esa-sceva/satcom-chunk-collection huggingface dataset:
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 2 Related Work
### Network Architecture for Key Points Detection  
Key points detection is mainly used in human pose estimation [24; 25], and the positions of key points can be located by estimating the heatmap. For key points heatmap estimation, the backbone first decreases the input image resolution to facilitate feature extraction and convolution calculation, and then generates a feature heatmap with the same resolution as the input. The main body of the network is possibly augmented with an intermediate supervision and fusion strategy.  
Representative network architectures include: (i) a U-shaped mirror symmetric framework. A stacked hourglass network [26] and its subsequent variants [25; 27] adopt high-to-low and low-to-high mirror symmetric structures, which transmit information from various scales to deeper depths in order to help the whole model to integrate local and global features and to perform well in detecting centers or edge corners of objects; (ii) anasymmetrical framework with heavy high-to-low and light low-to-high. In [24; 28], the high-to-low process is based on a large classification backbone network, such as ResNet or VGGNet, whereas the low-to-high process is simply composed of a few layers of bilinear up-sampling or transpose convolution; (iii) a high-to-low and low-to-high framework with an intermediate supervision or fusion strategy. [PERSON]. [29] proposed a feature aggregation and a coarse-to-fine supervision strategy for multi-stage networks, which improved the results of some existing models to some extent. In the cascaded pyramid network [24], the features generated in the high-to-low process are gradually combined into the low-to-high process through convolutions. [PERSON]. [28] proposed a deep network consisting of several parallel subnetworks, and these subnets conducted a repeated information exchange to eliminate the loss of spatial resolution in the sampling. Figure 1 depicts three representative network architectures mentioned above.  
In this paper, we use a set of discrete key points to describe lane lines abstractly, which is similar to the task of human pose estimation. Due to thin and narrow characteristics of lane lines, the backbones with series structures tend to miss subtle details related to lane lines in challenging scenes (such as occlusion, markings wear, dark-light, etc.). To cope with that, this work designed a new network architecture centering on the requirements of this detection task. Although part of the work was inspired by [11; 24; 25; 28; 29], there were significant differences overall.
## 3 Deepfake Technologies and the Emergence of Deepfake-Driven Social Engineering  
### The Definition of Deepfakes  
Deepfakes refer to artificially generated or modified digital media--videos, images, audio, or text--created through advanced machine learning (ML) techniques. In particular, _generative adversarial networks_ (GANs) are particularly notable for their ability to synthesize or manipulate content in a highly realistic manner [5; 6]. Initially, GANs were used for tasks such as image enhancement and style transfer [16]. However, malicious applications of GANs have significantly increased in recent years [7]. By leveraging large amounts of training data, such as publicly accessible videos or voice recordings, attackers can create deepfakes that convincingly replicate the appearance, voice, or even mannerisms of real individuals. In addition to GANs, other models like transformers and diffusion-based models have gained popularity for generating deepfakes. These models are readily available and easy to use in popular large language models (LLMs) like GPT-4 from OpenAI, Dall-E 2 or Gemini Flash 2.0 from Google [17].  
From a technical perspective, different categories of deepfake manipulations exist [10]:  
* **Face swaps and face reenactment**: A common application of deepfakes is to map a person's face onto another person's body or reenact someone's facial expressions in real time;
* **Lip-sync and voice cloning**: Attackers can synchronize a speaker's lip movements with _audio_ that is artificially generated or adapted so that it appears as though the speaker is delivering a different message.  
Together, these technologies lower the barrier of entry for highly convincing content creation, thereby opening the door to more sophisticated social engineering attacks [10].
# Research on Freight Transportation Carbon Emission Reduction Based on System Dynamics
## 3 System Flow Diagram and Structural Equation
### Model Parameter Estimation
#### 3.4.1 Main Parameters  
1. Carbon dioxide emissions Carbon dioxide emission coefficients of various energy sources are shown in Table 3.
2. Energy efficiency Energy efficiencies of different modes of transportation are shown in Table 4.
3. Freight volume Freight volumes were extracted from the National Bureau of Statistics of China [55], and are listed in Table 6. What needs to be explained about the freight volumes is that the data coming from freight surveys are often inconclusive. A study by Italian scholars showed the problems in describing the modal split in the freight models based on stated preferences (SPs) and revealed preferences (RPs) [56]. Indian scholars suggested that this can be improved by designing effective survey instruments, data collection strategies to improve response rates, etc. [57].
4. Proportion of diesel locomotives  
Proportions of diesel locomotives are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Railway**} & \multicolumn{2}{c}{**Road**} \\ \cline{2-5}  & **Diesel Fuel** & **Electricity** & **Petrol** & **Diesel Fuel** \\  & **(kg/10\({}^{4}\) t\(\cdot\)km)** & **(kWh/10\({}^{4}\) t\(\cdot\)km)** & **(L/10\({}^{2}\) t\(\cdot\)km)** & **(L/10\({}^{2}\) t\(\cdot\)km)** \\ \hline
2001 & 25.7 & 113.1 & 8 & 6 \\
2002 & 25.9 & 110.8 & 8 & 6 \\
2003 & 25.4 & 110 & 8 & 6 \\
2004 & 25 & 111.2 & 8 & 6 \\
2005 & 24.6 & 111.8 & 8 & 6.3 \\
2006 & 24.3 & 110 & 7.9 & 6.5 \\
2007 & 24.6 & 109.5 & 8 & 6.7 \\
2008 & 24.9 & 110.6 & 8 & 6.9 \\
2009 & 25.2 & 107.9 & 8 & 7.1 \\
2010 & 26.4 & 102.4 & 8 & 7.3 \\
2011 & 26.5 & 100.6 & 8 & 7.3 \\
2012 & 26.8 & 102.1 & 7.9 & 7.4 \\
2013 & 27.3 & 101.9 & 7.8 & 7.4 \\
2014 & 27.2 & 103.3 & 7.8 & 7.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy efficiency of different modes of transportation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Proportion** & **Year** & **Proportion** \\ \hline
2000 & 0.71552 & 2007 & 0.648663 \\
2001 & 0.708659 & 2008 & 0.636883 \\
2002 & 0.709282 & 2009 & 0.608359 \\
2003 & 0.697334 & 2010 & 0.547223 \\
2004 & 0.693079 & 2011 & 0.515314 \\
2005 & 0.684777 & 2012 & 0.4
# Macro-Modelling Approach for the In-Plane Cyclic Response of Cold-Formed Steel Partition Walls
## 3 Development of the Proposed Analytical Model
### Structural Characteristics of CFS Partition Walls
#### 3.2.3 CFS Studs  
Similar to the installation of GBs, there is gap between a CFS stud and a top track for easy erection. The deformation shape and damage of the stud-to-track connection under increasing upward displacements of a stud are presented in Figure 5. Typically, a stud is connected to top and bottom tracks with a single PAF, which develops negligible lateral resistance until the contact shown in the figure occurs. Once the gap, \(g_{st}\) reaches \(\delta_{h,gap,ST}\), the stud pushes against the track web, which develops the contact mechanism resulting in the local buckling of a CFS stud. Since the stud is enveloped within the sheathing GBs in the out-of-plane direction, its global buckling is restrained in the direction of their strong axis. Torsional buckling in the direction of stud's weak axis may occur when in-plane deformations are subjected to a CFS partition wall.  
Considering the boundary condition of the stud between screw connections, an unbraced length, \(l_{\textit{eff}}\) is conservatively assumed to be equal to two times of the screw spacing [29]. The buckling strength of the stud in the in-plane direction, \(f_{c}\) and its corresponding stiffness, \(K_{a}\) are, respectively, calculated from:  
\[f_{c}=\frac{\pi EI}{l_{eff}^{2}} \tag{14}\]  
Figure 4: Free-body diagram and monotonic behavior of gypsum boards.  
Figure 5: Determination of monotonic behavior of the stud.  
\[K_{a}=\frac{EA_{st}}{h} \tag{15}\]  
where \(E\), \(I\), \(A_{st}\), and \(h\) are, respectively, the elastic modulus, the moment of inertia, the area, and the height of a stud. From the force equilibrium of a free-body diagram in Figure 5, the stud located at the edge of a partition wall is critical for the buckling behavior. Lateral force, \(F_{ST}\) of the stud is derived from a bearing force before the buckling, according to the following:  
\[F_{ST}=\left(\frac{W}{H}\right)\!K_{a}(\delta_{b}-g_{st})=\frac{WK_{a}}{2} \!\left[\frac{\left(H/\delta_{h}\right)+\left(W/H\right)}{\sqrt{\left(H/\delta _{h}\right)^{2}+1}}-\frac{2g_{st}}{H}-1\right](F_{ST}\leq(W/H)f_{c}) \tag{16}\]  
After the buckling, the residual strength of the stud is assumed to be 30% of \(f_{c}\), as described in ASCE 41-17 [30].  
As shown in the idealized monotonic behavior of a CFS stud at the right figure in Figure 5, when lateral displacements \(\delta_{h}\) equals to \(\delta_{h,gap,ST}\), the bearing behavior of the stud is initiated. Applying the additional horizontal displacement, the stud develops elastic behavior with the stiffness of \(K_{a}\). After the maximum load equal to the buckling capacity of the stud is applied, the lateral force is rapidly degraded to the residual strength level.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 5 Conclusions  
The generation of angular trajectory for a MW-level MR-SPS using Bezier shaping approach is demonstrated in this paper. The precise solar and earth orientation for the MR-SPS is analyzed and the ideal angular trajectory without dynamic constraints is given which is helpful for the initial value selection of the following optimization. The attitude dynamics and kinematics are modeled for calculating the inequality constraints of the optimization. The angular trajectory of the MR-SPS is assumed to be described by Bezier curve functions. The angular trajectory design around equinox and solstice is performed. The numerical simulation verifies that the Bezier shaping approach can generate an angular trajectory that meet the dynamic constraints. The energy receiving efficiency can be near 100% and the demand for torque actuators is reduced.  
Conceptualization, S.X.; methodology, S.X. and M.H.; software, S.X. and M.H.; validation, S.X. and W.F.; formal analysis, S.X. and M.H.; investigation, S.X. and W.F.; resources, S.X.; data curation, T.L.; writing--original draft preparation, S.X.; writing--review and editing, Z.L. and T.L.; visualization Z.L. and S.X.; supervision, N.Q. and M.H.; project administration, N.Q.; funding acquisition, N.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Science Foundation of China, grant number 11702072; in part by the China Postdoctoral Science Foundation, grant number 2017M611372; in part by the Heilongjiang Postdoctoral Fund, grant number LBH-Z16082; and in part by the National Natural Science Foundation of China, grant number 61903245.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Concepts for Discriminating the Oil-Slick Category
#### 2.3.4 Concept 4: Feature Selection Methods  
During the initial exploratory analysis [29; 30], two feature selection methods were tested in the R-mode [64]. While dealing with large numbers of variables at the start (\(>\)500), these methods selected similar attributes:  
* **Correlation-Based Feature Selection (CFS):** Automatic-configured routine specifying a \"Merit\" to evaluate inter-statistical correlations among different groups of variables using the information of the categories being discriminated [65; 66].
* **Unweighted Pair Group Method with Arithmetic Mean (UPGMA):** Semi-automated method exploring rooted-tree diagrams (i.e., dendrograms). Its attribute selection process forms groups based on a similarity measure (e.g., Pearson's \(r\) correlation coefficient) in which each element of the matrix undergoes a simple linear two-by-two correlation. This method is adjustable to the user's needs as groups of correlated variables are relative to a user-defined cut-off to select them, that is, a phenon line (e.g., \(r\) = 0.5 and 0.9), which is a horizontal line draw across the dendrograms [67; 68; 69].  
Once variables were selected with these methods, they were separately put onto an orthogonal transformation [64; 69]:  
* **Principal Component Analysis (PCA):** Linear transformation approach used to select the most relevant principal component (PC) axes. The PCs' scores were the ones used as input to the LDA. Conversely, during the refined study [31; 32], four feature selection methods were tested:  
1. Do nothing--i.e., all variables were directly inserted onto the LDA;
2. Using all variables straight to PCAs without passing thought the UPGMA selection;
3. Same approach as used in the initial exploratory analysis [29; 30] but using only the UPGMA analyses as it offers more control in the attribute selection process than the CFS. This time, the application of a stricter similarity phenon threshold (i.e., \(r=0.3\), instead of 0.5 or 0.9) guarantees variables are deemed to have no significant statistical correlation from one another [70]. This leads to using the values of the attributes directly to the LDA; instead of the PCs' scores. This alternative circumvents the application of PCAs and simplifies the seep-spill discrimination process; and
4. The sole and strict UPGMA cut-off but this time with PCA.  
Of these four methods, the third has been reckoned to be the simplest, direct, and most efficient [31; 32].
## 3 Results and Discussion
### Infrared Spectral Split Peak Fitting of Extracts
#### 3.2.4 Results of Peak Splitting Fits for Extracts in the 3000-3600 cm\({}^{-1}\) Interval  
Figure 5 shows the fitted IR peak splitting curves for the three extracts in the 3000-3600 cm\({}^{-1}\) interval, and the corresponding fitted data results are shown in Table 5. There are six main forms of hydroxyl functional groups present in coal, namely hydroxyl-N hydrogen bonding near 3150 cm\({}^{-1}\), ring-conjugated hydroxyl hydrogen bonding near 3200 cm\({}^{-1}\), hydroxyl-ether oxygen hydrogen bonding near 3300 cm\({}^{-1}\), hydroxyl self-conjugated hydrogen bonding near 3410 cm\({}^{-1}\), hydroxyl-\(\pi\) hydrogen bonding near 3516 cm\({}^{-1}\), and the free hydroxyl group near 3611 cm\({}^{-1}\)[18,19]. The fitted parameters of the spectra show that all three extracts are dominated by hydroxyl self-conjugated hydrogen bonds and contain a large number of ring-conjugated hydroxyl hydrogen bonds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Peak \#**} & \multirow{2}{*}{**Position \(\sigma\) (cm\({}^{-1}\))**} & \multirow{2}{*}{**Assignment**} & \multicolumn{3}{c}{**Area Percentage (\%)**} \\ \cline{4-6}  & & & **ACE** & **CS\({}_{2}\)** & **ACE/CS\({}_{2}\)** \\ \hline
1 & 2956 & Aliphatic -CH\({}_{3}\) & 12.95 & 8.36 & 7.70 \\
2 & 2919 & Asymmetric aliphatic -CH\({}_{2}\) & 38.90 & 22.16 & 6.87 \\
3 & 2881 & Aliphatic -CH & 5.71 & 63.51 & 53.16 \\
4 & 2850 & Symmetric aliphatic -CH\({}_{2}\) & 42.44 & 5.97 & 32.27 \\ \hline \end{tabular}
\end{table}
Table 4: Infrared spectra of three solvent extracts 2800–3000 cm\({}^{-1}\) split peak fitting ratio of each absorption peak area.  
Figure 4: Infrared spectra of extracts in the interval 2800–3000 cm\({}^{-1}\). (**(a)** is the carbon disulphide fit, (**b**) is the acetone fit and (**c**) is the mixed solvent fit. The red line is the fitted curve and the blue line is the sub peak).
# An Explainable Scheme for Memorization of Noisy Instances by Downstream Evaluation
## 1 Introduction  
With the rapid expansion of artificial intelligence technologies across various application domains, such as medical diagnostics [1, 2], financial investment [3], and autonomous driving [4], the explainability of deep learning models has become increasingly critical. Deep learning [5] is a subset of machine learning that focuses on utilizing deep neural models to learn from data and perform tasks such as classification, regression, and representation learning. Explainability [6, 7] refers to the mechanism that allows humans to understand and explain how a deep model makes decisions, including the details and processes underlying these decisions. Given that some applications may involve legal and ethical considerations, such as medical decision-making and autonomous driving, the attribution of liability can be challenging when outcomes deviate from expectations. Therefore, decision transparency in models is indispensable. Understanding how a model makes decisions ensures its controllability, allowing researchers to make necessary adjustments when errors occur.  
Moreover, explainability is essential for enhancing model security and robustness. Robustness refers to a model ability to remain stable and perform well under uncertainties or abnormal conditions, such as when testing on data that differs from the training data or data that is affected by noise. Currently, most models are trained and tested on clean and well-organized datasets, but in real-world applications, models frequently encounter disturbances like blurring, occlusion, or varieties of noise. These uncertainties couldlead to incorrect inferences, resulting in significant performance degradation and even raising security concerns. For instance, in [PERSON]'s [8] study, he explains how an experiment of [PERSON] [9] demonstrated that a panda image, when slightly perturbed with noise, was misclassified as a gibbon, despite both images appearing nearly identical to the human eyes. This phenomenon indicates that models could be vulnerable to any white-box or black-box attacks. Therefore, assessing a model's performance under different levels and types of perturbations is crucial for ensuring its scalability and viability.  
For the aforementioned reasons, this study introduces a memorization method that provides model explainability when learning from noisy data. This approach allows for a detailed examination of what a model learns or memorizes from training data during a pre-trained task by evaluating the memorization matrices in a downstream model. The subsequent sections of the paper are organized as follows: Section 2 briefs the previous research and related works related to this study. Section 3 details the proposed method of evaluating memorization for noisy instances. Experimental results and ablation tests are presented and discussed in Section 4, and the conclusion follows in Section 5.
## 5 Case Study (Results (Step 2 to 5 in Figure 3))
### Gap of MWL in Each Factor (Step 4 in Figure 3)
#### 5.2.2 Manning Option  
Figure 7 shows the scenarios considered to have a significant difference between manning options in the same level of autonomy (\(p\) < 0.05 of \(t\)-test) in Table 8. Cells (scenarios) at the start point and endpoint of an arrow line in the figure have a significant difference. Table 11 shows the difference of MWL in the scenarios extracted in Figure 7. Table 12 breaks down the difference into six sub-elements and describes the sub-elements considered to have a significant difference between them (\(p\) < 0.05 of \(t\)-test). These suggest the following findings:  
* Formation changes between the onboard bridge and RCC (MO-1 and MO-2) and the decrease in lookouts (MO-3) generally do not have apparent effects on MWL of the responsible operators (ROs), regardless of the level of autonomy when they are onboard a ship;
* MWL of the ROs significantly worsens when they are at RCC (MO-4, MO-5, and MO-6) compared to when they are on the bridge of a ship (MO-1, MO-2, and MO-3). The effects especially emerge when ROs operate alone at RCC (MO-6: (1), (2), (9)-(11), (21)-(23) in Figure 7 and Table 11). Negative effects on MWL are much smaller when an autonomous navigation system has been installed (LoA-3) compared to no autonomy (LoA-1) and navigation support (LoA-2). In other words, an autonomous navigation system works well to alleviate the mental stress of ROs at RCC (Figure 7);
* MWL of ROs at RCC decrease when a lookout onboard a ship supports them (MO-5), compared to the other cases (MO-4 and MO-6) when they are at RCC ((3), (6), (13), (16) and (24) in Figure 7 and Table 11);
* The set of mental demands and frustration are the overwhelming sub-elements that show the clear effects according to the change of navigation manning ((1), (2), (4), (7)-(11), (14), (15), (17)-(26) of Table 12); \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**No** & \(p\) **< 0.05** & **No** & \(p\) **< 0.05** & **No** & \(p\) **< 0.05** & **No** & \(p\) **< 0.05** & **No** & \(p\) **< 0.05** & **No** & \(p\) **< 0.05** \\ \hline (1) & MD & (2) & - & (3) & MD/TD & (4) & EF & (5) & MD & (6) & MD/FR & (7) & EF \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Sub-elements of MWL that have a significant difference on each number in Figure 6 (\(p\) < 0.05 of \(t\)-test). Figure 6: Scenarios that have a significant difference between LoA in the same MO according to \(t\)-test (\(p\) < 0.05) (described by arrow lines). * Time pressure, effort, and own performance emerged in two cases as significant sub-elements that increase MWL. Both cases are related to the situation where the lookout supports RO navigating at RCC ((6) and (13) in Table 12).
# Mini-Review: Potential of Diatom-Derived Silica for Biomedical Applications
## 5 Silica for Biomedical Applications: Advantages
### Surface Functionalization for Drug Loading and for Biosensing Clips for Biomedical Applications
Diatoms were magnetized with iron oxide nanoparticles, and then conjugated with the monoclonal antibody Trastuzumab; this system was effective in selectively targetingand separating breast cancer cells, SKBR3 cells (HER2 positive cells), from HER2-negative cells under a magnetic field. The optical properties of these diatoms allowed to detect this specific binding ability by fluorescence microscopy, thanks to the optical properties of the silica. Similar studies on antibody-functionalized nanoparticles deriving from living cultures were already performed more than ten years ago, and they exploited the potential of two modified centric diatoms as photoluminescence biosensors. Functionalization of _Coscinodiscus wailesii_ frustules was one of the pioneer studies highlighting antigen recognition from antibodies that had been covalently bound to frustules [88]. [PERSON] and co-workers [89] succeeded in transforming _Cyclotella_ sp. frustules with the model rabbit IgG antibody, showing a correlation between the photoluminescence associated with the frustule/antibody complex and the antigen (goat anti-rabbit IgG) concentration. The main types of diatom silica functionalization are summarised in Table 2.