esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Methods
#### 2.2.4 Structural Characterization  
Attenuated total reflectance-Fourier transform infrared spectroscopy (FTIR-ATR) was applied to study the chemical interactions between TPS, GR and gum rosin derivatives (CM, LF, RD and UG). The analysis was performed in a Perkin Elmer Spectrum BX (FTIR system) coupled with an ATR Pike MIRacle \({}^{\mathrm{TM}}\) (Beaconsfield, United Kingdom). All the formulations as well as neat TPS and the raw resins were assessed within the range of 4000-700 cm\({}^{-1}\) with a resolution of 16 cm\({}^{-1}\) and 20 scans.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Formulations** & **Type of Resin in the Blend** & **Resin Commercial Name** & **TPS (wt. \%)** & **Resin (wt. \%)** \\ \hline TPS & - & - & 100 & 0 \\ TPS-GR & Gum rosin or unmodified colophony & Gum rosin & 90 & 10 \\ TPS-CM & Maleic anhydride modified gum rosin & Colmodif R-330 & 90 & 10 \\ TPS-LF & Penterythitol ester of gum rosin & Lurger 125 & 90 & 10 \\ TPS-RD & Disproportionated gum rosin & Residis 455 & 90 & 10 \\ TPS-UG & Glycerol ester of gum rosin & Unik Gum S88 & 90 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: TPS–resin blend studied formulations and their composition.  
Scanning electron microscopy (SEM) of the cryo-fractured surface of each formulation was carried out on a Phenon SEM equipment of FEI (Eindhoven, The Netherlands), with a voltage of 5 kV. Prior to SEM observation, the samples were coated with a gold-palladium alloy layer to make the sample surface conductive in a Sputter Mod Coater Emitech SC7620, Quorum Technologies (East Sussex, UK).
## 4 Experiments
### Ablation Study: Mislabeled Data Correction  
Our algorithm employs a similarity-based approach to determine species categories. Consequently, we propose using similarity measures to identify and eliminate mislabeled data. To validate this approach, we conducted the following experiment: We selected 271 species categories, each containing over 1000 images. The dataset for each category was divided into training and validation sets at an 8:2 ratio. From each training set, we randomly selected 10 images and inserted them into other categories while keeping records of these modifications. During model training, we collected five types of metrics every 300 iterations: training accuracy, validation accuracy, prediction accuracy for misclassified data, classification accuracy with a threshold of 0.8, and the proportion of misclassified data with similarity scores exceeding 0.8. The training process terminated after 69 epochs, and the collected data were visualized in a line chart (Figure 7).  
Figure 7 reveals several key findings: (1) Both training and validation accuracy improve with increasing training iterations; (2) The prediction accuracy for misclassified data and the proportion of misclassified data with similarity scores >0.8 initially increase before decreasing. We attribute this phenomenon to observed instances where the training accuracy reached 100% with a batch size of 420, suggesting potential overfitting within individual batches. In such cases, the algorithm appears to forcibly learn mislabeled data as correct, which subsequently impacts validation performance. As the frequency of these 100% accuracy batches increases, the validation accuracy stabilizes; (3) The proportion of misclassified data with similarity >0.8 consistently remains lower than the  
Figure 7: Model training process shows: (**a**) the training accuracy, validation accuracy; (**b**) Mislabeled Data Top1 Accuracy, Mislabeled Data Top1-0.8 Accuracy, and Top1-0.8 Proportion in Mislabeled Data at different global steps.  
prediction accuracy for misclassified data because the validation process does not apply the 0.8 threshold--some predictions with similarity below 0.8 are still counted as correct; (4) When applying the 0.8 threshold, the prediction accuracy for misclassified data remains relatively high throughout training, peaking during initial stages. This suggests the algorithm's initial lower degree of fitting to mislabeled data.  
In summary, after randomly selecting 2710 images and reassigning them to incorrect categories in the training set, our algorithm demonstrates the capability to correctly reclassify a significant proportion of these mislabeled samples after training completion.
## 3 Engineering Overview and Rock Mass Temperature Variation Characteristics with Depth  
### Engineering Overview  
The Wushan Open\(-\)Pit Mine, operated by China National Gold Group Inner Mongolia Mining Co., Ltd. in Hulunbuir City, Inner Mongolia Autonomous Region, China, holds the distinction of being the first modernized large\(-\)scale non\(-\)ferrous deep open\(-\)pit mine in China's alpine/permafrost regions. The mining area covers 9.84 square kilometers and is characterized by low resource grades (average copper grade of 0.29%, molybdenum grade of 0.039%), high mining intensity (daily ore processing capacity of 75,000 tons, annual ore volume of 24.75 million tons), substantial mining depth (current slope height exceeding 200 m, designed final slope elevation difference reaching approximately 510 m), steep slope angles (overall slope angle 43-45 deg, bench slope angle 65 deg), high clay mineral content in surrounding rocks (locally up to 60%), and pronounced freeze-thaw effects (annual temperature variation exceeding 80 degC).  
Prolonged intensive mining operations combined with structural discontinuities, jointing, water infiltration, and cyclic freeze-thaw actions have led to progressive deterioration of rock mass stability, with consequent geohazard escalation. Multiple localized slope failures have been documented across western and southern mining benches (Figure 2). Notably, the western and southern slopes accommodate critical conveyor and haul truck infrastructure, respectively. Slope instability at these strategic locations poses direct threats to material transport systems, potentially disrupting mining production continuity. Consequently, rigorous stability assessment of open\(-\)pit slopes under freeze-thaw cycling constitutes an essential prerequisite for operational safety assurance.  
Figure 2: Landslide in the Wushan Open\(-\)Pit Mine slope: (**a**) 645 m platform schematic; (**b**) western slope wedge; (**c**) southern slope landslide.
# Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
## 7 ET Support for Vision Screening and Training (RQ3)
### Usage and Types of ET Technologies  
The use of eye-tracking technologies has significantly increased in cognitive neuroscience, the assessment of functional vision, applied engineering, education, and customer visual attention research [122, 123, 124]. In recent years, research studies have shown a potential use of eye-trackers to quantify visual information processing, visual function assessment (VFA), and diagnose nystagmus and strabismus vision problems [33, 125].  
The eye-tracking method aims to record the gaze direction at a specific time and understand visual attention. To achieve this aim, four types of eye-tracking techniques have developed over time. The classifications of four general ET techniques are: scleral search coil, electro-oculography (EOG), infrared oculography (IOG), and video oculography (VOG) [126]. Most modern eye-trackers use the video oculography (VOG) technique to record eye movements by applying the pupil center corneal reflection (PCCR) method [127]. The advanced image processing algorithms are also applied to the subject's eye position, relevant to stimuli [128]. The commercial eye-trackers are mobile, remote, and embedded in a head-mounted display (HMD), or add-ons for HMD. Remote eye-trackers require the participants to maintain head position throughout the entire process, while mobile or HMD eye-trackers do not have such conditions.  
Generally, the refresh rate of eye-trackers varies from 30 Hz to 1200 Hz for recording eye movements, such as fixations and saccades. Eye trackers with low-frequency sampling have high latency in evaluating the point of gaze. The eye-trackers with a high-frequency sampling rate and low latency come with high-resolution cameras and they cost.  
Several research studies showed the use of ET for vision screening, especially the visual functional assessment (VFA). [PERSON] et al. [33] observed and quantified ET data to examine the oculomotor control and visual function assessment of visually impaired children. [PERSON] et al. [35] demonstrated the reliability of ET by cluster analysis to determine the normative values of oculomotor metrics, such as saccades and fixations. Their results showed 85% of eye movement metrics were highly reliable and acceptable. [PERSON] et al. [23] developed C&Look software that uses ET for complementing VTs and  
Figure 5: Current recommendations from the literature to fill the missing gaps in school vision screening programs. The colors in the figure depict recommendations for various methods and from various stakeholder groups.  
VT students to diagnose vision problems associated with oculomotor dysfunction in school children [111].  
Furthermore, some research studies have shown the advantages of eye-tracking technologies for diagnosing strabismus and pathological nystagmus [129; 130]. Table 4 shows the directions of ET used by researchers in the literature for vision screening.
## 6 Simulations and Discussion
### Comparison of Cluster-Based Self-Organization Schemes  
The final set of experiments compares the robustness of the SAMCA algorithm with other cluster-based self-organization schemes. Simulation study is conducted to show that the performance of the ESAC [43] approach is superior to those in [3; 41; 33; 42]. In ESAC, both diameter and size of  
Figure 18: The comparison of the number of clusterheads (left) and distributed gateways (right) when applying the SAMCA approach and the simplified models.  
the cluster are restricted in the purpose to generate balanced clusters. Two thresholds \(\mathrm{Thresh}_{Lower}\) and \(\mathrm{Thresh}_{Upper}\) are applied to control the size of clusters. Moreover, the distance between cluster members and their corresponding cluster-head is at most two hops. Based on the above characteristics of cluster formation, the ESAC heuristic may provide a good way to benchmark the performance of SAMCA scheme. To compare SAMCA with ESAC, the cluster stability is investigated given the network density and cluster formation parameters. For the ESAC heuristic, experiments are performed with two distinct values for threshold \(\mathrm{Thresh}_{Upper}=10\) (\(\mathrm{ESAC}\_10\)) and \(\mathrm{Thresh}_{Upper}=15\) (\(\mathrm{ESAC}\_15\)) and a fixed value for threshold \(\mathrm{Thresh}_{Lower}=5\). For the SAMCA scheme, the values of cluster formation parameters are _LB_ = 4, \(z\) = 8, and _UB_ = 12 (SAMCA[4, 8, 12]).  
Figure 19 shows and compares the average number of clusterhead changes when applying the ESAC and the proposed SAMCA. Observe that, unlike the LCC algorithm [42], both the ESAC and SAMCA schemes ensure cluster stability when nodes' speed increases. However, the frequency of clusterhead changes in ESAC is higher than that of the SAMCA with varying the node speed. Thus, the SAMCA algorithm may be more suitable for network topology control since the frequent clusterhead changes may degrade the system performance. The above set of experiments implies that the SAMCA is competitive with the ESAC heuristic in terms of cluster formation and topology management. Furthermore, [43] shows that more study may be needed for determining appropriate times to trigger the operation of cluster maintenance in the ESAC heuristic. In comparison, the SAMCA may be reliably applied to govern the topology change.
## 5 Privacy Risk Assessment: Advancements within the PRUDEnce Framework
### Optimizations
#### 5.2.3 Reuse Risk Value  
Various knowledge levels are typically employed when assessing the _empirical privacy risk_ across a dataset's user population. To enhance computational efficiency, we adopt a strategy where risk information from the previous evaluation (at knowledge level \(x\)) is reused for individuals whose data received a risk of one. This reuse principle extends to the next knowledge level (\(x+1\)), implying that individuals maintaining a risk of one for knowledge level \(x\) will continue to exhibit the same maximum risk value for the subsequent knowledge level, as can be seen in Algorithm 4.  
```
1:for each user trajectory \(traj\)do
2:if\(traj\) was evaluated for \(k-1\)then
3:if\(traj\) risk is 1 then
4:\(risk=1\)
5:else
6: Compute all possible background knowledge instances with size \(k\)
7:for each background knowledge instance \(inst\)do
8: Compute risk for \(inst\) using matching function and update maximum risk
9:endfor
10:endif
11:endif
12:endfor
```  
**Algorithm 4** Risk Computation with Reuse Approach  
This approach proves advantageous, especially when dealing with considerable datasets and multiple knowledge levels. By capitalizing on the consistency of risk values across consecutive knowledge levels, redundant computations are avoided, facilitating the risk assessment process.  
**Claim:** for any set \(D\), all combinations of \(x\) elements of \(S\) are also present in the combinations of \(x+1\) elements of \(S\).  
**Proof.** we will prove this claim by mathematical induction.  
**Base Case (\(k=1\)):** for \(k=1\), the combinations of one element of \(S\) are just the elements of \(S\). The combinations of two elements of \(S\) (\(C_{2}\)) will be a subset of the combinations of three elements of \(S\) (\(C_{3}\)).  
**Inductive Step:** We assume that all combinations of two elements of \(S\) are in \(C_{3}\). Now, we consider the combinations of two elements of \(S\) (\(C_{2}\)). To form \(C_{3}\), we can choose any element \(x\) of \(S\) and combine it with each combination of two elements of \(S\) (\(C_{2}\)). So, for each combination \(c\in C_{2}\), we can form a combination \(cx\) (where \(x\) is an element of \(S\)). Therefore, all combinations of two elements of \(S\) extended by one more element \(x\) are in \(C_{3}\). This implies that all combinations of two elements of \(S\) are in \(C_{3}\).  
By mathematical induction, we have shown that, for any finite set \(S\), all combinations of \(x\) elements of \(S\) are also in the combinations of \(x+1\) elements of \(S\).  
The next section will present the experimental results of implementing the proposed strategies.
# Polyacrylonitrile Nanofibers Containing Viroblock as Promising Material for Protective Clothing
## 3 Results and Discussion
### Antibacterial Activity of VB-Loaded PAN Nanofibers  
The antibacterial activity of PAN nanofibers loaded with four different dosages of VB was analyzed for antibacterial activity against Gram-positive and Gram-negative bacterial strains by using the disc diffusion method. Figure 7 shows the antibacterial images of VB-loaded PAN nanofibers. The activity against _S. aureus_ was more pronounced in comparison to _E. coli_. The possible reason could be that the cell membrane of _E. coli_ was relatively thicker than that of _S. aureus_.  
Figure 5: TGA spectra of PAN and VB/PAN nanofibers.  
Figure 6: In vitro assessment of PAN and VB/PAN nanofibers.  
The zone of inhibition was calculated using Image J software by measuring the clear area around the nanofiber specimens. Figure 8 shows the histogram of the results. The values of the inhibition zone against _S. aureus_ bacteria were 15.3, 15.7, 16.0, and 16.8 mm corresponding to 0.5%, 1%, 1.5%, and 2% VB-loaded PAN nanofibers, respectively. Similarly, in the case of _E. coli_ bacteria, the values of the halo zone were 13.5, 14.85, 15.15, and 15.65 mm. VB mechanisms of _E. coli_ and _S. aureus_ termination were bacterial membrane perturbation and damage inflicted by vesicle and the ROS generated by nano silver intracellularly disturbing the functionality of DNA, enzymes, and protein. After the disruption of the bacterial membranes, silver penetrated the bacterial cells, interacting with sulfur-containing proteins and thiol groups of enzymes, causing impaired functionality and DNA damage [28].  
Figure 8: Competitive analysis of the zone of inhibition against _S. aureus_ and _E. coli_ bacteria.  
Figure 7: Antibacterial activity of VB-loaded PAN electrospun nanofibers against _E. coli_ and _S. aureus_ bacteria.
# A Scalable Spatial-Temporal Correlated Non-Stationary Channel Fading Generation Method
## 2 Correlated Channel Fading Model
If the scattering or reflection comes from the same scatterer or reflector, there will be a cross-correlation between the fading of each sub-channel in the communication system, its SCF can be expressed as  
\[R_{k,l}^{p^{i}}(\tau)=\frac{\mathrm{E}\big{[}p_{k}^{i}(t)p_{l}^{i}(t+\tau)\big{]} -\mathrm{E}\big{[}p_{k}^{i}(t)\big{]}\mathrm{E}\big{[}p_{l}^{i}(t+\tau)\big{]}} {\sqrt{\mathrm{var}\big{[}p_{k}^{i}(t)\big{]}}\cdot\sqrt{\mathrm{var}\big{[}p_ {l}^{i}(t+\tau)\big{]}}} \tag{8}\]  
where \(\forall k\
eq l\) and \(k,l\in\{1,2,\cdots,N\}\), \(R_{k,l}^{p^{i}}\) represents the cross-correlation coefficient between the fading of the k-th channel and the i-th channel.
# Image Semantic Segmentation Fusion of Edge Detection and AFF Attention Mechanism
## 4 Experiment and Results
### Dataset  
The Cityscapes dataset is used in autonomous driving. It has street view images of more than 50 different cities; there are 8 large categories and 30 small categories of images in the dataset. The user can optimize the dataset according to the needs of the design, and 19 subcategories are used in this paper. The dataset contains the original image and the corresponding classification label image. These label images can be used in combination with the original image to better complete the tasks of image instance segmentation, semantic segmentation, and object detection. In this experiment, image enhancement is used to improve the generalization computing  
As shown in Tables 5 and 6, the semantic segmentation of AFF is added to the feature fusion part of the improved HED network and the improved PSP-Net is better than others. The main reasons are as follows: The PPM in PSP-Net architecture can extract features very well, and AFF can accelerate the speed of feature learning and the depth of feature propagation. When AFF is introduced into the encoder of MobileNetV3, the AFF attention mechanism will make the structure of the model complicated. Unnecessary hyperparameters will be generated, which makes model training slow and wastes resources. When AFF is introduced into improved HED network, the feature fusion of five bypass structures with different output scales is accelerated, and channels are unified.  
In Figure 18, Experiment I and Experiment IV were analyzed by image visualization on the Cityscapes dataset. Experiment IV is the proposed model in this paper.  
In Figure 19, Experiment I and Experiment IV were analyzed by image visualization on the SIFT Flow dataset.  
In Figures 18 and 19, Experiment IV, the segmentation edges of \"car\", \"vegetation\", \"road\", \"light pole\" and \"land\", \"ocean\", \"cloud\" are continuous, and the small objects  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **I** & **II** & **III** & **IV** \\ \hline MIOU & 82.71 & 79.94 & 80.63 & 83.68 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Feature fusion MIOU comparison of different semantic segmentation networks on SIFT Flow dataset.  
Figure 19: Images of Experiment I and Experiment IV on the SIFT Flow dataset.  
\"light pole\" and \"cloud\" are finely segmented. In Experiment I, the edge is well segmented, but there are still some problems in the segmentation of some small objects.  
From the experimental results shown in Tables 5 and 6, it can be seen that in Experiment IV, adding the AFF attention mechanism to the feature fusion part of the improved HED network and the improved PSP-Net has better effect than the other three positions.  
So, the proposed model in this paper is shown in Figure 15 with the AFF joining position IV, the loss function is Equation (8) in Section 3.3.
## 3 Discussion  
Due to its high growth rate, excellent product yield and mature gene manipulation, _Escherichia coli_ was chosen as the host strain for functional verification of novel genes and efficient production of \(\beta\)-carotene in this study. It was previously reported that multiphasic transformation inhibited the growth and biomass of carotenoid producing stains due to the expression burden [49]. In this work, five functional genes were integrated into one expression vector (Figure 4), preventing potential growth inhibition (Figure 5a,c). This indicated that the strategy balanced well between biomass and high product yield. However, some challenges still needed to be considered for heterologous expression in _E. coli_, especially the post-translational modifications and availability of membrane binding sites. As reported, the transmembrane trifunctional synthase (CrtlBY) from _Schizochytrium  
Figure 6: (**a**) Colony state of constructed strains with different intensity of pigmentation. (**b**) \(\beta\)-carotene yield measured by HPLC analysis.  
lacked the activity to produce \(\beta\)-carotene in _E. coli_[49]. In this work, no transmembrane domain was predicted in AllDI and ALGORPS (Figure 1), which might help them to function efficiently in _E. coli_. Furthermore, the application of these two novel functional genes could be extended to other prokaryotes, such as _Synechocystis_, which is regarded as a potential cell factory for carotenoid synthesis [53; 21; 54].  
Recent studies provided guidance for further modifications of AlGGPPS and AIIDI. As reported, the rationally designed GGPPS with multisite mutations from _Nicotiana tabacum_ exhibited significantly higher efficiency for GGPP synthesis than the wild-type gene, increasing carotenoid levels greatly [29]. A directed evolution method was applied to improve the enzyme activity, half-life and substrate affinity of IDI from _Saccharomyces cerevisiae_[56]. Additionally, multienzyme assembly using docking domains to increase cascade catalytic efficiency was also a potential strategy for enhancing carotenoid production [23]. As previously reported, the 3D model of thraustochytrid squalene synthase (SQS) provided important insights into possible binding and active sites [57]. Thus, results of protein structure and substrate molecular docking in this work (Figures 2 and 3) facilitated further engineering modifications of AlGGPPS and AIIDI for higher catalytic efficiency and activity. Notably, GGPP also functioned as the common precursor of various medicinal terpenoids, including sclareol [58], taxadiene [59; 60; 61; 62], miltiradiene [63] and rographolide [27]. Thus, the excavated AlGGPPS and AIIDI with superior catalytic properties could also be applied for the biosynthesis of valuable GGPP-derived isoprenoid drugs.