esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of AOD Inter-Comparison
### Evaluation of the VIIRS_ML AOD at 550 nm  
Comparing the VIIRS_ML to CE318 (Table 2), there are 817, 755, and 683 matchups for QF > 0, QF > 1, and QF = 3, respectively. Since each algorithm has its own definition of QF, there are different relative contributions of each QF level [6; 8]. Overall, the VIIRS_ML AODs show a high correlation with CE318 (R is 0.93-0.94) but overestimates over this region with high MBs (0.13-0.14) and large slope values for the equations of best fit (1.26-1.27). More than half of the VIIRS_ML data are within the expected error envelope (%EE > 50%). As compared to VIIRS_EDR, the VIIRS_ML has a higher bias, but has larger correlation with more data within the EE.  
Following site-by-site comparison for the VIIRS_EDR, we evaluate the VIIRS_ML AOD at each site (Table 4). Like the VIIRS_EDR, the VIIRS_ML shows the largest bias over the Beijing urban site. Since the urban surface reflectance may be underestimated in the \"dark-target\" algorithm, this can lead to an overestimation of AOD [37]. The VIIRS_ML AOD over XiangHe performs the best with the highest values of R and %EE but the MB is not the lowest and it is higher than that between the VIIRS_EDR and CE318 (MB of (\(-\)0.02)-0.00). However, the R values between the VIIRS_ML and CE318 at all three sites are higher than those found for the VIIRS_EDR and CE318. The values of %EE of the VIIRS_ML over the XiangHe and Xinglong sites are higher but lower over the Beijing site compared to those between the VIIRS_EDR and CE318.  
The results of the quality flag analysis of the VIIRS_ML AOD are similar to those for the VIIRS_EDR. Using high quality data leads to the best performance at the XiangHe and Xinglong sites but it is not suitable at the Beijing site.
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 2 System Model and Problem Formulation
### System Capacity
#### 2.2.2 Downlink Phase  
When the \(m\)th CU uses the downlink spectrum, the received SINR for the \(m\)thCU can be written as  
\[\gamma_{m,down}^{c}=\frac{P_{Bm}^{c}g_{Bm}}{\sigma^{2}+\sum\limits_{k\in K} \rho_{mk}^{d}P_{k}^{d}h_{km}} \tag{7}\]  
where \(P_{Bm}^{c}\) is the transmission power of the BS, \(g_{Bm}\) is the channel gain of the signal link between the BS and \(m\)th CU, and \(h_{km}\) is the channel gain of the interference link between the transmitter of \(k\)th D2D pair and \(m\)th CU.  
In the downlink, the D2D pair is interfered by the BS, thus we can obtain the SINR of the \(k\)th D2D pair under channel \(m\)  
\[\gamma_{k,down}^{d}=\frac{P_{k}^{d}g_{kk}}{\sigma^{2}+\sum\limits_{m\in M}{{{\rho }_{mk}^{d}}P_{Bm}^{c}h_{Bk}}} \tag{8}\]  
where \(h_{Bk}\) is the channel gain of the interference link between the BS and the receiver of \(k\)th D2D pair.  
In downlink phase, combining Equations (7) and (8), we can give the overall system capacity of CU and D2D pair  
\[R_{sum}^{down}=B\log_{2}\Bigl{(}1+\gamma_{m,down}^{c}\Bigr{)}+B\log_{2}\Bigl{(} 1+\gamma_{k,down}^{d}\Bigr{)} \tag{9}\]  
According to Equations (6) and (9), the total system capacity can be expressed as  
\[R_{sum}={{\rho}_{mk}^{u}}R_{sum}^{up}+{{\rho}_{mk}^{d}}R_{sum}^{down} \tag{10}\]
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 2 System Model and Problem Formulation
### System Model
The queue only stores the packets that arrived within the past \(T_{th}\) time slots, and when the delay of a packet in the queue exceeds this threshold, the packet will be discarded. The packet buffer queues of all beam positions in time slot \(t\) are defined as \(\Phi=\{\Phi_{t}^{m}\mid m\in\Psi\}\), where \(\Phi_{t}^{m}=\{pa_{t}^{m},pa_{t-1}^{m},\cdots,pa_{t-T_{th}+1}^{m}\}\) represents the packet buffer queue of beam position \(m\) in time slot \(t\). Since the current packet queue cannot be known when simulating the satellite-to-ground communication environment, it is assumed that the packet arrivals at each beam position before entering the beam hopping period follow a Poisson distribution (with an arrival rate of \(\lambda\)). After entering the beam hopping period, all beam positions generate new service packets with a probability of \(p_{in}\) in each beam hopping time slot, and the newly added packets are stored in the queues of each beam position. Considering the diversity of users' communication services, the packets arriving at the beam positions are simply divided into three types, and their data amounts are different. The traffic demands of all beam positions in time slot \(t\) are defined as \(D_{t}=\{D_{t}^{m}\mid m\in\Psi\}\), where \(D_{t}^{m}=\{d_{t}^{m},d_{t-1}^{m},\cdots,d_{t-T_{th}+1}^{m}\}\) represents the traffic demand of beam position m in time slot \(t\), meaning the total traffic demands of all users at this beam position within the past \(T_{th}\) time slots. \(d_{t}^{m}\) is the data amount corresponding to the packet \(pa_{t}^{m}\) in the packet queue.
## 4 Experimental Results and Discussion
### Integrated Model Ablation Experiment  
To better evaluate the performance of GRU-LR-TCN, the estimation results of different improved TCNs combined with different gated recurrent network models are experimentally analyzed and compared in seven metrics and time as a way to prove that GRU-LR-TCN has excellent estimation performance, and the experimental results are shown in Table 4. From Table 4, it can be seen that in the monitoring station 1001 dataset, GRU-LR-TCN outperforms all other combination models in seven metrics. In addition, compared with LR-TCN, the seven metrics are improved, and the SMAPE index has increased by 7.1%.
# Organic Nanostructured Materials for Sustainable Application in Next Generation Solar Cells
### Bulk Heterojunction (BHJ) Nanomaterial in OPVs  
In OPV, a BHJ is basically a multilayer nanostructure with each layer in the device design formed using a different manufacturing method. To begin with, it is made up of an absorbing (photoactive) layer, that consists of two components, a donor material is usually a conjugated polymer [1], conjugated pigments [24] or oligomers, and for an acceptor material it is often fullerene derivatives but recently BHJ have been replaced by their counterparts, non-fullerene derivatives. As shown in Figure 3, the absorbing layer is located between the anode and the top relatively low function (WF) cathode. Interfacial layers such as a hole transport layer (HTL) and an electron transport layer (ETL) [3] are typically put between the anode-photoactive and cathode-photoactive interfaces to improve the performance and stability of BHJ OPVs [25]. [PERSON] et al. proposed that a nanoscopic BHJ shape of the donor-acceptor network with continuous interpenetrating phase characteristics is critical for highly efficient OPVs. They attributed this to BHJ materials' low dielectric constant, in which a photon produced by an electron-hole pair (exciton) had an unusually high exciton binding energy and a short diffusion length [3].  
Figure 2: Illustration of charge transfer process in a hybrid organic-inorganic solar cell.  
They suggested that the key to achieving high PCE is to enhance dissociation efficiency. Because of the strong bonding of the exciton, efficient separation of the electron-hole pair requires the application of an additional field, which may be done by utilizing a large chemical potential difference at the donor-acceptor interface and depending on fast electron transfer. A p-n junction, similar to those seen in silicon solar cells, can be formed via the donor-acceptor contact. As a result, improving the charge separation ratio against recombination by raising the PCE by increasing the donor-acceptor interfacial area is a viable option. An example of an OPV device that employs BHJ is an inverted OPV device. In an inverted device, the bottom transparent electrode acts as the cathode, while the top electrode serves as the anode. Inverted solar cells offer equivalent performance to regular solar cells and have a higher level of environmental stability [26].
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 3 Multi-Technology Cooperative Communication and Drone Data Relaying
### Multi-Technology Communication Algorithm  
The main intuition behind the proposed heuristic is that cluster formation can opportunistically leverage the local connectivity of nodes at the different tiers; CHs can then be selected to uniformly spread energy consumption between nodes, both over clusters and tiers. CHs need to transmit data over network interfaces \(n_{u}\)\(u\leq U\). Hence, they end up consuming energy faster than the other nodes. To maximize clusters lifetime, nodes within the same cluster take turns becoming cluster head for  
Figure 1: Multi-tier network architecture: (example of three communication technologies).  
a time interval \(\delta t\) according to their energy level (i.e., the node having the highest energy level will be the CH). The heuristic is described in Algorithm 1.  
```
Input nodes location and \(\delta t\) (time-slot) Output: switch ON/OFF interface \(n_{i}\) foreach\(\delta t\)do
1 i = 1;
2 activate interface \(n_{1}\);
3 ACTIVATED = TRUE;
4 while(ACTIVATED==TRUE) and (\(i<U\))do
5 discovers \(n_{i}\)-neighbors and power budget;
6 select \(n_{i}\) tier CH with higher power-budget (potentially itself);
7 if\(CH_{i}=m\)then
8 if\(me\) node is the CH for \(n_{i}\)-tieractivate \(n_{i+1}\) interface;
9 i++;
10 else
11 ACTIVATED == FALSE
12
13 if\(i=U\)then
14 exchange data with UAV
```  
**Algorithm 1**Dynamic CH selection run at each node \(m\).  
**Complexity.** A node only has to identify cliques within its neighborhood (in \(O(n.log(n))\) if \(n\) is the number of neighbors) and sort the nodes of the clique according to their Id, so in \(O(m.log(m))\) where \(m\) is the number of nodes in the clique. Determining close bounds for \(n\) and \(m\) is a very interesting study that we may complete finely. Nevertheless, an upper bound for \(m\) is \(n\) (\(m\leq n\)). So an upper bound for the complexity is \(O(n.log(n))\).  
**4. Mobile Devices Performances: Smartphone Use Case**  
Testing experiments have been carried out to measure the performance of smartphones in terms of network interfaces (based on energy consumption and transmission range) and clock synchronization. We believe that such experimental results can support technological choices for rescue operations but also for many other applications relying on smartphone performances.  
We have conducted different experiments featuring six smartphones Wiko Tommy 2 and exploiting two network technologies Bluetooth and Wi-Fi. The main specifications of used smartphones are shown in Table 1. Testing scenarios target to measure and evaluate the performances of the smartphones using COPE application and considering the energy consumption, clock synchronization and transmission range metrics.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 4 Processing of Small Berries and Their Effects on Bioactive Compounds  
Fresh berries, like most fresh fruits, are available throughout the year for a limited period. Various conventional and alternative processing technologies are usually applied to transform these perishable materials into stable products, such as juices, beverages, pures, nectars, powders, concentrates, extracts, and spreads. Conventional methods have been used for producing safe berry products for decades; however, they could lead to quality changes, limiting consumers' acceptability. Moreover, many of them require increased resources in terms of heat, water, energy, and time, exerting a negative impact on the environment. The use of alternative technologies has opened new opportunitiesfor producing safe products with enhanced bioactive properties in a sustainable manner. Although not all the alternative methods are cost-effective, a combination of methods has good potential for overcoming this drawback.  
During processing, some unit operations have a higher impact on the berries' nutritional and technological profile, while others influence the functional properties of the phytochemicals to a greater extent. The stability of bioactive compounds, such as anthocyanins, is highly dependent on the processing conditions applied. However, some other factors like pH, concentration, light, oxygen, and the presence of enzymes, proteins, and metallic ions should also be considered [57]. In this section, the main operations with a significant impact on the bioactive compounds of berry products are briefly discussed (Figure 2).
# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
## 5 Overview of the Provided Results
### Identifying problematic time intervals  
Once the DSOs know that a given network is facing communication problems, it is very important to determine when they are happening. As has been already mentioned, the conditions in the PLC channels vary quite frequently, so it may happen that when the DSOs send their O & M crew to the field, the network is working again without problems. PRIME Analytics can be used to identify patterns in the communications problems and plan the in-field operation appropriately.  
For the identification of problematic time intervals, the list of instants with the greatest number of unregistrations can be used. As an example of this, Figure 15 shows that, in the scenario under study, 817 smart meters unregister between 7:45 and 8:30 AM. Although it is not the case in this example, a similar analysis could be done to try to identify patterns.  
Figure 14: Graphs of average SNR for (**a**) a problematic scenario and (**b**) a non-problematic scenario.  
In this case, this datum will be used to narrow down the diagnostics to find where the problem was and which nodes were involved, as explained in the next section.
# XGBoost-Based Detection of DDoS Attacks in Named Data Networking
## 3 The Attack Model and Detection Method
### _The Detection Mechanism of Improved XGBoost_
To further enhance classification accuracy, particularly in detecting the I-CIFPA class, we perform Bayesian optimization on the hyperparameters of the XGBoost model. This tuning process yields the final optimized version, called Improved XGBoost.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 1 Introduction  
Power system load forecasting, which encompasses power demand and active power predictions, involves predicting future system loads based on historical data, weather conditions, and the implications of current policies and regulations [1]. Electricity load forecasting is essential in power planning, transmission, and distribution within the grid sector. Short-term forecasts (less than two weeks) form the foundation for unit start-ups and shutdowns and scheduling and operational plans within the grid. Medium-term forecasts (spanning several months) can guide rational grid operation and maintenance decisions, ensuring consistent electricity supply for businesses and everyday life; these forecasts also offer a valuable foundation for grid operation and scheduling decisions across different industries. Long-term forecasts (looking years ahead) provide data vital for decision making concerning the grid system's renewal and transformation, aiming toenhance societal benefits. By predicting the electrical load for businesses, large industries, specialized industries, and general industries, one can understand the operational status of each sector, which in turn aids in the resumption and subsequent growth of enterprises.  
Forecasting and analyzing the power output of regional power systems allows for accurate electricity supply [2]. By systematically analyzing electricity consumption forecast data, power supply companies can plan and allocate future electric energy more effectively, enhancing the precision and efficiency of their planning. This approach helps avoid issues like blind, transitional, or insufficient power planning. Moreover, electricity consumption can also serve as an indicator of local economic development. By utilizing electricity forecasting, government departments can obtain valuable insights into economic growth, which can subsequently inform and support the formulation of government economic policies.  
Complex factors, such as volatile climate conditions, can introduce uncontrollable effects on power load forecasting, rendering traditional models' predictions uncertain. As power systems evolve, their structural levels become more diverse, necessitating further research into the challenges of power load forecasting.
###### Abstract  
MEC servers (MESs) support multiple queues to accommodate the delay requirements of tasks offloaded from end devices or transferred from other MESs. The service time assigned to each queue trades off the queue backlog and energy consumption. Because multiple queues share the computational resources of a MES, optimally scheduling the service time among them is important, reducing the energy consumption of a MES and ensuring the delay requirement of each queue. To achieve a balance between these metrics, we propose an online service-time allocation method that minimizes the average energy consumption and satisfies the average queue backlog constraint. We employ the Lyapunov optimization framework to transform the time-averaged optimization problem into a per-time-slot optimization problem and devise an online service-time allocation method whose time complexity is linear to the number of queues. This method determines the service time for each queue at the beginning of each time slot using the observed queue length and expected workload. We adopt a long short-term memory (LSTM) deep learning model to predict the workload that will be imposed on each queue during a time slot. Using simulation studies, we verify that the proposed method strikes a better balance between energy consumption and queuing delay than conventional methods.  
mobile edge computing; online resource allocation; task arrival prediction; long short-term memory; Lyapunov optimization +
Footnote †: journal: Applied  
_Applied_  
_Applied_  
_Acticle_  
_Acticle_  
==WARNING: Truncated because of repetitions==
_{i}(t)+b_{i}(t)^{2}-2b_{i}(t)(Q_{i}(t)+w_{i}(t))+Z_{i }(t)(Q_{i}(t)-b_{i}(t)+w_{i}(t))+\delta_{i}^{2}.\] (19)  
If we let \(C_{i}(t)=B_{i}(t)+Z_{i}(t)(Q_{i}(t)+w_{i}(t))+\delta_{i}^{2}\), we obtain:  
\[\Delta_{Z_{i}(t)}\leq C_{i}(t)+b_{i}(t)^{2}-b_{i}(t)(2Q_{i}(t)+2w_{i}(t)+Z_{i }(t)).\] (20)  
As \(\Delta L(Z(t))=\mathbb{E}[L(Z(t+1))-L(Z(t))|Z(t)]=\mathbb{E}[\sum_{i\in N} \Delta_{Z_{i}(t)}|Z(t)]\),  
\[\Delta L(Z(t
+++  
From Equations (21) and (22), the \(\Delta_{p}L(Z(t))\) is upper bounded as  
\[\Delta_{p}L(Z(t))\leq B+\mathbb{E}[\sum_{i\in N}b_{i}(t)^{2}-b_{i}(t)(2Q_{i}(t)+2 w_{i}(t)+Z_{i}(t))+Vb_{i}(t)\kappa f_{mes}^{2}|Z(t)],\]  
where \(B=\sum_{i\in N}C_{i}(t)\) is a positive constant unrelated to \(b_{i}(t)\).  
Note that the space defined in Equation (10) is the subset of the space defined by Equation (9). Then, from Proposition 1 and the concept of opportunistically minimizing the expectation, we can translate Problem P1 as the following per-time-slot optimization problem.  
\[\textbf{P2:}\argmin_{\Omega(t)=\{\alpha_{i}(t):i\in N\}}\mathbb{E}[\sum_{i\in N }b_{i}(t)^{2}-b_{i}(t)(2Q_{i}(t)+2w_{i}(t)+Z_{i}(t))+Vb_{i}(t)\kappa f_{mes}^{ 2}].\]  
\[s.t.\sum_{i\in N}\alpha_{i}(t)\leq 1. \tag{23}\]
# Non-Thermal Technologies in Food Processing: Implications for Food Quality and Rheology
## 7 Utilisation of Non-Thermal Food Processing Technologies  
There is now an increased acceptance of non-thermal food-processing technologies compared to the previous decade due to their various advantages over conventional methods, such as improved energy efficiency, a better retention of nutrients, reduced reliance on preservatives, and less threat to the environment. Numerous phases are involved in food processing, including raw material storage, washing, heating, chilling, emulsification, and drying. During thermal food processing, waste steam is released into the environment by the equipment, and this can contain the proteins, lipids, carbohydrates, and cleaning agent residues used in these procedures [167]. The environment may be negatively impacted by the waste items produced. Therefore, using greener and cleaner technologies that use raw materials without posing much environmental harm is crucial. The food industry has traditionally employed thermal technologies which include cooking, drying, pasteurisation, and sterilisation, all of which require the combustion of fossil fuels while electricity is utilised for power generation and refrigeration [80; 167]. With benefits including shorter cooking times, the preservation of quality characteristics, and increased product safety, non-thermal technologies offer a substitute for these thermal technologies [5]. Furthermore, they have found wide applications in foods with complex rheologies in which the conventional heat-based methods of processing or preserving may not be feasible. Importantly, these technologies can be utilised in different food matrices such as liquid foods, solid, and semi-solid foods.
## 4 Numerical Simulation Applied to the FFF Process
### Material Property Characterization
(2020) [13] demonstrated the use of active thermography supported by machine learning to predict internal defects in polymer-based additively manufactured components. Their methodology enabled the non-destructive, real-time identification of internal flaws from thermal sequences, improving process monitoring and reliability. Building on this, [PERSON] et al. (2022) [14] employed step-heating thermography in combination with simulation and ML models to accurately estimate internal defect sizes. Their results underscore the potential of integrating thermal diagnostics with data-driven techniques to support in situ structural validation and quantitative defect characterization. These contributions reinforce the growing relevance of hybrid experimental-computational methods in advancing defect-aware modeling for FFF. However, despite their potential, current validation strategies still face critical gaps. Traditional methods, such as mechanical testing under simplified geometries [59,60] or dimensional accuracy analysis [61], often neglect microstructural irregularities and transient thermal effects. Techniques like micro-computed tomography (\(\mu\)CT) [64], simulation-in-the-loop validation [12], and thermography supported by machine learning [13,14] have improved the ability to detect and quantify internal defects, but their adoption remains limited due to equipment costs, a lack of standardized protocols, and challenges in integrating experimental data into numerical frameworks. Therefore, advancing reliable FFF simulation will require the broader implementation of high-fidelity, non-destructive validation tools and coordinated efforts to establish shared benchmarks across materials, geometries, and simulation platforms. Building on this foundation, future research should prioritize advanced modeling techniques capable of capturing anisotropy, residual stresses, and defect formation more effectively. Integrating machine learning with multi-scale physics-based simulations and real-time process monitoring could significantly enhance predictive accuracy and efficiency. In the next section, the influence of fiber reinforcement on material performance is examined, specifically addressing how fiber type, orientation, and distribution impact composite properties. Figure 18: Strain distribution progression in a sample with an internal defect subjected to tensile loading. The sequence shows the evolution from the initial unloaded state to post-fracture, highlighting the average axial strain. Adapted from [12].