esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Aggregated Channel Attenuation Model
### Path Loss  
The path loss is denoted by \(h_{l}\), and it is commonly induced by unfavorable weather conditions. FSO link transmission is minimally affected in clear weather conditions. However, harsh weather conditions such as rain, snow and haze will cause a certain attenuation in the optical link and result in a significant reduction in optical signal power during communication. The Beer-Lambert law was utilized to determine the amount of optical signal power loss in the communication path [19].  
\[h_{l}(d)=\exp(-\sigma d) \tag{11}\]  
where \(d\) represents the link distance, parameter \(\sigma\) is the attenuation efficiency. Different weather conditions result in different values of \(\sigma\). In this paper, we focused on the scenario of sunny weather.
## 4 Limitations of Using Plant Products as Bioherbicides
### Solution Stability and Soil Biodegradability of Nepetalactone and Phloretin
No dose-dependent pattern in the dissipation of dihydrochalcone, isoflavue, and aliphatic phenol was evident, with the exception of the Prufbericht soil, where dihydrochalcone and isoflavue exhibited substantially higher DT\({}_{50}\) (time for concentration to decrease to 50% of its initial value), and DT\({}_{90}\) (time for concentration to decrease to 10%) values at the higher dose rate. Rapid degradation of pinocembrin dihydrochalcone was also observed, with a DT\({}_{50}\) ranging from 0.85 to 1.7 days and a DT\({}_{90}\) between 4.55 and 5.26 days [261]. The study of the adsorption of pinocembrin dihydrochalcone showed the character of a Freundlich curve model, indicating that pinocembrin dihydrochalcone interacts with the soil via functional groups on soil constituents that vary in quantity and quality and influence the strength of adsorption. The measured partition coefficient (KF) of 0.0661 indicates a low affinity to soil, which means that pinocembrin dihydrochalcone is probably not strongly bound to soil particles. This result suggests that pinocembrin dihydrochalcone may leach out of the soil more rapidly, reducing its potential for long-term soil persistence and efficacy. Given that the phytotoxicity of phloretin or its derivates has not been tested in greenhouse or field conditions, there are no available data in the literature on its stability in soil. However, the half-life of phloretin in soil is likely influenced by various factors, including microbial activity, soil pH, composition, weather conditions, and other environmental variables. [PERSON] et al. [262] reported that phloretin is stable in acidic conditions and high temperatures but prone to degradation in alkaline and strongly oxidizing conditions. Several studies have examined the chemical composition and levels of phloretin and phloretin in the soil of apple orchards affected by ARD and changes due to environmental and positioning factors [235; 263; 264]. [PERSON] et al. [265] reported that the W6 strain of _Ochrobarrum haematophilum_, commonly found in soil and the plant rhizosphere, was capable of degrading dihydrohalcones in replanted soil. The W6 strain exhibited significant activity in degrading phlorizin, phloretin, ferulic acid, cinnamic acid, and \(p\)-hydroxybenzoic acid in replanted soil by 30.00, 32.26, 48.10, 42.86, and 70.00%, respectively, compared to control soil. Considering the relatively short half-life of the dihydrohalcones studied in soil [260; 261], it is crucial to conduct a detailed stability analysis of phloretin. This analysis is necessary to better understand its persistence and behavior in the soil environment before proceeding with field experiments, ensuring more reliable and accurate results in practical applications. These results are essential to evaluate their long-term effects and effectiveness, particularly in agricultural and environmental contexts.
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 2 Explicative Head-Mounted Fault Display System Design
### Fault Diagnoser Model Computation
#### Predicting the Future Normal Expected Turbine Behavior  
The model is trained as a sequence-to-sequence model using the available data to predict future sensor states of the system. Considering that the number of errors during the observation time is significantly narrower compared to the period when the system is functioning normally, we chose to use two stacked LSTM layers preceded by an embedding layer to encode the sensory data. The overall model architecture with the capacity to predict non-linear dynamics is presented in Figure 4[36; 37]. In its simple form and derived from relations (1), the model can be defined as follows:  
\[h_{t}=\Gamma(x_{t}^{n}). \tag{2}\]  
The training of the function \(\Gamma\) parameters is performed with a defined time window \(k\) defining the recurrent model. Then, the input sequence is defined as \(x_{t}^{n}:\{S_{t}^{n},S_{t+1}^{n},\dots,S_{t+k-1}^{n}\}\) while the output sequence is \(h_{t}:\{S_{t+1},S_{t+2},\dots,S_{t+k}\}\), where \(t\) is the current timestamp, and \(S^{n}\) is the system state vector of \(n\) training inputs. As presented in Figure 5, each cell in the stacked LSTM architecture is composed by the following:  
* Input Gate \(\mathbf{i_{\text{t}}}\): modulates the incorporation of input signal \(x_{t}\) into the cell state \(c_{t}\)[10].
* Forget Gate \(\mathbf{f_{\text{t}}}\): applies a decay factor to the cell state, allowing the model to forget irrelevant past information as depicted in [10].
* Output Gate \(\mathbf{o_{t}}\) : determines the contribution of the cell state to the output signal \(h_{t}\)[9].
* Cell State \(\mathbf{c_{\text{t}}}\): serves as the memory component of the LSTM, updated by the input and forget gates [38].  
Figure 3: The basic diagram of the long short-term memory (LSTM) neural network [35].  
Moreover, during the training phase, we use the Huber loss function defined as:  
\[l_{t}=\begin{cases}\frac{1}{2}\delta(h_{t}-\hat{h_{t}})^{2},&\text{if }|h_{t}-\hat{h_{t}}|<\delta\\ |h_{t}-\hat{h_{t}}|-\frac{1}{2}\delta,&\text{otherwise}\end{cases} \tag{3}\]  
where \(t\) is the training timestamp index, and \(h_{t}\) and \(\hat{h_{t}}\) are respectively the recorded data and the output predicted by the LSTM model. \(\delta\) determines the transition point between the quadratic and linear regions of the Huber loss function. Huber loss combines the advantages from both the L1 and the mean squared error (MSE) loss functions. It prevents gradient exploding by using the L1 loss in extremities where the gradient will be independent of the error itself (constant gradient) while maintaining the smoothness of the cost function around the minimization objective.
## 2 Technologies in DTx
### Data Analysis
#### 2.2.2 Federated Learning for DTx
Furthermore, collecting and analyzing user data will require implementing an FL system that can be comprehensively applied to the DTx system, as data security or data ownership problems remain regardless of how deidentifiable processing is adopted.
# MODIS Retrieval of Aerosol Optical Depth over Turboid Coastal Water
## 1 Introduction
Based on the principle that liquid water absorption increases in shortwave infrared (SWIR), we present a new approach that uses MODIS-measured radiance at 2.1 \(\upmu\)m to retrieve AOD over turbid coastal water no matter whether the aerosol is fine or coarse mode dominated. In the spectral range of 0.55 to 2.1 \(\upmu\)m, the transparency of pure water to sunlight decreases rapidly with the increase of wavelength; the penetration depth (at which light attenuation is 90%) is 41 m at 0.55 \(\upmu\)m and drops to 0.001 m at 2.1 \(\upmu\)m [8]. Hence, unless the water is shallower than 1 mm, the water-leaving radiance contributed from the sea floor, sediments, and other contaminants in the water at 2.1 \(\upmu\)m is nearly negligible; this vastly simplifies the lower boundary condition of the retrieval. We present the data and the new approach in Sections 2 and 3, respectively, followed by description of retrieval results and the validation against measurements from six Aerosol Robotic Network (AERONET) coastal sites in Section 4. Discussions and conclusions are in Sections 5 and 6, respectively.
# Effect of Hydrophobic Silica Nanochannel Structure on the Running Speed of a Colloidal Damper
## 3 Fractal Seepage Model
### Seepage Theory of Nanochannels
The relationship between air pressure and volume can be described by the Van [PERSON] equation [10]:  
\[P_{G}+\alpha_{1}\frac{n}{V_{G}}(V_{G}-n\alpha_{2})=nRT \tag{8}\]  
where \(a_{1}\) is a constant related to intermolecular gravity, 0.1358 Jm\({}^{3}\cdot\)mol\({}^{-2}\). \(a_{2}\) is a constant related to the volume of the molecule itself, 3.64 \(\times\) 10\({}^{-5}\) m\({}^{3}\cdot\)mol\({}^{-1}\). \(n\) is the mole number, \(p_{G}\) is the gas pressure, \(V_{G}\) is the gas volume, and \(R\) is the universal gas constant. For the seepage of water in nanochannels, the continuum theory is no longer applicable, and the molecular discontinuity effect is not particularly obvious in the slip zone, so the slip boundary of the wall should be considered for correction. The slip effect refers to the phenomenon where the velocity of fluid near the wall of the channel/pipeline is not equal to zero. [PERSON] proposed a generalized slip boundary condition, which considered that the slip velocity \(u_{s}\) is proportional to the shear rate of fluid on the wall [22]:  
\[u_{s}=l\frac{\partial_{u_{s}}}{\partial_{y}}\bigg{|}_{y=0} \tag{9}\]  
where \(l\) is the slip length and \(l=0\) is the nonslip boundary condition. The slip length of water in nanochannels is related to the hydrophobicity of the wall--the stronger the hydrophobicity, the longer the slip length of water on the wall. At present, the slip length of smooth hydrophobic surfaces is mainly studied by microscopic experiments. [PERSON] et al. [15] measured the slip length of a smooth Octadecyl trichlorosilane (OTS) surface by using a colloidal probe in Atomic Force Microscopy (AFM), and they found that the slip length of the smooth OTS surface was 6 to 10 nm. The slip velocity \(u_{s}\) was calculated from the slip boundary. Combined with the first-order slip boundary, the seepage velocity of water in a capillary tube can be written as follows [9]:  
\[u_{m}=u_{s}-\frac{\lambda_{av}^{2}}{32\mu l}\frac{d_{p}}{d_{x}}=\frac{l\lambda _{av}}{4\mu}\frac{d_{p}}{d_{x}}-\frac{\lambda_{av}^{2}}{32\mu l}\frac{d_{p}}{d _{x}} \tag{10}\]  
Figure 3: Flow patterns of water in hydrophobic silica nanochannels. where \(d_{p}/d_{x}\) is the pressure gradient in the nanochannel. Water flow \(q\) through the capillary can be calculated based on \(u_{m}\):  
\[q(\lambda)=\pi\lambda^{2}u_{m}=\frac{\pi\Delta p}{\mu L_{0}}\left(\frac{\lambda_ {av}^{3}l}{16}-\frac{\lambda_{av}^{4}}{128}\right) \tag{11}\]  
where \(L\) is the length of the capillary, and \(\mu\) is the moving viscosity of water. \(\Delta p\) is the pressure difference at both ends of the capillary, and the total pressure difference at both ends of a capillary with length \(L\) can be obtained based on the formula \(\Delta p=p_{m}-(p_{G}+p_{e})\).
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 2 Problem Statement  
For quite some time, the occurrence and evolution of rail corrugation have seriously affected the development of urban rail transit. The rail corrugation not only causes strong vibration of the vehicle-track structure but also strengthens the wheel-rail rolling noise, which reduces the passenger comfort and increases the track maintenance cost. Therefore, it is critically important to identify and assess rail corrugation automatically and intelligently. The corrugation is a periodic irregular wear phenomenon on the rail surface and a rail image contains corrugation is shown as in Figure 1, which is collected from the track line between the Naniuoguxiang Station and the Dongsi Station of the Beijing Metro Line 6.  
In this paper, for real application, three issues will be investigated. First, the identification method of rail corrugation. Second, the method of estimating the rail corrugation period. Finally, the severity assessment method of rail corrugation. The ideas how to solve these problems are introduced in the following.  
**Rail corrugation identification based on improved SPM model.** First of all, to reduce the impact of noise and uneven illumination, a two-stage pre-processing method is considered to improve the original image quality. After that, rail head surface positioning is achieved by enhancing the rail its edge followed by weighted gradient projection analysis. Finally, according to the local texture and gradient features of the corrugation, an improved SPM model is proposed to identify rail corrugation.  
**Estimation of rail corrugation period.** To estimate the rail corrugation period more efficiently, the horizontal gray gradient statistical analysis and mathematic morphology algorithm are first used to localize the corrugation interval. After that, the proposed method by integrating the fast Fourier transform and normalized distance matching function is applied to estimate the rail corrugation period.  
**The severity assessment of the rail corrugation.** Based on the visual significance analysis for the rail corrugation image, the concept of wear saliency is introduced to assess the corrugation severity. In detail, the normalized frequency-tuned model is used to calculate the corrugation saliency map.  
Figure 1: The corrugation on a railway track.  
Subsequently, each corrugation area is localized by employing the adaptive threshold and mathematic morphology algorithm. In the end, after calculating the wear saliency, each corrugation area severity is determined with the help of fuzzy theory.
## 3 Stratum-Foundation Pit-Station-Track Interaction Model
### Comparative Analysis of Monitoring Results and Numerical Simulation
#### 3.3.1 Comparison of the Horizontal Displacement of the Retaining Pile
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Construction Phase** & **Serial Number** & **Construction Name** \\ \hline  & Stage 0 & Initial ground stress balance \\  & Stage 1 & Grouting properties of grouting layer soil \\ Phase 1 & Stage 2 & Drilling piles, temporary columns and pile foundations for foundation pit envelopes \\  & Stages 3–6 & Earth 3.5 m in the first stage of excavation \\  & Stage 7 & Poured crown beams and first internal supports \\  & Stages 8–15 & Earth 6 m in the second stage of excavation \\ Phase 2 & Stage 16 & Pouring the waist beam and the second inner support \\  & Stages 17–24 & Earth 5 m in the third stage of excavation \\ Phase 3 & Stage 25 & Pouring the waist beam and the third inner support \\  & Stages 26–33 & Earth 6 m in the fourth stage of excavation \\ Phase 4 & Stage 34 & Laying the anchor cable and applying preaxial force \\ Phase 5 & Stages 35–42 & Excavation of the fifth stage of the foundation pit earthwork 5 m to the bottom of the pit \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Finite element model numerical simulation procedure. certain errors in the equivalent substitution of the support structure simulation, leading to certain deviations between the calculated results and the actual measurement results. Figure 22b shows that after the foundation pit excavation is completed, the maximum horizontal deformation position of the retaining pile at measuring points ZQT-15 and ZQT-21 is basically the same, being 0.6 times the excavation depth of the foundation pit at both positions. The simulated value of the maximum deformation of the retaining pile at measuring points ZQT-15 and ZQT-21 is basically the same as the measured value.
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
## 2 Image and Bubble Characterization  
### Image Acquisition  
Bubble flow imaging with and without applied magnetic field was conducted at the thermal neutron imaging beamline NEUTRA (SINQ, PSI, 20 mm aperture, 10\({}^{7}\) n cm\({}^{-2}\) s\({}^{-1}\) mA\({}^{-1}\) flux) at the Paul Scherrer Institute (PSI) using a medium spatial resolution set-up (MIDI) [56]. Experiments were performed with both horizontally and vertically oriented inlet nozzles. The setup with the horizontal inlet is described in [8; 43], whereas a modified version of that model gallium/argon system was designed for the new experiments with the vertical inlet--the latter will be described in a follow-up publication. A thin-walled (4 mm) rectangular 150 mm \(\times\) 90 mm (95 mm) \(\times\) 30 mm (interior dimensions) glass (boron-free) vessel filled with liquid gallium (linear attenuation coefficient, LAC, for neutrons at NEUTRA is 0.56 cm\({}^{-1}\)) up to the 130- to 140-mm mark was used. Neutron flux was parallel to the 30-mm dimension of the vessel. A square field of view (FOV, 112.8- or 123.125-mm side) above the gas inlet was imaged at 100 FPS. The distance between the liquid metal layer and the scintillator and was [4; 32] mm depending on the setup (magnetic field system used, if any). A sCMOS _ORCA Flash 4.0_ camera was used to collect the scintillator (200 \(\upmu\)m thick \({}^{6}\)_LiF/ZnS_ screen).  
Reference experiments were performed at the cold neutron beamline ICON (SINQ, PSI, 20 mm aperture, \(\sim\)1.3 \(\times\) NEUTRA flux) to validate the developed image processing methodology [57]. A 20 mm \(\times\) 20 mm \(\times\) 30 mm rectangular brass reference body (stationary and moving, LAC at ICON is 0.90 cm\({}^{-1}\)) with a central spherical cavity (5 mm radius) was imaged at 100 FPS within a 120.06 mm square FOV (a sCMOS _ORCA Flash 4.0 V2_ camera with a 200 \(\upmu\)m \({}^{6}\)_LiF/ZnS_ scintillator) to reproduce the imaging conditions for argon bubbles in liquid gallium. The reference body was imaged with neutron flux directed along its shorter or longer axes. In addition, the distance between the scintillator and the body was either 0- (static body)/2 mm (moving body) or with an extra 1 cm. These adjustments to the test conditions were meant to determine how the reference shape acquisition error depends on SNR which these conditions modify.  
For each image sequence recording at the NEUTRA and ICON camera, dark current signals and neutron beam profile signals were recorded to be used for subsequent image normalization during pre-processing.
## 2 Materials and Methods  
### Computational Framework  
Figure 1 depicts the computational workflow framework used to build the IL-10-induced peptide prediction model. The analysis workflow involved multiple phases, such as data retrieval from the IL10 Pred server [33], feature extraction, handling of imbalanced data, feature fusion, feature selection, utilization of ML algorithms, model assessment, and IL10-Stack server construction.  
Initially, an IL-10 dataset was obtained from the IL10 Pred method. Next, protein sequence features were extracted using AAIndex and UniRep. Additionally, we adopted the SMOTE method to rectify the data imbalance in the dataset. After feature fusion, the process of feature selection was performed utilizing LGBM.  
We proceeded to split the data into training and testing sets, maintaining a ratio of 4:1. The methodology employed for training and validating the model incorporated both 5-fold cross-validation and independent testing. Two new methods for IL-10-induced peptide prediction were proposed, namely the non-stacking model, IL10-Fuse, and the stacking model, IL10-Stack. In the construction of IL10-Stack, we first trained individual models using SVM and LGBM separately. Subsequently, we made predictions on the test and validation sets using these two models, obtaining the test set predictions and validation set predictions. Next, we used the predictions from both models to create new test and validation sets, and then trained a new SVM model. Through this process, we developed a stacking algorithm based on amino acid sequence features for predicting IL-10-induced peptides. To facilitate better understanding and usage of our new algorithm by other scientists, we also established a web server in which users simply need to input a peptide sequence for prediction, and they will receive results indicating whether the peptide is an IL-10-induced peptide, along with corresponding confidence levels.
# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 2 Environment Perception System
### RADAR (Radio Detection and Ranging)  
Radar systems work in wavelengths of the order of millimetres; these are used in a wide variety of military and civil applications, such as aerial or terrestrial threat detection systems, shooting systems, and airports or meteorological systems. The emergence of smart vehicles and the need to increase road safety have triggered the use of this type of device in the automotive sector. Radar systems for intelligent vehicles work at frequencies of 24/77/79 GHz, known as millimetre wave radar (MMW). The radar measures the distance between the emitter and the object by calculating the time of flight of the emitted signal and the received echo. The radars not only allow the detection of the distance to several targets, but are also capable of accurately supplying the direction and speed of the targets. The new radars for vehicles use an array of micro antennas capable of generating a set of lobes that allow improvement of the range and a processing system for the detection of multiple targets (Figure 4a,b).  
Range millimetre-wave RADAR is applied in Blind Spot Detection (BSD), Lane Change Assistant (LCA), Rear Cross Traffic Alert (RCTA), Forward Cross Traffic Alert (FCTA) or radar video fusion. Radar waves have higher penetrability because they offer good features in all weather conditions, and can accurately detect short-range targets in front, to the side, and to the rear side of vehicle. For this reason, they are used in several ADAS systems. RADAR can significantly improve vehicle safety performance and reduce the decision-making burden of people at the wheel. Furthermore, it can  
Figure 4: Millimetre-wave RADAR CAR70 from Nanoradar: (**a**) Microarray radar antenna; (**b**) multi-lobe system.  
Figure 3: (**a**) Ultrasonic car sensors from Bosch; (**b**) an assistance parking system from Audi.  
be installed besides the bumpers of the vehicle. Some disadvantages of this sensor type are the lack of precision, its reduced Field of View (FOV), and the fact that it can produce false positives due to bouncing of the emitted signal.