esa-sceva/satcom-chunk-collection huggingface dataset:
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
In this work, the application of an SPR-POF platform with MIP as a specific receptor for the selective detection of 2-furalheide (2-FAL) in aqueous solutions for food safety surveillance is presented. The 2-FAL detection in aqueous solutions or beverages, for instance, wine, is becoming a crucial task not only for its relevance in affecting the flavor and aroma [28], but also for its suspected toxic and carcinogenic effects on human beings [29; 30; 31]. Moreover, furanic compounds have been proposed to assess the aging of food and beverages due, for example, to inappropriate storing conditions [31; 32; 33; 34]. An MIP for 2-FAL has been recently tested for sensing by electrochemical transduction by our group [35], taking advantage of the redox properties of 2-FAL. Here, the same MIP previously investigated is proposed with optical SPR transduction to improve the detection limits while maintaining similar characteristics of low cost and fast development of the biomimetic receptor. The portability of the apparatus is assured by the use of the POFs with a low dimension apparatus. In this study, the effect of the thickness of the MIP layer on the sensor response is investigated, particularly to modulate the SPR resonance wavelength according to the characteristics of the sample, mainly its refractive index (RI).
## 2 Study Area and Datasets  
### Study Area  
Beijing, as the capital city of China, is the national politics, culture, and technological innovation center of China and comprises 16 urban zones. It is located in the northern part of the North China Plain, 115.7\({}^{\circ}\)-117.4\({}^{\circ}\)E and 39.4\({}^{\circ}\)-41.6\({}^{\circ}\)N, covering an area of about 16,410.54 km\({}^{2}\). It is adjacent to the east of Tianjin city. The topography of Beijing is higher in the northwest and lower in the southeast, with an average altitude of about 43.5 m above sea level. It is surrounded by hills to the west, north, and northeast. The climate in Beijing is sub-humid and sub-arid monsoon with abundant rainfall in summer [35; 36]. Due to the smaller swath of high-resolution Gaofen-3 SAR images compared to Sentinel-1 SAR images, we select the coverage of Gaofen-3 as our study area (indicated by the brown rectangle in Figure 1), which includes the districts of Changping, Shunyi, Chaoyang, Dongcheng, Xicheng, Haidian, and Shijingshan.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SAR** & **Time Range** & **Resolution** & **Maximum** & **Studies** \\
**Datasets** & & & **Velocity** & \\ \hline ERS-1/2 & 1992–2000 & 25 m & \(-\)48 mm/a & [PERSON] et al., 2016 [19] \\ Envisat & 2003–2010 & 30 m & \(-\)143 mm/a & [PERSON] et al., 2017; [PERSON] et al., 2020; [PERSON] et al., 2020 [20; 21; 22] \\ ALOS-1 & 2007–2011 & 10 m & \(-\)120 mm/a & [PERSON] et al., 2021; [PERSON] et al., 2013; [PERSON] et al., 2012 [23; 24; 25] \\ Radarsat-2 & 2010–2016 & 30 m & \(-\)141 mm/a & [PERSON] et al., 2020; [PERSON] et al., 2022; [PERSON] et al., 2019 [26; 27; 28] \\ TerraSAR-X & 2010–2019 & 3 m & \(-\)117 mm/a & [PERSON] et al., 2022; [PERSON] et al., 2017; [PERSON] et al., 2022 [20; 29; 30] \\ ALOS-2 & 2014–2017 & 10 m/60 m & \(-\)150 mm/a & [PERSON] et al., 2018; [PERSON] et al., 2021; [PERSON] et al., 2019 [31; 32; 33] \\ Sentinel-1 & 2014–2020 & 20 m & \(-\)135 mm/a & [PERSON] et al., 2019; [PERSON] et al., 2022; [PERSON] et al., 2020. [22; 27; 24] \\ Gaofen-3 & 2020–2021 & 3 m & \(-\)**80 mm/a** & **This study** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the relevant studies of land subsidence over Beijing using SAR satellites.
## 3 Review of Existing Channel Models for Underground Mines
### Geometrical Optic Model  
For smaller-wavelength operations in underground mines, the GO model is useful to estimate the characteristics of the propagated signal in complex environments. Unlike the single-mode waveguide model, it can be used to investigate branches of mines for signal propagation. It can provide accurate results when the dimensions of the environment are larger than its wavelength. This means that it can satisfy the higher UHF-band frequencies for propagation loss in underground mines. The use of this model is based on optical ray theory, where EM waves used for signal transmission are considered to be optical rays [24]. The distributed fields of propagated signals can be computed with the addition of replicated images produced by the interior surfaces of a tunnel. The GO model can also be applied to analyze an occupied mine, but this model is not valid for characterizing lower frequencies. Moreover, it does not cover diffraction loss in mines. Another drawback is its computational complexity on high link distance [14; 25].
## 4 Conclusions  
This work investigated the impact of particle size distribution for colloidal particles on contaminant transport by performing a numerical simulation. The colloidal-particle-associated contaminant transport model along with the Langmuir adsorption model for the reaction between contaminant-sand and contaminant-colloidal particles was introduced, and a parametric study was performed at varied mean and standard deviation of particle size distribution. Based on the observations from simulated breakthrough curves, the key findings can be summarized as follows:  
1. The experimental breakthrough curves of Pb(II) under the presence of retained kaolinite particles were described well by the colloidal-particle-associated contaminant transport model introduced in this paper. This implies that the model can be used for the prediction of transport of heavy metals under the presence of immobile kaolinite particles or other particles that favorably adsorb the heavy metals.
2. As the median size of the colloidal particles and the standard deviation increased, the transport of contaminants was retarded. The impact of a standard deviation on the retardation of transport became more significant as the median size of the colloidal particles increased. This is attributed to the variation in the amount of retained colloidal particles by the median size and the standard deviation of particle size distribution. Therefore, the particle size distribution of particles should be taken into account in the prediction of contaminant transport.
3. A quantitative description of simulated breakthrough curves from the calculated \(\beta\) and \(\theta\) provides the remaining available adsorption sites of the bed and the prediction of the breakthrough point at a given condition.
4. Further investigation is needed for the impact of particle size distribution on contaminant transport when particle and contaminant transport simultaneously. In addition, the unsaturated flow can also be considered in the modeling work to predict the contaminant transport from the surface to groundwater.  
Writing draft paper, J.W.; Investigation and discussion, J.W., D.L., K.P, H.L; Visualization, H.L; Supervision and editing the manuscript, H.C.  
The authors appreciate the support partially by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport of the Korean government (No. 18 SCIP-B108153-04). The authors also wish to thank the anonymous reviewers for their careful review of the manuscript and valuable comments.  
The authors declare no conflict of interest.
## 4 Discussion  
Above-band gap excitation on the surface of the AlGaAs film leads to instantaneous creation of electron-hole plasma. As they diffuse into the bulk, they impart their energy to lattice via electron-phonon scattering to generate heat and non-thermal processes such as deformation potential (DP) coupling. Consequently, the temporal evolution of the free charge carrier distribution results in generating various forms of transient strains until the free-carrier dynamics evolve towards equilibrium. In particular, the DP contribution is intimately linked to the free carrier concentration and can be used to trace its evolution. Due to symmetric sample geometry, we are only sensitive to the DP coupling and thermal expansion of the lattice. By monitoring the temporal evolution of the Bragg peak shifts, we follow propagation of strain across X-ray probe depth into the depth direction. As shown in Figure 4, the general trend of the peak movements in early timescales (\(\Delta t<200\) ns) shows that the excess energy (large strain) flows from the film layer into the substrate. Subsequently, both layers come to a near equilibrium state at much longer timescale of several hundreds of nanoseconds.  
In Figure 5a, a few nanoseconds should allow sufficient time for free electron-hole pairs to diffuse away from the film surface and traverse across the epitaxial interface introducing the effect of the DP coupling driven lattice expansion in the substrate. However, aside from the impulsive lattice response due to the sound wave propagation, we do not measure any additional contribution of tensile strain until thermal diffusion takes place in the substrate. Apparent lack of tensile strain within intermediate timescales (few nanoseconds) implies that strain measurement is indeed insensitive to the presence of optically induced free electron population.  
It can be inferred from our result that only mobile electrons have migrated into the substrate (i.e., lack of holes), of which excess energy is used to generate heat via electron-phonon coupling. Consequently, the substrate becomes hotter than the initially excited layer, effectively making it a secondary heating source. Assuming that the electrons have thermalized with the lattice at sufficiently long time-scales (\(>\)hundreds of nanoseconds), we can convert the lattice strain into temperature based on Bragg's law using the thermal expansion coefficient of the materials as shown in Figure 5b. We note that the apparent temperature offset between the two layers implies a non-negligible thermal boundary resistance even for an epitaxially-grown interface [18]. However, evaluating the exact value of the  
Figure 4: Centroid shifts of AlGaAs (blue) and GaAs (red) taken from Figure 2. Only impulsive sound wave propogation is observed in the GaAs bulk at early times.  
thermal boundary resistance would require additional data sets at different laser fluence and elaborate modeling of the strain evolution based on dynamical X-ray diffraction theory [19; 20], which is beyond the scope of present study.
# Generative Enhancement of 3D Image Classifiers
## 5 Models and Methods
### 3D Convolutional Neural Network Classifiers  
The first two models are identical deep 3D convolutional networks consisting of three convolutional layers. Stride is used instead of pooling layers for downsampling. Batch normalization and Leaky ReLU activation function is applied after each convolutional layer. The output is produced by a single fully-connected layer with a unit count of \(C\), equal to the number of classes. A softmax function is used in order to convert the output logits to class probabilities.  
Figure 1: Simplified flowchart showing the basic steps of the proposed Generative enhancement methodology. The starting point is a network, which classifies training and testing samples \(x\), assigning them a predicted label \(\hat{y}\). Using our methodology, the network is expanded to contain a generator (\(G\)), producing original training examples from a Gaussian noise input vector \(z\).  
Figure 2: High-level architecture of the tree models designed for the proposed experiment. The first model (**a**) is a plain 3D convolutional (CNN) classifier trained directly on ModelNet10. The other CNN (**b**) is trained on an augmented dataset created by combining ModelNet10 examples and the conditional generator (\(G\)) output. The generator output is labeled according to its input condition vector. The last model (**c**) is a three-dimensional (3D) conditional Generative Adversarial Network (GAN) network where the discriminator (\(D\)) is used as the classifier during the testing phase.  
The loss function used to train these classifiers is a categorical cross-entropy shown in (1).  
\[L_{C}=-\sum_{i=1}^{M}\sum_{j=1}^{C}\mathbf{y}_{j}^{(i)}\log(\hat{\mathbf{y}}_{j}^{ (i)}) \tag{1}\]  
In the equation, \(M\) stands for the number of examples (in a minibatch or total), \(C\) is the number of classes in the dataset, \(\mathbf{y}\) is a vector of one-hot encoded training data labels, and \(\hat{\mathbf{y}}\) is the classifier prediction of the class vector. The \(\mathbf{y}_{j}^{(i)}\) notation refers to the \(j\)th element of the vector that belongs to the \(i\)th sample within the minibatch. The optimizer used to train these classifiers is RMSprop.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Graph-Based Homology Mapping Methods
#### 2.3.3. BubbleZ  
BubbleZ is a bioinformatics algorithm designed for the identification and analysis of genomic structural variations (SVs), specifically large insertions and deletions (indels), in whole-genome sequencing data. It is a fast whole-genome homology mapper which detects pairwise chains in homologous blocks using a de Bruijn graph [47]. The algorithm was recently tested on closely related mammalian genomes and a large collection of bacterial genomes. The algorithm identifies bubbles in the de Bruijn graph, which represent regions of potential structural variation. A bubble occurs when there are multiple alternative paths (alleles) through the graph for a specific genomic region. Then, it expands each detected bubble by traversing alternative paths through the de Bruijn graph. It performs variant calling to classify the structural variations into different categories, such as insertions, deletions, duplications, or complex rearrangements. Based on the graph topology and read alignments, the algorithm determines the size, orientation, and sequence content of each variant and assigns confidence scores to the detected structural variants based on various factors, including read coverage, allele frequency, and consistency across sequencing reads [48]. The complexity of BubbleZ depends on the size and complexity of the input sequences. It typically has a worst-case time complexity of \(O(n^{2})\), where \(n\) is the length of the input sequences.
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 4 3D Bioprinting Techniques
### Laser-Based Bioprinting
#### 4.1.2 Two-Photon Polymerization (TPP)
Cytotoxicity assays showed an over 95% cell viability rate in 21 days, thus confirming the bioprinting suitability of this method. This is due to the fact that TPP does not apply shear force stress to cells caused by extrusion, and the literature demonstrates that IR radiation is safer than UV radiation [118; 119; 120]. The in vitro degradation analysis indicated that the size of the hydrogels fabricated with 800 mW lasers decreased by almost 90% after 60 min, whereas an increase in laser power resulted in a greater resistance to degradation; therefore, 1600 mW lasers were capable of fabricating hydrogels with 60% size reductions in 60 min [121].
## 2 Method to Measure Railway Station Infrastructure Quality
Platforms, for example, form their own asset group and are hence weighted as 100%. The grades of the asset groups are then aggregated according to their weighting, and the grade of a station is determined. Table 1 shows all relevant infrastructure classes, asset groups, and their weightings for the aggregation of the BAQ grade of one station. The grade for a station is calculated according to Equation (2) [7]. If an asset class is not present at a railway station, the remaining weightings must each be reweighed according to their current weighting so that the sum of the weightings still amounts to 100%. The remaining percentages are distributed among the other classes in proportional shares according to their previous weighting. Thus, if an asset class is missing, the platforms, for example, receive 30% of the missing shares of the weighting. The factor \(p\) indicates this weighting within the station. \[BAQ_{station}=\sum_{i=1}^{num\ groups}\varnothing grade_{group\ i}\cdot p_{i} \tag{2}\]  
To calculate the nationwide grade in the next step, all of the stations are assigned to groups according to the volume of passengers using this station. For each passenger volume group, the mean of the grades of all stations assigned to this group is formed (see Equation (3) [7]). These mean values are weighted with a factor for the corresponding group (see Table 2). \[grade_{passengergroup}=\varnothing grade_{station} \tag{3}\]  
The sum of these weighted averages results in a nationwide BAQ grade. The equation for calculating the nationwide BAQ grade is given in Equation (4) [7]. \[BAQ_{nationwide}=\sum_{i=1}^{num\ passengers}\varnothing grade_{passengergroup\ i} \cdot b_{i} \tag{4}\]  
The nationwide BAQ grade is determined annually and compared to the targets of the LuFV. The BAQ grade is reported in an infrastructure asset quality report (\"Infrastrukturzustands- und -entwicklungsbericht\") [19; 20]. The historical development (2016-2022) of the nationwide BAQ grade and its corresponding target values are shown in Figure 2. There has been a steady improvement in the nationwide grade from a grade of 2.92 in 2016 to a grade of 2.77 in 2022. The target is an annual improvement of 0.02 grade points. In 2020, there was a change from LuFV II to LuFV III. This resulted in a one-off deviation from the improvement rate. \begin{table}
\begin{tabular}{c c} \hline \hline
**Passenger Volume Groups [Passengers per Day]** & **Factor b** \\ \hline \(>\)50,000 & 6/27 \\
10,001–50,000 & 6/27 \\
3001–10,000 & 5/27 \\
1001–3000 & 4/27 \\
301–1000 & 3/27 \\
100–300 & 2/27 \\ \(<\)100 & 1/27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Weightings of the stations according to passenger volume groups [7]. After providing a definition of the infrastructure quality of railway stations, the following section contains a discussion of the methodology for modelling degradation functions and the effect of the measures. These two aspects form the main elements of the cause-effect relationship.
## 6 Methodology
### Evaluation Metrics  
In order to evaluate the performance of the models, we used the following four evaluation criteria: accuracy, precision, recall, and F1 score (calculated as in the equations below):  
* _Accuracy_ (A): is a measure of a classifier's ability to correctly identify information as false or true. Equation (16) can be used to calculate the accuracy: \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (16)  
Figure 3: BERT\({}_{base}\)-CNN-BiLSTM architecture.  
* _Precision_ (P): is a metric that measures the exactness of the classifier, with a low value indicating a high amount of false positives. The precision is determined using Equation (17) and indicates the number of positive predictions divided by the total number of positive class values predicted. \[Precision=\frac{TP}{TP+FP}\] (17)
* _Recall_ (R): is calculated by dividing the total number of true positives by the total number of true positives and false negatives, as shown in Equation (18); it is thought to be a measure of a classifier's completeness (for example, a low recall value suggests a high number of false negatives). \[Recall=\frac{TP}{TP+FN}\] (18)
* \(F1\)_score_ (F1): Equation (19) is used to calculate the \(F1\)_score_ as the weighted harmonic mean of the classifier's precision and recall measures. \[F1=\frac{2*Precision*Recall}{Precision+Recall}=\frac{2*TP}{2*TP+FP+FN}\] (19) where \(TP\), \(TN\), \(FP\), and \(FN\) represent true positive, true negative, false positive, and false negative, respectively.
## 4 Findings and Discussion
### Explainable Frameworks in XAI
#### 4.3.2 What Are TRUST and LIME and What Is Their Applicability to IDSs for ICVs?  
TRUSTvs. LIME: TRUST is a framework for assessing AI models' transparency, robustness, and fairness [108]. It evaluates the comprehensibility and interpretability of intrusion detection models for ICV, considered black boxes because they are difficult to interpret [108]. Applying TRUST to the trained IDS model facilitates the transparency of the model's decision-making process and identifies how well the model can detect intrusion attempts. TRUST's feature importance and decision path analysis aid in understanding the most important features and how it makes its predictions. The framework includes several robustness assessment techniques for evaluating IDS in ICVs, such as robustness testing and adversarial example generation [93; 108]. It enables the interpretability of the model's generalization to new and unseen data and its resistance to adversarial examples [93; 108]. TRUST can also evaluate the model's fairness by assessing the demographic parity and equalized odds. It assists in clarifying the model's fair treatment of various features and identifies potential bias in the model [93; 108]. It is helpful for intrusion detection in ICVs as it provides more interpretable and transparent explanations of the decision-making process of the IDS, which can help to improve the security and safety of the vehicle and its occupants by ensuring the robustness and fairness of the model.  
Similarly, LIME is a technique for explaining the predictions of any machine learning model by approximating the model locally with an interpretable model. It is model-agnostic, following the ability to explain the output of any machine learning model, regardless of its architecture [107]. It is a common approach for interpretability because it provides human-understandable explanations for complex models [107]. LIME enables a more intuitive and interpretable comprehension of the complex decision-making process of IDS models, which is vital for building trust in the model and its predictions [107]. Its utilization enhances the understanding of the significant features relevant to intrusion detection predictions, which improves the security and safety of the ICV and its user.  
The benchmark XAI explainer model is known as the Local Interpretable Model-agnostic Explanations (LIME), introduced by [107]. This model, however, was outperformed by the Transparency Relying Upon Statistical Theory TRUST) explainer introduced by [108]. It achieved 25 times faster results and is a promising candidate for real-time and critical applications such as the ITS. In addition, the TRUST model took care of the computational complexity deficiency of SHAP.