esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Life Cycle Assessment (LCA)
The Single-Use Plastics (SUP) Directive [22], in force since 2021, significantly limits the use of disposable plastic products, prompting the industry to explore alternative materials. In the context of chain design, this opens up the possibility of developing durable polymer-based composites capable of replacing steel in select applications, while simultaneously complying with environmental regulations. However, the mechanical properties of poly mers often fall short of those exhibited by steel. For this reason, fiber reinforcement--such as glass or carbon fibers--is necessary to enhance material strength. Modern processing technologies enable precise control over material structure and geometry, potentially allowing such composites to be used in demanding applications, including mining or urban systems infrastructure, especially in the case of luminescent chains. A critical element in the development of new materials is the implementation of Life Cycle Assessment (LCA), which provides insight into their true environmental impact. Process optimization and appropriate material selection may allow for the production of chains that meet both structural performance criteria and sustainability goals. Innovative design approaches can help align industrial practice with regulatory expectations while reducing waste and supporting the transition to a circular economy. Recent cradle-to-gate LCAs show that manufacturing 1 t of chains from recycled carbon fiber-reinforced polymer (rCFRP) can cut the environmental load by roughly one-third relative to steel. * Greenhouse gas emissions fall by \(\approx\) 35-45% (from \(\sim\)2.0 t CO\({}_{2}\) e \(\cdot\) t\({}^{-1}\) for BF-BOF steel to \(\approx\)1.1-1.3 t CO\({}_{2}\) e \(\cdot\) t\({}^{-1}\) for compression-molded rCFRP with \(\geq\)30 vol% fiber) [23; 24; 25]. * Primary-energy demand (PED) drops by \(\approx\) 33-43% (18-20 GJ \(\cdot\) t\({}^{-1}\) for conventional steel vs. \(\approx\)10-12 GJ \(\cdot\) t\({}^{-1}\) for the same mass of rCFRP) [25; 26]. Because recycled carbon fibers could be used in reinforced recyclate, under specific conditions, every ton produced contributes directly to the SUP Directive's target of recycling at least 55% of plastic packaging by 2030 and achieving 30% recycled content in beverage bottles [27]. This alignment between quantifiable LCA benefits and forthcoming EU obligations strengthens the argument for substituting steel with composite chains wherever mechanical requirements permit.
## 4 Analytical Framework for Heterogeneous Service Provisioning
### Problem Overview and Healthcare Service Requirements
#### 4.1.7 Reinforcement Learning for Adaptive Rate Control  
To achieve dynamic and continuous rate adaptation, we implement an RL agent based on the Deep Q-Network (DQN) architecture. The RL agent is trained using a policy-gradient method under an on-policy learning scheme. The agent interacts with the network simulation environment, receiving rewards based on delay-aware utility improvements and penalizing rate allocations that lead to link congestion or deadline violations. Training is conducted in episodic rounds using historical and synthetic traffic traces to ensure generalization across different healthcare service scenarios. The environment is modeled using a Markov Decision Process (MDP) where state transitions reflect traffic fluctuations and resource updates in the edge-cloud topology. The agent observes the system state comprising current delivery rates \(g_{s,i}\), link utilization, estimated delays, and historical rewards and selects rate control actions to maximize cumulative network utility while minimizing penalties from excessive latency. The reward function at time \(t\) is defined as:  
\[r_{t}=\mathcal{U}_{s}(g_{s,i})-\varphi_{s}\sum_{t\in T_{s,i}}d_{s}(g_{s,i}^{t }), \tag{15}\]  
where \(\varphi_{s}\) denotes the service-specific delay penalty weight. The DQN model enables the system to learn an optimal rate allocation policy over time, even in the presence of non-stationary network dynamics and partial observability. This allows the system to learn optimal strategies over time, adapt to changing network and service dynamics, and ensure QoS across a variety of healthcare applications.
# Sustainable Italian Cities: The Added Value of Biomethane from Organic Waste
## 3 Materials and Methods
### Municipal Solid Waste  
As part of its Circular Economy Action Plan, the European Commission adopted a package of legislative proposals on waste in December 2015. This package requires member states to take specific measures to prioritize prevention, re-use, and recycling, according to the waste hierarchy. Recycling targets for municipal waste are as follows: (i) 55% by 2025, (ii) 60% by 2030, and (iii) 65% by 2035 [66].  
The Italian Institute for Environmental Protection and Research (ISPRA) acts under the vigilance and policy guidance of the Italian Ministry for the Environment and the Protection of Land and Sea. Based on the data provided by this institute [67], Italian waste generation amounted to 30,116.6 ktons in 2016 (+2% from 2015), with a separated collection rate of 52.5% (+5% from 2015) (Table 2). Within the separate collected waste of the MSW, the OFMSW assumed a predominant position, representing about 41.2% of this mix, followed by paper and cardboard (20.3%), glass (11.7%), and plastic (7.8%).  
Moving from a national to more local perspectives, the present analysis concentrates on municipalities with populations greater than 200,000 (criteria fixed by ISPRA) (Figure 2). Table 3 presents the data for 15 municipalities that, in 2016, had a collective population of 9.9 million (16.4% of the Italian population) and produced 18.1% of the total national waste.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **2015 (1000*tons)** & **2016 (1000*tons)** & **2015 (kg/capita)** & **2016 (kg/capita)** \\ \hline Total MSW & 29,524.3 & 30,116.6 & 486.7 & 497.1 \\ Total SC of MSW & 14,020.9 & 15,821.9 & 231.1 & 261.1 \\ Total OFMSW & 6071.5 & 6516.9 & 100.1 & 107.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical data in Italy regarding municipal solid waste (MSW) [67]. OFMSW—organic fraction of municipal solid waste; SC—separated collection.
## 3 Results
### Main and Two-Way ANOVA Interaction Effects of with or without * Load Level of EMG Values  
A summary of ANOVA for the effects of load (L) with- or without-Exo (W) for nRMS values of nEMG data are given in Table 3. The two-way ANOVA method was used in the calculations. Significant changes of the load main effects in nRMS are found for almost all the measured muscles (\(p<0.05\)), except for the muscle BF (\(p>0.05\)). nRMS values greatly increased over load levels in the knee extensors RF, VM, and VL (\(p<0.001\)). The main effects of with- or without-EXO are significantly different for almost all the muscles (\(p<0.05\)), except for the muscle VM (\(p>0.05\)). Load * with or without interaction effects were significant in some muscles such as RF, PL, and GL (\(p<0.05\)).  
The main and interactive effects could also be seen from plots of Figure 9. Although the overall characteristic is that the mean nRMS from the exoskeleton assisted load walking was higher than those from the conventional load walking, almost all the rates of nRMS that increased during the exoskeleton assisted load walking slowed down with the increasing of load level. Even in some muscles the rates are decreasing with the increase of the load level, such as muscles BF, ST, and GL. Especially for the loaded muscle VM, when the load level is over 30 kg, the exoskeleton augmentation potential appears.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Muscles**} & \multicolumn{5}{c}{**Load (Mean Activities)**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**R2**} & \multirow{2}{*}{\(p\)**} \\ \cline{2-2} \cline{5-7}  & **EXO + 0** & & & & & & \\ \hline RF & 16.35 & 23.15 & 25.8 & 24.35 & 4.17 & 0.68 & 0.17 \\ VM & 19.2 & 18.15 & 21.45 & 26.2 & 6.68 & 0.77 & 0.12 \\ VL & 26.1 & 20.45 & 22.4 & 22.2 & 0.78 & 0.28 & 0.47 \\ BF & 4.81 & 6.28 & 6.57 & 8.285 & **32.40** & _0.94_ & _0.03_ \\ ST & 4.39 & 4.94 & 7.96 & 8.81 & **24.12** & _0.92_ & _0.04_ \\ PL & 22.8 & 31.5 & 53.75 & 65.5 & **72.34** & _0.97_ & _0.014_ \\ GL & 10.75 & 11.915 & 10.81 & 12.76 & 1.54 & 0.43 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean Muscle Activities During 0 kg, 15 kg, 30 kg, and 45 kg Load Levels With the Exoskeleton. (Note: The determination coefficient R2 and the statistics F of Fischer used to confirm the linearity in whole cases are shown. The bold and italic style indicates linearity.).  
Figure 8: Normalized averaged muscle activities of with-Exo and without-Exo load carriage walking.
## 4 Case Studies  
### Description of Case Studies  
The integrated power-natural gas-heating energy systems shown in Figure 2 is composed by 39-bus power system and Belgian 20-node gas system [9; 16; 23]. The integrated network has 5 coal-fired units, 3 gas-fired units, 2 wind power units, 2 P2G facilities, 24 pipelines, 2 gas wells, 3 gas storages and 2 compressors. Total power generation capacity is 3903 MW. Power demand, gas demand and heating demand are shown in Figure 3 where maximum power load is at 19:00 and maximum gas/heating load is at 20:00. The parameters of cost and emissions for power systems and gas network are shown in Tables 1-3. In addition, initial line pack is given as 0.952 MSm\({}^{3}\) and the initial capacity of gas storage is given as 0 MSm\({}^{3}\), 0 MSm\({}^{3}\) and 0.003 MSm\({}^{3}\). Predicted wind power generation is 29,335.399 MWh. Other parameters can be found in [16]. The optimal economic/environmental dispatch of the integrated power-natural gas-heating energy systems with P2G and gas storage is studied to illustrate behavior of the proposed model and calculate metric of flexibility in two case studies using MATLAB language programming.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Unit No.** & _a/(10\({}^{3}\) 5/h)_ & \begin{tabular}{c} _b/(10\({}^{3}\)_ \\ **S/(MW\({}^{2}\).h)** \\ \end{tabular} & \begin{tabular}{c} _c/(5/(MW\({}^{2}\).h))_ \\ \end{tabular} & \
\begin{tabular}{c} _d/(10\({}^{3}\) 5/h)_ \\ \end{tabular} &
\begin{tabular}{c} _e/_MW\({}^{-1}\) \\ \end{tabular} \\ \hline Coal-fired unit 1 & 0.786 & 0.038 & 0.152 & 0.45 & 0.041 \\ Coal-fired unit 2 & 0.451 & 0.046 & 0.106 & 0.6 & 0.036 \\ Coal-fired unit 3 & 1.05 & 0.041 & 0.028 & 0.32 & 0.028 \\ Coal-fired unit 4 & 1.244 & 0.038 & 0.035 & 0.26 & 0.052 \\ Coal-fired unit 5 & 1.658 & 0.036 & 0.021 & 0.28 & 0.063 \\ Gas-fired unit 1 & 2.713 & 0.076 & 0.036 & / & / \\ Gas-fired unit 2 & 2.801 & 0.074 & 0.028 & / & / \\ Gas-fired unit 3 & 2.904 & 0.073 & 0.024 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cost coefficient of thermal units in power systems.  
Figure 2: The integrated power-natural gas-heating energy systems with power to gas (P2G) and gas storage.
## 3 System Model  
As shown in Figure 1, we consider an IoV scenario where the RUS and vehicles are regarded as ISCC nodes with sensing, communication, and computation capabilities. We model the road as a two-lane and unidirectional straight line with a set of RSUs \(\mathcal{N}=\{1,2,\cdots,N\}\) deployed along it. For simplicity, we assume that each RSU covers different areas based on its sensing range. In addition, each RSU \(n\) (\(n\in\mathcal{N}\)) is associated with a set of vehicles \(\mathcal{U}_{n}=\{1,2,\ldots,U_{n}\}\) within its coverage area, and vehicle \(un\) represents the \(u\)-th vehicle \((u\in\mathcal{U}_{n})\) associated with RSU \(n\). Furthermore, all RSUs equipped with mobile edge servers are interconnected via fiber optics to form a local area network (LAN) that enables mutual communication.  
We assume that each vehicle \(un\) has a batch of sensing tasks (i.e., raw sensing data obtained by the sensors on the vehicle) to be processed. Specifically, the arrival of the sensing task at vehicle \(un\) follows a Poisson process with the rate \(\lambda_{un}\). Further, we define the attributes of each sensing task for vehicle \(un\) as \(M_{un}=\{d_{un},f_{un},t_{un}^{tol}\}\), where \(d_{un}\) represents the task size, \(f_{un}\) denotes the number of CPU cycles required to compute one-bit data, and \(t_{un}^{tol}\) is the maximum tolerable processing delay for the task.
###### Abstract  
In order to develop dynamic analysis technologies regarding the design of offshore wind turbine generators (OWTGs), a special project called Offshore Code Comparison Collaboration Continuation (OC4) was conducted by IEA (International Energy Agency) in 2010. A similar project named INER-OC4 has been performed by the Institute of Nuclear Energy Research (INER) to develop the OWTG technologies of Taiwan. Since the jacket substructure will be applied to Taiwan OWTGs before 2020, the INER-OC4 project has been devoted to the design and analysis of jacket support structure. In this work, the preliminary result of INER-OC4 is presented. A simplified analysis procedure for jacket support structure has been proposed. Both of the NREL (National Renewable Energy Laboratory) 5 MW OWTG FAST model and OC4 jacket substructure model have been built and analyzed under severe design load cases (DLCs) of IEC (International Electrotechnical commission) 61400-3. Simulation results of six severe DLCs are performed in this work and the results are in agreement with the requirements of API (American Petroleum Institute) and NORSOK (Norwegian Petroleum Industry) standards.  
offshore wind turbine; jacket-type support structure; dynamic analysis; design standard comparison
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Deep Learning LSTM Model
#### 2.2.3 Configuration and Training  
We used MATLAB's deep learning toolbox to perform LSTM sequence-to-sequence regression forecasting, and the foF2 value after 1 h was forecasted from the time series sequence. At each time step of the input sequence, the LSTM network learns to forecast the  
Figure 3: A block diagram of the LSTM model.  
value of the next time step. The function predicts one timestep at a time and updates the state of the network at each forecasting step.  
In the LSTM model shown in Figure 3, stacked LSTM layers are used to extract representative features from historical input data and then are connected to the fully connected hidden layer. To obtain the relatively superior architecture of the LSTM model, we have tried several experiments with different sets of hyperparameters and hidden nodes based on the experience of previous works [46]. At last, the neuron node number of the hidden layer is chosen to be 21, which is determined by training the LSTM model from node 1 to 30. Consequently, the recommended configuration for our LSTM model is set as: the number of hidden nodes is 21, the size of batch data used in each iteration is 3, and the maximum number of training rounds is 250.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 3 AMAN Air Traffic Controller Support
### Trajectory Generation
In this way, however, it can always be ensured that deviations around severe weather areas always take place in a soft arc and thus enable a realistic flight path.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 4 Discussion
### Future Research Opportunities
Thirdly, outstanding cellular network planning foresees the minimum number of required aerial nodes to cover a given geographical area, either partially connected or entirely disconnected. Hence, to maximize the total covered zones, there should be a previous identification of users and obstacles. Beneath this regard, prior frequency planning and signaling overhead analysis can assure greater network throughput, especially for high-frequency bands. Finally, an in-depth design of the bespoke-solution construction affords countless advantages to aim for in a fully connected countryside. In this case, embracing an expected radius of 30 km [109]--or even a greater value--assures the coexistence of either LAPs or HAPs with terrestrial systems, and sharing the same spectrum can extend the coverage in rural and remote areas. Other strategies involve a novel antenna array design and aerial swarms or constellations, which are expected to be further researched in broader 5G investigations.
## 1 Introduction
These formal verification techniques allow us to know whether or not they comply with design policies (_Detection of configuration failures_), and then also identify possible mistakes in system and product configurations (_Diagnosis of configuration failures_). The nature of cybersecurity models is highly context-dependent [18]. Therefore, feature models should be adapted according to the objectives and the contexts of cybersecurity policies [19]. This article aims to offer a framework that covers the following objectives:  
* **OBJ1. ** Provide users with a set of tools for the definition and the maintenance of a catalogue of cybersecurity policies based on feature models. These policies are associated with different contexts of software products and systems related to cybersecurity. * **OBJ2. ** Provide users with a set of tools to obtain properties of feature models. For example, a property could be the verification of the correctness of the model, that is, to know the conformance between the products and the model. Another example could be the extraction of all configurations of products or systems supported by the cybersecurity policy. * **OBJ3. ** Automate the detection of configuration failures to validate with cybersecurity policies through the description of features of the different cybersecurity contexts. * **OBJ4. ** Provide the diagnosis of the established configurations to isolate and identify the configuration mistakes that cause the non-conformance of the cybersecurity policies. * **OBJ5. ** Validate the proposal through complex use cases. Such as the configuration of cybersecurity mechanisms of a web application server. In order to verify the proposal, the Design Science Research methodology [20] is used to evaluate it. The methodology has been previously used in research papers that propose a software artifact to solve a business problem [21]. In this case, the artifact (CyberSPL) provides mechanisms for verifying cybersecurity policies according to possible configurations. Design Science Research is formed of six phases used as a guideline of the structure of this paper: problem identification and motivation that contextualise the perceived difficulty to manage configurations in cybersecurity, as is presented in the Introduction; objectives of the proposed solution, analysed comparing previous work and lack of functionalities found; design and development, as a prototype of the artifact presented in [22]; demonstration by means an evolution of the previous prototype with and improvement of the previous implementation; evaluation, applied to a real example, and; communication, as mechanism to be known by the community and possible users, objective of this paper. The article is organised as follows: Section 2 presents an overview of related works. Section 3 introduces features model concepts and analysis. Section 4 tackles the proposal, including the architecture, the workflow, and the description of operations in a real scenario. Section 5 shows and discuss the results obtained from the experimentation applied to a proposed use case. Finally, conclusions are drawn and future work is proposed.
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 3 Results
### Temporal Analysis
#### 3.2.1 One Satellite: Sentinel-1  
For the Sentinel-1 data approach, dual-polarization (VV and VH) and multiple orbit (Ascending and Descending) products were used (DS_SAR). Even when using a temporal approach, the precision for this satellite was considerably lower than for Sentinel-2 in the static analysis, fluctuating between an F1-Score of 0.6208 and 0.6872 for all models and with a low Kappa score between 0.2508 and 0.3769. Nevertheless, the RF obtained an F1-score of 0.69. On the other hand, SVM and KNN obtained lower results, since they were unable to classify correctly treated sections, classifying most of the samples as non-treated sections. This behavior is also true for the other models, thus the low Kappa agreement values.  
When analyzing in more detail the confusion matrix (Table 4) of the RF results, it shows that the metrics of the untreated sections had a great influence on the value of the global F1-score, since the treated sections only had an F1-score 0.44.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods  
### Study Area and Experimental Design  
Maize plants cultivated at the teaching and research base of South China Agricultural University were used as the experimental materials for this study. A site map of the maize test fields, showing the data collected from two different test fields, is provided in Figure 1. Field 1 covers an area of approximately 0.3 hectares, and Field 2 covers approximately 0.2 hectares. The aerial images shown in Figure 1 were captured using a UAV system on day 30 after maize emergence. These data provide a comprehensive overview of the experimental setup. This study involved five sets of trials conducted in three distinct maize plant environments across two fields. The fields are referred to as Fields 1 and 2 and the maize plant groups as a, b, c, d, and e. Four composite trial groups were designed for this study: trial 1 included plants b and c; trial 2 included plants d and e; trial 3 included plants a, b, and c; and trial 4 included all five sets (a, b, c, d, and e). In trials 1 and 2, the experimental conditions were the same maize plants in the same field. In trials 3 and 4, the conditions involved different maize plants in different fields. The composite trial groups are summarized in Table 1.  
Figure 1: Aerial views of the experimental maize test fields. (**a**) Test Plot 1: covers an area of approximately 0.3 hectares, and (**b**) Test Plot 2: covers an area of approximately 0.2 hectares. Images were captured on day 30 after maize emergence using UAV-based multi-view imaging.