esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Traffic Multi-Maps Generation Based on Disjoint Path Flows
### Semi-Disjoint K-Shortest Paths
In general, it is solved by a get_DjKSP procedure (16) that receives as inputs the traffic network \(\Theta\), the commodity \(d\) (origin-destination pair), the routing algorithm to compute shortest paths \(\delta\), the number of paths to find \(K\) and the minimal disjoint level threshold between paths \(\phi\). We use the Dijkstra algorithm for convenience [66]. However, other strategies may be used for the path flow calculus such as A*, or hyper-paths [67; 68; 69; 62]. \[get\_DjKSP:\Theta,d,\delta,K,\phi,DjC_{n},DjERC_{n})\rightarrow\{r_{i}^{d}\} \tag{16}\] \[\{r_{i}^{d}\}\subset\{p_{i}^{d}\}\]  
Performance-oriented heuristic algorithms provide good enough solutions, such as the ESX-C proposed by [PERSON] [55]. It uses a heap of shortest paths that is iteratively incremented by recalculating a new shortest path (SP) while removing selected edges from the paths in the heap. A new SP is added to the heap when it fits the disjoint level criterion chosen. The algorithm loops until the Dj-kSP is set or no more SP can be found, and it returns the \(\{r_{i}^{d}\}\subset\{p_{i}^{d}\}\) Dj-kSP. Sometimes it is not possible to find precisely \(K\) disjoint paths that fulfill the disjoint level requirement for the \(x^{d}\) traffic flow, so only the \(K^{*}<K\) found paths can be used. We use the ESX-C algorithm for our purposes implemented by the \(get\_DjKSP_{ESX-C}\) procedure. The count of all the returned Dj-kSP path flows is referred to as \(numPF=count(\cup\{r_{i}^{d}\})\), and it is widely used in the optimization process. Two main factors determine the selected Dj-kSP set: (a) the similarity criterion \(DjC_{n}\) used, and (b) the edge removal criteria at each iteration. Edge removal criterion \(DjERC_{n}\) may be applied to the edge removal function to solve different traffic situations:  
* Smallest/largest free-flow edge weight \(c_{e}^{FF}\), \(DjERC_{minW/maxW}\). If the MaxW is selected, the algorithm quickly selects highly disjoint paths though similar weighted paths are not considered. On the other hand, MinW enables a detailed full-scan approach. Minimum/maximum stretch, \(DjERC_{minS/maxS}\), where stretch relates to the relative weight link contribution to the shortest path. * Least/most local shortest paths, \(DjERC_{minP/maxP}\) considering the amount of shortest paths that include an edge. We consider the \(\{r_{i}^{d}\}\) Di-kSP as the alternative paths that any vehicle in the flow should consider for its trip in the traffic assignment problem. In this case, the \(\{x_{i}^{d}\}\) path flows will be directly affected by these \(\{r_{i}^{d}\}\) routes. It is important to note that our model does not impose that vehicles must strictly follow these routes. The \(\{r_{i}^{d}\}\) routes are used by the TWM generation algorithm to create the TWM maps, which will be selectively distributed to the vehicles.
# Risk Perceptions on Social Media Use in Norway
## 5 Analysis and Results
### Risk Perceptions
#### 5.2.5 Susceptibility to Phishing  
The questionnaire had a section that attempted to measure susceptibility to phishing. The narrative that was presented was a typical malicious Facebook messenger message, and we asked how they would perceive it coming from typical social circles. The message that was shown to the respondents can be seen in Figure 9. Table 21 shows how people reported to react to the phishing message when asked if they would click it. The results show that this type of phishing can be expected to get between 8% and 15% hit rate of people clicking these kinds of links. With 6.1% of people saying they might click the link if it is sent from close family, it is 4.1% from a family member, 4.1% from a friend and 1.3% from acquaintances. Furthermore, if we examine the _maybe_ answers, we also see that these are lower for the acquaintances than for the others. The numbers might be lower than what is expected with this being self-reported, but that would probably skew the numbers towards the lower end of the scale, and the success rate might be higher.  
We found no differences between the sexes or age groups for these variables, but for the group that had suffered ID theft reported to be more trusting of messages received from family  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Answer** & **Count** & **Percentage** \\ \hline \multirow{3}{*}{Acquaintance} & Yes & 4 & 1.30\% \\ \cline{2-4}  & No & 289 & 92.00\% \\ \cline{2-4}  & Maybe & 21 & 6.70\% \\ \hline \multirow{3}{*}{Friend} & Yes & 13 & 4.10\% \\ \cline{2-4}  & No & 269 & 85.70\% \\ \cline{2-4}  & Maybe & 32 & 10.20\% \\ \hline \multirow{3}{*}{Family} & Yes & 13 & 4.10\% \\ \cline{2-4}  & No & 275 & 87.60\% \\ \cline{2-4}  & Maybe & 26 & 8.30\% \\ \hline \multirow{3}{*}{Close family} & Yes & 19 & 6.10\% \\ \cline{2-4}  & No & 264 & 84.10\% \\ \cline{2-4}  & Maybe & 31 & 9.90\% \\ \hline \hline \end{tabular}
\end{table}
Table 21: Who the respondents thought they might get tricked into clicking a link if they received it from. N = 314.  
Figure 9: Example of Norwegian phishing message that has been circulating from hacked Facebook accounts and sent to people on their friends list.  
and close family. Even though the ID theft group represents only 15% (48 of the 314 total) of the sample, they are over-represented in both the _yes_ and _maybe_ categories for both the Family and the Close family variables. Only 5 out of 13 of the _yes_ answers in the family and 6 out of 19 in the close family variable are from the hacked-group, 38% and 31%, respectively. The differences are visualised in Figure 10 where each bar on the x-axis counts as 100% to illustrate the difference.
## 3 Results and Discussion
### Detection of 3,4-Benzopyrene and Benzidine in Chloroform  
Since many toxicants, such as 3,4-benzopyrene, quintozene, and aflatoxin, are practically insoluble in water, it is difficult to use the conventional hydrophilic colloidal gold nanoparticles as efficient SERS substrates to detect the hydrophobic analytes. The hydrophobic gold nanomaterials in the current work provide a new option for the substrate used in hydrophobic organic solvents such hexane, toluene, and chloroform. Figure 5 demonstrates the SERS spectra of 3,4-benzopyrene and benzidine in chloroform using synthesized hydrophobic spherical gold nanoparticles as a substrate. As shown in Figure 5, the characteristic Raman signals of 3,4-benzopyrene at 1617 cm\({}^{-1}\) and 1575 cm\({}^{-1}\) were hardly observed in chloroform at a concentration as high as 1000 \(\upmu\)g/mL in the absence of the spherical gold nanoparticles. A significant increase in signal intensity was detected when the gold nanoparticles were mixed with 3,4-benzopyrene and used as a substrate even at a substantially lower concentration of 3,4-benzopyrene (40 \(\upmu\)g/mL). Similar results were obtained for benzidine, as shown in Figure 5.
# The Synergy of Renewable Energy and Desalination:
## 2 Overview of Desalination Technologies  
Depending on the separation method used, the technologies for water desalination can be broadly divided into thermal and membrane desalination approaches (Figure 1). A heat source is used to bring the supply of seawater to the proper temperature during thermal operations. Conventional fossil fuels, nuclear electricity, and RES can all provide heat.
## 6 Illustrative Policy Analysis  
The application of the proposed model to evaluate the impact of two illustrative policy scenarios is presented in this section. These include (i) improving the total access and egress travel time (referred to as out-of-vehicle travel time or OVTT) and (ii) improving the footpath infrastructure around the metro stations. Two levels of improvement are evaluated for each scenario, as follows: 10% improvement and 25% improvement. These improvements are implemented at 13 priority metro stations that are classified as levels 1-5, shown in Figure 10. Eight stations are located on Line 1 and five on Line 2, where consideration or footfall levels fall into low or medium categories. These are potential target stations for interventions in order to increase the overall metro train usage. Table 7 illustrates the effect of these facility improvements on the consideration rate and daily footfall of metro stations based on numerical simulations using the proposed model. For the first scenario, the OVTT change is analyzed. Assume, for example, that OVTT = 20 min was present at one of the 13 priority metro stations. It was reduced by 10% and 25%, i.e., 18 and 15 min, respectively, and the proposed models were used to compute the corresponding consideration probability and footfall at that station. As shown in Table 7, a similar process was carried out for each of the 13 stations. In the second scenario, the effect of improving the footpath infrastructure around the metro stations is evaluated. In this scenario, footpath conditions near metro stations were assessed using a binary variable: 0 for poor conditions and 1 for good conditions. In the improvement scenarios, for each of the 13 stations, 10% and 25% of the respondents, respectively, with poorly conditioned footpaths were randomly selected, and the corresponding footpath usability perception was improved from a poor to a good rating (0 to 1). The proposed model was then used to estimate consideration probability and footfall for both improvement levels based on the updated footpath usability values. Table 7 also presents the increase in the probability of consideration and footfall due to improving the footpath usability by 10% and 25%, respectively. Since the variable related to the footpath is significant only for the stations in Metro Line 2, there is only a marginal increase in footfall in stations on Metro Line 1. By evaluating both policies, it is observed from Table 7 that the OVTT scenario has a greater effect on consideration than footpath improvement. Stations 2 and 5, in particular, exhibit notable increases in footfall and consideration (for the corresponding percentage reduction in OVTT shown in green bold font in Table 7). Overall, the percentage increase in daily footfall is greater than the increase in consideration propensity (3.81% vs. 2.29%).
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
## 3 Related Work
### Caching in Information-Centric Networks
In [24], the authors propose an ICN-based caching scheme for improving retrieval time in 5G networks with mobile video consumers. They do not consider mobility of producers (videos are normally stored in servers in the core network) and use LRU for cache replacement. When the CCN architecture is used for devices which require low latency and/or mobile, the cache decision policy needs to be simpler and more efficient. Ref. [12] studies the performance of various caching decision schemes for content-centric networks using a realistic YouTube-like catalog. The major take-away of this study is that simple schemes out-perform complex ones and popularity of content plays a crucial role in the performance of a cache replacement policy. Accordingly, T-Move proposed in this paper is a simple scheme that works online and caches content based on its trendiness. This leads to content being cached at routers where it is most likely to be accessed, irrespective of the hop-count or geographical distance.
## 4 Conclusions  
We presented a new dTDLAS NO\({}_{2}\) instruments are designed to be operated as an optical gas standard (OGS) and can thus perform absolute (\"calibration free\") NO\({}_{2}\) amount fraction measurements directly, i.e., without any chemical conversion which is needed in the current standard reference method CLD. Nitrogen dioxide amount fractions in the range of 100-1000 umol/mol were delivered by the instrument and are in good agreement with certified reference values attributed to the reference gas mixtures. The relative uncertainty of the results is currently at 5.1% (\(k=1\)) and is almost completely (96%) dominated by the inaccurate molecular reference data. The relative repeatability of the results at 982.6 umol/mol is 0.1% and the instrument response time ranges from 0.5 s with a 10-scan average to 0.06 s in the single scan mode. Future work will focus on measuring a new SI-traceable NO\({}_{2}\) line strength value to further reduce the line strength uncertainty influence and thus the instrument accuracy. The instruments' detection limit of 0.825 umol/mol makes it well suited for direct measurements of the NO\({}_{2}\) concentration in car exhaust gases. Therefore, further plans are to extend the capability of the instrument for simultaneous measurements of NO\({}_{2}\) and NO concentrations (in both atmospheric, i.e., less than 1 umol/mol level, and car exhaust gas species).  
Experiment concept: J.A.N., Z.Q., V.E. Experiment realization and data collection: J.A.N., Z.Q. Data evaluation and discussion: J.A.N., Z.Q., O.W., V.E. Paper conceptualization writing--review and editing: J.A.N., Z.Q., O.W. and V.E. All authors have read and agreed to the published version of the manuscript.  
This work was internally funded.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Acknowledgments**: The authors would like to acknowledge the support from the PTB working group 3.42 (Spectrometric Gas Analysis) in the development of the instrument.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Proposed Approach  
This section describes the basic idea of our engine based approach. As described in Section 1.1, video exploration can be defined as combination of different tasks namely:* Retrieval
* Navigation
* Synopsis or summarization  
The proposed approach RAAVE is designed to be used in conjunction with a video search and retrieval system and therefore focuses only on the navigation and synopsis aspects. As described in Section 2.3 current approaches underutilize the multimodality in content representation and granting user control in the level of detail of the information, in an interactive manner.  
This is because current researchers approach the problem by creating highly customized interfaces that augment supplement informational and/or control elements around a video. The problem with such solutions is that they cannot change with evolving user needs thereby limiting the exploration experience of the user.  
The proposed approach (RAAVE) solves this issue by taking a different strategy. Instead of supplementing information or customizing the end interface, the proposed approach represents automatically extracted multimodal features in a configurable manner, independent of the end user interface.  
By configuration, we mean that content representation is configurable not just in terms of the amount of detail, but also in the choice of combination of different modalities. The configuration of extracted features in the representation is done with the help of templates. Hence, the representation of content can be modified by changing the template selection. In order to select templates for representing the content, the proposed approach utilizes a representation engine that uses a template collection and a template matching process. The template collection and template matching process are inspired by the findings of the experiment performed by [PERSON] et al. [59]. The following section details the design of the proposed approach.
# A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
## 2 Related Works
### Deep Learning and CNN-Based Models  
The advent of deep learning revolutionized FER by enabling end-to-end learning of expressive features from raw image data. CNNs such as VGGNet, ResNet, and InceptionNet have been widely adopted as FER backbones due to their hierarchical feature extraction capabilities [21]. Notably, VGG-Face and ResNet-50 variants trained on large-scale face datasets demonstrated significant improvements in FER accuracy [5]. To further refine CNN performance, attention mechanisms were introduced--particularly spatial attention [22], which emphasizes emotionally salient regions of the face [23]. Models like DAN (Dual Attention Network) and MA-Net utilized such attention modules to dynamically highlight informative regions, thereby improving emotion recognition under challenging visual conditions [24]. Despite their success, CNN-based models exhibit inherent limitations. They often struggle to model long-range dependencies and rely heavily on local receptive fields, making them insufficient for capturing holistic facial relationships. Additionally, these models are typically trained on RGB images alone, which restricts their capacity to generalize across modalities or temporal contexts. Inspired by breakthroughs in natural language processing and vision tasks, transformer architectures have recently gained traction in FER research. Unlike CNNs, transformers rely on self-attention mechanisms to model global dependencies, enabling them to capture long-distance relationships between facial features. Vision Transformers (ViTs), for instance, partition the image into non-overlapping patches, which are then processed as token sequences [25]. This allows for the modeling of interactions between disparate facial regions, such as coordinated movements of the brow and mouth in expressions of fear or surprise. Several transformer-based FER models have emerged in recent literature. For example, EMOFormer introduced a token-based transformer encoder tailored to emotional expression learning [26], while [27] combined self-attention with hierarchical feature extraction for robust recognition under noise. However, these models often incur high computational costs, making them less suitable for real-time applications. Moreover, many fail to account for the temporal dynamics of fa cial expressions or suffer from attention overfitting--where the model disproportionately focuses on dominant features while ignoring subtle cues.
## 4 Artificial Intelligence Approach for Crack Length Prediction
### Introduction to Methodology  
In the work presented herein, the AlexNet convolutional neural network (CNN) was used to develop a crack length prediction capability from AE signals. AlexNet is an image classification deep neural network trained on the millions of images contained in ImageNet [41]. The proposed method is not limited to AlexNet; it is simply a generic example. Existing or to-be-developed neural network architectures can also be used to achieve the crack length estimation with system accuracy being the affected result. The general concept of the neural networks follows the standard multilayer perception model which involves appropriately training its neural connections by a backpropagating error and adjusting connection weights following the standard steepest gradient descent [42]. Figure 13 shows the model of a deep neural network, which includes multiple hidden layers of nodes, each connected to the nodes of previous and ensuing layers by weighting factors.  
For AlexNet, an image recognition convolutional neural network, images of input size \(227\times 227\) pixels are required. To adopt the experimental AE signals to this criterion, the Choi-Williams transform (CWT) of the waveforms is processed to generate an intensity plot yielding information about the time-domain and frequency-domain of the wave, simultaneously. This intensity plot is then isolated from the legends and associated waveform plots and augmented using MATLAB to conform to the \(227\times 227\)-pixel requirement before being used as input by the AlexNet CNN. A schematic of this process is given in Figure 14.  
Figure 13: Schematic of the multilayer perception artificial deep neural network.
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 3 Results and Discussion
### PFAS Removal Efficiency of MIEX(r) GOLD Resin
This value is much higher than the previously reported anion exchange resins such as PFA300 (0.117 g/g) [31], granular activated carbon (0.153 g/g), powered activated carbon (0.434 g/g), and anionic-exchange MIL101(Cr) metal -organic frameworks (0.49 and 0.78 g/g) [32; 33]. Although this adsorption capacity is a little less than resin amberlite (IRA67, 1.16 g PFOA per gram), MIEX(r) GOLD resin requires comparably less time (12 vs. 48 h) to remove the same amount of PFOA [32].
## Discussions
### Reasons for Differences between SIF-GPP and SIF-CO\({}_{2}\)-GPP Model
Besides, there are several \"hotspots\" in the map of GPP\({}_{\text{S}}\) as shown in Figure 7h. We think that such \"hotspots\" could be wrong estimates. The SIF-CO\({}_{2}\)-GPP model corrects those \"hotspots\" in its results. We think that the homogeneous distribution of GPP with a very low value would be more in line with the growth state of vegetation in winter. Therefore,the SIF-CO\({}_{2}\)-GPP model also improves the accuracy of the SIF estimates for winter GPP. The inclusion of atmospheric CO\({}_{2}\) concentrations in the model will significantly improve the accuracy of the estimates and better present the spatial distribution of the GPP. Moreover, a reasonable mechanism to explain improvements brought by the SIF-CO\({}_{2}\)-GPP model can be expressed as follows. When the CO\({}_{2}\) concentration at surface leaves increases, the value of the CO\({}_{2}\) compensation point reduces and the carboxylation rate increases, which would increase the gross photosynthetic rate. Meanwhile, the respiration would be limited with the increasing the CO\({}_{2}\) concentration, resulting in increasing in the net photosynthetic rate. Eventually, the GPP would become higher and vice versa. Looking back to Figure 7, we can see that comparing with the SIF-GPP model, the SIF-CO\({}_{2}\)-GPP model overestimated GPP in winter and underestimated GPP in summer. Such results are consistent with the above mechanism.