esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response Effect on Base-Isolated Rigid Structures by Mass Eccentricity in Nuclear Plants
## 3 Sensitivity Analysis of Base Isolation Response Due to Mass Eccentricity
### Results of the Sensitivity Analysis of Seismic Response by Mass Eccentricity
In summary, the analysis results for the response spectra based on mass eccentricity in the one-axis direction indicate that, as eccentricity increases, the combined behavior due to the increased contribution of torsion leads to varying acceleration or displacement responses at specific locations of the upper structure, depending on its position. In the following analysis, a sensitivity analysis of seismic response due to 2D mass eccentricity in both horizontal directions was conducted. For the input of seismic motion, the EW direction design response spectrum from Figure 5 was utilized, along with the NS direction DRS, to perform acceleration and displacement response spectrum analysis. Figure 10 illustrates the changes in acceleration response for the X direction and Y direction while simultaneously increasing mass eccentricity in both horizontal directions. In Figure 10a, it can be observed that, for X-direction acceleration, the response sensitivity due to 2D mass eccentricity, where both Y-direction and X-direction eccentricities exist, shows nonlinear variations. As seen in Figure 10b, a similar trend is observed for Y-direction acceleration. In summary, the common feature is that, while in some regions the response may decrease, when mass eccentricity increases simultaneously in both horizontal directions, acceleration response can significantly increase. Thus, caution is required. Figure 10: 2-D mass eccentricity effect on horizontal max. acceleration. (**a**) Effect on max. acceleration in the X direction; (**b**) Effect on max. acceleration in the Y direction. Figure 6.0 Eccentricity sensitivity of horizontal displacement at A2 and A4 points. (**a**) A2 Point; (**b**) A4 Point. Figure 11 is similar to Figure 10, showing the influence on displacement response for the X direction or Y direction while simultaneously increasing mass eccentricity in the horizontal direction. As shown in Figure 11a, in the case of X-direction displacement, when Y-direction mass eccentricity is low, the displacement response linearly increases in proportion to the eccentricity. For X-direction non-eccentric cases, there is little effect from Y-direction eccentricity on displacement response until the X-direction eccentricity reaches around 50%, at which point the sensitivity to eccentricity increases significantly. In Figure 11b, Y-direction displacement demonstrates a different behavior. When X-direction mass eccentricity is low, the response sensitivity to Y-direction mass eccentricity remains relatively constant. As both horizontal mass eccentricities increase, Y-directional maximum displacement can increase by approximately 60%.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### HetNet Congestion Indicator
#### 3.2.2 Classify a UE as a MUE or PUE Type  
As mentioned in Section 2, a UE could be either MUE or PUE. If a UE receives signal from both Macro BS and pico BS, and it selects the Macro BS as its serving BS as the BS has the least HCI, then the UE is classified as a MUE; otherwise, it is a PUE. Due to the mobility, a UE may change its serving BS which will also change its type. As aforementioned, the different type of the UE will affect the priority of accessing resources. Specifically, a MUE can only access resources from the Macro BS, while a PUE can access resources from two BSs simultaneously. Figure 5 illustrates how a UE may enter the dual connectivity range or transits from PUE to MUE. Initially, a UE may be within the coverage range of a pico BS, but far from the Macro BS. In this stage, it is a PUE. As it moves closer to the Macro BS, it begins to receive signal from Macro BS and enters the dual connectivity range. At this stage, it is able to utilize resources of both BSs. As it continues to move closer the Macro BS, it eventually selects Macro BS as its serving BS based on the HCI cell selection scheme. At this stage, it becomes a MUE.
## 2 Modeling and Analysis of WMDAR
### Kinematic Model of Mobile Platform  
The following assumptions are made when analyzing the kinematics of the mobile platform. (1) There is no sliding between the wheels and the ground. (2) The speed traction axis is parallel to the plane. (3) The center point of the two driving wheels is the centroid of the robot mobile platform. The mobile platform is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Connecting Rod**} & **Quality/(kg)** & **Location of the Center of Mass/(mm)** \\ \hline \(1\) & 2.002 & [0, 5.955, 5.532] \\ \hline \(2\) & 1.268 & [0, 7.494, 185.070] \\ \hline \(3\) & 1.246 & [0, 5.864, 333.648] \\ \hline \(4\) & 0.710 & [0, 7.180, 490.024] \\ \hline \(5\) & 0.698 & [0, 4.821, 609.971] \\ \hline \(6\) & 1.461 & [0, 0.001, 817.380] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quality parameters of each link of the manipulator.  
Figure 3: The parameters of the mobile platform.  
Figure 2: Frame system definition. (**a**) Dual-arm robot. (**b**) Mobile platform.  
The generalized coordinates and velocity of the mobile platform are defined as: \(\theta=\left[x\quad y\quad\phi\right]\), \(\dot{\theta}=\left[\dot{x}\quad\dot{y}\quad\dot{\phi}\right]\). The steering angle and angular velocity of the left wheel are \(\phi_{l},\dot{\phi}_{l}\), and the steering angle and angular velocity of the right wheel are \(\phi_{r},\dot{\phi}_{r}\). Considering that the mobile platform satisfies the motion law of rigid body, there are:  
\[\mathbf{v}_{l}=\mathbf{v}_{c}-a\dot{\phi}\;\mathbf{v}_{r}=\mathbf{v}_{c}+a\dot{\phi} \tag{1}\]  
\[\mathbf{v}_{c}=\frac{(\mathbf{v}_{r}+\mathbf{v}_{l})}{2}\;\dot{\phi}=\frac{(\mathbf{v}_{r}-\mathbf{v }_{l})}{2a} \tag{2}\]  
Since the wheels have no sliding, there is \(\mathbf{v}_{l}=r\dot{\phi}_{l},\;\mathbf{v}_{r}=r\dot{\phi}_{r}\) and \(\dot{x}=\mathbf{v}_{c}cos\phi\), \(\dot{\mathbf{y}}=\mathbf{v}_{c}sin\phi\).  
\[\dot{\theta}=\left[\begin{matrix}(r/2)cos\phi&(r/2)cos\phi\\ (r/2)sin\phi&(r/2)sin\phi\\ r/2a&-r/2a\end{matrix}\right]\left(\begin{matrix}\dot{\phi}_{r}\\ \dot{\phi}_{l}\end{matrix}\right) \tag{3}\]  
\[\dot{\theta}=\mathbf{G}\dot{\mathbf{r}},\dot{\mathbf{r}}=\left[\dot{\phi}_{r}\quad\dot{ \phi}_{l}\right]^{T} \tag{4}\]  
The Jacobian matrix of the differential drive wheel mobile platform is:  
\[\mathbf{G}=\left[\begin{matrix}(r/2)cos\phi&(r/2)cos\phi\\ (r/2)sin\phi&(r/2)sin\phi\\ r/2a&-r/2a\end{matrix}\right] \tag{5}\]
## 3 Background and Related Work
### Related Work  
XAI has emerged as a crucial development in healthcare, especially in emergency departments (EDs), where decision-making should be rapid, reliable, and transparent. Several studies have explored the application of XAI in predicting patient outcomes, optimizing resource management, and improving triage processes, all of which aim to enhance both the accuracy of ML models and the trust clinicians place in them.  
A common theme across many studies is the integration of XAI techniques to address the \"black box\" nature of AI models. [PERSON] et al. (2023) stressed the importance of post-model explainability methods, such as visualization and feature relevance, to mitigate trust issues among clinicians ([PERSON] et al., 2021). This foundational need for transparency is echoed in studies that focus on more specific applications, such as the prediction of reattendance in EDs. For example, [PERSON] et al. (2021) used SHAP values to explain individual-level predictions, demonstrating how XAI can be used to inform post-discharge interventions and reduce reattendance rates ([PERSON] et al., 2021).  
Expanding on this, [PERSON] et al. (2022) and [PERSON] et al. (2024) applied explainability techniques to forecast ED visits and manage resources ([PERSON] et al., 2021; [PERSON] et al., 2021). Both studies used XGBoost and SHAP to uncover the factors influencing patient flow, with [PERSON] et al. highlighting variables such as weekday and patient volume, while [PERSON] et al. focused on continuous training and multi-model regression approaches for both short- and long-term forecasting. The shared emphasis on feature importance and interpretability across these studies illustrates the growing consensus that transparency is key to improving operational efficiency in EDs.  
Similarly, in their systematic review, [PERSON] and [PERSON] (2023) emphasized the need for generalized approaches to AI in emergency medicine, noting that most studies remain narrowly focused on specific conditions or patient groups (Pilis  
pandemic-specific contexts, such as [PERSON] et al. (2020), which developed XAI models for COVID-19 risk prediction, the emphasis remains on transparent decision-making, illustrating the versatility and necessity of explainable AI across various healthcare applications [53]. Beyond the healthcare sector, the value of SHAP-based interpretability has been demonstrated in other critical domains such as port logistic optimization [54].  
In summary, the body of work around XAI in emergency medicine converges on the importance of explainability not only to improve model performance but to foster trust and ensure clinical applicability. From predicting patient reattendance and managing ED traffic to addressing broader operational challenges like resource allocation, explainability is emerging as a critical component in making AI both usable and trustworthy in the fast-paced, high-stakes environment of emergency care.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Modeling the Handover Latency
#### 3.2.2 Handover Latency of FMIPv6  
FMIPv6 arranges for a CoA beforehand in association with the new access router (nAR) with the help of L2 triggers. It then creates a bi-directional tunnel amid the new access router (nAR) and the old access router (oAR) to reduce the handover latency. Upon connection with the new link, the MN informs the nAR that packets can now be received. Once the MN successfully obtains the FBAck (Fast Binding Acknowledgement) message that has been sent by the oAR before moving to the nAR, predictive FMIPv6 is executed; otherwise, reactive FMIPv6 is performed. The handover latency for predictive FMIPv6 can be given as [27]:  
\[L_{HO}^{pre-FMIPv6}=T_{L2}+T_{PRE} \tag{2}\]  
where \(T_{PRE}\) represents the time the time at which the nAR receives the Unsolicited Neighbor Advertisement (UNA) message sent from the MN and the time at which the MN receives the first data packet sent from the nAR. \(T_{L2}\) is the link layer handover latency.  
The handover latency for reactive FMIPv6 can be given as [8]:  
\[L_{HO}^{re-FMIPv6}=T_{R}+T_{DAD}+T_{RE} \tag{3}\]  
where T\({}_{R}\) is for registration latency, T\({}_{DAD}\) is the duplicate address detection latency and T\({}_{RE}\) represents the time to send FBU (Fast Binding Update) message, exchange required information between the relevant ARs and receives the first data packet sent from the nAR.
## 2 Vibration Response Analysis of Different Roadbed-Foundation Systems
### Upper-Soft, Lower-Hard System  
When constructing roads on foundations with relatively good soil conditions, where the shear wave velocity of the roadbed is lower than that of the foundation, the propagation characteristics of the vibration waves induced by traffic vibration loads are similar to those in a homogeneous elastic half-space foundation. In this case, the roadbed-foundation system forms a two-layer upper-soft, lower-hard system, as illustrated in Figure 3a. The relevant parameters are listed in Table 1.  
Figure 3: Schematic diagrams of roadbed–foundation systems: (**a**) Upper-soft, lower-hard system, (**b**) Upper-hard, lower-soft system, (**c**) Soft-sandwiched system.  
As observed in Figure 4, the displacement of ground surface points in the upper-soft, lower-hard system generally exhibits minimal attenuation with increasing horizontal distance \(x\). Without considering damping, the displacement would not attenuate at all, indicating that the energy generated by the vibration propagates continuously outward in the form of vibration waves along the horizontal direction. Figure 5 demonstrates that the  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil Layer** & **Thickness \(H\) (m)** & \
\begin{tabular}{c} **Shear Wave** \\ **Velocity \(\
u_{\text{s}}\)** \\ **(m/s)** \\ \end{tabular} & \begin{tabular}{c} **Mass Den-** \\ **sity \(\rho\) (kg/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Poisson’s** \\ **Ratio \(\mu\)** \\ \end{tabular} & \
\begin{tabular}{c} **Damping** \\ **Ratio \(\xi\)** \\ \end{tabular} \\ \hline \hline
\begin{tabular}{c} Roadbed \\ Hard foundation \\ \end{tabular} & 10 & 400 & 2300 & 0.24 & 0.02 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for the upper-soft, lower-hard system.  
Figure 4: Variation of **(a)** vertical and **(b)** horizontal displacement of a ground surface point with horizontal distance \(x\) in the upper-soft, lower-hard system.  
Figure 5: Variation of displacement amplitude of a point at different cross-sections \(x\) with depth \(y\) in the upper-soft, lower-hard system: **(a)**\(x\) = 0.0 m, **(b)**\(x\) = 54.0 m.  
vertical displacement of points at different cross-sections decays rapidly within the 10-m-thick roadbed. The overall displacement of points decreases significantly with increasing depth \(y\).  
Therefore, in the upper-soft, lower-hard system, the kinetic energy induced by traffic vibration loads primarily propagates outward along the horizontal direction through wave propagation, while the propagation in the vertical direction is less pronounced. This aligns with the general understanding of the influence depth of traffic loads on conventional highway roadbeds.
## 4 Performance Evaluation
The compression rate measures the ratio of the compressed graph size to the original graph size, where the lower, the better. The runtime includes both mining and encoding phases. We can observe that the proposed scheme consistently outperforms the baseline (GraphZip) in terms of compression rate, especially on larger datasets (e.g., Skitter, NBER, and LiveJournal). Moreover, the performance gap tends to widen as the dictionary size increases ((**a**) 30, (**b**) 50, (**c**) 100), indicating the effectiveness of our dictionary maintenance strategy based on dynamic pattern scoring. On the other hand, the runtime of the proposed scheme is slightly higher than that of GraphZip in most cases, which can be expected due to the additional complexity of provenance-based pattern extraction. However, the gap is not significant and can be well justified by the substantial improvement in compression performance.
# Experimental Simulation of Lightning Current Discharge on Rocks
## 2 Materials and Methods
### Fault Extinction  
Fault extinction occurs when the circuit breakers open and replace a short-circuit condition (large current) with an open-circuit condition (zero current). Once the breakers open, the voltage across the electrodes remains at zero. Therefore, the fault extinction event must be detected within the current waveforms.  
Compared to the voltage waveforms, the current waveforms had a lower relative resolution and signal-to-noise ratio. As a result, the wavelet decomposition technique discussed above was less effective and more prone to error, thus an alternative method was used to detect fault extinction. A post-fault, open-circuit condition was considered to exist when the measured current amplitudes became indistinguishable from noise. For an event with an estimated fault onset sample \(k_{0}\), the procedure for identifying fault extinction was as follows:  
1. Characterize the pre-fault (open-circuit) noise level in the current measurement using the standard deviation (assume noise is Gaussian with mean \(\mu\) estimated from the data): \[\sigma=\sqrt{\frac{1}{k_{0}-1}\sum_{k=1}^{k_{0}}(i_{R}[k]-\mu)^{2}}\] (3)2. Find the post-onset sample for which all later data samples could be considered noise: \[k_{F}=\{k|\forall k\geq k_{F},\ |i_{R}[k]|<6\sigma\}\] (4)  
With a 6\(\sigma\) threshold and the assumption of Gaussian noise, less than two in one-million samples of noise in the current channel will fall outside of the threshold, on average. This threshold was selected through trial-and-error. Manual inspection of the waveforms showed that smaller thresholds resulted in the inclusion of a significant number of post-extinction samples. Theoretically, smaller thresholds yield higher false alarm probability, which is problematic because of the high sampling rate (250 kHz).
## 3 Results and Discussion
### Pore-Embedded Ratio  
For quantitative analysis of silver implantation, the pore-embedded ratio of the laser-irradiated surface with different laser fluences and scanning speeds was evaluated. Figure 11 shows the change in the pore-embedded ratio when irradiated by femtosecond pulsed laser at 256 fs. A pore-embedded ratio of around 60% was achieved in this study. It was found that there were different laser fluences for each scanning speed, which showed the best pore-embedded ratio; for example, 0.3 J/cm\({}^{2}\) showed the highest value at 10 mm/s, whereas 0.4 J/cm\({}^{2}\) was the highest at 50 mm/s, and 0.5 J/cm\({}^{2}\) was the highest at 100 mm/s. It seems that the highest value for each scanning speed did not significantly differ. Thus, it was concluded that the combination of both two laser parameters was important.  
To understand the change in pore-embedded ratio in more detail, the result of Figure 11 was summarized in a color distribution map. Figure 12 shows the color map overlaid on the parameter map shown in Figure 10a. It was found that materials were embedded in nanopores at the laser condition where the dried film was partially peeled off and where LIPSS were generated. In the parameter range where the dried film was completely  
Figure 10: Parameter map of dried nano-silver dispersion film and YSZ surface morphology irradiated at the different pulse widths: (a) 256 fs, (b) 10 ps, (c) 26 ns.  
removed from the surface, a higher pore-embedded ratio was obtained, up to approximately 60%. In the range where LIPSS were observed, the pore-embedded ratio tended to decrease. Especially when the fluence was increased at low scanning speeds, the embedding rate was low because the YSZ substrate surface with silver-loaded pores was processed.
## 2 Related Work  
### Knowledge Graph Representation  
Knowledge representation is a technique that aims at learning low-dimensional representations for KG entities and relations, consisting of two critical steps: (1) constructing a scoring function measuring plausibility for triples, and (2) embedding KG components in continuous vector spaces.  
TransE [6], the most representative method in KG embedding, represents entities as vectors and relations as an operations between entities, i.e., \(\mathbf{h}+\mathbf{r}\approx\mathbf{t}\). The scoring function is defined as the distance between entities and relations in latent space, written as \(f_{\mathbf{r}}(\mathbf{h},\mathbf{t})=-\parallel\mathbf{h}+\mathbf{r}- \mathbf{t}\parallel_{1/2}\). However, TransE fails to deal with one-to-many, many-to-one, and many-to-many relations [7,8]. For example, given a relation holding two facts \((\mathbf{h},\mathbf{r},\mathbf{t}\mathbf{1})\) and \((\mathbf{h},\mathbf{r},\mathbf{t}\mathbf{2})\), we can infer \(\mathbf{t}\mathbf{1}=\mathbf{t}\mathbf{2}\) even though they are totally different entities. To overcome the above defects, [PERSON], [PERSON], [PERSON], and [PERSON] proposed TransH [7] to obtain distinct representations for entities when dealing with different relations, by projecting entity representations onto a hyperplane, resulting in a normal vector. e.g., \(\mathbf{h}_{\perp}=\mathbf{h}-\mathbf{w}_{\mathbf{r}}^{\top}\cdot\mathbf{h} \cdot\mathbf{w}_{\mathbf{r}}\), with \(\mathbf{w}^{\intercal}\) as the normal vector.  
TransE and its extensions represent both entities and relations as deterministic points in vector spaces, ignoring the uncertainty for KG components. To resolve this problem, some recent works have introduced uncertainty into KG embedding by representing KG components as distributions, e.g., KG2E [17], proposed by [PERSON], [PERSON], and [PERSON] and [PERSON], represents both entities and relations as distributions via Gaussian embedding. Inspired by the previous works, we tackle the embedding problem for KG by modeling both entities and relations as Gaussian distributions and representing them in the semantic space to effectively measure the affinities between them.
# Model-Based Dynamic Toll Pricing: An Overview
## 4 Interactions with Recent Technology Applications  
The development of new communication and transportation technology is having various positive effects on road toll pricing, making it faster, easier, cheaper and more reliable [111]. In particular, connected traffic sensors enable the collection and storage of big amounts of data, which can be processed and mined to support real-time decision making [59], including dynamic toll pricing definition. Self-learning techniques have been proposed to gradually learn the parameters of the driver behavior model by the mining of the recorded traffic data [8, 13, 31], enabling a more accurate representation of the users' preferences, which is fundamental for price definition. Reinforcement learning algorithms for optimization of dynamic pricing strategies are employed in [41, 42] and random forest predictions are included in [62].  
Connected drivers and vehicles allow the definition of user-specific strategies, where toll authorities can guide users' mode and routing choices with user-dependent advice and dynamic pricing [109, 112, 113, 114].
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 2 Description of Satellite Data Transmission
### Conflict in Ground Resource Utilization
#### 2.2.2 Ruc  
RUC refers to a situation where two or more satellite data transmission tasks require the use of the same ground station hardware resource during a specific time period. As  
Figure 1: Structure of ground receiving system.  
Figure 2: Satellite visible time conflict diagram.  
illustrated in Figure 3, when satellites 2 and 3 are simultaneously in view, an RUC arises because antenna 2 is already allocated to satellite 1 for data transmission. Consequently, satellites 2 and 3 compete for antenna 1, resulting in a conflict. In such cases, antenna 1 must be assigned to either satellite 2 or satellite 3, while the other task without an allocated antenna must either be assigned to another available antenna or aborted.