esa-sceva/satcom-chunk-collection huggingface dataset:
Therefore, more and more researchers are turning to semantic segmentation methods [43; 44; 45] to achieve the pixel analysis of pavement defects, marking each pixel with a predictive label to quantify the defects. [PERSON] et al. [46] proposed a fully convolutional network (FCN) that combines the semantic information from the deep layers with detailed appearance information from shallow layers to make segmented predictions for each pixel. [PERSON] et al. [47] applied the FCN to crack the detection, which can detect cracks at the pixel level. [PERSON] et al. [48] used the FCN to detect 40,000 concrete crack images, which can accurately evaluate the density of the cracks. Considering the needs of practical engineering, the detection of multiple defects is necessary. [PERSON] et al. [49] studied a multi-defect detection method for concrete structures based on transfer learning and FCN, which can detect multiple concrete defects at the pixel level in real-world situations. However, although the above segmentation algorithms can accurately segment the pixel information of the defects, they do not have the function of defect localization. For this deficiency, [PERSON] et al. [50] proposed an instance segmentation model, that is, the Mask R-CNN, which integrates the advantages of object detection and semantic segmentation and realizes the functions of the classification, positioning, and segmentation simultaneously. [PERSON] et al. [51] introduced the Mask R-CNN into crack detection in shield tunnels. [PERSON] et al. [52] used the Mask R-CNN to detect and quantify cracks on a water tank. However, in the field of pavement engineering, the application of instance segmentation to pavement defects is rare, especially in complex backgrounds for multi-object, multi-class pavement defect detection, and there are currently no public standard datasets on pavement defects. Therefore, in order to solve the problem of pavement defect detection in complex backgrounds, we trained an excellent model to provide a reference for pave ment maintenance personnel. The contributions of this paper are as follows: (1) This paper applied the Mask R-CNN and transfer learning to pavement defect detection. (2) Using transfer learning, different backbone structures of Faster R-CNN and Mask R-CNN were compared, and the optimal model for pavement defect detection was obtained. (3) The defect data obtained by using the optimal model to quantify pavement defects can provide reference for pavement maintenance personnel. The structure of this paper is as follows: In Section 2, the information of the pavement defect dataset is described, the network structure of the Faster R-CNN and Mask R-CNN are introduced, and the concept of transfer learning is expounded. In Section 3, these two algorithms are used to train the dataset, transfer learning is used to compare the performance of each model, and the best model is used to quantify pavement defects. In Section 4, the work is concluded, and the limitations and future research are stated.
# Virtual Reality in Museums: Exploring the Experiences of Museum Professionals
## 6 Future Directions: What Does the Future Hold for VR in Museums?
### Suggestions for Incorporating VR Applications in Museums
Therefore, VR can be used to create the missing context, while having the object at the center of attention. For example, as shown in Section 3.1 many art museums use VR to allow the visitor to enter the world of a painting. However, interviewee 15 advises:  
_\"My advice would be not to dive into paintings, but maybe create the world around it, create the context around it. So, for example, you could dive into the 17 th century on the Dutch canals where [PERSON] painted the Night Watch. You can build the context, build the world around it, but don't go in it\"._  
As emphasized by interviewee 15, VR could be more effective if it was used to re-create the world around the painting, since the original and its aura can never be replaced by VR. On the other hand, the use of VR should not overload the visitor or cut him off from the experience of the museum visit, and thus a fair balance should be found between use and overuse. Especially in the case of VR, which tends to create an isolation of the user from the environment, it is important to match the experience with \"other sensory stimulus\" (Interviewee 10) to make it more effective. Such stimulus could include other senses, such as touch or smell but can also include other social stimuli by incorporating a social element in the VR and ways for people to share their experience and communicate. In this way, VR will seamlessly blend with the museum experience which in itself is inherently social and a \"360 completely immersive experience\", as noted by interviewee 15. The issue of social stimuli and the importance of offering collaborative activities through such technology has been recognized by some scholars, however, VR technology has still not reached a point of offering truly social experiences [37; 40; 41]. Finally, any VR experience that re-creates a holistic reality or a time in history should abide to specific standards of scientific accuracy and ethics criteria. This also relates to concerns raised in many cases by museum professionals regarding the authenticity of the experience offered [42; 43]. In many cases the amount of information needed to create a VR is very large and not always available. Thus, curators and other scientists will have to make interpretive choices which, if not made carefully and based on specific criteria, may result in a VR which is only meant to recreate and not merely enlighten, educate or even immerse the visitor.
# Inverse Coupled Simulated Annealing for Enhanced OSPF Convergence in IoT Networks
## 3 Simulation and Evaluation  
### Evaluation Metrics  
The experiment primarily evaluates routing convergence performance through four metrics: fault detection time (FDT), fault detection accuracy (FDA), routing overhead (RO), and packet delivery rate (PDR). The definitions and formulas for these metrics are as follows:  
(1) Fault detection rime (FDT): The fault detection time is defined as the average difference between the time \(\tau\) when all OSPF nodes detect a fault during network simulation and the actual time \(t\) when the link fails [35; 36]. A shorter time indicates faster routing convergence under the same conditions, reflecting the efficiency of the fault detection mechanism's impact on overall routing convergence speed.  
\[FDT=\frac{1}{n}\sum_{i=1}^{n}(\tau_{i}-t_{i}) \tag{11}\]  
(2) Fault detection accuracy (FDA): The fault detection accuracy is defined as the ratio of the number of successful link fault detections \(F_{n}\) by all OSPF nodes during network simulation to the total number of actual link failures \(F_{l}\). A higher accuracy indicates better detection precision in the face of link failures, reflecting the responsiveness of the detection mechanism to actual fault events.  
\[FDA=\frac{F_{n}}{F_{l}}\times 100\% \tag{12}\]  
(3) Routing overhead (RO): Routing overhead is expressed as the ratio of the total number of bytes in control packets \(B_{r}\) to the total number of bytes in successfully transmitted packets \(B_{s}\)[37; 38]. A smaller routing overhead indicates lower costs associated with protocol routing convergence.  
\[RO=\frac{B_{r}}{B_{s}}\times 100\% \tag{13}\]  
(4) Packet delivery rate (PDR): The packet delivery rate is defined as the proportion of successfully transmitted packets \(P_{r}\) to the total packets sent \(P_{s}\) by a node [39; 40]. A higher packet delivery rate indicates greater reliability of the routing convergence method.  
\[PDR=\frac{P_{r}}{P_{s}}\times 100\% \tag{14}\]
## 5 Results
### Data Processing  
After selecting the most important features for model training, the dataset underwent a comprehensive preprocessing phase to ensure data quality and consistency. This included cleaning, normalization, and an appropriate partitioning strategy to enable unbiased performance evaluation. The main steps are outlined below:  
* Null or blank values were removed, and columns lacking predictive relevance were eliminated to reduce noise and dimensionality.
* The data were normalized to ensure that all features were on a comparable scale, thereby avoiding biases during model training.
* The dataset was divided into 70% training, 15% validation, and 15% testing subsets to enable a fair and independent evaluation of model performance. To assess the presence of overfitting, we compared the F1-scores between the validation and test sets. The observed differences were minimal (less than 1%), indicating a low generalization gap. This consistency demonstrates that the models maintained strong generalization capabilities and did not overfit the training data.
* Finally, the machine-learning models were trained using the training subset and evaluated on the test data using standard performance metrics.  
At this stage, both datasets (CIC-DDoS2019 and N-BaIoT) were found to exhibit the following characteristics:  
* Both are datasets prepared for the training of machine-learning models, already generated for this purpose, so the data preprocessing is minimal [6; 7].
* In the case of CIC-DDoS2019, the features have a similar importance index, so no feature selection was performed.
* For both datasets, an imbalance in the classes was found, with more instances of malicious traffic than normal traffic. Therefore, an oversampling technique was applied to balance the classes and improve the performance of the models.  
To balance the classes, the oversampling technique was used with the imbalanced-learn library, which allows generating synthetic instances of the minority class to equalize the number of instances of both classes. For this, a Pipeline was used that worked in conjunction with the SMOTE technique (Synthetic Minority Over Sampling Technique), which generated synthetic instances of the minority class to balance the classes. With all this, both datasets are ready to be used in the training of the selected models, and we proceed to the experimentation and results analysis stage.
# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 3 Experiment  
We obtained a video dataset recorded with an iPhone 6s back camera under electric lighting in an indoor environment using different frame rates. The videos were static-scene videos recorded in Raleigh, USA, where the ENF nominal value is 60 Hz. The power mains signals recorded concurrently with the videos were also acquired and served as the ground-truth ENF signals. We selected seven cameras for the experiments and obtained their \(T_{ro}\) values reported in [21], as shown in Table 1. In a real-life scenario, our approach will need a database of the \(T_{ro}\) values of all cameras and a database of ENF reference signals. For this study, we used videos whose camera frame heights (L) = 480, frame rates = 23.0062, and \(T_{ro}\)s = 19.8 ms. Each \(T_{ro}\) value was used with our adapted method [29] to analyze the video. The \(T_{ro}\) that corresponds to the camera used to record the video will lead to the estimation of an ENF that best matches the reference signal. In our adapted ENF estimation method, \(T_{ro}\) is a sensitive parameter and is employed to compute the number of zeros to be inserted in the idle period before analyzing the video, which is critical in estimating an ENF signal with no distortion. Three performance metrics: the normalized cross-correlation (NCC), the root mean squared error (RMSE), and the mean absolute error (MAE) are used to evaluate the (dis)similarity between the estimated ENF signal \(\{v\}_{k=1}^{m}\) and the reference signal \(\{r\}_{k=1}^{m}\). The NCC is evaluated as:  
\[\frac{\sum_{k=1}^{M}r_{k}^{c}v_{k}^{c}}{\sqrt{\left(\sum_{k=1}^{M}r_{k}^{c} \mathbb{Z}_{k=1}^{M}v_{k}^{c}\right.}} \tag{7}\]  
where \(r_{k}^{c}=r_{k}-\frac{1}{M}\sum_{n}r_{n}\), \(v_{k}^{c}=v_{k}-\frac{1}{M}\sum_{n}v_{n}\), and the variables with overhead bars are respective sample means. The RMSE is evaluated as:  
\[\sqrt{\left(\sum_{k=1}^{M}(r_{k}-v_{k})^{2}\right)}/M) \tag{8}\]  
and the MAE is evaluated as:  
\[\sum_{k=1}^{M}\left|r_{k}-v_{k}\right|/M \tag{9}\]  
When a test ENF signal \(\{v_{k}\}\) is examined against the reference ENF signal \(\{r_{k}\}\), the (dis)similarity is evaluated using \(\{\theta_{k}\}\) instead of \(\{v_{k}\}\), \(\partial_{k}\)\(\triangleq\)\(\triangleq\)\(\beta_{1}v_{k}+\hat{\beta}_{0}\), and (\(\hat{\beta}_{0}\), \(\hat{\beta}_{1}\)) are the least-square estimates when \(\{v_{k}\}\) is regressed on \(\{r_{k}\}\). This measure will guarantee that the RMSE and MAE metrics can be compared directly to the fluctuations in the reference ENF signal.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Camera ID** & **Model** & **L** & \(T_{ro}\) **(ms)** \\ \hline
1 & iPhone 6s back camera & 480 & 19.8 \\
2 & Sony Cybershot DSC-RX 100 II & 1080 & 13.4 \\
3 & iPhone 5 front camera & 720 & 22.9 \\
4 & iPhone 5 back camera & 1080 & 27.4 \\
5 & Sony Handycam HDR-TG1 & 1080 & 14.6 \\
6 & Canon SY230-HS & 240 & 18.2 \\
7 & iPhone 6 & 1080 & 30.9 \\ \hline \end{tabular}
\end{table}
Table 1: Cameras and parameters used in our experiment.
## Computational Methods
### Electronic Structure Method
The atoms are gradually separated until the total energy of the cell converges and we define the result as the atomic energy. One also needs the formation energy of the iodine molecule and because our computational code is not efficient for molecules, we take that energy from experiments. UI\({}_{3}\) is an anti-ferromagnet [2] with a somewhat complex magnetic configuration. Here, we compute the energies assuming a simplified anti-ferromagnetic arrangement of the spin moments on the uranium atoms along the (001) direction in the orthorhombic cell. This configuration turns out to be slightly lower in energy (\(\sim\)0.5 mRy/atom) than the ferromagnetic state and DFT thus confirms anti-ferromagnetism in UI\({}_{3}\). For the 16-atom per unit cell UI\({}_{3}\) structure we apply 144 k-points in the irreducible part of the Brillouin zone and each energy eigenvalue is broadened with a Gaussian distribution having a 20 mRy energy width. For the elastic constants the total energies need to be very carefully converged and for this purpose we utilize up to 288 k points. The thermodynamic properties (heat capacity and Gibbs free energy) are obtained from quasi-harmonic Debye-Gruneisen theory up to 1000 K. This methodology has been thoroughly detailed in the literature [24]. In the present implementation [25] of the model only the energy-volume relationship is needed, which we obtain from the DFT electronic structure calculations. We recently applied this straightforward framework for uranium mononitride. For UN, we found that for lower temperatures (\(<\)1000 K) it produces heat capacities that compare well with the anharmonic treatment while above that temperature one definitely needs to consider anharmonic lattice dynamics [15] that go beyond the quasi-harmonic assumption.
## 3 Proposed Solution  
In this section, we introduce the DMMS, designed to minimize the impacts of the producer's mobility in content delivery in terms of delay and packet loss using minimal signaling overhead. Our strategy relies on three components:  
A distributed cooperative strategically deployed Anchor, which proactively forwards the consumers' packet toward MPs when handoff occurs to achieve seamless data delivery with minimum packet loss and delay.  
A forwarding strategy that combines the NDN's default forwarding and the location-based forwarding strategy for improving data delivery.  
A location prediction technique to anticipate the producer's future location coordinates before a handoff event occurs, which allows for proactive forwarding of consumers' Inputs through the Anchor to the coordinates of the next PoA, where the producer is expected to be connected after the handoff.  
We separately detail in this section the Distributed Anchor design, the forwarding strategy, and the producer's location prediction technique.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.8 Data Anonymization  
Data anonymization through privacy-preserving techniques is crucial in the context of smart cities. With the increasing amount of data being collected from IoT devices, it is important to ensure that sensitive information is protected while still allowing for data analysis and decision making.  
One category of tools that can be used for this purpose includes ARX, sdcMICRO and \(\mu\)-ARGUS. These tools are specifically designed for data anonymization and privacy compliance, allowing cities to automatically anonymize datasets according to specific privacy requirements, ensuring compliance with regulations such as HIPAA and GDPR.  
Another category of tools that can be used in smart city contexts include data integration and data masking tools such as CloverDX, BizDataX and Gramener's Data Anonymization Solution. These tools can be used to integrate and anonymize location data from various sources, such as GPS data from public transportation, traffic cameras and mobile devices. The use of these tools can provide valuable insights, such as traffic prediction, people's movement patterns and smart transportation management. However, it is important to note that these techniques and tools can also be applied to other use cases in smart cities.
## 3 Methodology
### Cryptographic Techniques  
To evaluate this issue in the model, we focused on two cryptographic algorithms that secure data using mathematical algorithms and protocols. While these models address various aspects, in MLS/DBMS they are specifically used for evaluating access authentication. In authentication, cryptography aids in verifying the identity of users and systems, ensuring that only authorized individuals can access specific resources. Techniques such as password hashing and two-factor authentication are commonly used. The two cryptographic algorithms used in this model are RSA and AES, which are briefly described in the following subsections.
# Experimental and Numerical Study on UHPC-RC Decks within Hogging Moment Region
###### Abstract  
Steel-concrete composite continuous bridges can take full advantage of concrete and steel, but in regions with hogging moments, cracking of the concrete deck is a big issue affecting the durability of bridges. In order to solve cracking problems within the hogging moment region, this study proposes a composite deck method using ultra-high-performance concrete (UHPC) and regular concrete (RC). In this way, the layers of UHPC and RC are composited to take advantage of the high tensile strength of UHPC materials to improve the anti-crack performance of the concrete deck within the hogging moment region. Four different specimens were designed to account for different layer thickness of UHPC. Bending experiments of the UHPC-RC composite deck were undertaken and a corresponding finite element model was established to study the behavior of the UHPC-RC composite deck. The regularity of crack development in different UHPC layer thicknesses was revealed, and the load-displacement results were compared to investigate the ultimate capacity of a steel-concrete composite bridge structure using a UHPC-RC composite deck. Finally, with consideration of material cost, a reasonable UHPC layer thickness suitable for the composite deck was obtained to provide a reference for the design of a UHPC-RC composite deck.  
UHPC-RC composite deck; hogging moment region; crack development; load-displacement; experimental analyses
## 3 Results and Discussion
### Initial pH  
In general, the pH of the solution significantly affects the reaction kinetics of KMnO\({}_{4}\)-mediated TCE degradation. This is primarily due to the decreasing oxidation potential (E\({}_{0}\)) of KMnO\({}_{4}\) with increasing pH [17; 56], which leads to a slower TCE oxidation rate.  
\[\text{MnO}_{4}^{-}+5\text{e}^{-}+8\text{H}^{+}=\text{Mn}^{2+}+4\text{H}_{2} \text{O}\text{E}_{0}=1.51\text{ V (pH < 3.5)} \tag{4}\]  
\[\text{MnO}_{4}^{-}+3\text{e}^{-}+2\text{H}_{2}\text{O}=\text{MnO}_{2}+4 \text{OH}^{-}\text{E}_{0}=0.59\text{ V (3.5 <pH < 12)} \tag{5}\]  
\[\text{MnO}_{4}^{-}+\text{e}^{-}=\text{Mn}\text{O}_{4}^{2-}\text{E}_{0}=0.56 \text{ V (pH > 12)} \tag{6}\]  
Figure 4a shows that after a 110 min reaction, the TCE degradation efficiencies were similar across the pH range of 2 to 12. This may be attributed to the excess KMnO\({}_{4}\), which is capable of oxidizing nearly all TCE present [17]. However, the oxidation rate was lowest at pH ~12, likely due to the reduced E\({}_{0}\) of KMnO\({}_{4}\) at a high pH. Additionally, the rate constants for the oxidation of TCE intermediates are known to strongly depend on pH, particularly in the range of 4-8 [41]. These intermediates are oxidized more rapidly at lower pH, facilitating faster CO\({}_{2}\) formation.  
As shown in Figure 4b, the \(\varepsilon\) values of KMnO\({}_{4}\)-mediated TCE degradation ranged from \(-26.5\pm 0.6\)% to \(-23.0\pm 0.4\)% over the pH range from 2 to 12. A slight increase in \(\varepsilon\) values was observed with increasing pH, indicating that while the effect of pH on carbon isotope fractionation is minor, it is distinct. The trend in \(\varepsilon\) values (12 \(>\) 10 \(>\) 8 \(>\) 5 \(>\) 2) is  
Figure 3: (**a**) Effect of initial concentrations of TCE on the oxidative degradation of TCE in the KMnO\({}_{4}\) reaction. (**b**) The evolution of \(\delta^{13}\)C values of TCE with initial concentrations of TCE. (**c**) The variation of Rayleigh plots of TCE with respect to initial concentrations of TCE. Experimental boundary conditions: initial suspension pH 8.  
opposite to the trend in E\({}_{0}\), suggesting that E\({}_{0}\) is likely a key factor in governing both the degradation kinetics and isotope fractionation of TCE by KMnO\({}_{4}\). Reported pathways of TCE degradation under varying pH suggest that cyclic esters are transformed into acyclic esters and subsequently hydrolyzed into carboxylic acid intermediates such as formic, oxalic and glyoxylic acids, intermediates known to be pH-dependent [41]. Thus, both the E\({}_{0}\) and the nature of intermediates may influence \(\varepsilon\) values across the studied pH range.  
Figure 4c demonstrates a linear relationship between pH and \(\varepsilon\) values. This is a useful basis for selecting appropriate \(\varepsilon\) values in practical applications of KMnO\({}_{4}\)-based TCE remediation under varying pH conditions.