esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Quantitative Evaluation  
We used the ISTD-TE and ISTD-CRTE test sets to compare and analyze ISTD-DisNet with the seven other current state-of-the-art semantic segmentation models. The comparison metrics used here are the segmentation accuracy, computational complexity, and inference efficiency, with the segmentation accuracy denoted by the \(F1\) score and _mIoU_, the computational complexity denoted by the number of network parameters (Par), the com  
Figure 5: Example images of ISTD-PDS7, where “TC”, “LC”, “CCC”, “ANC”, “BS”, “PA”, “PO”, and “NS” are the abbreviations for “transverse crack”, “longitudinal crack”, “cement concrete crack”, “alligator network crack”, “broken slab”, “patch”, “pothole”, and “negative sample”.  
putational complexity (CC) denoted by GFLOPs, and the inference efficiency denoted by frames per second (FPS). Table 2 lists the quantitative comparison metrics for all the experimental models, where V16 = VGG16 [61], MV2 = MobileNetV2 [62], R-50 = ResNet50 [63], H-V2 = HRNetV2 [60], X = Xception [64], and ST = Swin transformer [42]. It is worth noting that the test results of the comparison models in Table 2 are from our previous benchmark assessment of the ISTD-PDS7 dataset [20].  
From Table 2, it can be observed that, as far as the evaluation results for ISTD-TE and ISTD-CRTE are concerned, the performance of the different models in the crack segmentation task (ISTD-CRTE) is lower than that in the multi-type distress class dichotomous image segmentation task (ISTD-TE), which is because the structure of cracks is very complex and delicate. This requires the model to retain as much spatial information as possible, which is a challenge for most models. Compared to the other models, the proposed ISTD-DisNet achieves the most competitive performance in two evaluation metrics, and the original SegFormer takes second place in the ranking, which proves the superiority of the hierarchical transformer encoder in multi-scale distress feature extraction. In comparison, the _mloU_ values of Swin-UNet for ISTD-TE and ISTD-CRTE are decreased by 10.03 and 11.79, respectively. It is worth noting that, although Swin-UNet possesses a similar number of network parameters to SegFormer, it has a lower computational complexity (GFLOPs = 52.56) and a higher computational speed (FPS = 110.95). In addition, the convolution-based HRNet and U-Net achieve moderate performances, with the former performing better in the two-class semantic segmentation task and the latter performing better in the crack segmentation task. The two models also achieve the lowest FPS values and have a slow inference speed. SegNet, with the simple encoder-decoder architecture, performs the worst of all.
# Centrifugal Model Study of Seepage and Seismic Behavior in a Homogeneous Reservoir Dam with Parapet
## 1 Introduction
Given the above background information, the present study evaluates the effectiveness of dam reinforcement and flood prevention by constructing a parapet structure on the dam crest. Although a few parapet-designed reservoir dams exist, most of them are concrete or rock-fill dams. An example of an earthen dam, such as the one studied here, is the San Luis Dam in California, USA; its dam parapet wall is made of reinforced concrete and is 14 feet high and 4 feet wide. In some cases, parapets are designed only in certain sections of the dam crest along the upstream slope [13], whereas in this study, the parapet covers the entire width of the dam crest and is designed to ensure that its own weight is transmitted to the dam crest. The anticipated structural effects of constructing parapets on dam crests include the following: (a) The trapezoidal shape of dams can provide better water pressure resistance and load distribution, which helps crack suppression inside the embankment. The weight of the parapet can also restrain the seepage progression. (b) In Japan, most small-scale earth dams with sandy soil embankments have suffered damage because of earthquakes; even a 5.4 magnitude earthquake can cause embankment failure [14]. Parapets can improve seismic performance by preventing the displacement generated from the dam body. A centrifugal model test was conducted in this study to investigate this effect. Building full-scale circular dams can be expensive and time-consuming; however, centrifugal models can simulate such structures on a smaller scale. By rotating the model, a force similar to gravity is applied in the central direction, allowing the model to produce a deformation that approximates a circular shape. Previous studies have used centrifugal models to evaluate embankment deformation, hydraulic behavior, and reinforcement effects in soft ground or various ground conditions during earthquakes [15; 16]. One closely related study describes the characteristics of water pressure changes during an earthquake in a fill dam [17]. While the existing literature provides valuable suggestions for future experimental directions, it typically relies on single-shot experiments that do not link seepage and seismic behavior. In the context of centrifugal model experiments for hydraulic facilities, continuously considering the infiltration problem applied to the embankment can closely simulate field conditions. This study is novel because it focuses on linking seepage and seismic experiments to provide a more comprehensive understanding of potential issues that may arise in reservoir dams. Furthermore, the effectiveness of the parapet on the dam crest is evaluated.
## 4 Implementing L2S-M in a Cloud-Native Platform for Intra-Site Connectivity: K8s Case
### Virtual Network Management Flow
#### 4.2.3 Detachment of a Pod from a Virtual Network  
The procedure to delete a pod from a virtual network is very similar to the one for its deployment as seen in Figure 6:  
1. Once the pod is scheduled to be deleted from the cluster (either from a deletion event or a failure in the pod/node), L2S-M picks up the event generated from the K8s API and realises that the pod being removed is attached into a virtual network.
2. L2S-M removes the pod entry from its database, marking the interface as idle.
3. Simultaneously, L2S-M sends to the SDN controller the instruction to remove the pod from the virtual network (e.g., removing the previous intent(s) generated in the attachment phase).
4. The SDN controller will configure the forwarding tables from the switches to remove the entries related to the pod that have been deleted, effectively removing the pod from the virtual network.
## 2 Materials and Methods
### Analysis of Bacterial VOCs by HS-SPME-GC-MS
#### 2.4.3 GC-MS Analysis  
GC-MS analysis was performed on an Agilent 7890A GC coupled to a 5975C mass selective detector (MSD) (Agilent, Santa Clara, CA, USA). Chromatographic separation was carried out on an HP-5 MS capillary column (30 m \(\times\) 0.25 mm ID, 0.25 \(\upmu\)m film thickness). Helium flow rate was 1 mL min\({}^{-1}\). Injector temperature was maintained at 250 \({}^{\circ}\)C, and the splitless mode was chosen for injection. The oven programme was: initial 40 \({}^{\circ}\)C with a 2 min hold, ramped to 110 \({}^{\circ}\)C at 4 \({}^{\circ}\)C min\({}^{-1}\), ramped to 130 \({}^{\circ}\)C at 2 \({}^{\circ}\)C min\({}^{-1}\) (held for 2 min), ramped to 200 \({}^{\circ}\)C at 3 \({}^{\circ}\)C min\({}^{-1}\), ramped to 300 \({}^{\circ}\)C at 30 \({}^{\circ}\)C min\({}^{-1}\) (held for 5 min). The total run time was 68.5 min. MSD settings: electron impact mode at 70 eV, full scan acquisition mode, scan range 33-500 \(m/z\). Ion source temperature was 230 \({}^{\circ}\)C. Quadrupole temperature was 150 \({}^{\circ}\)C. The retention index (RI) of each volatile compound was calculated using \(n\)-alkanes (C\({}_{7}\)-C\({}_{40}\)) as reference compounds, as described by [PERSON] et al. [24]. Identification of bacterial VOCs was accomplished by comparing the obtained mass spectra with those stored in the National Institute of Standards and Technology (NIST) Mass Spectral Library (version 11) as well as by comparing the determined RIs with those reported in the literature. The relative amounts (RA) of the detected volatile compounds were calculated by dividing the area data of each component by the summed total peak area of all components. The analysis of NA medium was performed under the same conditions.
A Theoretical Study of the Energetic Stability and Geometry of Silicon-Vacancy Color Centers in Diamond (001) Surfaces  
[PERSON]  
1 School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (Y.P.); [EMAIL_ADDRESS] (W.S.)  
[PERSON]  
1 School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (Y.P.); [EMAIL_ADDRESS] (W.S.)  
Shenzhen Shen  
518057  
12 November 201920192019201920192019201920192019201920192019201920192019201  
nanodiamonds (NDs), which fail to meet the key requirements [10] for ideal single-photon sources. In contrast, negatively charged silicon-vacancy (SiV\({}^{-}\)) centers exhibit a sharp ZPL at 738 nm (1.681 eV) at room temperature, with a large DW factor of 0.7 [23] and a short luminescence lifetime of 1 to 4 ns [24]. Similar to SiV\({}^{-}\), the SiV center in a neutral charge state (SiV\({}^{0}\)) also possesses a high DW factor (-0.9), with most of its emitted light concentrated into the ZPL at 946 nm (1.31 eV) [25], rendering SiV\({}^{0}\) a promising candidate for serving as single-photon emitters.  
The SiV\({}^{0}\) has a ground state electron spin of \(S\) = 1. Single SiV defects in diamond are composed of a silicon substituting a C atom (Si) and a vacancy (V) in the nearest neighboring C site. Unlike the NV center, where the nitrogen atom is covalently bonded to its three nearest C atoms, and thus exhibiting \(C_{3V}\) structural symmetry, the SiV center theoretically forms a split-vacancy structure (i.e., a silicon atom located at a bond-center site) in bulk diamond (Figure 1), thus, exhibiting \(D_{3d}\) symmetry.  
To improve the integrability, sensitivity, and spatial resolution of single-photon sources, they are generally produced into NDs, or as diamond thin films, in an attempt to embed them close to the diamond surface. To date, many reports have been published on the successful fabrication and characterization of SiV centers into NDs or in diamond thin films via the in situ method, i.e., doping during the chemical vapor deposition (CVD) synthesis processing of diamond [15; 26; 27; 28; 29; 30], ion implantation [14; 25; 31] and femtosecond laser ablation [32]. However, to the best of our knowledge, the energetic stability and geometry of a single SiV center positioned in the vicinity of a diamond surface using different surface termination species remain poorly understood, despite theoretical and experimental studies on the effects of different surface states on the PL of SiV centers near a diamond surface [29; 33].  
The purpose of this study was to theoretically investigate the surface geometric structures and energetic stability of a single SiV\({}^{0}\) center positioned in the vicinity of diamond (001) surfaces (from the second to the seventh C layer, denoted as \(L2\) to \(L7\) in the following discussion), under fully H-, O- or N-terminated configurations. A diamond (001) surface was chosen, because at present, it is the most often employed and promising of diamond surfaces. Calculations were based on density functional theory (DFT) [34; 35] methods under periodic boundary conditions.
# Integration of LTE 230 and LTE 1800 in Power Wireless Private Networks
## 4 Integration of the Access Network
### Synchronization Signal of TD-LTE 1800
\(c_{0}(n)\) and \(c_{1}(n)\) are scrambling sequences decided by \(N_{\text{ID}}^{(2)}\) as  
\[ \begin{array}{l}c_{0}(n)=\widetilde{d}\Big{(}(n+N_{\text{ID}}^{(2)})\text{ mod}31\Big{)}\\ c_{1}(n)=\widetilde{d}\Big{(}(n+N_{\text{ID}}^{(2)}+3)\text{mod}31\Big{)}\end{array}  \tag{7}\]  
where \(\widetilde{c}(i)=1-2x(i),0\leq i\leq 30\), is defined by  
\[x(\overline{i}+5)=\Big{(}(x(\overline{i}+3)+x(\overline{i})\Big{)}\text{mod }2,0\leq\tilde{i}\leq 25, \tag{8}\]  
with initial conditions \(x(0)=0,x(1)=0,x(2)=0,x(3)=0,x(4)=1\). \(z_{1}^{(m_{0})}(n)\) and \(z_{1}^{(m_{1})}(n)\) are also scrambling sequences, which can be written as  
\[ \begin{array}{l}z_{1}^{(m_{0})}(n)=\overline{z}((n+(m_{0}\text{mod}8)\text{ mod}31)\\ z_{1}^{(m_{0})}(n)=\overline{z}((n+(m_{1}\text{mod}8)\text{mod}31)\end{array}  \tag{9}\]  
where \(\overline{z}(i)=1-2x(i),0\leq i\leq 30\), is defined by  
\[x(\overline{i}+5)=\Big{(}(x(\overline{i}+4)+x(\overline{i}+2)+x(\overline{i} +1)+x(\overline{i})\Big{)}\text{mod}2,0\leq i\leq 25, \tag{10}\]  
with initial conditions \(x(0)=0,x(1)=0,x(2)=0,x(3)=0,x(4)=1\). The sequence \(d(n)\) is mapped to resource elements according to  
\[ \begin{array}{l}a_{k,l}=d(n),n=0,\ldots,61\\ k=n-31+\frac{N_{RB}^{DLNRB}}{2}\\ l=N_{symbol}^{DL}-1\text{ in slot 1 and 11.}\end{array}  \tag{11}\]  
Figure 6 illustrates the distribution of the PSS and SSS in the TD-LTE 1800 subframe. The gap between the SSS and PSS is three OFDM symbols. Figure 6: Illustration of the primary synchronization signal (PSS) and secondary synchronization signal (SSS) in an LTE 1800 subframe.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 3 Different Channel Models
### Graph-Based Models  
Motivated by the limitations of the Euclidean distance-based channel model, we introduce a novel way of modeling the channel gain based on a graph. The rationale behind is as follows: When sending a signal from an AP to a location, the user at that location will receive the signal from different directions, which are referred to as multipath components. Among them, there may be one or two dominant ones, which can be approximated based on a non-obstructed path from the AP location to the user location. Based on this reasoning, we introduce a graph-based approach for modeling the channel gain. Instead of using the Euclidean distance \(d_{il}\), we consider a value \(\ell_{il}\), which is computed based on a path between a user location \(i\) and an AP location \(l\). Given that the angle \(\theta_{il}\) for the antenna gain also  
Figure 5: The limitations of the Euclidean distance-based model. The blue arrow represents the broadside direction for the array at the AP location. The green straight line indicates the Euclidean distance between user location \(i\) and AP location \(l\). The orange polyline indicates a possible propagation path from AP location \(l\) to user location \(i\).  
depends on the propagation paths, we directly incorporate the antenna gain into \(\ell_{il}\). For the graph-based models introduced in this section, we define the average channel gain as  
\[\widetilde{\beta}_{il}=\zeta^{\prime}\frac{1}{\ell_{il}^{2}}. \tag{14}\]  
The details of the methods for finding the shortest paths to be utilized in calculating \(\ell_{il}\) are presented in Section 3.2.2. Before going into these details, we first introduce a graph-based model for the environment.
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 6 Example: Design of a CNT-Nanoparticle Material for Photon Attenuation
### Material Design Based on Compton Scattering
Nanoparticle composites can be manufactured with different thicknesses, shapes, and volume fractions of nanoparticles to shield different energies of radiation. But an obstacle is that modeling the radiation absorption of nanoparticle composites using the Wikipedia equations or XCOM does not account for the nanoparticles size, shape, distribution, and the interactions in the composite matrix. Improved modeling is needed to optimize the design of nanoparticle composites for radiation shielding. Some characteristics of nanoparticles and gamma radiation are given. Due to their penetrating nature, gamma rays require a large shielding mass to reduce the health risk to cells of living tissue. High-Z elements have high density and provide high shielding per material volume. Nanoparticles made from elements with high atomic numbers (Z) like lead oxide (PbO), bismuth oxide (Bi\({}_{2}\)O\({}_{3}\)), or tungsten trioxide (WO\({}_{3}\)) are most effective at absorbing gamma rays due to their high electron density. An increased interaction surface may also be important. The small size of nanoparticles creates a larger surface area and more interfaces for interactions with gamma radiation, potentially enhancing absorption compared to larger particles. Nanocomposites also provide multiple components for shielding. A limitation of nanoparticle composites is they may require a significant thickness for effective protection, which increases the cost. CNT fabric can be sealed to contain the nanoparticles and form a composite with a thin sealant or binder material. An example of a nanocomposite is CNT-Fe-W fabric, which can be custom designed for radiation shielding. In forming CNT fabric nanocomposites, particles are added to CNT fabric to shield different types of radiation. The structure and composition of the fabric are customized to optimize the shielding. As an example, micro-size W particles are added as CNT fabric is wound onto a drum. The synthesis process is described in [89; 90; 91]. The Fe in the fabric is a residual catalyst, which is about 20% of the weight of the CNT. Shielding higher energy radiation will require a thick/dense shield. The CNT-Fe-W fabric is flexible and manufacturable, and the cost of the W particles is low. The research is continuing with the goal of reducing the weight of the fabric by using smaller nanoparticles. CNT-Fe-W fabric is also multifunctional--besides attenuating photon and particle radiation, CNT fabric is flame-resistant, attenuates microwave radiation, and is thermally conductive. The CNT-Fe-W fabric designed has a low density and is thick and may produce different shielding than solid material as predicted by XCOM [87]. Improved modeling is needed to predict the attenuation of low-density nanoparticle composites especially at high radiation energy levels.
# Research on Driving Scenario Knowledge Graphs
## 3 Methodology: DSKG Construction
### Knowledge Inference Models Based on Representation Learning
#### 3.4.1 Model Definition  
Knowledge graph representation learning models are a class of methods used to learn vector representations of entities and relations in knowledge graphs. These models play a crucial role in entity-linking prediction tasks in knowledge graphs. This section introduces several commonly used knowledge graph representation learning models, including the TransE [39], Complex [40], Distmult [41], and RotatE [42] models.  
The TransE model is a distance-based knowledge representation learning model. Its core idea is to learn the embedding representation of knowledge by translating the relation  
Figure 8: Integrated updated knowledge graph of the driving scenario domain (partial). Blue circles represent classes, orange circles represent instances, and green circles denote properties.  
vector as the translation operation from the head entity vector to the tail entity vector. For a given triple \((h,r,t)\), its scoring function is defined as follows:  
\[f_{r}(h,t)=\left\|h+r-t\right\|_{2} \tag{1}\]  
where \(p\) is the norm (usually 1 or 2), and \(\left\|\cdot\right\|_{p}\) is the norm. This function calculates the distance between the head entity after relation translation and the tail entity, where a smaller distance indicates a better match.  
The Complex model is a complex-based knowledge representation learning model. Its core idea is to represent entities and relations in the knowledge graph as complex vectors and use complex inner product to compute the similarity between entities and relations. Its scoring function is defined as follows:  
\[f_{r}(h,t)=\text{Re}\left(h^{T}r\tilde{t}\right) \tag{2}\]  
where \(\text{Re}(\cdot)\) represents the real part, and \(\tilde{t}\) represents the conjugate of the tail entity vector.  
The Distmult model is a dot-product-based knowledge representation learning model. Its core idea is to represent entities and relations in the knowledge graph as vectors and use dot product to measure the similarity between entities and relations. Its scoring function is defined as follows:  
\[f_{r}(h,t)=h^{T}r\odot t \tag{3}\]  
where \(\odot\) represents the element-wise product.  
The RotatE model is a rotation-based knowledge representation learning model. Its core idea is to use rotation operations to capture semantic correlations between entities and relations. Its scoring function is defined as follows:  
\[f_{r}(h,t)=dist(h\circ r,t) \tag{4}\]  
where \(\circ\) represents element-wise multiplication of complex numbers, and \(dist(\cdot)\) represents a metric function, usually 1 or 2-norm.
# Research on Wheel Flat Recognition Based on Wayside Wheel-Rail Force
## 3 Rigid-Flexible Coupling Dynamics Model Based on TODS  
To analyze the relationship between wheel faults and dynamic responses using TODS' discrete wheel-rail force concatenation method, a vehicle-elastic track coupling dynamics model was developed using multi-body dynamics software (SIMPACK 2018) and finite element software (ANSYS 17.0). Figure 5 shows the vehicle-track coupling dynamics modeling diagram. In this model, wheels and rails are treated as flexible bodies: finite element mesh models were first created in ANSYS and then imported into SIMPACK for co-simulation. The carbody is modeled as a rigid body, excluding onboard equipment vibrations. The rail support system employs discrete supports, incorporating under-sleeper stiffness and rail fastener properties, while the truck's elastic suspension nonlinearities are fully considered. Figure 4: Installation of a proximity switch. Figure 5: Rigid–flexible coupling dynamics modeling. The vehicle model comprises 90 degrees of freedom (DOF):  
Carbody, four side frames, two bolsters: Each with six DOF (three translational, three rotational). Eight bearing saddles: Each with one vertical DOF. Four wheelsets: Each with six DOF (three translational, three rotational), one bending DOF, and 1 torsional elastic DOF. Eight wedges: Each with one vertical DOF. Nonlinear wheel-rail contact geometry is integrated into the model. Key vehicle parameters are listed in Table 1. Wheel flat lengths are varied between 10 and 70 mm, and vehicle speeds between 10 and 80 km/h. The track model uses an elastic discrete support framework, where rail-under support elements represent fasteners and ballast layers. The hybrid coordinate system method was employed to model wheel elastic deformations while excluding the effects of parametric resonance from vehicle equipment. To generate wheel-rail force outputs consistent with field data collected by the TODS, a series of pressure sensors (a measurement module in SIMPACK, which can be understood as a data-readout tool) were integrated into the track model at 240 mm intervals between sleepers, as depicted in Figure 6. This sensor configuration enables precise timing of wheel passage through each detection zone, facilitating subsequent concatenation of discrete wheel-rail force measurements. Wheel flats can be categorized into new flats and old flats. The geometry of a new wheel flat approximates a circular chord, though idealized new flats are rarely observable in practice. Once a flat forms, continued train operation rapidly rounds the flat's sharp edges, transforming it into an old flat. For this reason, simulation analysis focuses on establishing a mathematical model of old flats to reflect real-world wear characteristics.