esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Macroscopic Transmission Control: Virtual Hub-Based Transmission
### Transmission Link Decision and Route Reply
#### 3.2.2 Virtual Delayed Flooding-Based Search  
In this search of Algorithm 2, each RREQ packet is propagated using some penalty delay, such as \(w\cdot c_{ij}\), where \(w\) is a proportional delay constant--i.e., as the link cost increases, the packet is delayed more. Therefore, the low-cost hub link arrives at the destination more quickly.  
```
1:At intermediate hub node \(H_{j}\) in the reduced hub map,
2:while (1) do
3:\(T\leftarrow\) current system time
4:if there is an arrival of \(RREQ_{i}\) from a neighbor \(H_{i}\)then
5:\(T_{i}\gets T\)
6: Calculate the link cost \(c_{ij}\)
7: Calculate corresponding delay \(wc_{ij}\)
8: Insert the \(RREQ_{i}\) to the arrived \(RREQ\) set, \(\Phi\)
9:endif
10: Check \(RREQ_{k}\), \[k=\arg\min_{i\in\Phi}\{T_{i}+wc_{ij}\}\]
11:if\((T_{k}+wc_{ij})\leq T\)then
12: Forward the \(RREQ_{k}\) packet
13: Discard the other \(RREQ\) packets
14: Break
15:endif
16:endwhile
17: At the destination, collect the hub paths into \(\Psi\) from the received RREQs;
18: At destination, select the best hub-path, \(P^{*}\leftarrow\arg\min_{P\in\Psi}\sum_{(i,j)\in P}c_{ij}\);
```  
**Algorithm 2** Virtual delayed flooding-based heuristic search.
# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 1 Introduction
In summary, despite existing research applying reinforcement learning and federated learning in dynamic spectrum access, the following challenges remain in smart construction site scenarios: (1) a single neural network cannot meet the access requirements in complex spectrum environments; (2) traditional RL methods suffer from overestimation bias in spectrum allocation due to their greedy strategy; and (3) existing federated learning frameworks do not consider the efficiency trade-offs in multi-site collaborative optimization. To address these challenges in complex construction site scenarios, this paper proposes an FL-MD3 QN-based intelligent access algorithm for the on-demand access problem of multi-site, multi-modal, and multi-user under the same BS, tailored to the new transmission requirements of IoT in smart construction sites. FL ensures privacy and enables collaborative optimization across sites. MD3 QN adaptively adjusts access strategies for dynamic requirements. Key contributions include:1. This paper proposes an IoT intelligent access framework tailored for multi-site, multi-modal, and multi-user scenarios, innovatively constructing a three-objective optimization model aimed at maximizing data transmission rate, minimizing delay, and maximizing reliability. When aggregating model parameters using federated learning, a dynamic weighted aggregation mechanism is introduced, which adaptively adjusts weights based on data quality and communication stability at each site, ensuring privacy security while improving data aggregation efficiency. 2. The federated learning mechanism boasts a unique design in addressing user privacy risks. By integrating heterogeneous data from multiple sites, it enhances model generalization capability. Local models are trained at each site and aggregated to form a global model, adapting to varying communication demands. 3. A dynamic spectrum access strategy based on MD3 QN is presented, featuring a shared base-layer mechanism in the MD3 QN architecture that reduces parameter redundancy and improves computational efficiency. Meanwhile, a dual-network update strategy decouples action selection and evaluation, effectively mitigating the issue of overestimated Q-values. A business-adaptive reward function tailored to differentiated business characteristics optimizes spectrum access strategies for each scenario by assigning varying weights. 4. The FL-MD3 QN algorithm exhibits significant advantages in performance. By integrating federated learning and optimizing reward function design, FL-MD3 QN surpasses traditional algorithms in key metrics such as delay, transmission rate, access success rate, and bit error rate. Furthermore, its lightweight deployment on the Jetson AGX Navier edge node further validates its scalability and practicality in real-world smart building scenarios.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 4 Monitoring Element  
### Introduction  
Presently, network monitoring encompasses a combination of efficient network measurement techniques and Big Data processing methods that, in SDN, are intended to provide a rich view of the network state to the centralised control plane. This eventually enables the automation and improvement of network control and management tasks, such as adapting the network configuration (as the use case previously presented in Section 3.3), predicting traffic and application trends, or preventing potential problems (e.g., performance degradation, security breaches).  
The huge scale and diversity of presently' networks makes it difficult to measure and maintain accurate and timely statistics of the network state. In this context, the SDN paradigm offers the advantage of a data-plane populated by devices with enhanced computing and storage capabilities, as well as a centralised control plane that permits the collection of all the state information maintained in data-plane devices. For instance, OpenFlow [7] offers support to maintain flow-level traffic measurements in forwarding devices (e.g., traffic volume, flow duration) and provides an API to report this information to SDN controllers.  
However, despite SDN solves some classic problems of network measurement in distributed environments, it brings new challenges to address. The decoupling of the control and data planes adds new implications that need to be identified and considered for the design of efficient network analytics solutions. For example, this separation introduces a latency in the communication between the control and data planes (i.e., between SDN controllers and forwarding devices). Thus, this latency is not only affected by the delay of the connection itself, but also by other factors such as the current workload of the devices and their availability. Likewise, the fact that SDN controllers are centralised entities that typically manage many forwarding devices, makes them prone to become bottlenecks. This adds the need to avoid possible scalability issues by carefully selecting the tasks that are processed in controllers and those that may be devolved to data-plane devices.  
In addition, the use of modern ML and Big Data processing techniques enables the provision of a deep insight about all the information collected from the network. In this vein, one application that may be particularly beneficial for network operation is to classify the traffic by applications. This can be done using supervised ML methods, such as decision trees, Support Vector Machines, or Deep Neural Networks, which can be trained with labeled data to then classify the applications from a limited set of network measurements. For instance, some works leverage basic flow-level traffic measurements (e.g., traffic volume) to discover --with ML--the applications in the network [32; 33].
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 3 System Model
### Reinforcement with Static Learning  
In traditional RL (refer to Equation (3)), the learning mechanism has a constant rate that can be determined based on the importance of the current or discount values. The SU receives its reward based on the channel availability (or white spaces, or the idle status of PU). Specifically, the learning rate can be higher (lower) when the PU activity level is lower (higher), as represented by the equation below [46]:  
\[\begin{split} Q^{i}_{t+1}(s^{i}_{t},a^{i}_{t})=[(1-\alpha)\times Q ^{i}_{t}(s^{i}_{t},a^{i}_{t})+\\ \alpha\times\min(r^{i}_{t+1}(s^{i}_{t+1}))]\end{split} \tag{3}\]  
where \(r^{i}_{t+1}(s^{i}_{t+1})\) is the traffic intensity, which refers to the channel available time of a route \(k_{n}\in K\). The traditional RL approach can be ideal for a less dynamic network with low PU activity levels so that no adjustment is required for learning.
# Supporting Immersive Video Streaming via V2X Communication
## 1 Introduction
* This study adopts different encoding technologies for various types of multimedia applications and dynamically adjusts video quality based on different network conditions to optimize video transmission efficiency. * In planning bandwidth allocation, the characteristics of applications and user levels are considered, with bandwidth being prioritized for real-time applications. When similar applications compete for bandwidth, premium users are given priority access. Additionally, when network bandwidth is insufficient, V2V communication technology can be utilized to access bandwidth from non-busy road sections, thereby effectively improving the overall utilization of bandwidth resources.
## 5 Case Study Analysis
### Pre-Evaluation Analysis  
The pre-evaluation indicator system includes the transfer of passenger flows, the scale of affected passengers, the changes in the number of rides, the passenger profile, and the change in a line's passenger section volume. The detailed indicators are shown in Table 4. Overall, the adjustment of Line 543 will not result in an increased number of unreachable passengers (including those who require more than two transfers to travel to their destinations), but there are 645 trips where the number of transfers increased by one accounting for 28% of all the passengers on Line 543. Of these trips, 328 of them (a total of 281 people) could be classified as rigid travel. Meanwhile, the affected flow of vulnerable groups such as the elderly, students, and the disabled were 128 trips, 14 trips, and 5 trips, respectively. There is also a certain rigidity in the overall travel habits, as they are more prone to complaints and require careful consideration.  
In addition, by randomly analyzing the change in section passenger volume in Line 238, as can be seen in Figure 10, the estimation results and the actual results are quite similar. This means that the data can also meet the requirements of capacity allocation calculation in bus operation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Passengers Flow** & **Rigid Passengers** \\ \hline Trips with more than 2 transfers & 0 & 0 \\ Trips with increasing riding numbers & 645 & 328 \\ Of which are elderly & 128 & 35 \\ Of which are students & 14 & 9 \\ Of which are disabled & 5 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pre-evaluation key indicators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Bus Line**} & **Actual Flow** & **Actual Flow After** & **Estimated** & **Relative** & **Difference** \\  & **Before** & **Adjustment** & **Flow** & **Error** & **Between Actual** \\  & **Adjustment** & **Adjustment** & **Adjustment** & **Adjustment** & **Adjustment** \\ \hline Line 238 & 4491 & 4759 & 4759 & 0.00\% & 268 \\ Line 539 & 5723 & 6052 & 5992 & \(-\)0.99\% & 269 \\ Line 215 & 12,579 & 12,600 & 12,842 & 1.92\% & 263 \\ Line 556 & 5690 & 6262 & 5898 & \(-\)5.81\% & 208 \\ Line 524 & 4708 & 4980 & 4867 & \(-\)2.27\% & 159 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Passenger flow changes in main lines (trips/day).
# Evaluation of BDS-2 and BDS-3 Satellite Atomic Clock Products and Their Effects on Positioning
## 2 Methods  
The following sections introduce the Allan deviation used to assess the clock stability, the method of clock datum comparison and the principle of ionosphere-free PPP.
## 4 COME-UP: Computation Offloading in Mobile Edge Computing with User Direction Prediction
### Location and Direction Prediction Model (LDMP)  
We propose LDPM as a method for predicting the location and direction of a mobile user. The user mobility characteristics in MEC networks are constrained, such as location, velocity, and direction, and the data set is nonlinear [10, 41, 42]. Therefore, considering the nature of input data and the length of required parameters, time series analysis is applied to the mobile user trajectories. Recurrent neural networks (RNNs) offer higher prediction accuracy for time series problems [43]. Therefore, we employ a variant of RNN known as LSTM [44] to predict the user location and direction using mobility traces, as shown in Figure 4.  
Figure 4: Location and direction prediction model (LDPM).  
Figure 3: COME-UP architecture.  
The base station density in 5G cellular networks is significantly higher than in previous generations [45]. However, the individual user's movement information contains a few parameters, as mentioned earlier. The LDPM model considers the mobile user's location (\(x\), \(y\)), velocity (\(v\)), direction (\(o\)), and the task execution time \(TE_{i}^{k}\) as base features. We derive the training characteristics by calculating the derivative of (\(x\)) and (\(y\)) for location change, (\(v\)) for acceleration, and (\(o\)) for change in user orientation, as illustrated in Equations (10)-(13), respectively. The LSTM layer of the LDPM model is fed with the input data based on the window size (no of previous values). The input data for the LSTM layer of the LDPM is fed based on the window size (no of previous values). LDPM then forecasts where the associated user will be residing in the near future based on the information provided. As detailed in the next section, the TPM model uses the predicted location, velocity, direction (\(x_{n}\)-\(y_{n}\), \(v_{n}\), \(o_{n}\)) to determine an appropriate edge server offering minimum latency, energy efficiency, and higher CPU utilization.  
\[\frac{d}{d(x)}=x_{2}-x_{1} \tag{10}\]  
\[\frac{d}{d(y)}=y_{2}-y_{1} \tag{11}\]  
\[\frac{d}{d(v)}=v_{2}-v_{1} \tag{12}\]  
\[\frac{d}{d(o)}=O_{2}-O_{1} \tag{13}\]
## 3 Results
### Deconvolution of the SXP 15.3-SXP 305 Pair  
Figure 3 demonstrates that deconvolution of the SXP 15.3/305 image enhances the visibility of fine structures, particularly in the region around the sources. The cropped image isolates the target and reduces background noise during computations. Then, image rotation helps to align the raw cropped image with the adopted input PSF image. After rotation, the alignment of features remains consistent, preserving the various morphological details. Finally, the RL iteration procedure sharpens the image and reveals details that were previously blurred due to the instrument's PSF.  
In the resulting image (right panel in Figure 3), the maximum X-ray luminosities are not the same in the two flux-enhanced regions marked by boxes. This is never the case in _NuSTAR_ observations of single sources, so this feature signifies the presence of two X-ray emitters in the cleaned image. However, the sources are not entirely resolved owing to their small separation of about 7'', for which the performance of the RL algorithm begins to degrade somewhat (simulations show strong performance for separations of \(\geq\)10''). Nevertheless, we believe the brighter region corresponds to SXP 305 in outburst, during which pulsations of 305 s were measured. Before this observation, only 15.3 s pulsations were detected from this region when SXP 15.3 was active and SXP 305 was presumably in quiescence.  
A comparison between the original, rotated, and deconvolved images of Figure 3 shows the effectiveness of the RL method in improving spatial resolution. The deconvolved image (right panel) can subsequently be processed using other source detection algorithms (e.g., Gaussian fitting of the signal) to pinpoint the precise positions of the two sources that were unresolved in previous observations [28; 29].  
Figure 3: Deconvolution of the original _NuSTAR_ image. (**Left**) Observed image of the sky field containing three sources, with our target circled. (**Center**) The SXP 15.3/305 region is cropped and centered by rotation. (**Right**) Final deconvolved image using the off-axis PSF; the brighter source is likely SXP 305 since only 305 s pulsations were measured in this observation [28].  
Figure 2: Deconvolution of simulated closely spaced sources demonstrating that the RL algorithm can enhance source detection in _NuSTAR_ images.
## The Case Study of Chios  
### Project \"Aigaia Zeufxi\" in North-East Aegean Islands  
The first application for large scale wind integration in the Greek islands and underwater interconnection with the mainland was the \"Aigaia Zeufxi\" in 2006. Today, the \"Aigaia Zeufxi\" project is still under development and aims at a cumulative capacity of 706 MW in Lesvos, Chios, and Limnos. It consists of 28 wind farms with wind turbines of 2 MW rated power and diameter of 80 m. Seven of these are located on the island of Chios, as shown in Figure 1.
## 5 Latency and Reliability Performance Evaluation in Wearable-Based MCPTT Applications
### Performance Evaluation Results
However, this action has two limitations: (i) the increase in the MCPTT access time values due to the long PSCCH periods (as seen in Figure 5) and (ii) the allocation size constraint due to the reduced bandwidth of wearable devices. Hence, reliable MCPTT communications with high packet delivery ratios can be provided at the expense of the access time performance. As a result of this performance evaluation part, we can deduce the need for latency and reliability trade-off mechanisms in wearable-based MCPTT applications. These findings can be useful for the standardization community to take into account the MCPTT reliability requirements. This is necessary, especially since current 3 GPP specifications only consider latency-related metrics for the evaluation of MCPTT services. Additional incentives from the research community can investigate these trade-off mech  
Figure 6: Impact of the PSCCH period and the PSCCH/PSSCH ratio on reliability performance in MCPTT calls. Figure 7: Impact of the sidelink allocation size on reliability performance in MCPTT calls. and thus, help further enable the support of reliable mission-critical applications over cellular systems.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 4 Modeling the Query Flow Within the Mini-Datacenter  
We assume that there are M gateways \((G_{1},G_{2},G_{3},\ldots,\ G_{M})\), and each gateway has a fixed inflow and outflow storage capacity \(B_{i}\), where \(i=1,2,3,\ldots,\ M\). Each gateway has a buffer capacity \((B_{max})\), and the forwarded queries stored in each gateway's buffer cannot surpass its capacity. Each query burst at the command center (CC) is itemized and categorized into two types: an initial query \(q_{i}\), which is part of the incoming query burst of size \(K\left(Q=\{q_{1},q_{2},\ \ldots,q_{K}\}\right)\), and a set of generated queries \(Q_{R}\), where \(Q_{R}=q_{i}\times R(t)\),  
Figure 1: Proposed three-layered mini-datacenter.  
which are redundantly forwarded through the gateways. Here, the term \(R(t)\) represents the number of redundant gateways selected at time instance t, and it varies according to the available gateways and backlog at the CC queue. Each gateway is associated with multiple sensors, and the incoming query \(q_{i}\) is micro-partitioned by the gateway for processing by the sensors. We have developed two query forwarding approaches to forward the query from CC to G: (i) coarse-grain and (ii) fine-grain, as shown in Figure 2a,b.  
In the coarse-grain approach, the query flow is directed from the command center (CC) to the sensors (S) through a selected gateway that can be viewed as a single directed acyclic graph (DAG). The sensor's responses are aggregated at the respective gateway and then sent to the command center as a single data stream. In contrast, the fine-grain forwarding approach involves redundantly forwarding each query through multiple parallel pathways, with at least two, thus forming a parallelized directed acyclic graph (PDAG). The fine-grain approach offers a finer level of granularity to manage the system's reliability and flexibility in response to variations in the query flow environment. Here, the flexibility refers to the system's ability to respond to changes in the network conditions, such as fluctuations in workload distribution and available buffer space, while the reliability is influenced by the degree of redundancy. A query processing is performed individually at each selected gateway, and the aggregated responses are then compared at the command center to select the most reliable one.
###### Abstract  
African tropical ecosystems and the services they provide to human society suffer from an increasing combined pressure of land use and climate change. How individual tropical tree species respond to climate change remains relatively unknown. In this study, we refined the species characterization in the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model by replacing plant functional type morpho-physiological traits by species-specific traits. We focus on 12 tropical tree species selected for their importance in both the plant community and human society. We used CARAIB to simulate the current species net primary productivity (NPP), biomass and potential distribution and their changes in the future. Our results indicate that the use of species-specific traits does not necessarily result in an increase of predicted current NPPs. The model projections for the end of the century highlight the large uncertainties in the future of African tropical species. Projected changes in species distribution vary greatly with the general circulation model (GCM) and, to a lesser extent, with the concentration pathway. The question about long-term plant response to increasing CO\({}_{2}\) concentrations also leads to contrasting results. In absence of fertilization effect, species are exposed to climate change and might lose 25% of their current distribution under RCP8.5 (12.5% under RCP4.5), considering all the species and climatic scenarios. The vegetation model projects a mean biomass loss of \(-\)21.2% under RCP4.5 and \(-\)34.5% under RCP8.5. Potential range expansions, unpredictable due to migration limitations, are too limited for offsetting range contraction. By contrast, if the long-term species response to increasing [CO\({}_{2}\)] is positive, the range reduction is limited to 5%. However, despite a mean biomass increase of 12.2%, a positive CO\({}_{2}\) feedback might not prevent tree dieback. Our analysis confirms that species will respond differently to new climatic and atmospheric conditions, which may induce new competition dynamics in the ecosystem and affect ecosystem services.  
tree species; traits; tropical Africa; climate change; ecosystem services; dynamic vegetation model +
Footnote †: journal: Journal of the American Statistical Association  
0000-0001-8