esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Scattering Phenomenon in Underground Mines and Distribution of Dust Particles
### Joint Distribution of the Arrival Time and the Arrival Angle of the Scattering Downlink Link  
As mentioned above, although there are \(N\) scatterers uniformly distributed within the hemispheric region, for our analysis, a scatterer (denoted by \(S\)) is considered in a random position (\(x_{s},y_{s},z_{s}\)) in the Cartesian coordinates and (\(r\),\(\theta\),\(\varphi\)) in the spherical coordinates, where \(r\geq 0\) the elevation angle (\(\theta\)) is defined for \(0\leq\theta\leq\pi\) and \(\phi\) is defined for \(\pi\leq\phi\leq 2\pi\).
## 4 Simulation and Analysis
### Comparative Analysis of the Observed Protocols  
In this section, we determine the performance parameters of the proposed EZ-SEP protocol, which is the extended version of Z-SEP algorithm. Various properties and evaluations mentioned before for the above studied protocols have been combined and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dead Nodes Representation** & **Packets Delivery Ratio** & **Nodes Energy Consumption** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different network condition comparison.  
listed in Table 4. Network level means energy homogeneity or heterogeneity. Energy efficiency is the energy consumption index of the algorithm. Stability period is the time interval between the life and death of the first node. Scalability defines the scalability of the network.  
SEP and LEACH show similar results to each other and are the worst compared to Z-SEP and EZ-SEP, on the other hand EZ-SEP performs better in all the parameters against them. According to the above parameters, EZ-SEP demonstrates better indexes than its parent protocols.
## 4 Results
### Comparison Using Different SAM Models  
The Segment Anything Model (SAM) provides three architectures with increasing parameter scales: VIT-B (base), VIT-L (large), and VIT-H (huge). In this study, we selected the lightweight VIT-B (60 M parameters) and high-capacity VIT-H (632 M parameters) to generate pseudo-labels for downstream segmentation training, representing a trade-off between computational efficiency and performance. As shown in Table 5, the VIT-H-based pipeline achieved a Dice score of 0.88 and IoU of 0.77, significantly outperforming VIT-B (Dice: 0.85, IoU: 0.73). The performance gap highlights the capacity advantage of larger models in capturing fine-grained object boundaries under complex visual contexts. However, VIT-H may suffice for resource-constrained applications where marginal accuracy drops are tolerable. Based on these findings, all subsequent experiments adopt the VIT-B backbone for pseudo-label generation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Dice** & **IoU** & \(p\)**-Value** \\ \hline Step a (Pseudo-label generation) & 0.83 \(\pm\) 0.02 & 0.70 \(\pm\) 0.03 & - \\ \hline Step b (Customized UNet++) & 0.84 \(\pm\) 0.01 & 0.71 \(\pm\) 0.02 & \textless{}0.05 \\ \hline Step c (Twin model refinement with VIT-B) & 0.85 \(\pm\) 0.01 & 0.73 \(\pm\) 0.02 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the ablation experiments on TV show data. \(p\)-Values (Dice) are computed using independent \(t\)-tests.  
Figure 4: Visual comparison of segmentation results across scenarios. Our method (3 rd column) demonstrates superior boundary precision and fewer false negatives compared to CVPR 2023 [46] and TMI 2021 [45]. One latest fully supervised segmentation model, EoMT, is also shown.
## 3 Analysis of Deformation Characteristics of Underground Structure
### Equivalent Stiffness Analysis of Elastic Foundation Beam  
In the case of box structures, equivalent calculation often results in overly large stiffness for the elastic foundation beam. Therefore, it is crucial to discuss the equivalent stiffness of box or hollow tubular structures with varying sections and materials.  
The statistics for shallow buried cut-and-cover tunnels beneath existing structures of various cross-sections and materials in China are provided in Table 3. Projects 1 and 2 feature ground box structures, while Projects 3 and 4 have large-diameter subway tunnels. Project 5 involves a small-diameter cast iron pipe, and Projects 6 and 7 utilize small-diameter municipal concrete pipes. To explore the foundation beam stiffness equivalence for the hollow structures in Projects 1 to 7, the box-type structure foundation beams are equated to unit width using 1D, 2D (\(D\) representing the base plate thickness), and the overall structure. For tubular structures, finite length beams are equated to unit width using 1D, 2D (\(D\) denoting the wall thickness), and the overall structure. Then, the two-stage theory is applied to calculate the equivalent foundation beam through various methods. By comparing the computed existing structural deformation with the additional bending moment, a reasonable equivalent method is sought. The foundation beam stiffness values are presented in Table 4.  
Where \(z\) indicates the net distance between the new tunnel and floor, \(h_{0}\) denotes the thickness of coated soil of the existing structure, \(L\) is the width of the existing structure, \(H\) represents the height of the existing structure, \(V\) is the stratigraphic loss rate, \(z_{0}\) indicates the height of the cover layer on the new tunnel, \(S_{\text{h}}\) is the horizontal clear distance between the tunnel and the pile, \(D\) represents the pile diameter, and \(L_{1}\) is the pile length.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**New Tunnel**} & **Construction** & **Excavation** & \multirow{2}{*}{**z**} & **Existing Structure** & \multirow{2}{*}{**\(\mathbf{h_{0}}\)**} & \multirow{2}{*}{\(\mathbf{L\times H}\)**} & \multirow{2}{*}{\(\mathbf{V}\)**} \\  & & **Method** & **Area (m\({}^{2}\))** & & & & & \\ \hline \hline
1 [51] & Culvert & \multirow{2}{*}{Bench method} & \multirow{2}{*}{20.43} & \multirow{2}{*}{3.667} & \multirow{2}{*}{Wukesong Station} & \multirow{2}{*}{4.833} & \multirow{2}{*}{18.3 \(\times\) 7.8} & \(V_{1}\) = 0.284 \\  & & & & & & & & \\  & (double-line) & & & & & & \\
2 [52] & Heat distribution tunnel & & & & & & \\  & (single-line) & & & & & & \\
3 [53] & Beijing Subway Line 6 & & & & & & \\
4 [54] & Beijing Subway Line 8 & & & & & & \\
5 [55] & Subway entrance (single-line) & & & & & & \\
6 [56] & Qingdao Suway Line 3 & & & & & & \\
7 [57] & Dalian Subway Line 4 & & & & & & \\  & (single-line) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Construction cases of underpassing existing structures.
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 4 Experimental Results and Analysis
### Evaluation Criteria  
The confusion matrix is a tool used to evaluate the performance of a classification model, helping to understand the types of errors that occur during prediction. It consists of four parts: true positives, true negatives, false positives, and false negatives.  
The performance of fault diagnosis results is evaluated based on three statistical parameters, namely accuracy (Acc), sensitivity (Sen), and specificity (Spe) [12], which are calculated from the respective confusion matrices of each classification result.  
\[Acc(\%)=\frac{TP+TN}{TP+TN+FP+FN}\times 100 \tag{9}\]  
\[Sen(\%)=\frac{TP}{TP+FN}\times 100 \tag{10}\]  
\[Spe(\%)=\frac{TN}{TN+FP}\times 100 \tag{11}\]  
where true positive (TP) and true negative (TN) represent the number of cases correctly classified, while false positive (FP) and false negative (FN) represent the number of cases incorrectly classified.
## 3 Results and Discussion
### Color Parameters Analysis and Sensory Analysis  
The results of the paired \(t\)-test for 3 color parameters and 11 sensory attributes of all Cabernet Sauvignon and Marselan wines are shown in Table 4. Among the 13 parameters, \(L^{*},a^{*}\), herbaceous, and oak attributes showed significant differences between 2 varieties. The result showed that the \(L^{*}\) value of Cabernet Sauvignon was higher than that of Marselan, indicating that the color intensity of Marselan wine was pronounced than that of Cabernet Sauvignon. This phenomenon probably could be explained by the higher concentration of anthocyanin derivatives (including vitisins, pinotin s, A-e-F, A-v-F and F-A/A-F) of Marselan wines in comparison with Cabernet Sauvignon wine (Table 3). These anthocyanin derivatives were in a low concentration though, they had almost one order of magnitude more molar fraction of pigmented molecules than their precursor thus making a sizeable contribution to wine color [33]. The \(a^{*}\) value of Marselan wines was higher than that of Cabernet Sauvignon wines, indicating that the Marselan wines had more red hue than Cabernet Sauvignon wines. Cabernet Sauvignon wines displayed stronger intensities in herbaceous and oak aromas. It is well known that methoxypyrazines are mainly responsible for the green pepper or herbaceous aromas in Cabernet Sauvignon wines [34]. In a recent  
Figure 2: Principal component analysis of Cabernet Sauvignon and Marselan dry red wines from five regions in China based on phenolic compounds. CS, Cabernet Sauvignon; MA, Marselan; HT, Huangtu; LP, Loess Plateau; JD, Jiaodong; NX, Ningxia; XJ, Xinjiang.  
study, although it was reported that Cabernet Sauvignon and Marselan grapes contained high levels of methoxypyrazines, the differences in methoxypyrazines concentrations between two varieties were affected by factors, such as vintage, and no conclusion was reached [35]. The intensities of astringency were higher in Cabernet Sauvignon wines, and the tannin quality was higher in Marselan wines. The previous study had shown that phenolic acids can made a difference in the quality and intensity of astringency [36; 37]. Further studies are needed to confirm the contributions of methoxypyrazines to Marselan wines and the differences among various varieties. The difference in oak aroma may be caused by the application of different aging techniques, such as the origin and toasting degree of oak barrels [38].  
In this study, although the \(b^{*}\), floral, fresh fruit, dried fruit, and sweet attributes had no significant difference between two varieties, their \(p\)-values were relatively low, ranging from 0.075 to 0.175. Among them, all Marselan wines have floral and sweet that were higher in intensities than Cabernet Sauvignon wines, which could be associated with higher terpen in Marselan wines mentioned above. There was no statistical difference in the performance of red fruit, black fruit, astringency, and tannin quality between two varieties of red wine.
## 4 Results and Analysis
### Consistency of the NPP Estimates across Scales  
From the distinct stand density related trend in Figures 4 and 5, we learn that stand density needs to be taken into consideration to provide consistent and comparable results across scales. To ensure that our findings are also consistent across the heterogeneity of the forests across Austria, we test our data for potential variations according to key forest site and stand parameters: (i) ecoregion; (ii) dominant tree species; (iii) mean stand age and (iv) elevation of a given forest inventory plot.  
Austria has very distinct biogeographic growth conditions due to the east-west alignment of the Alps. According to [PERSON] _et al._[31], these differences lead to nine typical ecoregions (see Figure 1), which are characterized by similar environmental, macro-climatic and geological conditions leading to differences in the potential vegetation.  
Ecoregions strongly affect the species distribution. Thus, we next group our data by dominant tree species according to the main relative proportion of the basal area at a given inventory plot (see data section). Norway Spruce and Silver Fir, as more shade-tolerant species, are combined in one group (_PA_ + _AA_). The light demanding tree species Scots Pine and European Larch form the group (_PS_ + _LD_) and the group \"_other C_\" combines all other coniferous trees. The broadleaf species groups are the Common Beech (_FS_), the Oak (_QS_) and _other B_, which summarizes all other broadleaf species. The left images in Figure 6 show the difference between MODIS and terrestrial NPP estimates grouped by ecoregion. The differences are in general positive, both for ecoregions and tree species, before correcting for stand density. The right images show that correcting for stand density reduces this (Figure 6).  
Austrian forests grow across a large gradient in elevation, which affects species and growing conditions. Again, we also group all our data by the following elevation classes: (i) \(<\)500 m; (ii) 500-1000 m; (iii) 1000-1500 m; (iv) 1500-2000 m and (v) \(>\)2000 m in elevation. The same procedure is applied for the mean stand age at a given inventory plot to assess if any age related influences of our findings exist. The six age classes across all data are: (i) \(<\)30 years; (ii) 30-60; (iii) 60-90; (iv) 90-120; (v) 120-150; and (vi) \(>\)150 years. The same pattern as in the previous Figure 6 is apparent in Figure 7 as well.  
Figure 6: Difference between MODIS NPP and NFI NPP (MODIS minus NFI) grouped by ecoregions **(top)** and dominant tree species **(bottom)**. Left original MODIS NPP (\(\Delta\)NPP) is used, right the detrended MODIS NPP (Corr. \(\Delta\)NPP) is used. Properties of illustration analogous to Figure 2.  
Figure 7: Difference between MODIS NPP and NFI NPP (MODIS minus NFI) grouped by elevation **(top)** and mean stand age **(bottom)**. Left original MODIS NPP (ANPP) is used, right the detrended MODIS NPP (Corr. \(\Delta\)NPP) is used. Properties of illustration analogous to Figure 2.
# Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 Technologies
## 1 Introduction  
Automotive vehicles are one of the most dangerous people transportation options [1] and human errors are the main cause of accidents. There are different types of users and different types of obstacles on public roads that change their position over time, making it difficult to predict their immediate location.  
The intelligent transport system (ITS-G5) is the European standard that uses vehicle networks to share messages between vehicles, people, and infrastructure to make the public road safer and more traffic efficient [2]. The use of vehicle networks by the automotive industry and consortia that implement smart cities has grown steadily and these mobile networks ensure digital communications between all the users of these ITS services.  
IEEE 802.11p is the standard chosen by ITS for wireless communication on public roads for Vehicular ad-hoc networks (VANET) [3], due to its features, namely low latency and reduced loss of information [4] and allowing communication vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X) and vehicle-to-pedestrian (V2P) [5].  
This paper describes a functional ITS-G5 showcase application included in the ITS-G5 road safety application classification. The developed prototype consists of two stations, a road side unit (RSU) installed close to a crosswalk and an onboard unit (OBU) inside the vehicle. The RSU station includes a motion sensor, a video camera, image processing technology and ITS-G5 technology. The motion sensor is used to detect the presence of objects on crosswalk, the video camera to collect images of the crosswalk, the image processing technology for pedestrian detection and ITS-G5 technology for sending DENM (decentralized environmental notification message) [6] messages. The OBU station inside the vehicle supports 802.11p communication to receive and process DENM messages and to show them on the car infotainment system. These two modules working together establish an ITS application to improve the information quality available to drivers about the current conditions on the public roads, near crosswalks.  
The computer vision system is used to detect pedestrians on a crosswalk which will trigger the ITS-G5 technology to create and send the DENM messages. It uses deep learning algorithms implemented on YOLO CNN [7] and on the OpenCV [8] framework.  
The next section provides a related work overview of the ITS-G5 and pattern detection technologies domains. Section 3 provides the prototype specification, the complete prototype component description, the RSU and OBU stations, and explains the integration of two technologies to create an ITS application, computer vision and ITS-G5. Section 4 presents the implemented test scenarios and the results obtained. Section 5 provides the conclusions about the implemented prototype, some remarks on the ITS capabilities and directions for future work.
## 5 Application of the TOPSIS Method for Turbine Selection  
TOPSIS (technique for order preference by similarity to an ideal solution) [38] is an MCDM technique that selects the best solution from a pool of solutions. The primary concept of the method is that the chosen solution should be the closest to the positive ideal solution and the farthest from the negative ideal solution [39]. The method is simple and computationally efficient. The procedure of TOPSIS consists of a series of steps [39] listed below:  
1. Calculate the normalized decision matrix. The calculation of the normalized value \(n_{ij}\) is done as \[n_{ij}=\frac{x_{ij}}{\sqrt{\sum_{i=1}^{m}x_{ij}^{2}}}\ \ \ i=1,...,m\ \ \ j=1,...,n\] (1)
2. Calculate the weighted normalized decision matrix. The calculation of weighted normalized value \(v_{ij}\) is done as \[v_{ij}=w_{j}n_{ij}\ \ \ \ \ i=1...,m\ \ \ \ j=1,...,n\] (2) where \(w_{j}\) is the weight of the \(i_{th}\) criterion, and \(\sum_{j=1}^{n}w_{j}=1\)
3. Determine the ideal solutions (both positive and negative): \[S^{+}=\{v_{i}^{+},...,v_{n}^{+}\}=\{(max\ v_{ij}|i\in I)(min\ v_{ij}|i\in I)\}\] (3) \[S^{-}=\{v_{i}^{-},...,v_{n}^{-}\}=\{(min\ v_{ij}|i\in I)(max\ v_{ij}|i\in I )\}\] (4) where \(I\) is associated with benefit criteria, and \(J\) is associated with cost criteria.
4. Calculate the separation measures, using the \(n\)-dimensional Euclidean distance. The separation of each solution from the positive ideal solution is given as \[d_{i}^{+}=\{\sum_{j=1}^{n}(v_{ij}-v_{j}^{+})^{2}\}^{1/2}\ \ \ i=1,...,m\] (5) Similarly, the separation from the negative ideal solution is given as \[d_{i}^{-}=\{\sum_{j=1}^{n}(v_{ij}-v_{j}^{-})^{2}\}^{1/2}\ \ \ i=1,...,m\] (6)
5. Calculate the relative closeness to the ideal solution. The relative closeness of the solution \(S_{i}\) with respect to \(S^{+}\) is defined as \[R_{i}=\frac{d_{i}^{-}}{d_{i}^{+}+d_{i}^{-}},i=1,...,m\] (7)Note that since \(d_{i}^{-}\geq 0\) and \(d_{i}^{+}\geq 0\), then \(R_{i}\in[0,1]\)
6. Rank the preference order. The solution with the highest value of \(R_{i}\) is the best solution.  
Figure 2 illustrates the pseudo code of the TOPSIS method. In order to apply the TOPSIS method to the multi-criteria turbine selection problem, several requirements need to be fulfilled. The first and foremost requirement is to identify the decision criteria. In this work, five decision criteria are assumed. These are hub height, wind speed, zero output percentage, rated output percentage, and annual energy production. Note that while five criteria are assumed, the proposed TOPSIS approach is scalable and can be extended to as many criteria as required without the loss of generality. The second requirement is to assign weights to each of the five criteria to calculate the weighted normalized decision matrix. A simple weighting assignment is to treat all criteria equally, thereby resulting in a weight of 0.2 for each criterion. The third requirement stipulates the need of satisfying the incommensurability, which is inherently dealt with by the TOPSIS approach. Incommensurability refers to situation where different decision criteria have different units and magnitudes.
# QRLIT: Quantum Reinforcement Learning for Database Index Tuning
## 2 Background
### [PERSON]'s Search Algorithm  
Quantum computing can speed up various search processes on unordered data due to the ability to superposition quantum states, thus allowing for the use of quantum parallelism. In 1996, a search algorithm that uses these quantum properties was proposed by [PERSON] (1986). [PERSON]'s algorithm evaluates whether a given solution, called \"good state\", is contained in the domain of N possible solutions. By increasing the probability of the \"good state\" and reducing the probability of the remaining ones, it allows for a search with a time complexity \(\mathrm{O}\Big{(}\sqrt{\mathrm{N}}\Big{)}\), presenting a great advantage in relation to the classical one with a time complexity \(\mathrm{O}(\mathrm{N})\).  
The algorithm is built with three main layers (Figure 1), each encapsulating a different function. The initial layer, designated as State Preparation, initiates the process by placing all qubits into a superposition state. The Oracle, representing the second layer, encodes the \"good state\" and changes its signal (phase shift) through a combination between multiple controlled Pauli-Z and Pauli-X gates ([PERSON] _et al._, 2016). Finally, the Amplification Layer, or Diffusion Operator, serves as a third layer and uses a combination of Hadamard, multi-controlled Pauli-X, and Pauli-X gates ([PERSON] _et al._, 2016). Its function is to phase shift again and amplify the probability of obtaining the \"good state\" during the observation process.  
Following [PERSON]'s definition, to achieve the maximum probability of measuring the good state, we need to add more iterations by repeating layers two and three by t times (Equation (14)) for a unique solution ([PERSON] _et al._, 2016).  
\[\mathrm{T}=\mathrm{int}(\frac{\pi}{4}\sqrt{\mathrm{N}}-\frac{1}{2}),\ \ \ \ \ \mathrm{N}=2^{\mathrm{number\ of\ qubits}} \tag{14}\]
## 5 Experiments
### Ablation Study  
Finally, we conduct an ablation study by separately removing components of ms-SDRGN, including multi-scale encoder, GAT layers, and GRU. We evaluate each case for 1000 episodes and the average results are listed in Table 3.  
It can be observed in Table 3 that when removing any components, our ms-SDRGN will generally result in performance degradation. Firstly, removing local CNN encoder in case '-ms' will reduce average CFE score and reward, which demonstrates the validity of CNN encoder, as discussed in Section 5.5. Secondly, case '-Soft' demonstrates the stochastic policy outperforms the deterministic policy by improving exploration and coverage efficiency. Thirdly, case '-1 GAT' disables one GAT layer and limits the ad-hoc communication to one-hop range, which decreases 0.08 points on CFE score and 530 points on reward. Case '-2 GAT' disables both two GAT layers, which completely cuts off the communication of the UAV swarm and causes further performance loss. This finding suggests the necessity of GAT mechanism for modeling the communication between agents. Moreover, case '-GRU' removes the memory unit and significantly reduces the average reward and CFE score. For complex MARL tasks such as SAG-MCS in this paper, the memory unit can help agents recall long-term experiences, especially when the positions of PoIs and obstacles are fixed.