esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time Lags**} & \multicolumn{2}{c}{**Evaluation Metrics**} & \multirow{2}{*}{**S50**} & \multirow{2}{*}{**S60**} & \multirow{2}{*}{**S70**} & \multirow{2}{*}{**T50**} & \multirow{2}{*}{**T60**} & \multirow{2}{*}{**T70**} \\ \cline{3-3} \cline{5-8}  & \multicolumn{1}{c}{R\({}^{2}\) (\%)} & 0.817 & 0.818 & 0.818 & 0.826 & 0.825 & 0.824 \\ \multirow{3}{*}{1} & RMSE (kWh) & 22.61 & 22.53 & 22.55 & 22.05 & 22.09 & 22.14 \\  & CVRMSE (\%) & 0.249 & 0.248 & 0.248 & 0.243 & 0.243 & 0.244 \\ \hline \multirow{3}{*}{2} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.829 & 0.826 & 0.821 & 0.832 & 0.830 & 0.827 \\  & RMSE (kWh) & 21.86 & 22.06 & 22.34 & 21.64 & 21.76 & 21.96 \\ \cline{1-1}  & CVRMSE (\%) & 0.240 & 0.243 & 0.246 & 0.238 & 0.239 & 0.242 \\ \hline \multirow{3}{*}{3} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.841 & 0.837 & 0.843 & 0.847 & 0.846 & 0.849 \\  & RMSE (kWh) & 21.07 & 21.33 & 20.96 & 20.69 & 20.71 & 20.55 \\ \cline{1-1}  & CVRMSE (\%) & 0.232 & 0.235 & 0.231 & 0.228 & 0.228 & 0.226 \\ \hline \multirow{3}{*}{4} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.846 & 0.854 & 0.836 & 0.862 & 0.860 & 0.864 \\  & RMSE (kWh) & 20.75 & 20.20 & 21.37 & 19.65 & 19.77 & 19.52 \\ \cline{1-1}  & CVRMSE (\%) & 0.228 & 0.222 & 0.235 & 0.216 & 0.217 & 0.215 \\ \hline \multirow{3}{*}{5} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.849 & 0.858 & 0.852 & 0.871 & 0.874 & 0.869 \\  & RMSE (kWh) & 20.56 & 19.91 & 20.25 & 18.99 & 18.74 & 19.10 \\ \cline{1-1}  & CVRMSE (\%) & 0.226 & 0.219 & 0.223 & 0.209 & 0.206 & 0.210 \\ \hline \multirow{3}{*}{6} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.860 & 0.861 & 0.856 & 0.874 & 0.874 & 0.875 \\ \cline{1-1}  & RMSE (kWh) & 19.75 & 19.72 & 20.07 & 18.75 & 18.73 & 18.68 \\ \cline{1-1}  & CVRMSE (\%) & 0.217 & 0.217 & 0.221 & 0.206 & 0.206 & 0.205 \\ \hline \multirow{3}{*}{7} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.858 & 0.852 & 0.840 & **0.876** & 0.874 & 0.876 \\ \cline{1-1}  & RMSE (kWh) & 19.94 & 20.30 & 21.06 & **18.61** & 18.74 & 18.64 \\ \cline{1-1}  & CVRMSE (\%) & 0.219 & 0.223 & 0.232 & **0.205** & 0.206 & 0.205 \\ \hline \hline \end{tabular} The best performance obtained was highlighted in bold.  
\end{table} 
Table 4: Performance in the metrics evaluated when forecasting the energy consumption of the HVAC, using different time lags and HPs, applying the C1 configuration.
# Gaussian Parameters Correlate with the Spread of COVID-19 Pandemic: The Italian Case
###### Abstract  
Until today, numerous models have been formulated to predict the spreading of Covid-19. Among them, the actively discussed susceptible-infected-removed (SIR) model is one of the most reliable. Unfortunately, many factors (i.e., social behaviors) can influence the outcomes as well as the occurrence of multiple contributions corresponding to multiple waves. Therefore, for a reliable evaluation of the conversion rates, data need to be continuously updated and analyzed. In this work, we propose a model using Gaussian functions, coming from the solution of an ordinary differential equation representing a logistic model, able to describe the growth rate of infected, deceased and recovered people in Italy. We correlate the Gaussian parameters with the number of people affected by COVID-19 as a function of the large-scale anti-contagion control measures strength, and also of vaccines effects adopted to reach herd immunity. The superposition of gaussian curves allow modeling the growth rate of the total cases, deceased and recovered people and reproducing the corresponding cumulative distribution and probability density functions. Moreover, we try to predict a time interval in which all people will be infected or vaccinated (with at least one dose) and/or the time end of pandemic in Italy when all people have been infected or vaccinated with two doses.  
coronavirus; growth rate model; statistical modelling; gaussian fitting Article
## 6 Conclusions and Future Works  
One way to increase the certainty in a complex system creation is to remove silos between systems engineering and security teams and tackle security risks during the systems engineering lifecycle. The literature analysis, previous research, and feasibility study showed that systems engineers and security engineers recognize the value of integrating security and system design processes; also, they agree that detecting security issues at the early stages of the development lifecycle helps to reduce the cost and risk of the engineering project.  
This paper proposes the MBSesec method for combining systems engineering and security engineering disciplines at an early stage in an efficient and model-based way. The MBSesec method consists of the SysML/UML-based profile, security process definition, and recommendations on how the specific security technique should be implemented. The method covers phases starting from security requirements identification, continuing capturing assets and modeling threats and risks, and finally deciding security control objectives and appropriate controls. Furthermore, the MBSesec method is aligned to the ISO/IEC 27001 information security standard.  
The MBSesec method usage was presented by extending the Hybrid Sport Utility Vehicle (HSUV) model from the OMG SysML specification. There was created the HSUV security model for the  
Figure 17: Chart of the question: “Did your work quality improve when you moved from document-based system engineering to model-based system engineering?”  
Power Control ECU that represented the phases from the MBSEsec method. The HSUV application case study proved that all the necessary security artifacts could be created in the SysML model (security requirements, misuse cases, asset structure definition, attack scenarios, threat and risk definition, security controls, and supporting matrices, relation maps, verification rules). The MBSEsec method principles and expectations were validated with system and security engineering practitioners. The participants agreed that the MBSEsec method could bring a better work quality in terms of completeness, consistency, and communication with comparatively small training effort.  
We are planning to expand and update the MBSEsec approach according to the users' feedback as well as research whether our method is appropriate for security issues identification while rebuilding legacy software systems.  
Conceptualization, D.M.; methodology, D.M.; software, D.M.; validation, D.M.; formal analysis, D.M.; investigation, D.M.; resources, D.M.; data curation, D.M.; writing--original draft preparation, D.M.; writing--review and editing, D.M. and R.B.; visualization, D.M.; supervision, R.B.; project administration, R.B.; funding acquisition, R.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 4 Topology-Based Routing Protocols in FANETs
### Reactive Routing Protocol
The MDRM-Routing method ensures the establishment of stable routes with high-speed data transmission via wireless networks. MDRMA contributes effectively to network stability mitigation by generating fast and stable routes and reducing connection failures, as demonstrated by simulation findings [100]. ADRP is a novel FANETs adaptive density-based routing technology. ADRP is an upgraded AODV protocol that uses route freshness information to optimize its route-finding process. The primary purpose is to adaptively calculate forwarding probability in order to maximize forwarding efficiency in FANETs. ADRP dynamically adjusts a node's rebroadcasting probability to request packet routing based on the number of adjacent nodes. Indeed, it is more interesting to prioritize retransmissions made by nodes with fewer neighbor nodes. The simulation findings indicate that ADRP outperforms AODV in end-to-end delay, packet delivery percentage, routing load, normalized MAC load, and throughput [101]. AODV is a reactive routing protocol that operates on a hop-by-hop basis. It establishes the route between source and destination only when the source begins it and maintains it for as long as it desires. The source node broadcasts a route request (RREQ) packet to discover the destination. The number of hops necessary to reach the destination is contained in a route reply (RREP) packet. In the event of an invalid route, a route error packet (RERR) is issued to warn the source node of the link failure and to allow the source to restart the route discovery process. AODV automatically adapts the dynamic link-state, memory overhead, and network utilization [102].
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Laser-Induced/Scribed Graphene ECG Electrodes
#### 2.4.1 Porous LIG ECG Electrode  
When preparing LIG electrodes, a thin film polyimide (PI) was used as a substrate material to obtain different graphene structures. When the laser intensity applied to PI is high enough, the laser ablation carbonizes the PI, and the resulting nitrogen and carbon oxide gases spill out to form a porous structure on the surface of LIG [61]. The resulting porous LIG has high electrical conductivity and flexibility, and the porous surface helps increase the contact area between the skin and electrode, suitable for the electrode conductive layer. Based on porous LIG, [PERSON] et al. prepared a LIG ECG electrode using a 2.4 W CO\({}_{2}\) laser [62]. The graphene properties of laser-ablated carbides were proven by  
Figure 2: (**a**) The fabrication process (**left**) and photos (**right**) of the cotton-textiles-based graphene ECG electrode. Reprinted with permission from Ref. [45]. Copyright 2020, IOPscience. (**b**) The fabrication process of the Gr/SF/Ca\({}^{2+}\) tattoo electrode and its stable adhesion on human skin. Reprinted with permission from Ref. [47]. Copyright 2020, Wiley. (**c**) The preparation process of the nanofiber-based graphene ECG electrodes (**above**), its applications in ECG and EMG monitoring (**lower left**), and its SEM images (**lower right**). Reprinted with permission from Ref. [51]. Copyright 2021, American Chemical Society. (**d**) The graphene–nylon-based textile electrodes and their integration in a garment. Reprinted with permission from Ref. [53]. Copyright 2017, MDPI. (**e**) Preparation processes of the PVA/graphene biodegradable electrode. Reprinted with permission from Ref. [56]. Copyright 2020, International Journal of Engineering Research and Applications.  
Raman spectroscopy. The sheet resistance of the porous LIG electrode is 360 \(\Omega\)/cm\({}^{2}\), and the contact impedance of the electrode is 700 \(\Omega\), similar to commercial gel electrodes. When used to detect ECG signals, typical ECG features can be well recognized.
# A Dual-Adaptive Approach Based on Discrete Cosine Transform for Removal of ECG Baseline Wander
## 3 Results  
Experiments were carried out on both real ECG records and simulated ECGs to evaluate the proposed method qualitatively and quantitatively. The real ECG records were from the MIT-BIH Arrhythmia Database and the PTB Diagnostic Database. The former database is a collection of 48 half-hour two-lead ECGs whose sampling rate is 360 Hz. The latter is a database of 549 high-resolution 15-lead ECGs digitized at 1000 samples per second. The simulated \"clean\" ECG was constructed by repetitively concatenating an identical ECG cycle chosen from real ECG records. The simulated BW was generated by calculating low-frequency sinusoid waves and a low-frequency random component [13].  
For comparison, the performances of the following five algorithms were investigated:  
1. A DCT-based filter [27] with the proposed dual-adaptive scheme to search for the optimal cut-point between BW and the true ECG. \((\theta=0.65,\;G=10)\).
2. A DCT-based filter [27] which let the cutoff frequency be 0.9 \(\times\) CFF.
3. A linear phase, sharp cut-off FIR filter [28] with a cutoff frequency of 0.9 \(\times\) CFF.
4. A WT-based algorithm using Daub-4 mother wavelet [29].
5. A weighted median filter [30] with parameters \((a=500,\;\alpha=1,\;\beta=40)\).  
The algorithms are referred to using Roman numerals I-V.
## 4 The Study on Gas Control Technology of High Gas-Drainage Roadway
### Numerical Simulation  
#### 4.2.1 Numerical Model  
Based on the field conditions of the 91-105 working face at Wangzhuang Coal Mine, a geometric model was established in Fluent v.2022. Goaf dimensions are500 m \(\times\) 283 m \(\times\) 50 m, and all roadway dimensions are 5.5 m in width and 3.6 m in height. The haulage roadway and the airway serve as intake air roadways, introducing fresh airflow, while the reserved roadway within the goaf functions as the return air roadway. The intake airflow volume is 2400 m\({}^{3}\)/min for the haulage roadway and 600 m\({}^{3}\)/min for the airway.  
Due to the complex underground conditions in the real environment and the complexity of our research content, it is necessary to make idealized assumptions about some conditions in the model. The main assumptions include the following:  
1. As the gas flotation phenomenon is considered, and buoyancy is caused by pressure differences, a gravitational field must be added to the model.
2. The gas in the goaf is an ideal gas and can be compressed during the study.
3. Since the effect of the temperature field is not studied, the temperature in the model is assumed to be constant.
4. The porous medium characteristics of the goaf remain isotropic.
5. The gas flow state in the goaf is stable, assumed to be turbulent, and the gas migration process conforms to [PERSON]'s law.
6. The gas emission sources are the goaf, which are uniformly distributed in the fractured zone and caving zone of the goaf.
7. The air entering the model contains 79% nitrogen and 21% oxygen, without carbon dioxide or gas.
8. The influence of various underground devices and equipment on airflow migration in roadways is ignored. The transportation roadway, air roadway, gob-side entry retaining, and mining face are all regarded as cuboid channels with smooth surfaces, assuming that the gas flow within the channels belongs to turbulent flow.  
Through theoretical analysis, the horizontal position of the high gas-drainage roadway ranges from 14.8 to 94.3 m away from the return air roadway. Accordingly, six horizontal distances--15 m, 30 m, 45 m, 60 m, 75 m, and 90 m--were selected for numerical simulation analysis. The vertical position of the high gas-drainage roadway ranges from 20.5 to 40.7 m. Thus, four vertical heights are selected for simulation: 20 m, the middle position of the fracture zone at 28 m, 35 m, and 40 m. The numerical model of high gas-drainage roadway in the 91-105 working face is shown in Figure 10.
# Improved PEG-Based Construction of Analog Fountain Codes
## 3 Improved PEG-Based Construction
### Simulation and Discussion  
In this subsection, we present the numerical results of the proposed construction and the constructions in the references. Here, we select \(d=8\) and \(W=\{1/2,1/3,1/5,\)\(1/7,1/11,1/13,1/17,1/19\}\) for AFC constructions, which are designed in [2]. In addition, \(d=4\) and \(W=\{1/2,1/3,1/5,1/7\}\) are also considered. Various construction methods such as random AFC, S-AFC, and WC-PEG are included as comparison. The proposed construction is labeled as IM-PEG. These settings are in line with the previous references. The CS-BP decoder with a maximum iteration number of 15 is applied in the receiver side.  
First, we simulate the BLER of different AFC constructions including random AFC, S-AFC, WC-PEG, and the proposed construction. Let \(R=k/n\) denote the code rate. Simulations are conducted with fixed rate \(R=2\) and \(k=128\). Various degrees \(d=4\) and \(d=8\) are considered, respectively. The AWGN channel with zero mean and variance \(\delta^{2}\) is employed for the transmission of AFC codewords. Signal-noise ratios (SNRs) range from 16 dB to 24 dB. All of the parameters are set according to [2; 6]. Please note that \(R\) can be larger than 1, since one AFC symbol involves \(d\) bits. It can be seen in Figure 3 that the improved PEG construction has good BLER performance. For example, when \(d=4\), the proposed construction offers about 1 dB gain when BLER is lower than \(10^{-4}\) over WC-PEG and S-AFC.  
Then, the achievable rates of different AFC constructions with \(k=128\) are simulated. Similarly, various degrees \(d=4\) and \(d=8\) are considered, respectively. Figure 4 shows that the rates of the proposed construction are higher than others in a large range of SNRs, in both \(d=4\) and \(d=8\).  
Moreover, BLER performance with various weight sets is presented in Figure 5, \(k=128\), \(d=4\), and \(R=1\) are set for the simulation. \(W^{\prime}=\{0.8670,0.432,0.2155,0.1073\}\)[6] is selected for comparison. It can be seen from Figure 5 that the proposed construction provides good error performance with \(W^{\prime}\) as well.  
Figure 3: BLER of different AFC constructions with \(d=4\) and \(d=8\).
## 4 Discussion
### Future Research Directions  
It is important to highlight some limitations of this study and suggest directions for future research. In our work, one cultivar and one season data were considered; future research work should aim to improve the model's performance by considering different olive cultivars, multiple seasons, and deficit irrigation trials across various climatic conditions. This approach should also be validated through long-term studies involving the collection of testing data over multiple years. Additionally, other parameters related to photosynthetic efficiency and gas exchange, such as the electron transport rate, net assimilation rate, stomatal conductance, and transpiration rate, could be considered.
## 2 Study Area and Data
### Data  
#### 2.2.1 SAR Data  
The data used for monitoring are Sentinel-1 radar imagery. Sentinel-1 operates in the C-band with a wavelength of 5.6 cm and a repeat cycle of 12 days. The regular and evenly distributed acquisition of data over time enhances its capability to detect rapid deformations. The time range for this paper using Sentinel-1 data is from 25 August 2017, to 11 August 2022 (Table 1). DEM adopts SRTM 30 m resolution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **Path** & **Frame** & **Track Direction** & **Resolution** & **Model** & **Width** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-1A data list.  
Figure 1: Overview of the study area.
# Forest Fire Detection Using New Routing Protocol
## 3 Conventional PCE-OLSR Method  
Power Consumption Efficient-Optimized Link State Routing (PCE-OLSR) is a method proposed for forest fire surveillance by using mobile sensor networks [32]. The PCE-OLSR method is based on an OLSR protocol and it aims to increase the network's lifetime by consuming node energy in an efficient manner. The technique of PCE-OLSR uses those nodes that are located in the fire area for the purpose of completely utilizing their energy before they burn and die by the fire. In this case, the other nodes that are located in safe areas do not use their energy. Therefore, the energy of those nodes may be conserved, continue working for as long as possible, and thus increase the network lifetime. Each node of the PCEOLSR has a threshold for temperature and it is set at 60 \({}^{\circ}\)C, where a node detects fire when its temperature reaches 60 \({}^{\circ}\)C. In other words, when a node's temperature reaches this threshold, all its neighbors will change their routing to this node to exploit its energy before it is destroyed and dies by the fire.  
However, such a routing process can cause a bottleneck problem, as depicted in Figure 2. The temperature of the \(B\) node reaches 60 \({}^{\circ}\)C, as shown in Figure 2B. In this case, all neighbors of the \(B\) node (i.e., \(D\), \(E\), \(F\), and \(G\) nodes) change their parent to the \(B\) node for the purpose of totally utilizing the energy of the \(B\) node. In Figure 2C, the temperature of node \(B\) reaches 130 \({}^{\circ}\)C and it is burned. In addition, the temperature of the \(A\) node reaches 60 \({}^{\circ}\)C, and therefore, \(D\), \(E\), \(F\), and \(G\) nodes change their routing to \(A\) as well for the purpose of exploiting the \(A\) node's energy. However, the bottleneck problem leads to accumulation of many data packets in the node's buffer located in the fire area, wherever this node will be destroyed before transmitting all information. Consequently, many data packets will be dropped and lost in the network.
# Advanced Micro/Nanocapsules for Self-Healing Coatings
## 5 Types of Self-Healing Capsules
### Optimization of Micro/Nanocapsule Design for Self-Healing Coatings

Table 1: Types of self-healing capsules along with their description, applications, and self-healing mechanisms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types of Self-Healing** & **Description** & **Self-Healing Mechanisms** & **Applications** \\ \hline Core–shell microcapsules & These capsules have a core that contains the healing agent, surrounded by a protective shell. & Upon shell rupture, the core releases the agent to fill cracks or gaps. & Used in structural materials, protective coatings, and smart textiles. \\ \hline Multicore capsules & These capsules contain multiple cores within a single shell, each filled with different healing agents or components. & Upon damage, multiple heating agents are released sequentially or simultaneously to enhance the repair process or to address different types of damage. & Used in complex systems where multiple types of damage might occur. \\ \hline Encapsulated liquid & These capsules are filled with liquid metals, which can flow and solidify to repair damage. & When a crack forms, the liquid metal flows into the damaged area, filling the voids, and solidifies to restore the material’s integrity. & Used in batteries, electronic circuits, and advanced materials requiring rapid and robust healing. \\ \hline Temperature-responsive capsules & These capsules are triggered by changes in temperature. & When the material is exposed to a certain temperature range, the capsule releases its healing agent to address thermal damage. & Used in high-temperature environments, such as in automotive or aerospace applications. \\ \hline pH-responsive capsules & These capsules are designed to release their healing agent in response to changes in pH levels. & When the material’s environment becomes more acidic or basic due to corrosion or damage, the capsules respond by releasing the healing agent. & Used in materials exposed in harsh environments and corrosion-resistant coatings. \\ \hline \multirow{4}{*}{Polymer capsules} & These capsules contain polymer precursors that can form a solid polymer when released. & Upon capsule rupture, the polymer precursors are released, and through chemical reactions, a solid polymer is created that heals the crack. & Used in adhesives, coatings, and structural polymers. \\ \hline \multirow{2}{*}{Emulsion-based capsules} & These capsules contain emulsions that can release healing agents when triggered. & The emulsion can release the healing agent either through a gradual diffusion process or by capsule rupture. & Used in paints and self-healing coatings, where gradual healing over time is desired. \\ \hline Time-delayed capsules & These capsules are designed to release their healing agent after a specific time delay. & The capsule slowly degrades or reacts over time, eventually releasing the healing agent. & Used in systems where immediate repair is not necessary or where gradual healing is needed. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key types of self-healing capsules.