esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 4 Post-Earthquake Fire (PEF) Damage Assessment  
Structures affected by earthquakes are particularly vulnerable to fire for several reasons. First, physical deterioration of the structure increases its susceptibility to fire. Second, earthquakes can affect fire protection systems, rendering them less effective or completely inoperable during a fire event. Third, earthquake-induced damage can obstruct access for firefighters, facilitating the unchecked spread of fire.  
The assessment of damage to structural components is a pivotal aspect of evaluating a building's resistance to post-earthquake fire (PEF) [48]. Prior to an earthquake, buildings are primarily subjected to gravity loads, comprising both dead and live loads. During seismic events, lateral vibrations inflict damage and cause permanent lateral deformation, impacting the structural integrity of the components.  
In this compromised state, the structure, now burdened with residual stress from the earthquake, must withstand additional stresses imposed by any ensuing post-earthquake fire. The severity of seismic damage is contingent upon the earthquake's duration and intensity, which subsequently dictates the building's drift, acceleration, peak structural temperatures, and deflections [3]. These factors are crucial for assessing the extent of damage to structural components and for identifying necessary modifications to maintain safety.
## 6 Presentation and Analysis of Results
### Statistical Analysis
When the car is ON, the radar is recording high-powered vibration responses as opposed to when the car is OFF. Figure 23: Received power histogram for the Kia Forte5 gear modes at 23 Hz. Figure 24: Received power histogram for the Kia Forte5 gear modes at 45 Hz. Figure 25: Spectrogram histogram comparison between acoustic and radar data at 23 Hz. Similar patterns, shown in Figure 26, also apply to the power distribution for each gear mode (off, park reverse, neutral, and drive) on the front side of the Kia vehicle at the frequency of 45 Hz since it has also been deduced from the corresponding spectrogram as an important feature. When the vehicle is OFF, the samples for the acoustic data hover around \(-5\) to 5 dB. Most of the samples in the radar data hover between \(-6\) and 2 dB. On the contrary, when the vehicle is ON and in DRIVE, the acoustic and radar samples are mostly above 5 dB. Again, this shows good evidence that when the car is ON, the radar is recording high-powered vibration responses as opposed to when the car is OFF. Another interesting observation is when comparing NEUTRAL to DRIVE and REVERSE. NEUTRAL tends to have more samples at a slightly lower power level than DRIVE and REVERSE. NEUTRAL for acoustic data has samples less than 20 dB for the 23 Hz feature and 10 dB for the 45 Hz feature. When looking at the REVERSE and DRIVE samples, they tend to be mostly above 20 dB for 23 Hz and 10 dB for 45 Hz. Radar samples in NEUTRAL have samples less than 10 dB for the 23 Hz feature and less than 5 dB for the 45 Hz feature. Just as in the acoustic data, the opposite occurs in the radar data for REVERSE and DR  
states more accurately. However, in this case, by monitoring only 23 Hz and 45 Hz, we can see patterns in the radar's returned power to decode the vehicle's current gear mode.
## 3 Results and Discussion  
### Surfactant and CNT Concentration  
It was important to ensure that the sodium dodecyl sulfate (SDS) concentration was below the critical micelle concentration (CMC) to ensure that the SDS properly assists in suspending the SWCNTs [34]. The CMC of SDS is 8.2 mM [40].  
It was found that the CNT concentration used in these experiments was 0.2 mg mL\({}^{-1}\).
## 2 Related Work
### Mental Illness Models  
Recent work has shown the effectiveness of using BERT-based transformers for social media texts [34; 35; 36] as well as transformers on audio text transcripts [37; 38], for detecting mental illnesses such as depression and anxiety. These methods show the promising potential of transformer-based methods for detecting signs of mental illness. Methods based on social media texts have the advantage of data being readily available and easy to acquire, but have the limitation of not being very useful in a clinical setting for two main reasons; (1) the lack of direct specific prompts may discourage participants from expressing their underlying feelings; (2) the nature of text in social media differs substantially from natural speech.  
Datasets such as the Distress Analysis Interview Corpus (DAIC) for English [39] and Emotional Audio-Textual Depression Corpus (EATD-Corpus) for Chinese [38] address these issues by providing transcripts from clinical interviews. As a result, these datasets are more clinically useful than their social media counterparts. Both corpuses utilize a virtual interviewer that asks questions. In the case of DAIC some combination of 170 questions is asked, while the EATD-Corpus simplifies the interview into three questions.  
There are four main advantages of the PHQ-V and GAD-V assessments over these datasets. First, the dataset size for the PHQ-V/GAD-V assessments is roughly an order of magnitude larger, which can improve generalizability; the validation study described below shows that an increase of 20% in the number of samples improved the model scores significantly. Second, the standardization of five questions provides a simplified and repeatable experience compared to the 170 possible questions from DAIC. Third, the removal of the need for a virtual interviewer further simplifies the practical application of these assessments. Lastly, the asynchronous nature of the PHQ-V/GAD-V assessments provides a convenient way for patients to provide this information in their own time from their home computer or mobile device, providing consistent feedback to the reviewer.  
Multimodel Models  
Various research studies have shown the potential value of fusing multiple modalities such as audio and text [40], including when using transformer models [41]. We hypothesized that similar gains would be observed by adding audio and video modalities to text, but this result did not materialize during this research. The observed result came as a big surprise; we discuss this result in further detail in the results section, and provide some possible explanations for these results, as well as discussing potential further work that should be undertaken.
## 5 Experiments and Results
### Benchmark Model Comparison and Estimation Metrics  
#### 5.2.1 Baselines  
We compare the performance of the proposed model with the following benchmark models:  
* Constant Velocity (CV) model [39]: It assumes a constant speed for the vehicle and predicts its future trajectory based on historical states. In our work, with the 3 s historical trajectory input, the next 5 s movements are indicated through calculating the trajectory average speed of the previous time window.
* GRU model [40]: The model is a common GRU network with no aggregation mechanism, and it considers all trajectories to be independent of each other.
* Vanilla LSTM (V-LSTM) model [23]: This baseline conducts the vehicle trajectory task with an LSTM-based encoder-decoder process. Generally, the vehicle historical trajectories are taken into the encoder, and the decoder outputs the vehicle anticipation positions.
* Generative Adversarial Imitation Learning GRU (GAIL-GRU) model [41]: This baseline uses ground truth data from surrounding vehicles as input in the prediction task.
# Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer
## 5 Experimental Results and Analyses
### Ablation Experiment  
To verify the importance of the proposed two parts, the C3D time-stream module and multi-scale convolutional Transformer module, in the dual-stream network, ablation experiments were conducted separately. The recognition accuracy of the original two-stream network is compared with the network after the addition of the two modules, and the experimental results are shown in Table 2.  
Figure 6: Training Accuracy and Loss on the UCF101.  
From the results in Table 2, it can be seen that the recognition accuracy on the dataset UCF101 is improved by 3.2% and 1.9% after adding the C3D module and the multi-scale convolutional Transformer module, respectively. By employing data augmentation and optimizing the model training strategies, the recognition accuracy of the model can be further enhanced. To further verify the effectiveness of the proposed module, we conducted ablation experiments on six categories of the KTH video dataset, and the results are shown in Figure 7. In the figure, TS is the abbreviation of two-stream, C3D-TS is the abbreviation of two-stream based on C3D, and C3D-MS-TS is the abbreviation of two-stream based on multi-scale Transformer module and C3D.  
The results indicate that the use of 3D convolution instead of 2D convolution can enhance the extraction of dynamic features, and the fusion of multi-scale attention integration information can enhance the expression of features. Finally, our behavior recognition algorithm is compared with other behavior recognition algorithms on the UCF101 dataset and the comparison results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Modeling** & **Recognition Accuracy (ACC)/\%** \\ \hline Two-stream & 88.6 \\ C3D-Two-stream & 91.8 \\ Multi-Transformer-Two-stream & 90.5 \\ C3D+Multi-Scale Transformer & 92.9 \\ \hline \end{tabular}
\end{table}
Table 2: Recognition accuracy of different modules.  
\begin{table}
\begin{tabular}{c c c} \hline
**Modeling** & **UCF101 (ACC)/\%** & **HMDB51 (ACC)/\%** \\ \hline Two-stream [34] & 87.6 & 59.4 \\ P3D [4] & 88.6 & - \\ C3D [12] & 85.4 & 56.8 \\ VTN [27] & 92.7 & 72.3 \\ IDT [35] & 85.9 & 57.2 \\ AVID-CAM [36] & 87.5 & - \\ TDN [37] & 91.5 & 75.3 \\ STS [38] & 90.1 & 62.4 \\ YOWOv3 [39] & 95.4 & 68.8 \\ TDD [40] & 90.3 & 63.2 \\ Ours & 92.9 & 74.6 \\ Ours-DA & 94.2 & - \\ \hline \end{tabular}
\end{table}
Table 3: Recognition accuracy of different methods.  
Figure 7: Ablation Experiment on the KTH.  
As can be seen from Table 3, the accuracy of the two-stream algorithm with the addition of the C3D module and the multi-scale convolutional Transformer module is obviously enhanced in comparison with the other algorithms, which indicates that the behavioral recognition method designed in this paper achieves a better recognition effect after strengthening the extraction of the dynamic features as well as improving the accuracy of the feature extraction.
# Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review
## 5 Discussion
### Domains and Application Areas  
Another significant outcome of our analysis pertained to the domains where the proposed DRA models were applied (RQ2). Utilising a grouping technique, we sought to investigate three distinct domains. These were the SC domain, the ICT domain, and the ICS domain. More specifically, 3 DRA models (6%) were applied in the SC domain, 21 DRA models (42%) were applied in the ICT domain, and 26 DRA models (52%) were applied in the ICS domain. The obtained result was anticipated, given the inherent significance of the ICS domain in the context of cybersecurity. Moreover, the crucial functions performed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domains** & **AI/ML** & **Mathematical** & **Unclassified** \\ \hline ICT & 9 & 6 & 6 \\ ICS & 13 & 7 & 6 \\ SC & 2 & & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Primary analysis method applied to each domain.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Domains** & **Bayesian Networks** & **Attack Trees** & **Neural Networks** & **Artificial Immunity** & **Association Analysis** \\ \hline ICT & 4 & 3 & & 2 & \\ ICS & 7 & 3 & 2 & & 1 \\ SC & & 1 & 1 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: AI/ML primary analysis methods applied to each domain.  
by ICS and the potential severe consequences of cyber attacks, which could even result in fatalities, naturally drive significant attention towards bolstering cybersecurity measures for ICS systems. The successful execution of a cyber attack on an ICS infrastructure could indeed lead to catastrophic outcomes. Hence, there exists a compelling rationale for intensifying efforts in detecting and mitigating cyber threats specifically targeting the ICS domain.
## 4 ROLQ-TEE Scheme
### Detailed Construction
```
Input: The candidate facility list \(\mathcal{F}_{\mu}\), the query key \(sk_{\mu}\), and the customer set \(U_{\mu}\) Output: The encrypted candidate set \(\mathcal{CF}_{\mu}\) and the encrypted customer list set \(\mathcal{CL}_{\mu}\) 1: Select a random value \(r_{\mu}\in Z_{q}^{*}\);
2: Construct the facility candidate set \(M_{\mathcal{F}_{\mu}}=f_{1}||f_{2}||,\ldots,||f_{\hat{s}}\);
3: Compute the encrypted candidate set \(\mathcal{CF}_{\mu}=\{\mathcal{CF}_{\mu,0},\mathcal{CF}_{\mu,1},\mathcal{CF}_{ \mu,2}\}\), where \(\mathcal{CF}_{\mu,0}=g^{r_{\mu}}\), \(\mathcal{CF}_{\mu,1}=M_{\mathcal{F}_{\mu}}\cdot e(H_{1}(rid_{\mu})^{r_{\mu}}, h_{\mu})\), and \(\mathcal{CF}_{\mu,2}=H_{2}(T_{t})^{r_{\mu}}\);
4: Select a random value \(r_{\mu}^{\prime}\in Z_{q}^{*}\);
5: Construct the customer list \(L=F(sk_{\mu},y_{\mu,1})||F(sk_{\mu},y_{\mu,2})||\ldots||F(sk_{\mu},y_{\mu,n_{ \mu}})\);
6: Compute the encrypted customer list set \(\mathcal{CL}_{\mu}=\{\mathcal{CL}_{\mu,0},\mathcal{CL}_{\mu,1},\mathcal{CL}_{ \mu,2}\}\), where \(\mathcal{CL}_{\mu,0}=g^{r_{\mu}}\), \(\mathcal{CL}_{\mu,1}=L\cdot e(H_{1}(rid_{\mu})^{r_{\mu}},h_{\mu})\), and \(\mathcal{CL}_{\mu,2}=H_{2}(T_{t})^{r_{\mu}}\);
7: Generate the query token \(\mathcal{Q}_{\mu}=\{\mathcal{CF}_{\mu},\mathcal{CL}_{\mu}\}\)and send \(\mathcal{Q}_{\mu}\) to the \(\mathcal{CSP}\). ```  
**Algorithm 2**OptLocQue Algorithm  
```
Input: The encrypted location data set \(c_{i}\), the encrypted query token \(\mathcal{Q}_{\mu}\), the query key \(sk_{\mu}\), \(sk_{\mathcal{DO}}\), the map table \(MT\), and the public system parameters \(mpk\) Output: The encrypted optimal query result \(\mathcal{C}_{res}\) 1: Load the encrypted query token \(\mathcal{Q}_{\mu}=\{\mathcal{CF}_{\mu},\mathcal{CL}_{\mu}\}\);
2: Decrypt \(\mathcal{CF}_{\mu}\) to \(M_{\mathcal{F}_{\mu}}=\frac{\mathcal{CF}_{\mu,1}\cdot e(sk_{\mu,1},\mathcal{CF} _{\mu,2})}{\epsilon(sk_{\mu,2},\mathcal{CL}_{\mu,0})}\);
3: Decrypt \(\mathcal{CL}_{\mu}\) to \(L=\frac{\mathcal{CL}_{\mu,1}\cdot e(sk_{\mu,1},\mathcal{CL}_{\mu,0}),2}{ \epsilon(sk_{\mu,2},\mathcal{CL}_{\mu,0})}\);
4:for\(1\leq i\leq n_{\mathcal{MO}}\)do
5:for\(1\leq j\leq n_{\mu}\)do
6:if\(L.H_{1}(y_{\mu,j})=MT.H_{1}(x_{i})\)
7: Load the \(c_{i}\) of \(MT.H_{1}(x_{i})\) and decrypt \(c_{i}\) to \(l_{i}\);
8: Obtain the location intersection set \(U_{I}=\{l_{i}\}\);
9:for\(1\leq\xi\leq s\)do
10:foreach\(l_{i}\)in\(U_{I}\)do
11: Compute the euclidean distance \(d(l_{i},f_{\xi})\) between \(f_{\xi}\) and \(l_{i}\);
12: Compute accumulated value \(\sum=d(l_{i},f_{\xi})\);
13: Select the query type, namely RNNQ, AVGQ, or MAXQ;
14: Obtain the optimal query result _res_;
15: Select a random value \(r_{R}\in Z_{q}^{*}\);
16: Compute encrypted optimal query result \(\mathcal{C}_{res}=\{\mathcal{C}_{res,0}=g^{r_{R}},\mathcal{C}_{res,1}=res\cdot e (H_{1}(rid_{\mu})^{r_{R}},h_{\mu})\), \(\mathcal{C}_{res,2}=H_{2}(T_{t})^{r_{R}}\}\);
17: Send the \(\mathcal{C}_{res}\) to the \(\mathcal{DU}_{\mu}\).
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Pol-SAR Features Extraction
#### 2.1.3 Freeman-Durden Decomposition  
The Freeman-Durden decomposition determines three-component scattering mechanisms, including double-bounce, volume, and surface scattering, based on physical models. The Freeman decomposition is shown as follows [24]:  
\[\left[C\right] = f_{\text{v}}\left[C\right]_{\text{v}}+f_{\text{d}}\left[C\right] _{\text{d}}+f_{\text{s}}\left[C\right]_{\text{s}} \tag{9}\] \[P_{\text{v}}=\frac{8f_{\text{v}}}{3}\] (10) \[P_{\text{d}}=f_{\text{d}}\left(1+\left|\alpha\right|^{2}\right)\] (11) \[P_{\text{s}}=f_{\text{s}}\left(1+\left|\beta\right|^{2}\right), \tag{12}\]  
where \(\left[C\right]_{\text{v}^{\prime}}\left[C\right]_{\text{d}}\), and \(\left[C\right]_{\text{s}}\) are the covariance matrices of the three scattering mechanisms, and \(f_{\text{v}}\), \(f_{\text{d}}\), and \(f_{\text{s}}\) are the scattering coefficients of double-bounce, volume, and surface, respectively. \(\alpha\) and \(\beta\) are the polarized coefficients of double-bounce and surface, respectively. \(P_{\text{v}}\), \(P_{\text{d}}\), and \(P_{\text{s}}\) are the scattering powers of the three scattering mechanisms. They can be used as features describing the scattering mechanisms because they are related to echo power from the different mechanisms.
## 2 Handle Recognition and Positioning Method Description
### Kinematics Model of the Robotic Arm  
In the experiment, the Denavit-Hartenberg (D-H) modeling method was used to establish the kinematics model of the mobile robotic arm [23]. This method is widely used in robot kinematics modeling to establish an integral link coordinate system. In the multi-link series robotic arm system shown in Figure 4, the kinematics model of the robotic arm is established by homogeneous coordinate transformation.  
Figure 4: Reference frame representation used in kinematic calculations of the robotic arm.  
Figure 3: The object recognition of the N state.  
Equations for each link of the robotic arm:  
\[n^{-1}T_{n}=\left[\begin{array}{cccc}\cos\theta_{n}&-\sin\theta_{n}\cos\alpha_{ n}&\sin\theta_{n}\sin\alpha_{n}&a_{n}\cos\theta_{n}\\ \sin\theta_{n}&\cos\theta_{n}\cos\alpha_{n}&-\cos\theta_{n}\sin\alpha_{n}&a_{n} \sin\theta_{n}\\ 0&\sin\alpha_{n}&\cos\alpha_{n}&d_{n}\\ 0&0&0&1\end{array}\right] \tag{1}\]  
The kinematic model of the base corresponding to the end of the robotic arm can be obtained by multiplying each link coordinate system equation. \(P^{0}\) represents the coordinates of the endpoint of the end robotic arm in the base coordinate system.  
\[P^{0}=\ ^{0}T_{1}\cdot\ ^{1}T_{2}\cdot\ ^{2}T_{3}\cdot\ ^{3}T_{4}\cdot\ ^{4}T_{5}\cdot\ ^{5}T_{6} \left[\begin{array}{c}0\\ 0\\ 0\\ 1\end{array}\right] \tag{2}\]  
This method derives the transformation matrix from the base coordinate system to the end coordinate system. During the forward kinematics calculation, the position and posture of the coordinate system of the end of the robotic arm are obtained according to the known joint variables. The inverse kinematics calculation solves the value of the joint variables based on the known pose of the end coordinate system. An accurate model is established to lay a good foundation for subsequent experiments.
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 3 Methodology
### Isolation Forest
#### 3.2.2 Testing Scheme  
The way the isolation forest algorithm is used in this research, requires no selection of a set of normal conditions to adjust the parameters of the model. Instead, data are analyzed through a rolling window of 1 month width and instances lying at the border of the data distribution are marked as anomalies. The rolling window is shifted each time by one week, effectively sweeping the entire dataset. The choice of the window length is the fruit of a compromise between computation time and the ability to capture a complete set of operating conditions of the turbines.
## 4 SD-EAR in Detail
### Optimum Route Selection  
Whenever a node \(n_{i}\) wants to communicate with another node \(n_{i}\), it sends an opt-route-select message to the SDN controller. The attributes of opt-route-select are:  
1. message-type-id (3)
2. message-type-id (4)
3. message-type-id (5)
4. message-type-id (6)
5. message-type-id (7)
6. message-type-id (8)
7. message-type-id (9)
8. message-type-id (10)
9. message-  
+++ ==WARNING: Truncated because of repetitions==
type-id (11)  
++  
* source-id (n\({}_{\rm i}\))
* destination-id (n\({}_{\rm j}\))
* timestamp
* traversed-zone-list (initially it is {\(Z_{\rm p}\)} only; this field is particularly required for inter-zone communication. Whenever opt-route-select arrives at a new zone, the identification number of the new zone is added to the list)  
Receiving opt-route-select from n\({}_{\rm i}\), the SDN controller of zone \(Z_{\rm p}\) checks to see whether destination n\({}_{\rm j}\) belongs to the same zone or not. This is performed by searching n\({}_{\rm j}\) in the NODE-STAT\({}_{\rm p}\) table. If n\({}_{\rm j}\in\) extr(NODE-STAT\({}_{\rm p}\), 1), then an intra-zone route selection is performed; otherwise, an inter-zone route section is performed. Please note that extr(NODE-STAT\({}_{\rm p}\), 1) is a function that extracts 1-th field from the NODE-STAT\({}_{\rm p}\) table.  
After the selection of the optimal route, the SDN controller informs about the chosen sequence of routes to the source of communication in an opt-route-reply message. The attributes of opt-route-reply are:  
* message-type-id (4)
* source-id (n\({}_{\rm i}\))
* destination-id (n\({}_{\rm j}\))
* optimal sequence of routers with locations  
The flowchart of route discovery appears in Figure 7.  
Figure 7: Node registration process.
# Effects of Repolishing Systems on Surface Characteristics of a 3D-Printed Permanent Material
###### Abstract  
Background: 3D-printed (3 DP) resins for permanent restorations are increasing in availability and gaining popularity. Aesthetic stability is an issue of dental resins that may compromise the long-term success of restorations. A staining test has been performed to evaluate gloss, roughness, and color staining of a permanent 3 DP resin, and the effects of repolishing. Methods: Squared specimens of one 3 DP permanent resin (Crown permanent, Formlabs) have been CAD-designed and 3D-printed. After the roughness, gloss, and color measurements, they were immersed in a coffee staining bath at 44 \({}^{\circ}\)C for 24 h and then measured again. Subsequently, they were repolished with three different systems: propby cup and Nupro prophy paste, HiLuster Plus 2-step, and OptiStep Polisher 1-step polishing systems. Results: Gloss and Roughness were not significantly affected by the staining procedure. No significant changes were observed for gloss after repolishing, while one of the tested systems (prophy cup and paste) produced a worsening effect on roughness. Color was affected by the staining procedure. After repolishing, b* changes were almost completely recovered, while L* changes were recovered only partially. Conclusions: Color stability still represents a challenge, and 3 DP resins for permanent use are affected. Repolishing systems should be further developed and tested.  
3D printing; resin composites; staining; gloss; roughness; color stability Article
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 3 Data Collection and Methodology
### Proposed Framework
The IBC Recv Tx data reflects transactions added to the Osmosis blockchain following the relay processing of the IBC Transfer Tx data. Transactions processed by the relay and included in the Cosmos Hub blockchain are represented as IBC Ack Tx data. Because relayed transactions are included in blocks, IBC Recv Tx and IBC Ack Tx contain valid and invalid transaction data. Valid IBC Recv Tx and Valid IBC Ack Tx contain data that the Valid Tx Filtering process has validated. The IBC Msg Relay Time data is collected by aggregating time data in the Sequence Tracker merely by tracking the connectivity of a given fungible token transfer operation. The presence of IBC Tx Generation data and matching Valid IBC Ack Tx data ensure that the relevant fungible token transfer was completed successfully, allowing aggregation of message interval delivery time. These transaction data points assist the Cosmos Measurement Node in tracking and analyzing the time it takes for messages to transit across blockchains, exposing information on the efficacy and performance of inter-blockchain communication.