esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### V2X Application Enabler (VAE) Support  
The main role of VAE is to support the efficient utilization and deployment of V2X applications and services. Apart from the network-associated discussions, we provided an overview of VAE and various architectural interfaces for realizing V2X services from the 3 GPP technical specification (_TS 23.286 Rel-16_) in the application layer under system support. However, the 3 GPP also provides core network supports through various VAE services i.e., message delivery, file distribution, application requirement (e.g., reserve and notify network resource), dynamic group, service continuity, etc., and their operations in the technical specification (_TS 29.486_) of _Rel-16_ and _Rel-17_. In addition, the defined protocol and data model helps in: VAE server and V2X application server communication through Vs interface; and VAE servers communication through the VAE-E interface. The stated VAE services include APIs for the V2X applications to communicate with the VAE server. The APIs can be used by the researchers to make good use of VAE servers and V2X applications. Apart from this usage, the researchers are encouraged to propose new APIs for VAE network-related services.
## 4 Simulation Results
### Path Planning for 2-D Formation Rendezvous
On the other hand, the standard deviations of lengths with DCPSO are much smaller than those with CCGA, as seen in Table 4. This suggests that the proposed DCPSO has a better searching stability than the conventional CCGA. It can also be seen from the Monte Carlo results that the average computation time of the DCPDO algorithm is 2.35 s and that of CCGA is 3.11 s. We have to say that the proposed algorithm cannot be directly used in a real-time situation because the collision detection and avoidance among particles take a large amount of time. However, the proposed path-planning algorithm can be used on board if a prediction mechanism is added. For example, the UAVs in formation agree on a rendezvous maneuver time. Then, each UAV estimates its states at that rendezvous maneuver time. The estimated states of the UAVs are used as the input of the formation rendezvous algorithm. Using the algorithm, the UAVs can obtain their rendezvous paths. \begin{table}
\begin{tabular}{c c} \hline \hline
**UAVs** & \((x_{\text{fit}}\),\(y_{\text{fit}})\)/(km,km) \\ \hline UAV1 & \((0.6,0)\) \\ UAV2 & \((-0.3,-0.6)\) \\ UAV3 & \((-0.3,0.6)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Poses at UAVs’ starting points and rendezvous points (2-D). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & \(L_{1}\) (km) & \(L_{2}\) (km) & \(L_{3}\) (km) & \(\begin{array}{c}dis_{\text{min,12}}\\ \text{(km)}\end{array}\) & \(\begin{array}{c}dis_{\text{min,13}}\\ \text{(km)}\end{array}\) & \(\begin{array}{c}dis_{\text{min,23}}\\ \text{(km)}\end{array}\) & \(\begin{array}{c}dis_{\text{min,23}}\\ \text{(km)}\end{array}\) \\ \hline DCPSO without cooperation & 35.0610 & 31.3760 & 34.3439 & 0.7863 & 1.0817 & 1.2000 \\ DCPSO with cooperation & 35.0611 & 35.0590 & 35.0618 & 0.9868 & 1.0817 & 0.4879 \\ Cooperative co-evolutionary & 35.6054 & 35.5208 & 35.6208 & 0.9564 & 1.0817 & 0.3261 \\ genetic algorithms (CCGA) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical results of 30 simulations. \begin{table}
\begin{tabular}{c c} \hline \hline
**UAVs/Rendezvous Point** & **Pose** \(x\)**, \(y\), \(\chi\)/(km, km, rad) \\ \hline UAV1 & \((2,-5,\pi/6)\) \\ UAV2 & \((-1,10,-\pi/4)\) \\ UAV3 & \((-3,20,-\pi/5)\) \\ Rendezvous point & \((35,15,0)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Poses at UAVs’ starting points and rendezvous points (2-D). Figure 4: Paths of UAVs for formation rendezvous. (**a**) Produced by DCPSO without cooperation; (**b**) produced by DCPSO with cooperation; (**c**) produced by CCGA. \begin{table}
\begin{tabular}{c c} \hline \hline
**UAVs** & \((x_{\text{fit}}\),\(y_{\text{fit}})\)/(km,km) \\ \hline UAV1 & \((0.6,0)\) \\ UAV2 & \((-0.3,-0.6)\) \\ UAV3 & \((-0.3,0.6)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Desired formation configuration.
## 4 Discussion
The main advantage of the PSO algorithm over other feature selection methods, such as GA, simulated annealing (SA), and brain storm optimization (BSO), is its simplicity and efficiency. The PSO algorithm can converge quickly to an optimal solution, even in high-dimensional feature spaces. Other advantages of the PSO are easily controlled compared to other similar algorithms. In contrast, GA and SA algorithms can be computationally expensive and may require more complex fitness functions and encoding schemes to handle large feature sets. Moreover, genetic algorithms are prone to premature convergence and may require extensive parameter tuning to achieve optimal performance. Standard PSO algorithms use a simple update rule, which can limit their ability to explore complex search spaces and converge to optimal solutions. Advanced PSO algorithms, on the other hand, incorporate additional features such as adaptive inertia weights, constriction factors, and social learning strategies to improve the efficiency and effectiveness of the optimization process. These features allow the algorithm to balance exploration and exploitation of the search space, adapt to changing conditions, and avoid premature convergence to suboptimal solutions. The adaptive inertia weights adjust the trade-off between global and local search based on the fitness of the candidate solutions, while constriction factors reduce the step size of the particles as they approach optimal solutions. Our experimental results showed that advanced PSO algorithms can outperform standard PSO algorithms in terms of convergence speed and solution quality, especially in complex optimization problems with high-dimensional search spaces (see Table 3). Moreover, we demonstrated that advanced PSO algorithms have a strong capability to identify the most informative features and improve the accuracy for MCI conversion prediction. To our knowledge, this is the first study that utilized 3D-ZM features along with the IDPSO algorithm for MCI conversion prediction, which is the most challenging and demanding task in the area of AD detection. This study was conducted based on the ADNI dataset, which was created using highly standardized protocols to distinguish individuals at different AD stages. For our MCI conversion prediction framework to be adapted in clinical settings, further validation using other datasets with different protocols is needed.
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 2 Modeling a Converged Network Operator
The metropolitan segment--consisting in Metropolitan Area Networks (MANs)--aggregates  
Figure 1: High-level view of a Converged Operator Network (CON) as considered by ARCFIRE. the traffic of different service DIFs coming from access routers from multiple types of access networks. This segment can be modelled with one or two DIFs that multiplex traffic and deliver it to service routers in core Points of Presence. The residential customer DIF can be thought of as an internal network slice that manages the resources that the operator has allocated to residential customers through the different segments of the network (access, metro, core). Other types of customers (e.g., businesses, different verticals) may have their own separate service DIFs. The backbone (BB) segment connects together service routers in different metropolitan regions, as well as provider border routers that interconnect with other providers directly or via internet eXchange Points. The backbone can consist of one or more DIFs, depending on the scale of the CON core network. Access routers connect to the Customer Premises Equipment (CPEs) directly or via an access network segment, depending on the access technology. Figure 2 depicts a customer connected via a direct fiber from the access router (modelled as a point-to-point DIF). Figure 3 illustrates the cellular access scenario. The major changes with respect to Figure 2 are the introduction of Cell DIFs between Mobile Hosts (MHs) and Base Stations and the presence of the Serving Area DIF to manage mobility within a set of neighboring cells. More levels of DIFs can be employed to accommodate the load, scale, and rate of change of the mobile devices to be supported [7]. A full description of the RINA design for a converged operator network--including DIF structures and policy proposals for each segment of the network--is provided in Section 3 of ARCFIRE's Deliverable D2.2 report [5]. Figure 2: Example design for a CON: fixed residential access. Table 1 shows a summary of architectural properties exhibited by a converged operator network designed using RINA compared to the ones shown by a CON designed with current state of the art networking technologies as fully described in ARFIRE's Deliverable D2.1 report [8]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**CON Design** & **with Current Network Architectures** & **with RINA** \\ \hline \multirow{8}{*}{_Structure_} & Different layers, different functions & Different layers, same functions with different policies \\ \cline{2-3}  & Functions in different layers are not always independent & Strict layering: layers are black boxes, only the service interface is visible \\ \cline{2-3}  & Fixed and static number of layers, require additional constructs such as tunnelling, virtualization, etc.
## Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests  
**[PERSON] 1-*, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 4**  
* 1 IBIMET-CNR, Via Madonna del Piano 10, 50019 Firenze, Italy; E-Mails: [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (M.C.)
* 2 DISTAT, Universita del Molise, Contrada Fonte Lappone, 86090 Pesche, Isernia, Italy; E-Mail: [EMAIL_ADDRESS]
* 3 DISTAF, Universita di Firenze, Via S. Bonaventura 13, 50145 Firenze, FI, Italy; E-Mail: [EMAIL_ADDRESS]
* 4 DISAFRI, Universita della Tuscia, Via S. Camillo de Lellis, 01100, Viterbo, Italy; E-Mails: [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (P.C.).
* * Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-055-522-6024; Fax: +39-055-444-083.  
_Received: 15 October 2009; in revised form: 6 November 2009 / Accepted: 16 November 2009 / Published: 19 November 2009_  
**Abstract:** A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the inter-year GPP variability, are then applied to predict the effects of expected environmental changes (+2 \({}^{\circ}\)C and increased CO\({}_{2}\) concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO\({}_{2}\) concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes.  
Footnote †: journal: Remote Sens.  
**Keywords:** Mediterranean forest; GPP; modified C-Fix; environmental change
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 5 Cooperative Optimization for Privacy and Priority Based on DDPG
### State Space  
The state of the ICV-\(i\) at time slot \(t\) is denoted as \(s_{i(t)}\), which consists of the parameters of the vehicular task \(T_{i}\), such as partial offloading ratio \(\lambda_{i(t)}\), the computational resource required by the vehicular task \(C_{i(t)}\), the sizes of input and output data generated by the task execution \(D_{i(t)}^{in}\) and \(D_{i(t)}^{out}\), the maximum completion deadline \(t_{i(t)}^{max}\), and the vehicular task priority \(Q_{i(t)}\). The ICV-\(i\)'s time-variable driving characteristics at time slot \(t\) as the current position and speed \(p_{i(t)}\) and \(s_{i(t)}\) are also the components of \(s_{i(t)}\). In addition, \(s_{i(t)}\) involves the allocated RSU-\(j\)'s computational resource \(f_{j(t)}^{moc}\) and two privacy-related variables, \(N_{(t)}\) and \(t_{c,j(t)}\), which represent the ICV number of the pseudonym changing and the last time slot ICV-\(i\) changed its pseudonym, respectively. The state \(s_{i(t)}\epsilon\)\(S\) can be written collectively as  
\[s_{i(t)}=\left\{\begin{array}{l}\lambda_{i(t)},C_{i(t)},D_{i(t)}^{in},D_{i(t )}^{out},t_{i(t)}^{\max},Q_{i(t)},\\ p_{i(t)},s_{i(t)}\
eq f_{j(t)}^{MEC},N(t),t_{c,i(t)}\end{array}\right\}_{ \forall i\in\mathcal{V},\forall j\in\mathcal{R}}. \tag{24}\]
## 2 Calculation Model and Numerical Method
### Numerical Validation  
The case of square cylinder flow at a high Reynolds number was adopted to validate the numerical method. The Reynolds number was \(2.2\times 10^{4}\) based on the side length of the square, and the Mach number was 0.2. A set of grids contained 2.8 million cells, refined near the wall to ensure the y\({}^{+}\approx 1\) for the first grid spacing. The physical time step was 0.005 s, and the Courant number during the inner iteration was 10. Figure 3 illustrates the computational field and grid distribution.  
The \(Q\) criterion is commonly used to depict the coherent structures of the intricate wake flow. A detailed definition is provided in reference [23]. Figure 4 provides the \(Q\) isosurface distribution with a nondimensional \(Q\) = 0.45. The large-scale, three-dimensional vortex alternately shed in the wake. The quantitative results are provided and compared with the experiment results in Figure 5, which includes the time-averaged streamwise velocity and the Reynolds stress. The simulation results agreed with the experimental ones well, and we concluded that the numerical IDDES model provided higher numerical accuracy than the RANS model.  
Figure 4: \(Q\) isosurface of the square cylinder flow.  
Figure 5: \(Cont\).  
Figure 3: Computational field (**left**) and grid distribution (**right**) of the square cylinder flow.
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 3 Collision Avoidance Constraint Formulation
### Dynamical Merging of Occupied Grids
Therefore, we primarily focus on the differences between Algorithm 3 and Algorithm 2: Algorithm 3 takes as input the vertically merged occupied grid cells (labeled as VBoxes) and outputs the horizontally merged occupied grid cells (labeled as LBoxes). The specific implementation of the function Sort() (c.f. line 2 in Algorithm 3)is as follows: The grids are first sorted by the height of the bottom edge in ascending order, and for grids with the same bottom edge height, they are sorted by the position of their left boundary from left to right. The merging condition (c.f. line 5 in Algorithm 3) represents the primary distinction between the two algorithms. Specifically, if the current occupied grid and the next one share identical vertical boundaries and are either adjacent or overlapping in the horizontal direction, or if one grid entirely contains the other, the function MergeHorizontalGrids() (c.f. line 6 in Algorithm 3) is executed to merge them into a larger occupied grid along the horizontal direction. By comparing Figure 5a,c, it can be observed that after vertical and horizontal merging operations, the number of occupied grids representing obstacles is reduced from 79 to 36, a decrease of 54.4%. This significant reduction in grid count directly reduces the computational scale of subsequent collision detection. Moreover, the merging of vertical and horizontal grid cells is designed as a computationally efficient one-time preprocessing step. This process requires only a single traversal of all occupied cells, combined with basic sorting (see line 2 in Algorithms 2 and 3) and adjacency checks (see line 5 in Algorithms 2 and 3). The merging is accomplished by directly updating the cell boundaries. Once the merging is completed, the resulting grid map remains valid throughout the entire planning process without requiring repeated operations. Therefore, this step introduces minimal computational overhead and has a negligible impact on the overall planning efficiency. ```
Input: VBoxes; Output: LBoxes. 1. Initialize \(i=1\); LBoxes = \(\mathcal{O}\);
2. VBoxes \(\leftarrow\) Sort (VBoxes);
3. while\(i\leq\) length (VBoxes) do
4. \(j\gets i+1\);
5. while\(j\leq\) length (VBoxes) \(\cap\) VBoxes;\(y_{\min}\) = VBoxes;\(y_{\min}\) \(\cap\) VBoxes;\(y_{\max}\) = VBoxes;\(y_{\max}\)\(\cap\) VBoxes;\(x_{\min}\)\(\leq\) VBoxes;\(x_{\max}\)do
6. VBoxes;\(i\leftarrow\) MergeHorizontalGrids (VBoxes;\(y_{\max}\));
7. \(j\gets j+1\);
8. endwhile
9. LBoxes.add (VBoxes;);
10. \(i\gets j\);
11. endwhile
12. return LBoxes;
```  
**Algorithm 3** Horizontal Merging of Occupied Grids
# Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks
## 4 Experimental Results and Analysis
### Experimental Evaluation Metrics  
In order to accurately evaluate the performance of each model in the recommendation task, this paper adopts three evaluation indicators commonly used in the recommendation field, which are _Recall@K_, _MRR@K_, and _AUC_. This paper sets the parameter as \(K=20\). The recall rate (_Recall@K_) refers to the ratio of the number of relevant results retrieved in the top \(K\) results to the number of all relevant results in the library, and it measures the recall rate of the retrieval system, the formula is shown as (22).  
\[Recall@K=\frac{1}{M}\sum I_{hit} \tag{22}\]  
Among them, \(M\) represents the total number of recommendations, and \(I_{hit}\) represents the indicator function. If the target item appears in the current recommendation list, the value of the indicator function is 1, otherwise it is 0.  
The mean reciprocal rank (_MRR_) reflects whether the items we found are placed in a more obvious position for users, emphasizing the positional relationship and order, the formula is shown as Formula (23).  
\[MRR@K=\frac{1}{N}\sum_{i=1}^{N}\frac{1}{rank_{i}} \tag{23}\]where \(N\) is the total number of users, and \(rank_{i}\) is the position of the \(i\)-th user's real visit value in the recommendation list. If this value does not exist in the recommendation list, then \(rank_{i}\rightarrow\infty\).  
A positive sample and a negative sample are randomly given, the true representative meaning of \(AUC\) is the probability that the predicted score of the positive sample is greater than the predicted score of the negative sample. The larger the \(AUC\), the higher the probability that the model predicts the sample to be a positive sample than the probability that the model predicts that the sample is a negative sample, the formula is shown as Formula (24).  
\[AUC=\frac{\sum_{i\in PositiveClass}rank_{i}-\frac{M(1+M)}{2}}{M\times N} \tag{24}\]  
Among them, \(M\) represents the number of positive samples. \(N\) represents the number of negative samples, and \(rank_{i}\) represents the serial number of the \(i\)-th sample.
# Deep Learning Applications for Dyslexia Prediction
## 4 Research Results
### Classification and Performance of Deep Learning Models
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Datasets** & **DL Model** & **No. of Subjects** & **Performance** \\ \hline
[30] & Test score & ANN & Not mentioned & Accuracy: 75\% \\ \hline
[32] & EGG & MLP & N = 6 kids & Accuracy: 86\% for eye opened \\  & & & Nymal = 3 & and 85\% for eye closed. \\ \hline
[38] & fMRI and DTI & ANN & N = 56 kids & Accuracy: 94.8\%, Sensitivity: \\  & & & aged (9–12 years) & 94.7\%, Specificity: 95\% \\ \hline
[33] & EGG scan & ANN & N = 80 kids (7 to 13 ages) & Accuracy: 89.9\% \\ \hline
[34] & EGG & ANN & N = 32 & \\  & & & Normal = 15 & Accuracy: 78\% \\  & & & Dyslexics = 17 & \\ \hline
[25] & Handwriting image & CNN & N = 88 & \\  & & & Normal = 62 & Accuracy: 55.7\% \\  & & & Dyslexics = 11 & \\ \hline
[26] & Handwriting image & CNN & N = 100 & Accuracy: 77.6\% \\ \hline
[27] & Handwriting image & ANN/MLP & N = 30 & Accuracy: 73.33\% \\ \hline
[12] & MRI & CNN & N = 45 & Accuracy: 73.2\% \\ \hline
[37] & fMRI & 3D CNN & N = 66 children (9 and 12 years) & Accuracy: 72.7\%, F1 score: 67\%, Sensitivity: 75.0\%, Specificity: \\  & & & (9 and 12 years) & 71.4\%, Precision: 60\%\% \\ \hline
[28] & Handwriting image & CNN & N = 54 children (267 samples) & Accuracy: 86.14 \(\pm\) 1.02\% \\ \hline
[35] & EEG & CNN & N = 48 & The study did not mention the accuracy but stated the effectiveness of CNN for eliciting informative features to diagnose dyslexia. \\ \hline \end{tabular}
\end{table} 
Table 3: Summarizes the performance of the predictive model of each study in this review.
## 4 Performance Evaluation
### Number of Dropped Packets  
Figures 15 and 16 present the results in terms of number of dropped packets. According to the CSMA/CA algorithm of the IEEE 802.15.4 standard [21], the frame is repeated four times and is dropped if it is not acknowledged or not sent after the fourth repetition.  
For higher traffic rates, we obtain a higher dropped packet rates as we have more packet loss and more repetitions. For periodic traffic generation, we note that HMC-MAC reduces the number of dropped packets compared to other protocols. For example, for \(16\) pkts/sec/node, when we use a periodic traffic generation, HMC-MAC reduces the number of dropped packets by around \(77\%\), \(68\%\), \(64\%\) and \(60\%\) of that of cluster method, random method, 2-hop method and HMC without segmentation method, respectively. Results are substantially similar in case of burst traffic.  
Figure 14: Number of repetitions for burst traffic.  
Figure 15: Number of dropped packets for periodic traffic.