped module as the detection object feature. This improved the detection rate of objects, but increased the complexity of the network due to the complex U-shaped structure. The learnable weights for feature fusion were proposed in BiFPN [34], but ignored the influence of the dataset on weights. [PERSON] [35] proposed a data-driven pyramid feature fusion strategy that learns a method of spatially filtering conflicting information to suppress inconsistency, but its universality is low. Recently, [PERSON] [36] began to study the weights of feature fusion, and generated a set of fusion weights by statistical methods and introduced them into the FPN structure, which further improved the detection performance of small objects. Although these detection algorithms have excellent detection capabilities in natural images, their performance and application in small object detection in specific scenes are poor such as in UAV aerial images and satellite remote sensing images. The main algorithms of small object detection are summarized in chronological order as shown in Table 1.
# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 4 Experiments
### Dataset and Experimental Parameter Settings  
During the training and testing phases of LNDM-Net, portions of the ShapeNet dataset (16 categories) and the PCN dataset (8 categories) were selected for training and validation. To ensure consistency in the input data, all original point clouds were centered at the origin, with their 3D coordinates constrained within the interval \([-1,1]\). The model was built using the PyTorch deep learning framework. Training was performed on a high-performance workstation featuring an Intel(R) Core(TM) i9-14900K CPU @ 6 GHz and an NVIDIA GeForce RTX 4090 GPU, operating under the Ubuntu 18.04 system to ensure stability and compatibility.  
For the training process, the Adam optimizer was used with an initial learning rate of 0.001, a batch size of 24, and 400 epochs. To reduce overfitting and gradually lower the learning rate, a decay strategy was employed, where the learning rate was reduced by a factor of 0.5 every 20 epochs. Batch normalization (BN) and the ReLU activation function were applied in the multi-scale feature extractor and the attention discriminator, while only the ReLU activation function was used in the pyramid point cloud generator. Additionally, the sampling and local neighborhood dynamic fusion operations in the local neighborhood dynamic fusion layer were repeated twice, and the attention layer operation was applied four times.  
Figure 5: Attention discriminator.
## 3 Results and Discussion
### In Vitro Metabolite Identification in Rat and Human  
Ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was used to identify the metabolites in the positive and negative modes. A comparison of erinacine A metabolites, in this study, with rat and human liver S9 metabolites is shown in Table 2. Twenty-four metabolites were found after erinacine \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **t\({}_{\text{1/2}}\) (min)** & **CL\({}_{\text{int}}\) (mL/min/kg)** & **CL\({}_{\text{H}}\) (mL/min/kg)** & **E\({}_{\text{H}}\)** \\ \hline Rat & 43.32 & 87.02 & 3.7 & 0.61 \\ Human & 114.92 & 18.92 & 9.72 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hepatic metabolic stability of erinacine A in rats and humans. Figure 1: Erinacine A remaining versus time profile in rat and human liver S9. Data is presented in mean values of triplicate measurements. A incubation and were assigned as M1-M24 (Table S1). Five common metabolites were detected after incubation with rat and human liver S9. The mass errors were all \(\leq\)5 ppm. The top three abundant metabolites, based on peak area, were Glutathione conjugation + Demethylation (M6), Demethylation (M2), and Demethylation + Hydrogenation (M3). Alcohols Dehydration (M1), Demethylation (M2), Demethylation + Hydrogenation (M3), 2 \(\times\) Hydroxylation (M4), Demethylation, and two Hydroxylations (M5) metabolites were both identified in rat and human liver S9. The proposed metabolic pathway and possible metabolites of erinacine A are shown in Figure 2.  
The parent compound of the erinacine A formula was C\({}_{25}\)H\({}_{36}\)O\({}_{6}\). The MS spectrum of erinacine A was found at [M + H]\({}^{+}\) = 433.2573 and [M + Na]\({}^{+}\) = 455.2405 (Figure 3A). The MS/MS spectrum by the collision energy of 10 V is shown Figure 3B that correspond to the cleavage of the C-O ether bond ([C\({}_{20}\)H\({}_{29}\)O\({}_{2}\)]\({}^{+}\) = 301.2162 and [C\({}_{20}\)H\({}_{27}\)O]\({}^{+}\) = 283.2052) (Figure 3C) [15; 32]. The Alcohols Dehydration metabolites (M1) formula was C\({}_{25}\)H\({}_{34}\)O\({}_{5}\), with erinacine A losing an H\({}_{2}\)O [33; 34]. The MS spectrum of M1 was found at [M + H]\({}^{+}\) = 415.2472 and [M + Na]\({}^{+}\) = 437.2255. At a collision energy of 10V, the main product ions were _m/z_ 283.2036, 215.1701, 181.1249, 135.0817, 119.0856, 105.0709, and 69.0327 (Figure 4A). Two product ions were analogous to those of erinacine A ions (_m/z_ 283.2036 and _m/z_ 119.0856), and the product ion of _m/z_ 301.2162 disappeared. Dehydrated M1 could be used to indicate water loss in pentose fragments [35]. The Demethylation metabolites (M2) formula was C\({}_{24}\)H\({}_{34}\)O\({}_{6}\), with erinacine A losing a methylene (-CH\({}_{2}\)) [36]. At a collision energy of 40 V, the MS spectrum of M2 was found at [M + H]\({}^{+}\) = 419.2407. The main product
## 3 Architectural System Design
### System Overview: High-Level Architecture of the Pregnancy Monitoring Framework  
The pregnancy monitoring framework was designed as an end-to-end system integrating state-of-the-art deep learning techniques with advanced data processing and  
Figure 9: LASSO feature importance plot.  
explainability tools. The system follows a sequential modular pipeline, as illustrated in Figure 10.  
The system starts with the collection of the National Family and Health Survey-5 (NFHS-5) dataset. This dataset contains 136,136 records and 95 features spanning clinical, demographic, and lifestyle aspects of maternal health. In stage two, missing values are addressed through mean imputation for numerical features and mode imputation for categorical features. Numerical features are standardized to provide even scaling, whereas the sole categorical feature, 'State', receives one-hot encoding. To improve model efficacy and readability, the LASSO (least absolute shrinkage and selection operator) was used to scale down the dataset into 51 vital features responsible for making accurate predictions of pregnancy complications. Next, three deep neural network models were constructed: ResRNN-Net based on Bi-LSTM, CNN, and attention techniques, MultiScaleFusion-Net using GRU and multiscale convolution blocks for stronger feature fusion, and the multimodal model, which integrates images through CNN, time series by LSTM, and tabular by MLP.  
Models were trained with 5-fold stratified cross-validation to have data splits balanced for all folds. Accuracy, precision, recall, F1-score, and ROC-AUC were used as the evaluation metrics. Then, search was used to optimize the learning rate, batch size, and dropout rate to achieve optimal model performance. For model explanation, SHAP (SHapley Additive explanation) and Grad-CAM (gradient-weighted class activation mapping) were used, providing global and local understanding of feature importance. The final outcomes were assessed according to model performance criteria, providing robust predictions and explainable insights to inform healthcare decisions. This modular structure guarantees the system's scalability, stability, and clinical validity, making it flexible for operational deployment in real-world healthcare environments.
# Is Precision Therapy in Infantile-Onset Epileptic Encephalopathieshes Still Too Far to Call Upon?
## 2 Advances in Diagnostic Approaches  
The diagnostic landscape of EDEs has transformed with tools such as next-generation sequencing (NGS), whole-exome sequencing (WES), and whole-genome sequencing (WGS). These technologies have identified over 900 monogenic causes of EDEs, including mutations in SCN1A, KCNQ2, and CDKL5 [9]. Complementary functional assays and neuroimaging techniques, such as MRI spectroscopy and functional connectivity analyses, enable detailed correlations between genetic and brain abnormalities [10]. Integration of biomarkers, such as CSF neurofilament light chain levels and EEG patterns, further enhances diagnostic precision [10].  
Advanced functional methods have further revolutionized the understanding of genetic mutations in EDEs. For instance, the patch-clamp technique is a powerful electrophysiological method that allows for the analysis of ion channel mutations by assessing their functional properties. This method has been particularly impactful in characterizing gain-of-function or loss-of-function mutations in genes such as SCN1A and SCN2A, which encode sodium channels critical for neuronal excitability [11]. By elucidating the precise effects of mutations on channel behavior, patch-clamp studies provide essential data for tailoring specific therapeutic strategies.  
Additionally, molecular dynamics simulations have emerged as a crucial tool for investigating the interaction between mutated proteins and potential therapeutic compounds. This computational approach models the structural and dynamic behavior of proteins at an atomic level, enabling researchers to predict how specific drugs bind to and modulate the activity of mutated targets. For example, in GNAO encephalopathy, a rare condition caused by pathogenic variants in the GNAO1 gene, molecular dynamics has been employed to analyze how mutations in the G\(\alpha\)0 protein affect its interaction with guanosine triphosphate (GTP) and downstream signaling pathways [12]. In this article [PERSON] et al. [12] showed that the molecular dynamics approach was pivotal in identifying tetrabenzine as a candidate drug capable of modulating the dysfunctional G\(\alpha\)o protein and mitigating movement disorders and seizures associated with the condition. This discovery highlights the potential of combining computational insights with clinical pharmacology to develop targeted treatments for rare genetic disorders.  
Despite these advancements, conventional antiepileptic drug (AED) treatments remain seizure-focused and disregard specific genetic pathophysiologies. This limitation often leads to delayed interventions, continued developmental regression, and suboptimal patient outcomes. The integration of functional assays, such as patch-clamp and molecular dynamics, into diagnostic workflows holds the potential to bridge this gap, facilitating more accurate diagnoses and personalized treatments.
## 3 Results and Discussion
### In Silico Evaluation Results  
Computational tools have significantly advanced the prediction of interactions and chemical stability. Thus, a simulation model was developed to predict the interaction between forchlorfenuron (FCF), gibberell receptors (GA3 Ox2), and auxin receptors (IAA7). This simulation model served as the in silico evaluation. Additionally, we meticulously designed an in vitro growth model using the germination of lettuce seeds (_Lactuca sativa_) to corroborate the stimulation of these receptors, ensuring the reliability of our findings.