esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Effect of Map Orientation on Spatial Knowledge Development
#### 3.2.4 Route Retracing by Cognitive Style  
The egress task completion time from the endpoint to the start point was recorded in seconds. The results of mean differences in the egress time according to cognitive style are given in Table 8. In the north-up map condition, FD and FI participants took almost the same time on the route retracing task. The mean egress time for FD individuals was 145.769 s, and for FI individuals was 145.15 s. In the forward-up map condition, FI individuals took less time (M = 124.35, SD = 66.867), whereas FD individuals took considerably more time: M = 154.154, SD = 97.058 (see Figure 11).  
Further analysis was performed to investigate the statistical effect of map orientation on the route retracing task regarding cognitive style (see Table S7 in the Supplementary Materials). A repeated measures ANOVA was conducted with map orientation as a within-subject variable and cognitive style as a between-subject variable. There was no significant main effect of map orientation on the route retracing task: F (1, 38) = 0.998, \(p\) = 0.324. In addition, there was no significant interaction between map orientation and cognitive style: F (1, 38) = 0.891, \(p\) = 0.351.  
Figure 10: Landmark sequencing scores mean, in different map orientation conditions by cognitive style. Note: “FD” stands for field dependence and “FI” stands for field independence.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Map Orientation** & **Cognitive Style** & **Mean** & **Standard Deviation** & **Min** & **Max** \\ \hline \multirow{2}{*}{North-up} & FD & 145.77 & 108.38 & 43 & 375 \\ \cline{2-6}  & FI & 145.15 & 86.44 & 29 & 333 \\ \hline \multirow{2}{*}{Forward-up} & FD & 154.15 & 97.06 & 42 & 343 \\ \cline{2-6}  & FI & 124.35 & 66.87 & 37 & 260 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Egress time’s mean and standard deviation in route retracing task for two cognitive styles. Note: “FD” stands for field dependence and “FI” stands for field independence.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 8 Discussion of Limitations
### Security Barriers  
Also from a security standpoint, special attention needs to be paid to the use of privileged Daemonset. Such Daemonsets manage privileged pods residing in the same mount, IPC and network Linux namespace as the worker nodes. If attackers have access to such pods, they can launch attacks like installing malware in the host operating system, tampering with other pods on the same machine, switching the Kubelet to a malicious configuration, etc. Therefore, it is important to have some mechanisms to prevent such pods from becoming a backdoor for attackers. There are plenty of countermeasures. First, any ingress traffic to the Daemonset pods must be disallowed by network policies [8; 57]. Second, the privileges of the Daemonset pods with respect to the underlying host operating system could be restricted to only those they really need to implement the reconfiguration tactic by means of a proper security contet. Third, the role-based access control policies for the API server of the control plane could also be defined so that only the least possible privileges are given to the Daemonset pods. Fourth, the Daemonset and all its Pods must be removed after the reconfiguration has successfully ended or after a timeout. After removal, any backdoor or vulnerability caused by the removed Daemonset does not exist anymore.
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Cervical and Ovarian Cancer
#### 2.6.5 Other Cancer Types  
Piperine inhibits PKC\(\alpha\) and ERK phosphorylation and reduces NF-\(\kappa\)B and AP-1 activation, leading to down-regulation of MMP-9 expression in human fibrosarcoma HT-1080 cells. In B16F10 melanoma cells, piperine (2.5, 5 and 10 \(\upmu\)g/mL) inhibited activation of transcription factors NF-\(\kappa\)B, c-Fos, cAMP response element-binding protein (CREB), activated transcription factor (ATF-2) and consequently downregulated inflammatory and growth regulatory genes IL-1\(\beta\), IL-6, TNF-\(\alpha\), and granulocyte-macrophage colony-stimulating factor (GM-CSF) [72]. In ultraviolet-B-irradiated mouse melanoma cells (B16F10), piperine promotes cell death through the elevation of intracellular ROS formation, calcium homeostasis imbalance, and loss of mitochondrial membrane potential [73]. Synthetic piperine-amino acid ester conjugates exhibit cytotoxic activities against IMR-32, MCF-7, PC-3, DU-145, Colo-205, and Hep-2 cancer human cell lines [74].
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Human-Centric Design and Usability  
One important research direction on the merging of the IoV and smart cities lies in human-centric design and usability. This includes a deep investigation of user interfaces and interaction models that link drivers to IoV systems. The goal is to create interfaces that are both intuitive and informative, that allow drivers to comprehend the complexities of IoV technologies, creating a feeling of control and confidence. Not only does this improve safety, but it also increases the trust in the operation of autonomous vehicles by improving the user experience.
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 6 Performance Evaluation  
### Simulation Tool  
Simulations are carried out using SimuLTE [26], a system-level simulator based on OMNeT++ [27] that models the full LTE protocol stack and includes support for P2P and P2 MP V2V communications, while accurately capturing channel dynamics. In order to assess the performance of the proposal described in Section 4.2, we extended SimuLTE so as to support CAM exchange within a platoon  
Figure 4: Theoretical bounds for the sequential and simultaneous transmission schemes (M = 50). (**a**) CAM completion cycle in case of sequential transmission (\(N=1\)); (**b**) Maximum number of synchronized platoons; (**c**) CAM completion cycle in case of simultaneous transmission (\(N=1\)); (**d**) CAM completion cycle in case of simultaneous transmission (D = 5).  
and the latest C-V2X specification when considering the proposed scheduling modes for CAM transmissions. In particular, modifications affect the following modules.  
**Platoon CAM application**. We implemented two application modules to mimic the CAM exchange. The former models the behavior of the PL and sends one CAM to its PMs at the beginning of every CAM cycle, using P2 MP V2V transmissions. In turn, the application running at the PMs receives the PL's CAM and sends one CAM to the following PM using a P2P V2V transmission. We assume that each platoon is formed at the beginning of the simulations and that PMs know the address of the following vehicle, hence we do not simulate the platoon's joining phase. If a PM does not receive the CAM from the PL (e.g., due to unsuccessful decoding), it is configured to send its CAM in any case (i.e., with the latest speed/acceleration information available) at the scheduled time. In other words, the PM does not wait for receiving the CAM from the PL before transmitting, otherwise it could be stalled in case of packet loss. This is practical because PMs are aware of the timing of the update cycle, that is provided by the PL during the joining phase.  
**Propagation model**. The propagation model in [35] has been implemented to compute the path loss, with parameters set to match a highway environment and a convoy path loss. The same model is also considered as propagation scenario #PS9 of the METIS (Mobile and Wireless Communications Enablers for the 2020 Information Society ([[https://www.metis2020.com/](https://www.metis2020.com/)]([https://www.metis2020.com/](https://www.metis2020.com/))) project. In particular, the path loss for a receiver at a distance \(x\) from the intended transmitter is computed as follows:  
\[PL(x)=PL_{0}+10\cdot n\cdot log_{10}\frac{x}{x_{R}}+Y_{\sigma} \tag{1}\]  
where \(PL_{0}\) is the reference path loss equal to 63.3 dB, \(n\) is the path loss exponent set to 1.77, \(x_{R}\) is the reference distance (10 m), \(Y\sigma\) is a normally distributed random variable with mean \(\mu\) equal to 0 and standard deviation \(\sigma\) equal to 3.1.
## 3 Results and Discussion
### Satellite Image Price Modeling  
The satellite features dataset was analyzed considering price per hectare, data volume per hectare, minimum order area, revisit time, spatial resolution, radiometric resolution, raw image price, and CV of NDVI simulation using a PCA. Figure 5 shows the first and the second principal components obtained from the PCA. According to this analysis, price per hectare and data volume per hectare had similar loadings on the first and the second principal components. The CV of NDVI simulation had a large positive impact on the first principal component analysis.  
Figure 4: Signal-to-Noise ratio of sensors and CV of NDVI simulation as error estimation.  
Stepwise regression was performed estimating the price per hectare of satellite imagery considering data volume per hectare, minimum order area, revisit time, spatial resolution, radiometric resolution, raw image price, and CV of NDVI simulation. Although the result of the stepwise regression with the optimization of the Akaike information criterion (AIC) returned a multiple linear regression model based on data volume per hectare and CV of NDVI simulation, the effect of the CV of NDVI simulation was not significant (\(p\)-value > 0.05). Based on this, a linear model was fitted plotting price and data volume (Figure 6), returning the function showed in Equation (2).  
\[\centering\bar{\epsilon}\ \text{ha}^{-1}=0.0019\ \text{KB ha}^{-1}\@add@centering \tag{2}\]  
The definition of a price model to estimate agricultural tools is widely applied to estimate the profitability of agricultural practices [58]. According to the PCA and regression analysis results, the raw satellite price per hectare was primarily influenced by data volume per hectare, calculated according to Equation (1).
## 2 Experiment and Computing CZM Parameters
### Full-Scale Transverse Shear Test and Parameter Computation  
As it is difficult to implement the temperature load in full-scale transverse shear tests, the interface characteristics were analyzed by applying the transverse horizontal load to the cement concrete base plate in the composite plate, because the interaction mechanism of the two loading modes is same. The China Academy of Railway Science and Southeast University conducted field full-scale transverse shear tests on the interface characteristics between the asphalt concrete foundation and cement concrete base plate.  
A cement concrete base plate with dimensions of length 19.2 m, width 3.4 m, and height 0.3 m was constructed on the asphalt concrete foundation. The interface bonding strength was created by the cement hydration reaction. The experimental setup was composed of a cement concrete base plate, an asphalt concrete foundation, counterforce piers, hydraulic jacks, dynamometers, and displacement meters (see Figure 2). During the test, the transverse horizontal load was applied to the base plate by hydraulic jacks (see Figure 3a), and the relative displacement between the base plate and asphalt concrete foundation was measured using displacement meters (see Figure 3b). Ten counterforce piers (1 m \(\times\) 1 m \(\times\) 1 m) were placed at every 1 m in one line on the lateral side of the asphalt concrete foundation. Hydraulic jacks were installed between the slab plate and counterforce piers. They were controlled via an automation system and worked synchronously.  
Figure 3: Full-scale transverse shear test site: (**a**) Layout of hydraulic jacks; (**b**) Layout of displacement meters.  
Figure 2: Test setup of full-scale plate-pushing test. CCBP: cement concrete base plate. ACWL: asphalt concrete waterproofing layer.  
Two relative control points were established on the plain stage of the slope beside the track slab to ensure the relative accuracy of the control network. The lateral displacement monitoring points are marked in Figure 2. The stepwise loading method was adopted. Half a ton was loaded per stage until the total load reached 5 tons, and then, 0.2 ton per stage was added until the interface was debonded. The shear stress-displacement monitoring curve is plotted in Figure 4.  
The parameters of cohesive element in ABAQUS include interfacial shear strength \(t^{0}\), interfacial shear stiffness \(k\), and critical fracture energy \(G^{C}\). As shown in Figure 1, interfacial shear stiffness and critical fracture energy can be expressed as:  
\[k=\frac{t^{0}}{\delta^{0}} \tag{3}\]  
\[G^{C}=\int_{0}^{\delta^{f}}t(\delta)\ d\delta=\frac{1}{2}t^{0}\delta^{f} \tag{4}\]  
where \(\delta^{0}\) is the critical displacement, and \(\delta^{f}\) is the maximum displacement. \(\tau^{0}\), \(\delta^{0}\), and \(\delta^{f}\) are obtained from field experiments. In order to obtain \(G^{C}\), the force-displacement curve from the field test is extended with the original slope to intersect the horizontal axis. The CZM parameters obtained from the full-scale transverse shear test are listed in Table 1.
# Stability Analysis of Roadway Groups under Multi-Mining Disturbances
## 4 Numerical Simulation of Deep Well Roadway Group Disturbance Effect
### Influence of Mining Disturbance at Working Faces
When the distance between the working face and the roadway is between 50 m and 100 m, the maximum displacement of the ventilation roadway and the auxiliary transportation roadway is gradually reduced. When the distance between the working face and the roadway is 100 m, the changing rate of the maximum horizontal displacement has the lowest values. The plastic zone distribution of the surrounding rock of the model roadway is depicted in Figure 16.  
When the distance between the working face and the roadway is 50 m, the plastic areas of the four main roadways become larger, and the track roadway and the auxiliary transportation roadway change significantly. When the roadway is 75 m, the plastic areas of the ventilation roadway and the belt conveyor roadway change slightly while the track roadway and the auxiliary transportation roadway changes more significantly. When the distance is 100 m, the change of the plastic zone of the ventilation roadway and the belt conveyor roadway is relatively small, and the changes of the track roadway and the auxiliary transportation roadway are insignificant. In summary, when the distance between the working face and the roadway is 100 m, the mining face has an insignificant influence on the stress distribution, displacement distribution, and plastic zone of the roadway surrounding rock. The key strata theory and the theory of surface subsidence are verified. When the remaining width of the coal pillar exceeds the subsidence range of the rock strata, it would not affect the roadway. Therefore, it can be considered that retaining coal pillars of more than 100 m is beneficial to the stability control of the surrounding rock of the roadway. Figure 15: Maximum vertical displacement at different positions of working face and roadway: (**a**) the track roadway and (**b**) the auxiliary transportation roadway.
## 2 Materials and Methods
### Spectral Separability Analysis  
After creating the training dataset, a spectral separability analysis was conducted on the normalized median signatures of each land cover class. For each specific land cover class, the median spectral-temporal signatures were computed; subsequently, the values for each individual feature were subjected to normalization through the employment of a Min-Max scaler, thereby constraining them within the range of 0 to 1. This analysis identified clusters used as the foundation for training a series of Hierarchical Random Forest algorithms.  
To achieve this, the data for each class were compared using a hierarchical cluster analysis [75] approach based on Euclidean distance, identifying all nodes that effectively separated hazelnut (HZ) from other land cover types. The primary objective was to achieve the spectral and temporal separation of hazelnut (HZ) from the land cover class that exhibited the most similarity.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Data Source** & **Sentinel-2** & **Sentinel-1** & **DEM** \\ \hline EE-Collection & S2\_SR & S1\_GRD & SRTMGL1\_003 \\ Bands & B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12 & VV, VH & elevation \\ Filters & Max 10\% Tile Cloud Cover & Orbit: ASCENDING & - \\  & 01/Jan/2020 - 31/Dec/2022 & 01/Jan/2022 - 31/Dec/2022 & - \\ N. Images & 1182 & 73 & 1 \\ Reducer & Median, Monthly & Average, Monthly & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the data used.  
Figure 5: Creation of the Reference Dataset through Stacking and Training Dataset at 10 m/pixel Sampling.
# SPN-Based Dynamic Risk Modeling of Fire Incidents in a Smart City
## 2 Materials and Methods  
### Stochastic Petri Net  
Stochastic Petri nets are a potent tool for modeling distributed discrete systems with features like concurrency, synchronization, mutual exclusion, and conflict. A Petri net is a bipartite, weighted, directed multigraph defined by six basic elements, SPN = (P, T, F, W, M, \(\lambda\)), where:  
1. P is a finite set of places, representing the local operating states of the system at each stage.
2. T is a finite set of transitions, representing the key factors that drive the change of the system state.
3. F is a set of directed arcs, defining the connection relationship between places P and transitions T, that is, F \(\subset\) (P \(\times\) T) \(\cup\) (T \(\times\) P), where '\(\times\)' is the Cartesian product.
4. W is a weighted function of the arc FN, referring to the quantity of the resource that is required or can be produced. Connections between nodes of the same type are not allowed.
5. M is a marking vector function from PN. It is used to describe the current state of the Petri net. In particular, M0, as the initial marking vector, represents the state when the system starts. That is, the distribution of tokens on each place at the beginning of the SPN.
6. \(\lambda\) is a set of average firing rates associated with the transitions. Each transition follows a negative exponential distribution, where \(1/\lambda\) represents the average delay of the transition firing.  
After a transition is activated, tokens flow in from the adjacent places, generating new state identifications when passing through each place until the system reaches the final state. This continuous process describes the dynamic change and state transition of the system. For the continuous SPN model, the distribution function of each transition follows the memoryless exponential function in Equation (1):  
\[\forall\text{t}\in\text{T}:\text{F}_{\text{t}}(\text{x})=1-\text{e}^{-\lambda _{\text{t}}\text{x}} \tag{1}\]
# Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter
## 5 Assimilation Results
### Data Assimilation Validation Experiments  
The data assimilation algorithm, described in Figure 3, is validated using model-generated synthetic observations. This is performed with a HOSS setup to simulate the SHPB experiment described in Section 3. The synthetic observations experiment, referred to as a twin experiment, consists of selecting a set of \"reference\" parameter values for \(\sigma_{t}^{max}\) and \(E_{t}\), from which a HOSS model simulation for the SHPB is generated and used to get synthetic observations. In this case, the observations are the stress versus time curve generated from this reference HOSS simulation. Afterwards, an EnKF assimilation experiment is performed using these SHPB synthetic observations. To validate the EnKF assimilation method if the estimated parameters provided by the EnKF are close to the \"reference\" parameters chosen to generate the synthetic data, then the assimilation method is validated, since it provided the correct parameter values for this synthetic observation experiment.  
The parameter values given by the EnKF validation experiment show steady convergence from the very first iteration, and provides a very good approximation at three iterations (see Figure 4). This steady convergence proves that the EnKF can accurately estimate the correct parameter values for \(\sigma_{t}^{max}\) and \(E_{t}\) for the current setup. In order to ensure that the EnKF is actually converging to the correct solution, we compared the synthetic observed SHPB with the EnKF HOSS ensemble. As expected, the HOSS ensemble time-stress curves converge to the synthetic SHPB time-stress curve, as shown in Figure 5.  
To verify that the data assimilation performed consistently over different parameter values, an additional two experiments with different reference parameter values were performed. The assimilation results on those two additional experiments are consistent  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Description** & **Units** & **Interval** & **Reference val. (Validation)** \\ \hline \(\sigma_{t}^{\text{max}}\) & shear strength & \(10^{9}\) Pa & \([4.0\times 10^{-3},\,6.0\times 10^{-2}]\) & \(4.5\times 10^{-2}\) \\ \(E_{t}\) & specific energy in tangential direction & \(10^{6}\) J/m\({}^{2}\) & \([2.0\times 10^{-4},\,24.0\times 10^{-4}]\) & \(5.0\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table with the parameter use for data assimilation, with their corresponding interval values, as well as a “reference value” for the validation of the assimilation algorithm. The parameters are sampled using the LHS strategy, where each parameter is assumed to have a uniform distribution. The intervals for each parameter are derived in an ad hoc manner from previous HOSS simulations for granite materials.  
with the results presented in this section--that is, both parameters \(\sigma_{t}^{max}\) and \(E_{t}\) converged to the reference parameter values in no more than three EnKF iterations.
## 4 Numerical Experiments
### Korea Network  
This section conducts numerical experiments on the South Korea network. These experiments were performed to investigate (a) the convergence characteristics of the route-based GP algorithm for solving the multi-class TAP and (b) the effect of flow allocation. Figure 7 depicts the Korea network, which consists of 247 zones, 45,434 nodes, 96,621 links, and 51,541 O-D pairs. The Korea Transportation Institution provided the network structure, O-D trip tables, and link travel time function. Four scenarios were created to investigate the effect of the multi-class TAP with mixed modes on different roadway classes. Scenario 1 assumed that CAVs were not permitted to drive on any roads, which meant they drove manually on all roadways like HVs. On the other hand, Scenario 2 assumed that CAV travel was permitted exclusively on freeways. It was assumed that CAVs were permitted on freeways and arterials in scenario 3. Finally, scenario 4 assumed that CAVs were capable of traveling on all available roadways. The aggregated demands were assumed to be disaggregated by 50% and 50% to obtain the O-D trips for HVs and CAVs, respectively. Maximum capacity was expected to be 1.5 times more when driving only CAVs than when driving only HVs. The GP algorithm was coded in Intel Visual FORTRAN XE 2021 and ran on a 3.60 GHz processor with 16.00 GB of RAM.  
Figure 6: Trajectories of capacities.