esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Energy Consumption  
Over the last few years, energy consumption in Europe has grown significantly, both in the residential and industrial sectors. As a result, a number of energy policy measures  
Figure 1: Evolution of demand coverage [3].  
have been adopted, such as the European Green Pact [8], energy efficiency strategies and the development of Renewable Energy Plans.  
The industrial sector is particularly relevant in the European Union (EU), accounting for around 25% of total energy consumption in the region. At the EU level, the European Commission has promoted energy efficiency through directives and regulations that encourage electrification and the use of renewable energies in industry, aiming to reduce emissions and improve competitiveness. Projects such as the Energy Performance of Buildings Directive establish measures to reduce energy consumption in buildings, with the aim of achieving nearly zero-energy buildings [9].  
The transition to renewable energy such as solar PV and wind has contributed to the reduction in energy demand in several sectors, including the residential. However, energy price volatility, exacerbated by events such as the 2022 natural gas crisis, remains a challenge for energy markets. In 2021, prices reached historic peaks due to the energy crisis, although in 2022 they began to moderate as member states took measures to contain the crisis. This has affected both industrial competitiveness and household welfare, increasing energy costs. It has also raised concerns about energy stability and security. Despite the efforts to liberalize the market, structural challenges remain, such as the costs associated with distribution networks and taxation, which affect both industrial and residential consumers.  
Spain, in particular, has played a prominent role within this European framework. In 2023, 50% of the country's electricity generation came from renewable sources [10], with its leadership in wind and solar photovoltaic energy standing out. However, it has also been impacted by the natural gas crisis, with a considerable increase in energy prices. The government implemented measures such as the temporary reduction in VAT on electricity and the promotion of self-consumption with renewable energies.  
In addition, Spain has developed projects such as SPAHOUSEC, which focuses on the analysis of household energy consumption. This project seeks to identify energy consumption patterns by service and climate region, with the aim of improving energy efficiency and promoting the use of renewable sources.
## 3 Results and Discussion
### Reinforcement of Geopolymers with Basalt Grid
Their addition of 10% to a geopolymer mixture can increase the compressive strength (up to 37%) and flexural strength. This is a result of the specific microstructure of basalt fibers in which the particles are embedded in a dense aluminosilicate matrix [65]. [PERSON] et al. [63] conducted a study on a geopolymer matrix made of metakaolin and basalt fiber addition with different proportions (1%, 3%, 5%, 7%). The results showed an increase in flexural strength compared to a pure geopolymer (1.7 MPa) made with metakaolin by the following values:  
* For geopolymer with 1% basalt fiber--an increase of 1.9 MPa;
* For geopolymer with 3% basalt fiber--an increase of 6.9 MPa;
* For geopolymer with 5% basalt fiber--an increase of 11.8 MPa;
* For geopolymer with 7% basalt fiber--an increase of 14.8 MPa. [PERSON] et al. tested a pure fly ash-based geopolymer without any additives compared to a geopolymer made based on fly ash, slag, and fine and coarse aggregates, with the addition of basalt fibers in the amount of 0.5%, 1%, 1.5%, 2%, and 2.5% by weight. The tests showed an increase in compressive strength for all the tested samples with the addition of geopolymer fibers, and especially for the geopolymer containing 2% basalt fiber--the values were 58.4 MPa and 40.1 MPa [66]. [PERSON] and other researchers [67] conducted a study to compare the mechanical properties of a geopolymer concrete made with metakaolin and a traditional concrete made with Portland cement. Both composites were reinforced with 0.5% and 1% basalt fiber. The compressive strength test showed a decrease in the values of both the geopolymer composite and the traditional concrete. However, the geopolymer concrete had a higher value. For the flexural strength test, both composites recorded higher values after the addition of basalt fiber. For the unreinforced geopolymer concrete, the flexural strength was 16 kN. However, after reinforcement with 0.5% basalt fiber, the value was 17.2 kN, and for 1% basalt fiber, the value was 21%. In the case of traditional Portland cement concrete, the initial value was 9.6 kN; after the addition of 0.5% basalt fiber, the flexural strength increased to 12.5%, and with the addition of 1% basalt fiber, it increased to 14 kN.
However, we speculate that the subjects in their studies may simply not have been able to reach the exercise intensity that resulted in VO\({}_{2\text{max}}\) during exercise testing with ACE. Because heart rate (HR) shows a certain linear relationship with VO\({}_{2}\) during exercise testing over wide range of submaximal intensities [15], VO\({}_{2\text{max}}\) can be estimated as VO\({}_{2}\) corresponding to age-predicted maximal HR. Therefore, the estimated VO\({}_{2\text{max}}\) may be the same for exercise testing with ACE and CE if correlations between HR and VO\({}_{2}\) during exercise testing with ACE are equivalent for those with CE. If so, exercise testing with ACE can substitute for that with CE in evaluating exercise capacity. In that case, people with injuries or disabilities in their lower limbs will benefit from exercise testing with ACE. However, to date, it has not been elucidated whether the correlation between HR and VO\({}_{2}\) during exercise testing with ACE is equivalent to that during exercise testing with CE. On the other hand, a few reports concluded that the difference between VO\({}_{2\text{max}}\) attained during upper- and lower-limb exercise was more modest in the individuals with well-trained upper limbs compared to those with untrained upper limbs [11; 16]. Although these studies suggest the possibility that the training status of upper limbs might influence the correlations between HR and VO\({}_{2}\) during exercise testing with upper-limb exercise, there are no previous studies that examined whether that was true. Therefore, it needs to be clarified whether the presence of continuous, endurance activity of the upper limbs, such as in people who use manual wheelchairs, would affect the difference in the correlation between HR and VO\({}_{2}\) during exercise testing depending on exercise modalities. By doing so, useful suggestions may be provided for predicting VO\({}_{2\text{max}}\) obtained from lower-limb exercise tests from the results of exercise tests with the upper limbs. The aim of this study is to elucidate whether the exercise testing with ACE can substitute for that of CE in the assessment of exercise capacity. To clarify this, this study included collegiate student athletes with or without trained upper limbs. We conducted an incremental exercise testing using ACE and CE in these subjects and examined whether the correlations between HR and VO\({}_{2}\) during exercise testing is affected by exercise modalities. We also tried to examine the effect of the training status of upper limbs on the correlation between HR and VO\({}_{2}\) during exercise testing with ACE. The novelty of this study is providing practical guidance of exercise testing with ACE and CE in the general population, including those with injuries and disabilities.
## 4 Experiments
### Estimation of Absolute Nonlinearity Parameter  
In order to verify the validity of the proposed method, the absolute nonlinearity parameters of the two materials were measured first using the conventional calibration method [12], which uses a pre-measured calibration function converting the electrical output of the receiving transducer into the displacement amplitude. The results are shown in Table 3. The absolute parameter of aluminum was used as a reference value and the absolute parameter of copper was used for comparison with the result estimated by the proposed technique.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Longitudinal Wave (m/s)** & **Transverse Wave (m/s)** & **Surface Acoustic Wave (m/s)** & \(F\) & \(k^{\prime}\) \\ \hline Al2024 & 6403.2 \(\pm\) 2.4 & 3120.9 \(\pm\) 2.1 & 2935.0 \(\pm\) 3.8 & 3.73 \(\pm\) 0.080 & \\ Copper & 4373.0 \(\pm\) 1.3 & 2266.8 \(\pm\) 1.2 & 2150.8 \(\pm\) 4.7 & 4.00 \(\pm\) 0.001 & 0.501 \(\pm\) 0.018 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement results for wave velocities and wavenumber compensation factor.  
Figure 4: Experimental setup for measuring the velocities of longitudinal and transverse waves.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Absolute Nonlinearity Parameter (\(\beta\))** & \(\beta_{Copper}/\beta_{Al2024}\) \\ \hline Al2024 & 6.93 \(\pm\) 0.30 & 0.671 \(\pm\) 0.068 \\ Copper & 4.65 \(\pm\) 0.25 & 0.671 \(\pm\) 0.068 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Absolute acoustic nonlinearity parameters of Al2024 and copper measured by the direct method.  
Next, the absolute nonlinearity parameter of copper was estimated by substituting the measured wavenumber compensation factor \(k^{\prime}\) and the relative nonlinearity parameters of Al2024 and copper in Equation (8), in which the absolute nonlinearity parameter of Al2024 shown in Table 3 was used as the reference. The result is shown in Table 4. The absolute nonlinearity parameter measured using the direct method is shown for comparison. Considering the deviation, the estimated value agrees well with the direct measurement result.  
The results verify that the proposed indirect method using surface acoustic waves with a fully non-contact laser-ultrasonic technique is effective for estimating acoustic nonlinearity parameters.
## 5 Results
### Cluster Analysis
The results show the presence of significant interactions with cluster for most MO variables (with the exception of MO_AO) and for two of the four MI variables, namely, the ones with visual presentation (MI_VV and MI_VO). None of the two auditory-oral tasks MO-AO or MI_AO (roughly similar to traditional span tasks with letter and colour stimuli) showed any interaction with cluster. As planned, for each of the factors that had shown significant interactions with cluster, a further repeated-measures ANOVA was conducted in order to disentangle the interaction and clarify different effects among clusters. The results of these analyses are reported in Table 6.  
Since no interaction emerged among MO_AO, MI_AV, and MI_AO. It was concluded that the significant differences among clusters were not modulated by any factor and could be considered as differences related to the characteristics of the AO task (in its two item and order components), regardless of its content, and the AV task. The pattern of results for the various clusters on MO_AO (where cluster 3 showed no significant difference with respect to TD) is illustrated in Figure 7. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Variable** & **Factor** & **Significant Interactions** & **F** & \(\mathbf{p}\) & **Part. eta2** \\ \hline MO\_VV & Stimulus & Stim \(\times\) cluster & F(3,78) = 7.683 & \(<\)0.001 & 0.228 \\  & Presentation & & & & \\  & Distractors & & & & \\  & Suppression & & & & \\ MO\_VO & Stimulus & Stim \(\times\) cluster & F(3,77) = 3.235 & 0.027 & 0.112 \\  & Presentation & Pres \(\times\) cluster & F(3,77) = 3.938 & 0.011 & 0.133 \\  & Suppression & Stim \(\times\) sup \(\times\) cluster & F(3,77) = 4.530 & 0.006 & 0.150 \\ MO\_AV & Stimulus & Stim \(\times\) cluster & F(3,78) = 3.872 & 0.012 & 0.130 \\  & Distractors & & & & \\ MO\_AO & Stimulus & \(-\) & & & \\ MI\_VV & Stimulus & Stim \(\times\) cluster & F(3,78) = 4.706 & 0.005 & 0.153 \\  & Presentation & & & & \\  & Distractors & Distr \(\times\) cluster & F(3,78) = 7.288 & \(<\)0.001 & 0.219 \\  & Suppression & & & & \\ MI\_VO & Stimulus & Stim \(\times\) cluster & F(3,77) = 3.383 & 0.022 & 0.116 \\  & Presentation & Pres \(\times\) cluster & F(3,77) = 3.638 & 0.016 & 0.124 \\  & Suppression & & & & \\ ML\_AV & Stimulus & \(-\) & & & \\  & Distractors & & & & \\ MI\_AO & Stimulus & \(-\) & & & \\ \hline \end{tabular}
\end{table} 
Table 5: Results of the repeated-measures ANOVA assessing the interactions between all involved factors and clusters for each of the overall STM variables.
# Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant
## 2 Electromechanical Plant
### Discrete Time Model  
Since the control strategy to be implemented is PI, a continuous time PI controller is designed to establish the sampling time. Accordingly, the closed-loop system bandwidth \(w_{bw}\) can be used to determine the sampling time.  
Figure 6: Equivalent block diagram of the plant.  
Figure 7: Consideration of pole location.  
The recommendations given in [32; 33; 34] are applied to design the PI controller. The continuous PI controller is given by Equation (15).  
\[C(s)=K\frac{s+a}{s} \tag{15}\]  
According to [34], the design procedure consists of locating the zero \(s=-a\) close to the integrator pole so that the pole-zero pair of the feedback system \"almost cancel\". As the objective in this section is to determine the sampling time, the goal is to have the highest bandwidth with an overshoot lower than 22% (as a criterion of the system response) to design the PI controller. Thus, using the root locus, it is established that \(a=0.132\) and \(K=492\). Figure 8 presents the root locus, the step response, and the Bode diagram of the closed-loop system with the continuous time PI control.  
Thus, in Figure 8 it is observed that the bandwidth of the closed-loop system is \(w_{bw}=3.23\) rad/s. According to [35], the sample rate is usually taken between 10 and 20 times the bandwidth. For the design 20 times are taken; therefore, the sampling frequency is \(w_{s}=20w_{bw}\) and thus the sampling time \(T_{s}=2\pi/w_{s}\) is \(T_{s}\approx 0.1\,s\).  
The transfer function \(G(s)\) given by Equation (14) is converted to \(G(z)\) (discrete time system) using a sampling time of \(T_{s}=0.1\) s and the bilinear transformation method [30]. In this way, the model of the discrete time system is obtained given by Equation (16).  
\[G(z)=10^{-5}\frac{2.907+5.814z^{-1}+2.907z^{-2}}{1-1.767z^{-1}+0.767z^{-2}} \tag{16}\]  
In general, this transfer function can be represented as:  
\[G(z)=\frac{\Theta(z)}{U(z)}=\frac{B_{0}+B_{1}z^{-1}+B_{2}^{-2}}{1+A_{1}z^{-1}+ A_{2}z^{-2}} \tag{17}\]  
The respective difference equation for this plant is:  
\[\theta[n]=B_{0}u[n]+B_{1}u[n-1]+B_{2}u[n-2]-A_{1}\theta[n-1]-A_{2}\theta[n-2] \tag{18}\]
## 2 Materials and Methods
### Climate Change-Induced Physiological Change Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Ecosystem Service**} & \multicolumn{3}{c}{**Economic Value (EUR ha\({}^{-1}\) y\({}^{-1}\))**} \\ \cline{3-5}  & & **Minimum** & **Maximum** & **Average** & **Reference** \\ \hline \multirow{5}{*}{Cultural activities} & Recreational & & 925.37 & [34,35] \\ \cline{2-5}  & Educational & \(0.81\) & \(8.30\) & \(4.56\) & [31] \\ \cline{2-5}  & Aesthetics & \(8.30\) & \(2\,158.00\) & \(1\,083.15\) & [36] \\ \cline{2-5}  & Cultural activities & & 5,81 & & [34,35] \\ \cline{2-5}  & Oranmental & 3.32 & 177.62 & 90.47 & [34,36] \\ \hline \multirow{2}{*}{Provisioning} & Maintenance of fisheries & 2 012.01 & \(13\,271.86\) & \(7\,641.93\) & [9] \\ \cline{2-5}  & Seafood harvest & 0.39 & 387.63 & 194.01 & [9] \\ \hline \multirow{5}{*}{Regulating} & Coastal protection & & 6 835.88 & [9] \\ \cline{2-5}  & Flood and storm protection & 395.08 & \(12\,611.02\) & \(6\,503.05\) & [9] \\ \cline{2-5}  & Water purification & 71.38 & \(8\,207.04\) & \(4\,139.21\) & [37] \\ \cline{2-5}  & Biological and pest control & & 50.63 & & [34] \\ \cline{2-5}  & Water flow regulation & & 19.92 & & [34] \\ \cline{2-5}  & Nutrient cycling & 115.37 & \(28\,008.35\) & \(14\,061.86\) & [34] \\ \cline{2-5}  & Air quality & & 10.79 & & [34] \\ \cline{2-5}  & Blue carbon storage \({}^{1}\) & & 12.38 EUR ton\({}^{-1}\) C & & [38] \\ \cline{2-5}  & Blue nitrogen storage \({}^{1}\) & & 270 EUR ton\({}^{-1}\) N & & [39] \\ \cline{2-5}  & Blue phosphorous storage \({}^{1}\) & & 36 444 EUR ton\({}^{-1}\) P & & [40] \\ \hline Supporting & Habitat/refugia & 243.19 & 581.00 & 412.10 & [34] \\ \hline \multirow{2}{*}{Supporting} & \multirow{2}{*}{\({}^{1}\) Blue carbon, nitrogen, and phosphorous storage values are expressed on a mass basis.} \\ \cline{2-5}  & & & & \\ \end{tabular}
\end{table} 
Table 3: Salt marsh ecosystem service maximum, minimum, and average values (obtained from the maximum and minimum values) per ha per year, extracted from the referred bibliographic sources. The climate change stressor-driven impact on the salt marsh floristic inventory was calculated by applying the relative change percentage for a certain species in a specific scenario:  
\[\text{Predicted colonized area in the Scenario}_{i}=\ \sum\!\left(Area_{species\ n}\!\left(m^{2} \right)+Area_{species\ n}\!\left(m^{2}\right)\times\frac{Impact_{i}(\%)}{100}\right)\]  
where \(Impact_{i}\) are the values of relative change as described in Table 4. For species without available data in the literature, the average impact for a certain stressor was applied.
## 2 Literature Review
### Definition of Resilience  
The concept of resilience was originally derived from the Latin word \"resilio\", meaning to return to one's original state. Initially applied to physics as engineering resilience, the concept was later introduced to ecology by [PERSON] in 1973 [PERSON] (1973). In common definitions, resilience is often viewed as a static capacity or dynamic process that includes the potential ability of a system to anticipate, avoid, and adapt to environmental shocks [PERSON] (1973); the ability of a system to continue functioning in the face of a disruptive shock [PERSON] (1973); or the ability to withstand a shock and to recover from it [PERSON] (1973); [PERSON] (1973).  
The concept of resilience has then been widely used in physics, material science, psychology, environmental science, and emergency management [PERSON] (1973). In the field of project management, [PERSON] et al. [PERSON] et al. (2018) first proposed the concept of project resilience, which is considered to be the ability of a project to withstand and continuously adapt to change. Then, more and more scholars proposed different concepts of project resilience, which can also be generally categorized into two major kinds: process perspective and capability perspective [PERSON] (2018).  
(1) From a process perspective, project resilience is considered throughout the project and focuses on the long-term development of the project. For example, [PERSON] et al. [PERSON] et al. (2018) adopt a process perspective to understand and construct project resilience. They define project resilience as the ability to withstand shocks, cope with challenges, and recover. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) call for the development of the concept of project resilience and mention that it involves anticipating, understanding change, and planning for scenarios to minimize losses and adapt to new realities.  
(2) From a capability perspective, project resilience is often linked to \"resistance\", \"adaptation\", \"rebound\", and \"learning\" [PERSON] (1973). For example, [PERSON] et al. [PERSON] et al. (2018) conceptualize project resilience in their empirical study of engineering projects as the ability to prepare for, respond to, and mitigate the impacts of disruptions to ensure successful project delivery. [PERSON] et al. [PERSON] et al. (2018) see project resilience as a set of interrelated capabilities that projects need before and after a disruption occurs, and that should be complemented by the development of preparedness capabilities to recover from the disruption before it becomes apparent. The process perspective definition is more applicable when looking at the full life cycle of a project, whereas the capability perspective is more concerned with the state of recovery of a project after it has been subjected to a disruption. The research perspectives used vary depending on the focus of the research.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 3 GSFedSec: GS-Based SA for Privacy Preservation in FL  
Our GS-based protocol fundamentally solves the high computational cost problems of the traditional SA using the GS, as shown in Figure 2. Each client sends data to a server and signs it with the GS without the need to exchange unmasking keys with the server and other clients. The server can check whether a given client is from a valid group, though it cannot identify individual members. Thus, the GS allows a client device to be validated at a lower cost without revealing its identity. Furthermore, if a group member exhibits suspicious behavior, it can be removed from the group. Then, the GS is updated for the remaining group members. Admin maintains a membership table and a revocation table at its side. Whenever any member joins the group, an entry is entered in the membership table. Whenever any member leaves or is revoked from the group, its entry is maintained in the revocation table. If the member has been revoked by the admin because of its malicious behavior, it is not allowed to join again. In this way, clients are encouraged to exhibit good behavior in the group; otherwise, they might be revoked from the group.  
In addition, an adversary cannot target a particular model update because all are signed with the GS and secured with secret keys. Therefore, the GS-based technique offers several advantages over the traditional SA process, as follows:  
* GS is less costly than SA because the number of transmissions is reduced by removing the requirement for client-to-client communication.
* In our algorithm, clients do not need to know each other because they do not need to communicate with each other. They only need to obtain an updated signature from the admin, and this cost is significantly small compared to that of sending messages to many nodes in the network.  
The proposed protocol is improved from the short-GS algorithm presented in [25]. Group members transmit data using their GS, hiding their identities. When the server receives the data, it sees the GS, verifies that it is from a valid member, and extracts it. The server still does not know the exact identity of the sender; hence, it cannot distinguish the specific data belonging to each sender. Subsequently, it aggregates all collected data andcomputes a new system model. Therefore, we designed a new integrated protocol that simultaneously and efficiently ensures data and source privacy.
# Anti-Collision System for Small Civil Aircraft
## 4 Accuracy of Determining the Position of Flying Objects by Anti-Collision System  
According to the derived Equations (1) to (26), the operation of the anti-collision system was simulated, and the accuracy of determining the position of flying objects with this system was evaluated. The influence of the position of flying objects on the accuracy of the anti-collision system was monitored. The individual test examples were designed to examine the ability of the anti-collision system to determine the position of flying objects around an aircraft with sufficient accuracy. The selected simulation results are shown in Figures 3-10.