esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Surgical robots, embodying cross-disciplinary knowledge and integrating multiple high-tech elements [1], are comprehensive medical devices that can reduce patient incision trauma and infection risk through precise control and the replacement of manual operations [2]. They provide a work mode for surgeons that reduces physical exertion and focuses on disease analysis, thereby promoting efficient surgical treatment. The application and promotion of surgical robots are a manifestation of the transformation of technological achievements such as artificial intelligence and 5G, which are in line with the concept of the Global Digital Health Strategy (2020-2024) proposed by the World Health Organization. The potential for remote operations also provides more possibilities for future global diagnostic and treatment methods [3].  
HCI is the interactive process between people and the digital environment, i.e., the process of information control and exchange from the user to the system [4]. The goal of HCI design is to provide users with experiences that align with their specific knowledge background [5]. With the improvement and development of design disciplines, the design concept of user-centered design (UCD) [6] has gradually been recognized by the industry [7]. HCHhHCI, as an essential component of the surgical robot [8], is a channel for the surgeon to perceive, understand, and analyze feedback information, and make situational decisions by relying on their professional abilities [2]. Good HCI design can improve surgical efficiency and user experience [9], and is widespread concern of researchers in relevant fields.  
Minimally invasive surgery (MIS) refers to a surgical method that delivers special instruments, physical energy, or chemical agents into the human body through minor trauma or natural channels to deactivate, remove, repair, or reconstruct internal body abnormalities, deformities, or trauma for therapeutic purposes. Its outstanding feature is that it causes significantly less trauma to patients compared to traditional surgery [10]. Surgical robots, with their broad imaging field of view, high positioning accuracy, and remote control capabilities [11], have become an effective means of assisting in performing minimally invasive surgery. Depending on the type of surgery [12], MIS robots can be classified into six types--laparoscopic surgery robots, vascular interventional surgery robots, percutaneous puncture surgery robots, neurosurgical robots, natural orifice surgery robots, and orthopedic surgery robots [13]--as shown in Table 1.
## 1 Introduction
We capture the information of local and global joints by combining the operator of the improved spatial graph convolution with the modified transformer self-attention operator. Spatiotemporal graph convolution operation only operates on a local neighborhood node and is restricted by the size of the convolution kernel, it lacks the global information. Therefore, the autoencoder constructed with spatiotemporal graph convolution also lacks global information. We use self-attention to solve the locality of the graph convolution operator by capturing the global information in the skeleton data. Specifically, the extracted spatiotemporal graph of skeleton features is encoded to generate a latent vector using the encoder part of a spatial temporal self-attention augmented graph convolutional autoencoder (SAA-STGCAE). The deep embedded clustering layer is used to softly assign the latent vector to the clusters. We use the Dirichlet process mixture model to measure their distribution. We can obtain the normality score for each sample and determine whether the action should be classified as normal or not. An overview of proposed method can be viewed in Figure 1.  
The key contributions of this work are summarized in this paper as follows: (1) We propose a novel spatial temporal self-attention augmented graph convolutional clustering networks for skeleton-based video anomaly detection tasks by employing the spatial temporal self-attention augmented graph convolutional autoencoder to extract the relevant features and embedded clustering; (2) We design a new spatial self-attention enhancement graph convolution operator to understand the intra-frame interaction between different body parts and capture the local and global features of a skeleton in the frame; (3) Our model achieves state-of-the-art AUC of 0.789 for the ShanghaiTech Campus anomaly detection datasets and also exhibits excellent performance metrics for CUHK Avenue datasets.
## 5 Risk Assessment Method Based on Three-Parameter Interval Grey Number Decision-Making  
### Risk Probability Interval  
After the equipment fault probability \(P\), equipment importance \(E\), and loss degree _Loss_ are certain, the risk value \(R\) can be obtained from Formula (1), and the \(l\) order origin moment of the output variable \(R\) can be made certain by Formula (22).  
\[E(R^{l})=\sum_{k=1}^{n}\sum_{i=1}^{m}p_{x_{i},k}(R(k,i))^{l}\ (l=1,2,3,\ldots) \tag{22}\]  
The origin moments of each order of the output variable \(R\) are calculated, and its probability density function (PDF) and the cumulative distribution function (CDF) are obtained by expanding the Cornish-Fisher series [28]:  
\[z(\alpha)=\xi(\alpha)+\frac{\xi^{2}(\alpha)-1}{6}\beta_{3}+\frac{\xi^{3}( \alpha)-3\xi(\alpha)}{24}\beta_{4}-\frac{2\xi^{3}(\alpha)-5\xi(\alpha)}{36} \beta_{3}+\frac{\xi^{4}(\alpha)-6\xi^{2}(\alpha)+3}{120}\beta_{5} \tag{23}\]  
where \(\xi(\alpha)\) represents the inverse accumulative distribution function of \(\alpha\), \(\xi(\alpha)=\phi^{-1}(\alpha)\). The cumulative distribution \(F(\alpha)\) of \(z\) can be obtained by \(z(\alpha)=F^{-1}(\alpha)\).  
Since the transformer risk probability is given in the form of PDF and CDF, to fully consider the impact of uncertainty on risk judgment, the risk probability is expressed as an interval value--that is, \(R\) is represented by the corresponding interval of three sampling points: \(R=[u_{x_{i}}-\sqrt{3}\sigma_{x_{i}}u_{x_{i}}u_{x_{i}}+\sqrt{3}\sigma_{x_{i}}]\). The three-parameter interval grey number distance measure decision-making method is introduced to judge transformer risk grade by using this interval value.
###### Abstract  
Digital twin technology can be used for disaster safety management of underground utility tunnels, which are social infrastructures. To verify its efficiency in an underground utility tunnel, it must be implemented and monitored in the real world. Therefore, the state of information about an object and the position of an object in the real world, which can change upon movement, should be correspondingly reflected in the digital model. Underground utility tunnels are monitored through closed circuit television (CCTV) cameras because the main infrastructure facilities are installed in a long and narrow form, which limits access to them. Therefore, synchronization of a digital twin model of an underground utility tunnel requires a method for identifying the location of an object through an installed CCTV camera. This study compares the DenseDepth transfer learning method and the coordinate system conversion method through floor plan projection to estimate depth using a single CCTV camera installed in an underground utility tunnel. The coordinate system conversion method through floor plane projection showed an error of 0.23 m to 1.5 m within a 50 m range. The DenseDepth transfer learning method showed an error of 1 m to 3 m; therefore, it was considered unsuitable for distance estimation. The coordinate system conversion method through floor plane projection showed a smaller error even at a longer distance than the DenseDepth transfer learning method. Therefore, the coordinate system conversion method through floor plane projection was considered more suitable than the DenseDepth transfer learning method for depth estimation using a single camera.  
depth estimation; deep learning; coordinate conversion; digital twin +
Footnote †: journal: Medical Communication  
10.3390/app13042106
## 5 Benchmark Testing and Performance Analysis of MRSO
### Experimental Configuration and Evaluation Methods
#### 5.1.1 Evaluating with Averages (Mean)  
The average, or mean, is a fundamental statistical measure used to summarize the central tendency of a dataset. In optimization, the average objective function value across multiple independent runs provides a clear indicator of an algorithm's typical performance, facilitating straightforward comparisons with other algorithms. In minimization problems, lower average values indicate better performance, while in maximization problems, higher average values are desirable [34; 35]. This metric confirms the MRSO's ability to achieve optimal results consistently across different runs.
## 4 Experimental Design
### Experiment Implementation
#### 4.2.3 Communication Behavior  
Once the nodes and the application have been set up, the scenario must be configured. Algorithm 5 shows an example of how the environmental variables are set up in an ns-3 script. Algorithm 6 shows an example of the configuration of a communication channel, in this case Wi-Fi. As stated before, the scenario observes the IEEE 802.11b wireless standard with a Request To Send/Clear To Send (RTS/CTS) handshake set for frames above 2200 bytes, disabling fragmentation for frames under that value. Retransmission of lost packets due to collision or propagation loss is allowed [37], which means the nodes will make sure they can establish communication and then use a redundant protocol to make sure they receive packets correctly. The transmitted information consists of a device's contextual information (_i.e._, their feature vector), as well as its unique node identifier. The ns-3 offers several propagation models for signal delay and signal loss, which represent the power fluctuations of the signal under different real-world conditions. Examples are the Constant Speed Propagation Delay model, which has a constant signal speed independent of the distance; the Building Propagation Loss, which simulates shadowing and wall penetration signal loss; or the Okumura Hata, which is used to model path loss in open areas for distances of over 1 km and frequencies ranging from 150 MHz to 2.0 GHz [38]. A propagation delay model can be used together with a loss model, if they are compatible. ```
1. std::string phyMode (\"DsssRate1 Mbps\");
2. double distance = 50; // meters
3. uint32_t packetSize = 1000; // bytes
4. uint32_t numPackets = 10000;
5. double interval = 1.0; // seconds
6. Time interPacketInterval = Seconds(interval);
7. Config::SetDefault(\"ns3::WifiRemoteStationManager::FragmentationThreshold\", StringValue (\"2200\"));
8. Config::SetDefault(\"ns3::WifiRemoteStationManager::RtsCtsThreshold\", StringValue (\"2200\"));
9. Config::SetDefault(\"ns3::WifiRemoteStationManager::NonUnicastMode\", StringValue (phyMode));We began our experiments using the Friis propagation model, which gives the power received by one antenna under idealized conditions given another antenna some distance away is transmitting a known amount of power.
## 4 Dimensioning Analytical Model for the Optical Rings
### Evaluation of \(n_{\lambda,CPRI}^{u,i,j,k}\)  
Let us introduce the following events and probabilities:  
* \(\mathcal{E}_{S_{h}}\): It denotes the event that the RRUs of the sub-area handled by \(AS_{i,j}\) active the MIMO configuration \(S_{h}\) (\(h=0,\cdots,Q-1\)) with \(n_{h}\) (\(h=0,\cdots,Q-1\)) active antennas; \(p_{S_{h},k}=Pr(\mathcal{E}_{S_{h}})\) denotes the probability of the event \(\mathcal{E}_{S_{h}}\) in the \(k-th\) SI; the probabilities \(p_{S_{h},k}\) (\(h=0,\cdots,Q-1\); \(k=0,\cdots,N-1\)) are evaluated in Appendix B.
* \(\mathcal{E}_{\gamma_{0},\cdots,\gamma_{Q-1}}^{S}\): It denotes the joint event that \(r_{h}\) sub-areas handled by \(AS_{i,j}\), for \(h=0,\cdots,Q-1\), active the configuration \(S_{h}\); \(p_{\gamma_{0},\cdots,\gamma_{Q-1}}^{S,k}\) denotes the probability of the event in the \(k\)-th SI; due to the statistical independence (Assumption 4) mentioned above, we can express \(p_{\gamma_{0},\cdots,\gamma_{Q-1}}^{S,k}\) as follows: \[p_{\gamma_{0},\cdots,\gamma_{Q-1}}^{S,k}=\binom{D}{r_{0}}\binom{D-r_{0}}{r_{1 }}\cdots\binom{D-\sum_{h=0}^{Q-3}r_{h}}{r_{Q-2}}p_{S_{0},k}^{r_{0}}\cdots p_{S_ {Q-1},k}^{r_{Q-1}}\] (4)  
The introduction of these above events allows for the evaluation of the probabilities of the random variable \(N_{AS_{ij},CPRI}^{k}\) that denotes the number of wavelengths needed to carry CPRI flows from \(AS_{i,j}\) to the CO in the \(k\)-th SI. We can simply write:  
\[Pr(N_{AS_{ij},CPRI}^{k}=h)=\sum_{(r_{0}\cdots r_{Q-1})}\sum_{\sum_{h=0}^{Q-1} r_{h}=D}I(F^{\lambda}(r_{0},\cdots,r_{Q-1})=h)p_{\gamma_{0},\cdots,\gamma_{Q-1}}^{S,k} \tag{5}\]  
where \(I(*)\) is the indicator function and \(F^{\lambda}(r_{0},\cdots,r_{Q-1})\) is a function that establishes the number of wavelengths needed to support \(r_{h}n_{RRU}\) CPRI flows at bit rate \(f_{h}^{CPRI}\) for \(h=0,\cdots,Q-1\). The function \(F^{\lambda}(*)\) depends on type of mapping and multiplexing chosen for the OTN transport network. We assume the adoption of OTN multiplexing with at most two layers in which the CPRI flows are multiplexed in ODU2 [22] and ODU2's are multiplexed in a high level container transmitted on the wavelength; let \(\eta_{h}^{CPRI}\) (\(h=0,\cdots,Q-1\)) denote the maximum number of CPRI flows into one ODU2 generated by RRUs configured according to MIMO configuration \(S_{h}\) (\(h=0,\cdots,Q-1\)). If \(\eta^{\lambda}\) denotes the maximum number of ODU2s into a wavelength, we can express \(F^{\lambda}(r_{0},\cdots,r_{Q-1})\) as follows:  
\[F^{\lambda}(r_{0},\cdots,r_{Q-1})=\frac{\sum_{h=0}^{Q-1}\lceil\frac{r_{h}n_{ RRU}}{\eta_{h}^{CPRI}}\rceil}{\eta^{\lambda}} \tag{6}\]  
Finally, we choose \(n_{\lambda,CPRI}^{u,i,j,k}\) as the \(\alpha\)-th percentile of the needed number \(N_{AS_{ij},CPRI}^{k}\) of wavelengths, that is:  
\[n_{\lambda,CPRI}^{u,i,j,k}\text{ is chosen as the smallest value }x\text{ such that }Pr(N_{AS_{ij},CPRI}^{k}>x)<1-0.01\cdot\alpha \tag{7}\]
## 4 Results
### Correlation Analysis
#### 4.3.3 Correlation Between STAI Scales and RMSSD  
As shown in Figures 8-11, correlation analysis revealed varying degrees of association between changes in the STAI-C subscale scores and RMSSD (as a physiological indicator of relaxation). The change in STAI-C state anxiety (STAI-C S) showed a weak positive correlation with RMSSD (r = 0.276, \(p\) = 0.192), suggesting a potential relationship, although it was not statistically significant, as shown in Table 6. Similarly, the correlation between changes in STAI-C trait anxiety (STAI-C T) and RMSSD was weak and negative (r = \(-\)0.095, \(p\) = 0.507), indicating little to no significant interaction between long-term trait anxiety changes and physiological relaxation. The correlation between changes in the STAI-C total  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **r** & **\(p\)-Value** \\ \hline STAI-S & 0.770 & 3.98 \(\times\) 10\({}^{-8}\) \\ STAI-T & 0.948 & 2.01 \(\times\) 10\({}^{-18}\) \\ STAI & 0.916 & 4.65 \(\times\) 10\({}^{-15}\) \\ STAI-C S & 0.776 & 2.60 \(\times\) 10\({}^{-8}\) \\ STAI-C T & 0.934 & 1.00 \(\times\) 10\({}^{-16}\) \\ STAI-C & 0.852 & 4.42 \(\times\) 10\({}^{-11}\) \\ STAI-P & 0.830 & 3.90 \(\times\) 10\({}^{-10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pearson correlation coefficient of each stress scale (Pearson’s r).  
score and RMSSD was also weak and positive (r = 0.146, \(p\) = 0.559) and did not reach statistical significance. Regression analysis further confirmed these weak correlations, with regression coefficients of 0.905 for STAI-C S, \(-\)0.693 for STAI-C T, and 0.288 for the STAI-C total score. However, none of these relationships were statistically significant. Overall, the data suggest that the psychological changes in stress levels show a limited association with the physiological relaxation measured by RMSSD, with state anxiety exhibiting a stronger association compared to trait anxiety or the total score.  
Figure 8: Distribution of stress scales (STAI-C) and RMSSD.  
Figure 10: Correlation between STAI-C for each item and RMSSD.  
Figure 9: Scores and values change of STAI-C and RMSSD for each participant.
## 2 Mechanical and Durability Properties of Asphalt Concrete and Concrete
### 2.1 Influence of Mix Parameters on Pavement Performance
A detailed procedure is presented in Figure 5.  
[PERSON] and [PERSON] [53] highlighted that higher compaction should be avoided in the case of both Marshall and gyratory compaction. It may influence a decrease in the cold AC's workability. Then, a greater compression level can be challenging to apply in the field. The cold mix-design procedure is shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Authors** & **Types of Mix** & **Mix Parameters** & **Key Findings** \\ \hline
[42] & [PERSON] et al. & CPB & Aggregate packing & With appropriate packing optimization, the alternative (with only recycled aggregate) CPB’s compressive strength (fc) increased by 156\% from 30.9 MPa to 79.2 MPa. \\ \hline
[53] & [PERSON] and [PERSON] & AC & Method and level of compaction & Regarding gyratory compaction, 40 gyrations were enough for an adequate air void range (3–5\%) in cold mixes. \\ \hline
[54] & [PERSON] et al. & AC & Mixing temperature & Using a 3D X-ray computed tomography image, a higher mixing temperature led to a lower degree of clustering of recycled asphalt pavement (RAP) and a better distribution of the aggregates. \\ \hline \end{tabular}
\end{table} 
Table 2: Effects of mix parameters on properties of asphalt concrete and concrete paving block. Figure 5: Steps for advanced packing optimization (adapted from [42]). The research by [PERSON] et al. [54] analyzed 2D cross-sections taken for three AC cylindrical samples at the same depth in the fused 3D binary images, in which the voxels belong to recycled and natural aggregates. The X-ray analysis showed that the RAP aggregates were covered by a RAP binder, which tended to decrease by increasing the mixing temperature. This decrease can be due to a higher volatility loss of the AC's components with increasing mixing temperature. Furthermore, the spatial heterogeneities in the viscous properties can be responsible for the anisotropic aggregate distributions. Then, higher temperatures might contribute to a more homogeneous and isotropic spatial distribution of the aggregates, manifesting in better AC mechanical and durability properties.
## 2 Materials and Methods
### Data  
The main factors used for modeling the vulnerability to wind erosion processes were climate characteristics (wind speed, temperature, and precipitation), the presence of vegetation, and soil properties. The following datasets were used in this research:  
* Soil: soil texture (sand, silt, and clay content), the organic matter content and the CaCO\({}_{3}\) content. The soil data were obtained from a soil study of the AP Vojvodina soils [24];
* Climate: wind velocity, air temperature, and precipitation. The climate data (average monthly temperature, average monthly precipitation, and average monthly number of days with a wind velocity of above 12.4 m s\({}^{-1}\).) were obtained from the annuals (1997-2007) for the month of March from the Republic Hydrometeorological Service of Serbia (RHMOS) [23];  
Figure 2: A map of the research area in the AP Vojvodina (showing soil samples, meteorological stations and sample plots).  
* Vegetation: vegetation cover. The data concerning the vegetation cover were obtained from the Copernicus Global Land Service (CGLS) [25], a ready-made product of 10-daily difference vegetation index (NDVI) index images (resolution \(300\times 300\) m) for the area of the AP Vojvodina, for March 2016-2019. The data are available at [[https://land.copernicus.eu/global/products/ndvi](https://land.copernicus.eu/global/products/ndvi)]([https://land.copernicus.eu/global/products/ndvi](https://land.copernicus.eu/global/products/ndvi)) and they were accessed on 11 April 2019;
* Land use data: the data on land use were used without the areas which were not considered in this research as irrelevant for wind erosion processes, such as urban areas, water bodies, and mining areas. In order to exclude the abovementioned areas, CORINE Land Cover 2018 was used [26]. The data are available at [[https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download](https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download)]([https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download](https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download)) and they were accessed on 10 May 2019.
## 4 Results
### Features Metrics and Extraction
#### 4.1.3 Feature Analysis  
To improve the texture-feature-extraction efficiency, PCA was used to perform the principal-component extraction on multi-band data. After performing the principal-component transformation, we observed that the first principal component, PCA 1, contained the information of 82.58% of all the bands, and that the first three principal components, PCA 1-3, contained 99.08% of the information. In addition, eight texture features were extracted from four window scales (\(3\times 3\), \(5\times 5\), \(7\times 7\), \(9\times 9\)) using PCA 1, following which the best window for extracting the texture features was discussed. According to the Method 2.2, the ALOS-2 data were preprocessed to obtain the result, as depicted in Figure 1. Compared with the optical images, the texture of ALOS-2 images was more obvious; different surface roughness results in different texture features. In addition, the artificial surface and other features with strong dispersion characteristics have strong echoes, which are brighter in the image and have obvious features. High-resolution SAR images have better texture and strong scattering characteristics than those of optical images. We extracted the following eight texture features of the SAR images by using the GLCM method: contrast, variance, dissimilarity, angular second moment, mean, entropy, homogeneity, and correlation. These texture feature values were normalized to be combined into a multi-band image similar to a multi-spectral image. Owing to the certain correlation between texture features, upon increasing the number of texture features, the complexity of the image also increased, and a satisfactory combination effect was not necessarily achieved. Therefore, analyzing the correlation matrix between individual texture features, separability and important textures were selected, and strong scattering features were also required to be well preserved. On the basis of the above-mentioned analysis, this study selected four textures, namely, mean, variance, dissimilarity, and second moment, to generate the main texture feature.  
PCA was used to fuse the spectral, texture features of OLI images and texture features of ALOS 2 images (Figure 11).  
Figure 10: Texture values of land-cover types based on ALOS-2.
# Intelligent Network Service Optimization in the Context of 5G/NFV
## 4 Methodology of MAPE's Analysis Service
### Analysis Flow and Components  
The analysis (A) part of MAPE comprises two main components: the metric collector (MC), performing essential preprocessing operations on the monitoring data, and the actual analysis part, i.e., a DL classifier or an RL agent, performing the actual analysis of the processed metrics. The analysis architecture is modular, in the sense that its components can be easily replaced with alternative implementations without disruption of the overall operation, provided that their input and output data can be handled appropriately and follow a specified format.