esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B Correlator Output Variance Derivations
### Data Bits Misaligned by an Integer Number of Code Chips
\begin{array}{ll}\int_{s=\Lambda\atop s\in D_{3}}^{T_{b}}&e^{j2\pi fs}\sum \limits_{p=0}^{18-K}e^{-j2\pi f(\Delta+pT+vT_{c})}ds\\ =\sum\limits_{p=0}^{18-K}e^{-j2\pi fpT}\int_{s=\Delta}^{T_{b}}&e^{j2\pi f(s- \Delta-vT_{c})}ds\\ s\in D_{3}\\ =\sum\limits_{p=0}^{18-K}e^{-j2\pi fpT}\sum\limits_{n=0}^{18-K}e^{j2\pi f(n-K) T}\int_{(n-K)T}^{(n-K+1)T-CT_{c}}e^{j2\pi f(\alpha-\lfloor\alpha/T_{c}\rfloor T_{c})}d \alpha\\ =\sum\limits_{p=0}^{18-K}e^{-j2\pi fpT}\sum\limits_{n=0}^{18-K}e^{j2\pi fnT} \sum\limits_{m=0}^{C-1}\int_{mT_{c}}^{(m+1)T_{c}}e^{j2\pi f(\alpha-mT_{c})}d \alpha\\ =CT_{c}\frac{\sin(\pi fT_{c})}{(\pi fT_{c})}e^{j\pi fT_{c}}\frac{\sin^{2}(\pi f (19-K)T)}{\sin^{2}(\pi fT)}\end{array} \] (A19)  
The fourth integration of Equation (A16) is:  
\[ \begin{array}{ll}\int_{s=\Delta}^{T_{b}}&e^{j2\pi fs}\sum\limits_{p=0}^{19-K }e^{-j2\pi f(\Delta+pT+vT_{c})}ds\\ s\in D_{4}\\ =(2046-C)T_{c}\frac{\sin(\pi fT_{c})}{(\pi fT_{c})}e^{j\pi fT_{c}}\frac{\sin^{ 2}(\pi f(20-K)T)}{\sin^{2}(\pi fT)}\end{array} \] (A20)  
Finally, we combine Equation (A17) through Equation (A20) and will arrive at:  
\[ \begin{array}{ll}&E\Big{\{}\left|y_{k}\right|^{2}\Big{\}}\\ &=T_{c}^{2}[\sin c(fT_{c})]^{2}.\\ &\Big{(}(1023-C)\cdot\frac{\sin^{2}(\pi fKT)+\sin^{2}(\pi f(20-K)T)}{\sin^{2} (\pi f)}\\ &+C\cdot\frac{\sin^{2}(\pi f(K+1)T)+\sin^{2}(\pi f(19-K)T)}{\sin^{2}(\pi fT)} \Big{)}\end{array} \] (A21)  
This completes the development of the variance of correlator output when the data bits are misaligned by an integer multiple of primary code chips. Specifically, when \(C=0\), i.e., the differential time delay between undesired and desired signals are integer multiples of a code period, we arrive at:  
\[ \begin{array}{ll}&E\Big{\{}\left|y_{k}\right|^{2}\Big{\}}\\ &=T\cdot T_{c}[\sin c(fT_{c})]^{2}\frac{\sin^{2}(\pi fKT)+\sin^{2}(\pi f(20 -K)T)}{\sin^{2}(\pi fT)}\end{array} \] (A22)  
which is a special case and we put it here for ease of reference.
## 5 Simulation and Performance Analysis
### Network Performance Analysis  
The packet loss differentiation algorithm proposed in this paper requires the cooperation of the receiver because it uses information such as ROTT and IAT at the receiver. In other words, the final packet loss differentiation is completed at the receiver, necessitating the receiver to provide feedback on the judgment information to the sender. To achieve this, the explicit loss notification (ELN) mechanism is employed. When the receiver determines that the cause of packet loss is congestion loss, the ELN flag bit of the TCP packet header  
Figure 6: Comparison of AUC values of LDM-HEL, LDM-satellite, and LLD algorithms.  
Figure 7: Comparison of AUC values of voting, ACC weighting, and SDG weighting.  
is set to 0, and when the receiver determines that the cause of packet loss is bit error loss, the ELN flag bit of the TCP packet header is set to 1. Accordingly, the sender adopts the corresponding congestion control window adjustment strategy.  
In the experiment, the link bit error rate was set to \(10^{-1}\), and LDM-HEL, LDM-satellite, and TCP-RLLD [26] packet loss discrimination algorithms were applied to TCP New Reno. We evaluated the Goodput performance of the LDM-HEL algorithm and four baseline algorithms (LDM-Satellite, TCP-RLLD, New Reno, Westwood, LA, USA) at different end-to-end packet sending rates, as shown in Figure 10.  
The experimental results show that New Reno and Westwood do not exhibit significant Goodput improvement when the transmission rate increases. This is mainly due to their lack of an effective packet loss differentiation mechanism. In contrast, LDM-HEL demonstrates better performance. By employing a heterogeneous ensemble learning model, it comprehensively considers more network state characteristics, fully leverages the advantages of multiple classifiers, and enhances the model's generalization ability. This results in more accurate packet loss differentiation, helping to avoid unnecessary congestion window reduction. Compared with the LDM-satellite and the TCP-RLLD algorithms, its throughput increased by an average of 9.28% and 19.73%, respectively.  
Figure 8: Comparison of F1-score performance of three packet loss differentiation models.  
Figure 9: Comparison of training time of three kinds of packet loss differentiation models.  
We also compare the performance of each algorithm under different bit error rates, and the results are shown in Figure 11. We set the packet sending rate to 1250 packets/sec. In the case of a low packet loss rate, the Goodput of all protocols is relatively high. With the increase in bit error rate, the Goodput of each algorithm decreases obviously. However, LDM-HEL still exhibits a better Goodput.
# A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph
## 5 Lightweight V2V Identity Authentication and Key Agreement Scheme
### V2V Identity Authentication and Key Agreement Process
\[wk=r1\oplus r2 \tag{4}\]  
\[msg3^{\prime}=Enc_{wk}('success') \tag{5}\]  
(9) Equipment B calculates the working key, _wk_, and decrypts _msg3_ (received from Equipment A). Dec represents the symmetric decryption function. The _wk_ and _msg3'_ calculation methods are as follows. \[wk=r1\oplus r2 \tag{6}\]  
\[msg3^{\prime}=Dec_{wk}(msg3) \tag{7}\]  
(10) Equipment B verifies whether _msg3'_ is an authentication success message. If yes, it indicates Equipment A and Equipment B use the same working key, _wk_. Here, Equipment B completes the identity authentication of Equipment A. Subsequently, Equipment A and Equipment B use _wk_ as the working key to encrypt the communication data. The working key, _wk_, is time effective, and it is eliminated according to the set timeout and the amount of encrypted data. When the use time of _wk_ exceeds the set value, _wk_ invalid, Equipment A and Equipment B re-authenticate and negotiate the working key. When the amount of encrypted data or encryption times by _wk_ exceeds the set value, _wk_ invalid, Equipment A and Equipment B re-authenticate and negotiate the working key.
## 3 Results and Discussion
### Electromagnetic Field on Different European Roads
#### 3.1.4 Hungary  
In Hungary, electric field measurements were carried out in the car cabin during driving on motorways M0, M1, and M5, as shown in Figures 9-11. The selected driving roads are part of the International E-road network--E 75 (between points P1 HU-P2 HU and P2 HU-P3 HU) and E60 (P3 HU-P4 HU).  
Figure 8: Croatia: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
Figure 10: Hungary: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
Figure 9: Hungary: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
Figures 9-11 and Table 3 show that the maximum \(E_{total}\) values vary slightly from 5.33 V/m to 5.50 V/m. Although part of the road section P2 HU-P3 HU covers the Budapest Ring Road, no higher value was measured compared to the other two road sections. The median of \(E_{total}\) is between 0.24 V/m and 0.26 V/m. The lowest \(E_{total}\) value in all road sections is 0.11 V/m. We see that the \(E_{total}\) upper quartile is 0.29 V/m (P1 HU-P2 HU), 0.31 V/m (P2 HU-P3 HU), and 0.35 V/m (P3 HU-P4 HU). Additionally, the electric field on the probe \(y\)-axis has a slightly higher mean and median. We suppose that obtained results are caused by the E-field distribution inside the car, which varies due to the many different routes of propagation of electromagnetic waves from the transmitter/s to the LSProbe. The signal that arrives at the probe axis is composed of direct and reflected components. The higher measured values at the \(y\)-axis of the LSProbe 1.2 variant E are likely caused by the direct component.
## 3 FlexMonitor
### Scalability  
Generally, the scalability of the monitoring framework FlexMonitor is mainly reflected in two aspects: (1) the ability to reduce to computing overhead on the controller; and (2) the ability to reduce the communication overhead between the controller and switches.  
During the network monitoring, the computing overhead on the controller is mainly caused by the analysis of the monitoring data. In FlexMonitor, due to the increase of monitoring data sources, especially the use of event triggering messages, the monitoring data analysis would become relatively simpler. Therefore, the computing overhead on the controller is reduced to some extent. Furthermore, FlexMonitor takes various measures to reduce the communication overhead between the controller and switches. From the perspective of the controller, it collects the monitoring data only from switches in the optimal targeted switch set rather than all switches. From the perspective of the switch, it fully exploits the computing capability of the switches. On the one hand, it introduces the event triggering message as one of the monitoring data source, which can make the communication overhead minimal while maintaining the monitoring accuracy. On the other hand, it can send the monitoring data to the controller after aggregating or compressing it in the switches.  
Therefore, the monitoring framework FlexMonitor has good scalability and can be easily extended to large-scale networks.
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 3 Results
### Emissions and Vegetation Type  
Table 5 lists the annual percentage of emissions by vegetation type. Tropical forests accounted for the highest percentage of emissions: \(>\)29.4% in 2011, 2014, and 2013. This type of vegetation is distributed in Plain and Hills of the Yucatan Peninsula (15.2) and Southern Pacific Coastal Plain and Hills (14.5) (Figure 1). Dry tropical forests have lower emissions but a greater variability, from 8.2% to 17.4%. In 2012, emissions were distributed more uniformly in temperate forests, such as pine-oak, oak-pine, and oak in the 13.5 Southern Sierra Madre ecoregion, with the percentage in tropical forests at \(<\)15%. In cloud forests, which are restricted to the mountainous area in that ecoregion, the burned pixels ranged between 2.7% and 4.9% over the four years. Meanwhile, the emissions generated in tropical rainforests and hydrophilic vegetation represented less than 1% for all of the years; the former was restricted to small areas of the ecoregions 15.2 and 13.2, and the latter was limited to the coastal zone of the ecoregions 15.2 and 14.5. Grassland produced emissions at low percentages, between 3.7% and 5.9%, in all periods with the exception of 2014, when they reached 12.0%.  
The annual emission dynamic depends on the distribution of vegetation and precipitation. In January, the vegetation type that had more emissions was grassland, because it is light fuel and quickly loses the moisture that it accumulates during the wet season. As each year progressed, emissions significantly increased in temperate forests, reaching more than 50% in May and June, while in tropical forests the emissions decreased. In July, emissions were generated in the tropical forests. By the end of the year, emissions had again increased in temperate forests and grassland. Therefore, under the conditions described here, the distribution of emissions at the ecoregion level is due mainly to environmental characteristics.
## 2 Methodology
### Wind Load Modeling and Simulation  
Wind load is modeled using CFD analysis with the open-source Kratos Multiphysics tool [12; 13; 14]. This involves a finite element method (FEM) formulation for flow problems based upon a variational multi scale (VMS) formulation [15]. The computational domain setup is the one presented in ref. [16], which was set up for the CAARC B building [10; 11]. Tetrahedral elements are employed to discretize the fluid domain. The computational domain setup for the current simulation is illustrated in Figure 1. The figure also outlines the boundaries of the fluid domain and illustrates the applied boundary conditions.  
Inlet Boundary Condition  
The wind characteristics are taken care of by the inlet boundary condition. The wind effects in the atmospheric boundary layer (ABL) are modeled by decomposing the wind velocity field as a Reynolds decomposition, \(V=\overline{V}+V^{\prime}\), where \(\overline{V}\) is the steady mean profile component and \(V^{\prime}\) is the unsteady turbulent fluctuations component. The mean wind profile, denoted as \(\overline{V}\), represents the averaged contribution to the overall wind field \(V\). This profile undergoes changes over the time scale of several hours to days, as indicated in [17]. Therefore, for the numerical simulation, the mean profile is assumed to remain constant. The turbulent fluctuations of wind \(V^{\prime}\) represent the wind gusts with a time span of seconds. The gust wind speed triggers transient conditions of peak wind loading. If the structural eigenfrequencies of large structures align with frequencies in the gust-induced wind load pattern, resonant effects may occur. Separate models are employed for each term, \(\overline{V}\) and \(V^{\prime}\).  
An exponential profile is adopted for the study for the mean wind profile. The wind velocity at any height \(z\) is given by  
\[\overline{V}(z)=V_{\text{ref}}\bigg{(}\frac{z}{z_{\text{ref}}}\bigg{)}^{\alpha} \tag{1}\]  
where \(z_{\text{ref}}\) is the reference height and \(\alpha\) is the exponent which depends on the terrain. To capture wind statistics at the location of interest, wind fluctuations \(V^{\prime}(t,y,z)\) are modeled. Various numerical models are available to address this issue [18; 19; 20]. In this study, the well-established Mann model is utilized to simulate wind gust fluctuations [21; 22]. This model, widely accepted in structural wind engineering [23; 24; 25; 26], is known for its spectral approach in wind generation.
## 3 System Model and Description
### System Models of Existing and Proposed Protocols  
To explain the system models of existing and proposed protocols, a few key terms are defined here: the nodes near or at the layers are known as Cross nodes. The nodes that sense their own data before forwarding the data packets towards higher depth nodes are known as Normal nodes (relay nodes).  
The network consists of cross, normal, source and sink (sonobuoy) nodes with the surface gateways and channel pathways. These sonobuoys act as an embedded system in underwater environment. In the proposed protocols, relay nodes are randomly deployed for data transmission in the deep ocean by targeting minimum EC. Initially, the relay nodes are in a sleeping mode with minimum EC. However, they become active on receiving the data packets from the source and cross nodes. When these relay nodes complete their participation in communication, they sleep again. The binary tree formation is the main feature of both proposed schemes with some additional properties of SPF-Three. This approach finds the single shortest path towards the destination (based on\"Dijkstra and BFS-SPF-Three\" algorithm) which searches different possible shortest paths and performs opportunistic routing to minimize the EC and distance.  
Figure 2 demonstrates the system model of FLMPC protocol with non-homogenously distributed layers to overcome the noise hindrance. In the existing schemes, every cross node keeps two copies of data and forwards the data using IP-based broadcasting. The main aim is to balance the effect of collision till the packet reached the destined sonobuoy. Distribution of layers depends on different types of noises in the sea depth, i.e., in shallow water noise of ships is high as compared to sea depth. Therefore, in shallow water layers will be closer to each other. System models of both proposed schemes are shown in Figures 3 and 4. The Figure 5 demonstrates legends for the aforementioned proposed and existing protocols. In proposed protocols, there is a need to find the path which is the shortest path with the minimum EC and distance. Due to the ability of shortest path selection, SPF-Three and BFS-SPF-Three become more efficient. In this regard, network communication space is divided into unequal multiple layers and becomes narrow near the sea surface. To handle the effects of packet collision and data loss, binary tree generation idea is adopted from the existing protocols with 3-hop neighbors information. By considering the aforementioned issues, two efficient routing protocols are proposed. The detailed description
# A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN-NER +
## 5 Cloud-Oriented Services for the 4 CH Framework
### The 4 CH Cloud Platform: Design and Approach  
The development approach of the 4 CH cloud platform adopted the infrastructure-as-code (IaC) approach, i.e. the process of managing and provisioning computing resources through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. Following the IaC approach, the Kubernetes [29] solution has been adopted and implemented. Kubernetes is an open-source container orchestration system used to automate the management, scaling, and deployment of web services integrated into the cloud.  
By defining a set of building blocks, Kubernetes can collectively provide mechanisms that deploy, maintain, and scale applications based on CPU, memory, or custom metrics. Thanks to its loosely coupled nature, Kubernetes is enough extensible to meet different workloads.  
The Kubernetes cluster is composed of a master and several worker nodes suitable to host the 4 CH services, as the THESPIAN SaaS platform, comprising THESPIAN-Mask, THESPIAN-NER, and THESPIAN-XRF [10]. To expose services and applications to the users, an ingress object has been also made available.  
The platform was deployed by using the Rancher Kubernetes Engine (RKE).  
As explained above, on top of the Kubernetes cluster some other components have been deployed to improve functionalities of the cluster: Canal for the network service and Longhorn for the storage service.  
On top of the Kubernetes cluster, some components have been deployed to improve the cluster functionalities such as networking and storage.  
Networking is provided by Canal [30] which is a combination of Calico [31] and Flannel [32]. Calico is open-source networking and network security solution for containers, virtual machines, and native host-based workloads. Flannel is a dedicated networking plugin for Kubernetes which provides an overlay network which allows communication between Kubernetes workloads. Moreover, Calico increases the network security by setting policies that control the communications within it.  
On the other hand, storage services rely on Longhorn [33], an open-source software that implements distributed block storage by using containers and microservices. Longhorn creates a dedicated storage controller for each block device volume and synchronously replicates the volume across multiple replicas stored on multiple nodes. The storage controller and replicas are themselves orchestrated by using Kubernetes. Moreover, to increase  
Figure 10: Schema of the 4 CH competence centre cloud PaaS, also showing the ancillary software services.  
availability, Longhorn creates replicas of each volume. Replicas contain a chain of snapshots of the volume, with each snapshot storing the change from a previous snapshot.
## 4 Results and Discussion
Figure 5: Changes in total freeway tolls with number of iterations. necessary to adequately ascertain the rationality level of local freeway users and to pre-estimate the appropriate timing for evaluating the effectiveness of the implementation of the rate program. Theoretically, due to the competitive relationship between freeways and parallel national and provincial arterial roads, given all other conditions alike and the higher speeds of freeways and the lower speeds of parallel national and provincial arterial roads, the number of users choosing to travel on the freeway will increase, and, thus, freeway tolls generally increase. As shown in Figures 6-9, The simulation results are consistent with the above basic understanding. Remarkably, increasing freeway speeds beyond 110 km/h will have an attenuating effect on the freeway user share and the total tolls for freeway trips. Similarly, when we increase the speed of parallel state and provincial arterials over 70 km/h, the depressing effect on the freeway user share and total freeway tolls decays. From this, in conditions where the toll rate and other conditions remain unchanged, the freeway tolls can be increased through speed management and other means (such as limiting low speeds) to reduce the cost of freeway travel. For heavily congested road sections, we can increase the parallel national and provincial arterial roads' speed to absorb part of the highway travel demand, thus achieving the goal of congestion relief. Moreover, the difference in speed between the freeway and the parallel national and provincial arterial roads is also correlated with the freeway travel demand and tolls. Theoretically, under the same conditions, the larger the speed difference between the freeway and the parallel road, the more attractive the freeway is, and its travel demand and tolls will be larger. The simulations shown in Figures 10 and 11 verify this theoretical result. Furthermore, we find that when the speed difference between the freeway and the parallel road is less than 5 km/h, the growth of the speed difference will not have a positive contribution to the freeway toll collection. This shows that the parallel national and provincial arterial roads still have an advantage in attracting passenger traffic. In practice, it can be understood that, although freeway speeds are slightly higher than those of parallel roads, the accessibility of the freeway is relatively low due to its full closure nature, and the cost of getting to the freeway and entering and exiting the toll booths overrides the speed advantage of the freeway. Hence, in addition to improving the speed advantage of freeways, improving the accessibility of freeways can somehow increase the demand for freeway travel, which in turn can increase the toll revenues of freeway operators. Figure 8: Parallel road speed and total freeway tolls. Figure 9: Parallel road speed and freeway user share.