esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Discussion  
The results presented indicate that the road capacity with the proposed model is higher than with the ID model. Further, the variations in speed with the proposed model are small, and an approximately constant speed is achieved over time, which is realistic. Conversely, the ID model results in large speed variations that increase with \(\delta\), so the traffic behavior is unrealistic. These variations produce significant acceleration and deceleration that increase over time as shown in Figure 8. Conversely, with the proposed model, the CAVs exhibit small variations in acceleration and deceleration that decrease over time. These variations are greater at lower densities (high \(S/S^{\prime}\)) due to the increased freedom of movement, while at higher densities (low \(S/S^{\prime}\)), the variations are smaller due to the need for closer coordination and smoother driving behavior.  
With the proposed model, a lower density results in increased traffic flow, which is consistent with traffic dynamics. With the ID model, the flow increases as \(\delta\) increases, which results in unrealistic behavior as \(\delta\) does not represent real traffic dynamics. Moreover, the maximum speed with the proposed model is 18.0 m/s, but with the ID model, it is only 10.6 m/s as shown in Figure 6c. The proposed model can incorporate CAVs, whereas the ID model is only applicable to human-driven vehicles. This results in greater efficiency as CAVs can communicate and coordinate their behavior, leading to faster queue dissipation regardless of the density. Conversely, the ID model with human-driven vehicles results in significant delays, especially with high values of \(\delta\), indicating poor queue management.  
The results for cyberattacks show that the proposed model produces realistic traffic flows. The variations are large when the attack intensity is high and small when the intensity is low. Thus, the proposed model can be used to effectively analyze and manage cybersecurity threats.  
Figure 16: Traffic flow behavior of CAVs on a 1 km circular road with the proposed model under a cyberattack at \(c=30\) for (**a**) \(S/S^{\prime}=0.2\), (**b**) \(S/S^{\prime}=0.5\), and (**c**) \(S/S^{\prime}=0.8\).
###### Abstract
The controller is composed of three sub-systems: _(1) High-level layer_, which gathers information from the vehicle info bus to define the control objectives, estimated through a model-based approach; _(2) Intermediate-level layer_, dedicated to calculating the desired control actions; _(3) Low-level layer_, which generates the reference signals for the actuators. With respect to previous work based on Sliding Mode strategies [32; 33; 34; 35], typically using separated sliding surfaces that decouple the control dynamics, this algorithm relies on a 2 dimensions model for the states control. These challenges are well-known and established lateral stability SMC methods adopt a single sliding surface. Moreover, the application of desired yaw moment is based on Moore-Penrose Pseudo-inverse innovative criteria [12; 36] to solve a multi-Degree Of Freedom (DoF) problem for over-actuated systems. This allows us to achieve both vehicle stabilization and a driving path that is in accordance with the driver's intentions. Even if the controller is developed for 4 Wheel Drive (4 WD) architectures with independent traction wheels, the adopted approach ensures fundamental features, which consent to flexibility and portability of the solution with respect to different vehicle layouts and applications. To assess the goals of this work, a simulation campaign of closed-loop maneuvers is performed by comparing proposed controllers with a reference commercial ESP on a driving simulator. This Hardware-in-the-Loop (HiL) system is composed of a concurrent PC, in which reside the RT vehicle model and the control algorithms; a commercial braking unit and EPSiL steering bench; and driver interface devices, for example, pedal and steering wheel. In Section 2, a detailed description of the developed control strategies is shown. Instead, The HiL driving simulator is described in Section 3. In Section 4, the simulation results are presented. Finally, Section 5 summarizes conclusions and future developments.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 3 Evaluation Framework
### Node Locations  
Different strategies are being considered to evaluate the influence of the location of nodes on the network performance as shown in Figure 1. Here, the location of wireless nodes is defined. One hundred random houses are selected in each area of interest. The CPE has a fixed site placed on the wall closest to the street of the chosen house at 4 m of elevation or 50 cm under the roof height. A post is placed at the property's entrance when the closest street is further away than 25 m from the building. For the last use case (see Section 3.4), only 12 buildings are selected, and the CPE elevation is 25 m.  
Next, the possible ENs' locations are placed using three strategies.  
* Facade only (FA): Places the ENs equipment in the same way as the CPE at 4 m on the wall closest to the street in houses, public buildings, or other constructions.
* Lamp post only (LP): Place the ENs on lamp posts at the height of 4 m. Since detailed information on the lamp post is unavailable, it is assumed that lamp posts are placed at corners or intersections and subsequent ones 50 m along each street until covering the investigated area.
* Facade and the lamp post (FP): also called joint location, it is a mixture of the previous strategies, where nodes could be on walls or lamp posts.  
A comparison of the first two strategies is shown in Figure 3, where it is noticeable that for rural scenario, the lamp posts aids to connect further away nodes, while in the urban scenario, the distribution density is quite similar between facade and lamp post. In addition, it is worth mentioning that these are possible locations of the ENs, and after the allocation procedure, the algorithm selects the final used locations (See Section 4.1).  
Figure 3: Edge node placement comparison. (**a**) Least-Rural. (**b**) Liege-Urban.  
Figure 2: General tree network architecture with a example of pruning method (Section 4.1).
# Approximating the Temporal Neighbourhood Function of Large Temporal Graphs
## 3 Computing Temporal Neighborhood Functions through Reverse Temporal Cones
### Approximating the Size of the Reverse Temporal Cones
#### 3.1.3 Applying Sketches to Reverse Temporal Cones  
In the case of reverse temporal cones, the sets whose sizes we want to approximate are the reverse temporal cones at different time instants within a specific time interval \(\mathbb{I}\). Indeed, for each edge scanned by the dynamic programming algorithm described in the previous section, either we use the UPDATE operation in order to add a node to the sketch of a reverse temporal cone, or we use the UNION operation in order to compute the union of the sketches of two reverse temporal cones. Whenever all edges whose appearing time is in \(\mathbb{I}\) have been read, we can use the SIZE operation in order to estimate, for each node \(u\), the size of \(\mathcal{R}(u,\mathbb{I})\). Let \(\overline{r(u,\mathbb{I})}\) denote the obtained estimate of \(|\mathcal{R}(u,\mathbb{I})|\). Hence, atnf approximates \(|\mathcal{N}(\mathbb{I})|\) by using the estimates \(\overline{r(u,\mathbb{I})}\) as follows:  
\[\overline{|\mathcal{N}(\mathbb{I})|}=\sum_{u\in V}\overline{r(u,\mathbb{I})}.\]  
By linearity of expectation, the approximation performed by atnf is unbiased and has relative error bounded by \(\epsilon\) with high probability, whenever \(k\in\Theta\left(\frac{\log n}{\epsilon^{2}}\right)\).
## 2 The Communication Infrastructure of a Smart City
### Communication Challenges  
The main challenges the smart city communication infrastructure has to deal with can be summarized as:  
* Heterogeneity: heterogeneous communication technologies have to be integrated to provide reliable and functional access to the system elements in different environments. Indeed, in order to have a wide spread of smart city applications, it is required that people can use their own devices and not dedicated devices and software. This will allow low cost, flexible, and scalable solutions. The resulting communication infrastructure must integrate any technology that may be considered relevant by a smart city actor.
* Quality of Service: the diversity of smart city solutions determines the wide variability of supported services and applications. This leads to multiple traffic types within the network and, hence, the need to manage all of these respecting their different Quality of Service (QoS) requirements in terms of priority, delay, data rate, reliability, and security. Moreover, the amount of data varies tremendously during a day, so the traffic conditions change quickly, and the system must be able to adapt itself to the scenario variability.
* Security: smart city networks have to carry reliable and real-time information toward monitoring and control centres. This exposes the system to outside attacks, unauthorized accesses, and data modifications. Data can be captured and carried over the system. This makes it necessary to foresee suitable mechanisms to prevent cyber attacks that might block the city functionalities and carry unwanted alarms or data theft.
* Energy consumption: smart cities are based on large diffusion of smart devices and sensors whose operations are strongly affected by their battery life. Hence, energy efficient communication protocols are needed, especially for local connections. Moreover, the use of energy harvesting solutions should be considered.
* Communication resource availability: smart cities and smart devices will have an explosive growth in the next years. Hence, the traffic generated by the applications running on smart systems will require a huge amount of bandwidth and network resources, thus challenging the communication infrastructure. Especially in wireless communications, having sufficient and dedicated spectrum resources for smart city applications will be infeasible. Therefore, the availability of sufficient spectrum to accommodate current and future needs of smart environments is expected to be a critical requirement.
# High-Temperature Liquid-Liquid Phase Transition in Glass-Forming Liquid Pd43 Ni20 Cu27P10
## 3 Results and Discussion
### Bond Angle Distribution
#### 3.2.3 Atomic Dynamics  
In general, the structural transition in metallic liquids also affects their kinetic properties. From the AIMD simulations, the mean square displacement (MSD) for each temperature was calculated and the self-diffusion coefficient (\(D\)) was derived from the linear regime of the MSD (the results are presented in Figure 9a,b). It can be observed that the self-diffusion coefficients for all atoms kink near LLPT temperature, and the self-diffusion coefficients for P and Cu elements fluctuate the most. This means that the Arrhenius behavior of the self-diffusion coefficient changes at LLPT temperature. This non-Arrhenius behavior of diffusivity indicates a change of atomic dynamics when LLPT occurs.  
Figure 8: BADs of P-Cu-P in Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) liquid at different temperatures, where the black curve represents 1200 K and the red curve denotes 700 K. The blue vertical line corresponds to the characteristic angle of the icosahedral clusters, and the black vertical line corresponds to the characteristic angle of the octahedral ones.  
Figure 9: (**a**) Time dependence of the MSD of liquid Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) during cooling; (**b**) \(D\) for Pd, Ni, Cu, and P atoms as a function of temperature in the liquid Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\). The black dashed line in (**b**) represents the temperature at which LLPT occurs.  
From the temperature dependence of the self-diffusion coefficient, the activation energy can be derived. In the high-temperature range, the activation energies of Pd, Ni, Cu, and P components are 5.917 kJ/mol, 6.122 kJ/mol, 6.002 kJ/mol, and 5.565 kJ/mol, respectively, while the activation energies decrease after LLPT respectively to 2.981 kJ/mol, 3.013 kJ/mol, 2.774 kJ/mol, and 1.545 kJ/mol. This phenomenon runs counter to common sense. For a homogeneous liquid system, the activation energy at high temperatures should be lower than that at low temperatures. This abnormality in atomic dynamics further indicates that there is a structural change in Pd\({}_{\rm d3}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) liquid before and after LLPT, and the P and Cu elements have the greatest influence.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 2 Materials and Methods
### Method  
#### 2.3.1 Trend Analysis  
A univariate linear regression analysis was applied to per-pixel vegetation (NDVI, LAI, and GPP) and meteorological data (P and Ta) from 1991 to 2018. The regression yielded the slopes of NDVI, LAI, GPP, P, and Ta over multiple years using MATLAB 2018b (MathWorks, Natick, MA, USA), representing the vegetation change trends in Xinjiang during the study period. The calculation formula is as follows:  
\[\theta_{\text{Slope}}=\frac{n\times\sum\limits_{i=1}^{n}i\times X_{i}-\sum \limits_{i=1}^{n}i\sum\limits_{i=1}^{n}X_{i}}{n\times\sum\limits_{i=1}^{n}i^{2} -\left(\sum\limits_{i=1}^{n}i\right)^{2}} \tag{1}\]  
where \(i\) is the year, and \(X_{i}\) is the value of NDVI, LAI, GPP, P, or Ta for the year \(i\). The _slope_ calculated for each pixel represents the overall trend of NDVI, LAI, GPP, P, or Ta over multiple years for that pixel. \(\theta_{\text{Slope}}\) > 0 represents an increasing trend for NDVI, LAI, GPP, P, or Ta, while the negative value indicates a decreasing trend.
## 2 Related Work
### Cloud Deployment  
Nowadays, when most IT companies use cloud architecture services, most research activities have focused on the IaaS layer, neglecting the rest of the cloud stack [8]. For example, [PERSON] et al. present two approaches for PaaS: ASCETIC and SeaClouds [8]. There are multiple solutions, starting with IaaS, PaaS, and SaaS but there are also applications and architectures developed for multicloud. [PERSON] et al. explore different service models and different types of clouds [9], with an emphasis on InterCloud architecture, topologies, and types; InterCloud is a cloud of clouds, interconnecting the infrastructures of multiple cloud providers. There are two types of InterClouds: federation clouds, where the cloud providers are voluntarily collaborating in order to share resources, and multiclouds, where there is no voluntary collaboration between cloud providers and the management of resources, provisioning and scheduling is entirely the responsibility of the client. The multicloud architecture is used to offer any type of cloud service, most of the latest implementations and solutions being oriented on software services (SaaS) including modeling services [10].  
The challenges regarding multicloud systems are related to the deployment of the services. Having an architecture composed of multiple cloud systems makes the deployment process much more complex and different from platform to platform. Nonetheless, the deployment process can be different based on the specific application: enterprise-based services [11] or manufacturing services [12]. In order to solve the deployment problem, some research directions have been focused on the integration of third-party cloud platforms and services, and on the development of multicloud interfaces [13; 14]. The problem of modeling the optimal and automatic deployment of cloud applications was investigated in [15]. This was done by analyzing the specification of the computing resources required by the software application but also required by the executing environment, the description of deployment rules, and the computation of an optimal deployment to minimize the total cost.  
Most of the implementations for multicloud software services are based on container technologies like Docker [16] and OpenShift [17]. A set of tools has also been developed to automate the creation of software services, with one example being Source to Image (S2I), which is an open-source project that aims to automate the creation of container images directly from the source code of the application. S2I creates a container image that is populated with all the requirements of the application, the application is then compiled and deployed into the container without the programmer's intervention.
## 2 Materials and Methods
### Analysis of Temporal Variation in Saltmarsh NDVI Using PlanetScope  
To investigate the potential seasonal phenological response of saltmarsh, the mean NDVI values for all 47 quadrat locations were extracted from the 12 monthly PlanetScope images (April 2020 to March 2021). The significance of the mean NDVI variation between months was analysed using a one-way repeated measures ANOVA that detects differences between multiple related means [42]. For the herbs, grasses, and sedges quadrats, the monthly NDVI means met the assumption of sphericity using [PERSON]'s test [43], and no correction factor was applied. For the _J. kraussii_ quadrats, the monthly NDVI means violated the assumption of sphericity, and a Greenhouse-Geisser correction factor was applied [44].  
Any observed seasonal variation in saltmarsh NDVI may be due to actual vegetation response or may potentially be caused by a confounding factor unrelated to the changing biophysical properties of the saltmarsh vegetation, such as variations in soil moisture or tidal water levels [45; 46] or variations in sun/sensor geometry at the PlanetScope image acquisition times [47]. The seasonal variations in NDVI were compared to the monthly rainfall prior to and during the study period. Monthly rainfall data were acquired from the Australian Bureau of Meteorology [48]. Due to small gaps in the available data, mean monthly rainfalls from the three weather stations closest to the study area were used: Jervis Bay Airfield, Callala Treatment Plant and Point Perpendicular (Stations IDs 068264, 068245 and 068141, respectively). The relationship between the monthly rainfall and NDVI of the 47 sampled quadrats was tested using [PERSON]'s correlation coefficient. The tide level, sun elevation and view angle of the PlanetScope sensor were also qualitatively evaluated. Tide levels were sourced from a local tide forecast [49], and the sun elevation angle and sensor view angle were extracted from the PlanetScope metadata supplied with each image.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Outsourcing Process
#### 4.2.1 Design of the Fog Node Verification Object  
For each fog node identifier \(g_{i}\), the LBSP first uses \(AES\) to obtain the corresponding ciphertext \(c(g_{i})\), where \(1\leq i\leq s\) and \(s\) indicates the total number of fog nodes. Then, for each \(c(g_{i})\), the LBSP uses the pseudo-random function (i.e., \(prf_{k}\)) to obtain the secret value \(prf_{k}(c(g_{i}))\) and prepares a standard Bloom filter \(VO_{g_{i}}\) with \(m\) bits, where the initial value of each bit is 0. Further, the LBSP utilizes set \(H\) that is composed of \(l\) hash functions to hash \(prf_{k}(c(g_{i}))\) and obtains the set of hashed values \(\{h_{1}(prf_{k}(c(g_{i})))\in[0,m-1],\ldots,h_{l}(prf_{k}(c(g_{i})))\in[0,m-1]\}\). Finally, the LBSP enabling these hashed values can be represented in the standard Bloom filter and sets the corresponding bit \(vo_{g_{i}}[h_{j}(prf_{k}(c(g_{i})))]\) to be 1, where \(1\leq j\leq l\).
# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 4 Spatiotemporal Prediction Models
### Spatial Interpolation Techniques  
We use spatiotemporal data measured from discrete stations in our deep learning prediction models. Therefore, the prediction result of the spatiotemporal data must be visualized by interpolating discrete data in two-dimensional space. We apply the nearest, linear, and cubic interpolation to spatially interpolate and compare the predictions of the deep learning models as postprocessing. The nearest interpolation is the most basic interpolation technique, and the algorithm fills the empty space by copying the adjacent value. The linear and cubic interpolation can be applied as a higher-order interpolation technique, and these techniques usually produce excellent approximations for regularly distributed stations.
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 4 Performance Evaluation
### Network Density
#### 4.3.2 Highway Scenarios  
To implement the highway map, a section of A711 highway located near Clermont-Ferrand airport (Aulnat, France) is modeled, as shown in Figure 11. It has two lanes in each direction and has 2 km length. The data message is created by the fixed source node at the west end of the highway and gathered by all vehicles driving in the ROI. To detect the _traffic flow_, induction loop detectors were implemented under each lane as highlighted by the red circles in Figure 11. The unit of traffic flow represents the number of vehicles passed the detection point per hour (vph). We set five levels of traffic flow that include 443 vph, 883 vph, 1319 vph, 1764 vph and 2207 vph, which increase linearly. The simulation duration of each traffic flow is 200 s and repeated 10 times with different random seeds. All the obtained simulation results with a 95% confidence interval are shown in Figure 12.  
In the case of highway scenarios, AMD achieves high PDRs that are similar as those of TrAD, as shown in Figure 12a, due to the relatively simple two-dimensional road topology. The PDR of AMD exceeds 90% since 883 vph and finally reaches close to 100%. However, Figure 12a also highlights that the PDR of DV-CAST fails to reach 70% finally. The reason lies in that DV-CAST does not have the rescue mechanism for failed transmissions. Indeed, the transmitter vehicles forward the data message only once, _i.e._, one-shot deal. Theoretically, the operation principle of DV-CAST guarantees a high data coverage on the highway. However, if a transmission fails, there is no mechanism to trigger the retransmission again. It is possible that the consecutive transmissions would fail due to fading or collisions. Therefore, if a transmission is broken at the middle forwarder, the vehicles in the rear of this forwarder cannot receive the data message.  
In addition, TrAD uses the message list in the beacon to detect data messages needed by neighbors, so that the failed transmission can be rescued in the next beaconing process. Since the PDR values of TrAD and AMD are close, it makes sense to compare their number of transmissions and delay, as shown in Figure 12b,c. AMD costs less transmissions than TrAD in dense networks, which is consistent with the result of experiments in [7]. However, the delay of TrAD is lower than the one of AMD. This means that the extra transmissions of TrAD would come from the SCF mechanism in one-hop broadcasting, especially at the border of the ROI. TrAD always employs the breaker going out of the ROI to load data messages to the vehicles just coming into the ROI. That is why the PDR of TrAD gains the advantage over AMD at most of vehicular densities, e.g., 443 vph, 1319 vph and 1764 vph. Although the number of transmissions and the delay of DV-CAST are the lowest, its overall performance is still the worst, because the low transmissions and the low delay exactly benefit from the low PDR.  
Figure 11: A711 highway of Clermont-Ferrand, France.  
Figure 12: _Cont._