esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Full Anchor Cable Support Mechanism for the Immediate Roof of a Thick Soft Rock Mass Roadway  
### Support Mechanism Analysis  
The immediate roof of trackway 3606 has a maximum soft rock thickness of 4.91 m, which is larger than the length of an ordinary anchor bolt. This means that the bolt cannot be effectively fixed in the solid rock stratum, making it difficult to support the roof effectively. From Figure 3A, it becomes clear that the roof cannot be effectively supported with a bolt-based support scheme because of the insufficient prestressed anchorage range.  
The roof of the 3606 trackway is soft and thick, and its support body needs a large anchoring range. Only in this way can the soft rock be made more resistant to damage and keep the roof intact. Upon observing Figure 3B, the full anchor cable support scheme plays several important roles in supporting the roof of the 3606 trackway with its soft and thick rock mass. First, it provides support for the shallow surrounding rock, reducing the risk of dislocation and deformation failures. The strong prestress and large anchoring range of the anchor cables increase the compressive stress of the surrounding rock, which reduces the influence of tensile stress and prevents deformation and damage to the surrounding rock.  
Second, the full anchor cable support scheme securely suspends the shallow loose surrounding rock from the roof, preventing it from collapsing and potentially damaging  
Figure 3: Diagram of anchorage range.  
the roadway. The anchor cables are inserted deep into the stable rock strata, ensuring that the surrounding rock is held in place and the roof remains stable.  
Finally, the plastic expansion zone deep in the roof is supported by anchor cables, which helps to control the plastic failure range of the roof and avoid roof movement. This ultimately ensures the safety of the roadway and protects the integrity of the support system. Overall, the full anchor cable support scheme plays a critical role in providing effective support for the thick and soft rock mass roof of the 3606 trackway.
## 2 Apparatus
### Simulations
#### 2.3.2 High Resolution Electron Modes
In fact, the mass resolution is in this case limited by the influence of the drift region and not by the initial velocity of the ions, and the mass resolution for zero kinetic energy ions (not shown) coincides with the mass resolution shown in Figure 4a. Figure 4b shows the resulting energy resolution for ion VMI in Operation Mode 2(b) for three different settings for the extractor voltage, \(V_{\rm E}^{\rm i}\), calculated using the 27-particle model (blue, green and orange lines). The optimum extractor voltage, for which the best resolution was achieved for a kinetic energy of 6 eV, was found to be \(V_{\rm E}^{\rm i}=-8.802\) kV, and for this setting, the resulting energy resolution from the Monte Carlo simulation is shown (black line), indicating an energy resolution better than 2.5% for kinetic energies between 3 and 10 eV. This is a small degradation in resolution as compared to the resolution obtained in Operation Mode 1(b) (shown by the dashed black line), but it is still an acceptable resolution for ions in most experiments. It is interesting to note that even in Operation Mode 2(b), for \(V_{\rm E}^{\rm i}=-8.802\) kV, the mass resolution of the ion TOF is \(m/\Delta m=100\), which is acceptable unless isotope resolution of heavier fragments is required. In Figure 4c,d, the corresponding simulated detector image and the 3D momentum distribution after inversion are shown, respectively. Figure 4e shows the kinetic energy spectrum calculated from the 3D momentum distribution. Figure 4: Cont.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 6 Trusted Execution Environment (TEE) Support and Feasibility
### AMD SEV and SEV-SNP  
AMD SEV and SEV-SNP extend x86 virtualization with full memory encryption and integrity protection while maintaining compatibility with native x86 memory and cache management. Crucially, SEV supports huge pages (e.g., 2 MB, 1 GB) transparently to the guest OS [32]. This enables our covert channel design without requiring architectural modifications.  
Furthermore, AMD's MOESI coherence protocol enables PREFETCHW from one core to induce a modified (M) or owned (O) state in another core's cache, a property exploited in our experiments. Using Google Cloud's Confidential virtual machine (VM) instances, which feature SEV-enabled AMD EPYC CPUs, we demonstrate the practicality of our covert channel with both the sender and the receiver co-resident on a single VM, running on separate physical cores.  
The availability of coherent prefetching, the high-resolution timers clock_gettime() or rdtscp(), and huge pages makes SEV the most suitable TEE for this attack.
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 4 Results
### Mean and SD Analysis  
Finally, when analyzing the graphs of the mean and SD of the AC components, it is observed that the means in all the CF ranges are close to zero, indicating the absence  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CF Range** & **Mean** & **Std** & **Min** & **Max** & **Median** \\ \hline
0–0.2 & 2.80 & 1.37 & 0.56 & 4.91 & 2.86 \\
**0.2–0.4** & **3.07** & **1.38** & **0.91** & **5.67** & **2.86** \\
0.4–0.6 & 2.09 & 0.92 & 0.57 & 4.22 & 2.08 \\
0.6–0.8 & 1.56 & 0.94 & 0.65 & 5.20 & 1.50 \\
0.8–1.0 & 0.66 & 0.44 & 0.20 & 2.21 & 0.53 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Descriptive statistics of \(P_{SQI}\) by CF range, highlighting in bold the identified optimal range.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CF Range** & **Mean** & **Std** & **Min** & **Max** & **Median** \\ \hline
0–0.2 & 0.02 & 0.01 & 0.00 & 0.03 & 0.02 \\
0.2–0.4 & 0.02 & 0.01 & 0.00 & 0.03 & 0.02 \\
**0.4–0.6** & **0.02** & **0.01** & **0.00** & **0.04** & **0.02** \\
0.6–0.8 & 0.02 & 0.01 & 0.00 & 0.03 & 0.02 \\
0.8–1.0 & 0.01 & 0.01 & 0.00 & 0.05 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of \(Z_{SQI}\) by CF range, highlighting in bold the identified optimal range.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CF Range** & **Mean** & **Std** & **Min** & **Max** & **Median** \\ \hline
0–0.2 & 2480.98 & 1923.91 & 383.49 & 9166.99 & 1918.59 \\
0.2–0.4 & 4291.52 & 2365.41 & 863.76 & 9766.50 & 3862.66 \\
**0.4–0.6** & **4794.68** & **2030.75** & **1370.60** & **10.155.94** & **4597.64** \\
0.6–0.8 & 4080.38 & 2133.26 & 880.20 & 10,566.47 & 3821.63 \\
0.8–1.0 & 1985.56 & 2202.75 & 65.01 & 6443.84 & 977.02 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Descriptive statistics of \(E_{SQI}\) by CF range, highlighting in bold the identified optimal range.  
of a significant trend towards high or low values in the average measurements across these ranges. The presence of negative minimum and positive maximum values of similar magnitude shows that, although most of the measurements are close to zero, there are fluctuations that deviate both positively and negatively with the homogeneous variability in the measurements across the different CF levels. Furthermore, medians close to zero reinforce that the central tendency of the data is aligned with the mean, confirming the symmetry of the distribution around a central value. However, the SDs are higher in the low ranges of CF (0-0.6), indicating more significant variability in the amplitude of the signals. As the CF increases (0.6-1.0), a reduction in the median SDs is observed, associated with a decrease in the pulse amplitude. In the highest range of CF (0.8-1.0), the range of SDs decreases considerably due to the lower presence of pulses due to the compression of blood vessels.
# Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
###### Abstract
In contrast to the broadcast soccer video, the scouting video can adequately capture lengthy spatial-temporal relationships between most players since it does not focus on the ball carrier and does not include scene highlights, as shown in Figure 1. However, the extremely small image size of the ball in unedited soccer videos makes it difficult to focus on players performing actions with the ball. Hence, by constructing the relationships between players in each video frame as a graph with audio-visual information as nodes, the proposed method enables shot prediction from those videos. To introduce the importance of players based on field position and team information into the graph edge, we take advantage of knowledge that reflects detailed match situations. Then, our method extracts latent features from graphs by incorporating graph convolutional networks (GCNs) [35] and a graph convolutional recurrent network (GCRN) [36]. The graph convolutional recurrent neural network (GCRNN), which combines the above two deep learning methods, enables the extraction of latent features that consider both spatial and temporal relationships of the graph. Finally, the proposed method predicts shots with Bayesian neural networks (BNNs) [37]. The BNN is a method that can make predictions with uncertainty by introducing probability distributions for the weight parameters of the neural network. Thus, the BNN enables more reliable predictions than conventional methods based on deterministic machine learning. Note that we have previously presented the preliminary results in previous studies [38; 39; 40; 41], where we demonstrated the effectiveness of incorporating visual information and players' distances into graphs to achieve shot prediction from soccer videos. In summary, the main contributions of our method are as follows:  
* For the shot prediction from soccer videos, we use a graph that introduces soccer-specific knowledge (players' importance based on the field positions and team information) with audio-visual information as graph nodes. * Predicting and obtaining uncertainty with BNNs enables us to provide more robust predictions. The remainder of the paper is organized as follows. In Section 2, we describe the shot prediction method based on multimodal features in soccer videos with GCRNN and BNNs. Experimental results and the discussion are presented in Section 3 to verify the effectiveness of the proposed method. Finally, we conclude our work in Section 4.
## 2 Materials and Methods
### The MDCIM

Table 1: Display specifications assumed for the sRGB encoding color space. The value of 2.4 for the \(\gamma\) parameter in sRGB encoding color space is inherited from parametrizations from CRT displays before 1998, and also partly based on an optimization for a reference ambient illuminance level of 64 lx [22; 26]. The international standard that specifies sRGB color space remarks that the typical office viewing environment has an ambient illuminance level of 350 lx, considerably exceeding 64 lx [22]. But mobile displays are typically viewed under much higher illuminance than either of these two values. This is why the International Color Consortium recommends using different values for \(\gamma\) in the case of situations that differ from the reference viewing environment [27]. Furthermore, modern mobile displays have colorimetric specifications that differ significantly from those listed in Table 1 for sRGB, as this color space was originally designed for CRT displays. However, today's mobile and tablet displays, despite device-specific characterization proposed by the MDCIM and other models [28; 29], have proven to significantly enhance color reproduction accuracy; the need to individually characterize each device poses a challenge for practical implementation. Additionally, the MDCIM takes ambient lighting into account for color reproduction. The CIECAM02 model has been applied to account for ambient illuminance in mobile displays [30; 31], but studies indicate it lacks precision under varied lighting and leaves room for accuracy improvements, even after refinement [32]. Therefore, the MDCIM adopts an alternative approach to account for ambient illuminance. As previously mentioned, the MDCIM uses Equation (4) to determine display-specific maximum luminance and chromaticity coordinates. By combining Equations (4) and (1), tristimulus values with a luminance dimension are obtained. To calculate colorimetric parameters such as CIELAB values, the tristimulus values of the reference white must also be expressed in this dimension, which involves multiplication by the luminance \(L_{\text{rw}}\) of the reference white:  
\[L_{\text{rw}}\ =\ \frac{M_{L}}{\pi}\ =\frac{E\cdot\rho}{\pi}\ \ \ [\text{cd}/ \text{m}^{2}] \tag{5}\]  
where \(M_{L}\) is the luminous existence of reference white, and \(E\) is the ambient illumination level in lx [33]. Equation (5) assumes that the reference white is a Lambertian scatterer, and in what follows, it will also be assumed to have ideal reflectivity \(\rho=1\) independent of wavelength. The approach of estimating luminance from illuminance based on the assumption of Lambertian distribution follows the definition established by the MDCIM, as referenced in [10]. We chose to maintain consistency with this reference to ensure comparability.
## 3 Image Feature-Based Extraction of Damage Indicators
### Residual Bearing Capacity Extraction Algorithm  
#### 3.1.1 Static Analysis of Bridge Piers and Decks  
For the abutment and deck members of a girder bridge, which are simplified as two rectangular bodies without considering the mechanical influence brought by the natural environment, the abutment force is mainly composed of vertical loads, including the ground support force, the self-weight of the abutment, and the weight of the bridge deck and the loads such as the vehicles passing through the bridge, which will exert pressure on the connection between the abutment and the bridge deck. A measure of the bearing capacity of a bridge abutment can be expressed in terms of stress \(\sigma\). The stress is calculated as:  
\[\sigma=F/A, \tag{1}\]  
where \(F\) denotes the force applied to the stressed object and \(A\) denotes the cross-sectional area of the stressed object.  
Since the abutment is a vertical structure, \(F\) is the combined force of the ground support force \(Fo\), the abutment self-weight is \(G\), and the bridge deck transfer load is \(Q\). The force is manifested as pressure, and \(A\) is the horizontal cross-sectional area of the abutment. When the beam structure is complete, due to the existence of self-weight, in any transverse cross-section of stress with the height of the decrease and increase, when the abutment structure is missing, the missing part of the cross-sectional area decreases and will be in the missing part of the stress concentration. Therefore, the size of the residual bearing capacity of the bridge abutment is negatively correlated with the height of the strike location and  
Figure 1: Flowchart of damage assessment method for girder bridge.  
positively correlated with the strike intensity, and the simplified force situation is shown in Figure 2.  
Since the bridge deck is a transverse structure, \(F\) is the combined force of the reaction force of the pier support \(Fo\), the self-weight of the bridge deck \(G\), and the passing vehicle load \(Q\) on the bridge deck, and the force of the bridge deck manifests itself as a bending moment, calculated as:  
\[M=F/x, \tag{2}\]  
where \(F\) denotes the shear force applied at a location and \(x\) denotes the distance from that location to the support surface.  
When the bridge deck structure is complete, the bending stress in the bridge deck length direction is half the maximum, and when the bridge deck structure is missing, it will produce stress concentration in the missing parts. Therefore, the size of the residual bearing capacity of the bridge deck is negatively correlated with the distance of the strike location from the center of the bridge deck and positively correlated with the strike intensity, and the simplified stress situation is shown in Figure 3.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 4 Hybrid Simulated Annealing Algorithm
### Cost Function  
The cost of a solution, \(P\), is defined by Equation (1).  
\[Cost(P)=\frac{|U|-|C(p)|}{|U|} \tag{1}\]  
where \(|U|\) is the total number of users and \(|C(P)|\) represents the total number of users that are covered by \(P\).  
When \(Cost(P)\) equals 0, \(P\) is an optimal solution to the wireless communication tower placement problem. It should be noted that when \(P\) is an optimal solution to a wireless communication tower placement problem, however, \(Cost(P)\) may not be equal to 0. In other words, \(Cost(P)=0\) is a sufficient condition, but it is not a necessary condition for \(P\) to be an optimal solution.
## 3 Test and Results
### Shadow Detection Results
As observed in the results, the unbalanced data scenario tends to provide high F1-Scores but reveals noticeable under-segmentation during qualitative analysis. This is particularly evident in U-Net and U-Net++, where these methods successfully segment intricate shadows but still exhibit significant over-segmentation areas, especially in regions lacking an annotated ground truth. DSSNet demonstrates improved control over over-segmentation but fails to capture smaller shadowed regions accurately. MSASDNet  
Figure 10: Comparison of shadow segmentation results for the third sample: (**a**) original image, (**b**) ground truth, (**c**) U-Net (F1: 0.8964), (**d**) U-Net++ (F1: 0.9241), (**e**) DSSNet (F1: 0.9057), (**f**) MSASDNet (F1: 0.8667), (**g**) CADDN (F1: 0.9293), (**h**) proposed method (DKU-Net, F1: 0.9565). consistently misses small shadow regions and, in this sample, shows noticeable over-segmentation in the upper area of the image. CADDN achieves accurate shadow segmentation but misses fine details in smaller regions. Lastly, the proposed DKU-Net method also under-segments certain small shadow regions; however, when compared to the other tested methods, it significantly mitigates this under-segmentation while maintaining a balanced and robust performance across intricate and small shadow details. Summarizing, U-Net and U-Net++ consistently show strong generalization abilities, achieving high F1-Scores; however, they struggle with intricate shadow details, either over-segmenting small regions or failing to capture fine structures. DSSNet demonstrates improvements by reducing over-segmentation but lacks precision in capturing finer details and smaller shadowed areas, especially in complex scenarios. MSASDNet performs well in avoiding over-segmentation but consistently misses small shadow regions, particularly in unbalanced data cases, which impacts its robustness. CADDN achieves a good balance between under- and over-segmentation, capturing intricate shadow details more effectively than the previous methods. However, its performance is limited by missing finer structures within the segmented shadows, which could impact applications requiring higher granularity. The proposed DKU-Net architecture outperforms the tested methods across various metrics, achieving the highest F1-Scores and providing a notable balance between precision and recall. It effectively mitigates both over- and under-segmentation, capturing intricate shadow details while maintaining consistency across different data distributions. Despite occasional minor under-segmentations in small shadow regions, DKU-Net demonstrates superior robustness and adaptability, making it the most reliable method for shadow segmentation in aerial images among the tested approaches. These findings highlight DKU-Net's potential for real-world applications such as urban planning, environmental monitoring, or infrastructure analysis, where accurate shadow segmentation plays a crucial role in extracting meaningful insights from aerial imagery.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 4 Experimental Setup
### System Setup  
In recent speaker recognition challenge, e.g., NIST SRE [45] and VoxSRC [46], the x-vector (TDNN) [19] is used as a baseline by the organizer for its good performance.  
To verify the effect of our proposed PPL loss, we took the classical TDNN structure and statistics pooling (SP) [18] as the baseline and carried out experiments on the VoxCeleb database. The classical TDNN structure consists of five one-dimension convolutional layers, a pooling layer, two fully connected layers and a softmax layer. The outputs of the hidden layers are usually used as the speaker embedding for verification. Specifically, the kernel sizes of five one-dimension convolutional layers at the frame-level are 5, 3, 3, 1 and 1, respectively.  
The output of layer 5 is a 1500-dimensional vector, so if the input speech has T frames, we obtain a (\(T\times 1500\)) vector. The mean and variance of these \(T\) 1500-dimensional vectors are calculated and concatenated to be a 3000-dimensional vector. There are two 512-dimensional fully connected layers after the pooling layer. The output of the softmax layer is a (1 \(\times\) N)-dimensional result vector, where N denotes the number of speakers in the training stage.  
For the training with Pytorch, we cut the input speech into chunks of 200 frames for input model training during the network training. See Table 1 for network parameter settings. T represents the number of frames per chunk.
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 2 Related Work  
Performance evaluation via mathematical modeling and analysis has been the focus in prior works for deriving the fundamental limits of quantum networks, developing new insights, and providing a collection of baseline results to use in numerical or experimental settings. However, this closed-form assessment is generally tractable solely for simple scenarios: configurations with one or two nodes [10; 22; 23; 24; 25; 26], linear chains (i.e., isolated paths in a network) [11; 27], specific entanglement distribution protocols [12; 28; 29], or one-shot communication instances in which only a single request is considered at a time [30; 31].  
At the system level, models for performance analysis of quantum networks addressing queue stability, control, or scheduling policies have also been investigated [32; 33; 34; 35; 36], mainly under idealized assumptions on the diverse impairments that quantum devices or quantum channels may exhibit, e.g., loss, state/memory decoherence, noisy entanglement swapping, or depolarization errors.  
Discrete-event simulation is used in some of these works for validating the assumptions of the formal model, like in [13; 31], that exploit NetSquid to corroborate the results predicted by a Markovian analysis of a single switch and a single repeater. Different quantum memory technologies and simulator configurations were compared to evaluate the general performance of the system, but in either case, a generalized network architecture remained unimplemented, specifically the network size for the switch case and the quantum memory sizes in the two-switch case.  
Alternatively, numerical simulation forms the basis for studying the theoretical limits of quantum switches in works like [10], where the capacity region under different conditions is completely bounded through convex analysis. Nevertheless, numerical simulation has a high computational load, and the analysis here deals with a single switch as well. Another limitation of current simulation experimental settings is that they have not been optimized or conceived for implementing and evaluating key metrics like fidelity as a resource, yet fidelity is fundamental for routing, distributed quantum computing and quantum communication [8; 26; 37].  
Our work sets the basis for a simulation framework designed for experimentation on arbitrary network graphs, based on a more general operating model supporting quantum switches and routing, but with the capability of precise configuration of the physical properties of channels, sources, quantum memories, and quantum programs.