esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things in Marine Environment Monitoring: A Review
## 2 Overview of IoT in Marine Environment Monitoring
### IoT-Based Marine Environment Monitoring Applications  
IoT-based marine environment monitoring application areas include: (1) Ocean sensing and monitoring; (2) water quality monitoring; (3) coral reef monitoring; (4) marine (either offshore or deep-sea) fish farm monitoring; (5) wave and current monitoring. Different applications use different IoT system architectures, sensing and control technologies, and communication technologies.  
An ocean sensing and monitoring system is a general marine environment monitoring system, which existed for a long time, previously using oceanographic and hydrographic research vessels. A water quality monitoring system usually monitors water conditions and qualities, including water temperature, pH, turbidity, conductivity, and dissolved oxygen (DO) for ocean bays, lakes, rivers, and other water bodies. A coral reef monitoring system typically monitors coral reef habitats and the surrounding environments. A marine fish farm monitoring system monitors water conditions and qualities including temperature and pH, measures the amount of fecal waste and uneaten feed for a fish farm, as well as fish conditions and activities including the number of dead fishes. A wave and current monitoring system measures waves and currents for safe and secure waterway navigations.
## 4 Conclusions  
One of the main issues for the photovoltaics scientists and industry is to produce devices with high conversion efficiency, cost-effective, and competitive to traditional methods of generating electricity [30].  
With high costs and complexity of femtosecond devices on the one hand, and low efficiency and quality of long pulse micromachining on the other, it is a sub-picosecond laser that might be an alternative when it comes to scribing and cutting of thin silicon wafers. The unwanted thermal effects, i.e., melting, cracking, amorphization, and residual stress buildup, were not observed during the experiment. A sub-picosecond micromachining technique has a potential to be applied in thin film solar cell manufacturing for dicing (cutting, Figure 4), surface structuring (hatching, Figure 6), and edge isolation processes (scribing, Figure 5). The results such as micromachining quality, depth of cut, and crater width, demonstrated during the experiment, can be considered when applying for industrial use.  
In a wide variety of micromachining processes, the duration of each one would depend on the desired results and the setup applied (a galvo scanner, linear translation stages, etc.). The micromachining parameters that were found optimum in the presented experiments are considered for the specific applications. There is, however, a potential to further increase the scanning speed or decrease the number of repetitions, which will translate to lower ablation rate, but even better efficiency of the whole process.  
Comparing to the commercially available ultrashort pulse lasers described in the introduction (Table 1), we find a sub-picosecond laser more cost efficient and a reliable source. In addition, the prototype Yb:KYW design offers some unique parameters, such as repetition rate (100-400 kHz), UV wavelength, or obtainable laser fluence close to the silicon ablation thresholds. The M\({}^{2}\) = 1.2 parameter, indicating a beam quality, is also an advantage for above-mentioned laser.  
All authors have seen and approved the manuscript and have contributed significantly to the paper. All authors have read and agreed to the published version of the manuscript.  
This research is financed by the Institute of Fluid Flow Machinery, Polish Academy of Sciences O3/Z3/T1.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
The data that support the findings of this study are available from the corresponding author on request.  
Figure 6: Large scale images of the Si wafers sub-picosecond surface hatching. Laser beam parameters: 650 fs, 343 nm, 200 kHz, 27.5 uJ; processing parameters: 7 mm/s, 15 pattern repetitions, total processing time 1.5 h.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 2 Methodology
### Extended Kalman Filter
, \tag{15}\]  
which only maps the position into itself; therefore, the GPS-based EKF is a filter for the GPS noise.
# Digital Health Technology for Stroke Rehabilitation in Canada: A Scoping Review
## 4 Discussion
### Strengths and Limitations of This Review  
A notable strength of this review lies in its examination of the current landscape of DHTs for stroke rehabilitation, with a specific focus on Canada. This insight offers a solid foundation for researchers to explore existing gaps in evidence or build upon the current body of knowledge, as well as for Canadian clinicians to utilize evidence-based technologies in their practice. However, given that this is an emerging field, the review is based on only 14 studies, which limits the strength and generalizability of the conclusions. Additionally, the literature search was restricted to studies published in English, which may have limited the inclusion of relevant studies published in other languages. Moreover, the wide range of applications and the variability in study designs make it challenging to provide detailed recommendations or propose a targeted systematic review on a particular area.
# Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
## 3 Materials and Methods
### Surface Subdivision and Machining Time Estimation
#### 3.3.3 Region Merging  
Segmenting machining workpieces into regions can sometimes yield impractical small areas for effective machining. Such regions lead to the generation of numerous short, discontinuous toolpaths, which can compromise the efficiency of the machining process [29]. A systematic approach to merging smaller regions into adjacent, larger ones addresses this issue.  
The first step in region merging involves defining and identifying small regions based on a predefined area threshold. Regions whose areas fall below this threshold are flagged for potential merging to enhance the compactness and continuity of the machining paths. Once small regions are identified, their neighboring regions are examined. This involves analyzing the spatial adjacency and connectivity of the regions based on their proximity and shared boundaries.  
The small region is merged with each neighboring region, and the impact on the workpiece machining time is calculated. The adjacent region that leads to the most favorable reduction in machining time is chosen as the optimal candidate for merging. The results  
Figure 2: Regionalization process: (**a**) initial K-means clustering, (**b**) regions with created boundaries, (**c**) merged regions, and (**d**) final surface finishing regions with smoothed single boundaries. The arrows represent the optimal machining directions or VVF directions.  
can be seen in Figure 2c, where the initial number of 13 regions from Figure 2b was lowered to 9.
## 3 Results and Discussion
### Mechanical Characterization of the Hybrid Coupons Based on the Small Punch Tests
Another interesting result is that there is a significant difference in the mechanical properties of Inconel 718 for the different conditions studied. Thus, the selected characterization method is sensitive enough to evaluate the mechanical properties of these types of hybrid components. [PERSON] et al. in [23] studied the efficacy of small-scale test methodologies to derivate the mechanical properties of additive manufactured materials. In the study, Inconel 718 produced by laser bed powder fusion (PBF-LB) was analyzed by means of a small punch test. The estimation of mechanical properties was carried out with a correlation where five materials were tested, including the Inconel 718. The estimation of the mechanical properties used in the presented study employs 16 materials among which Inconel is not included. Therefore, a comparison between both correlations is calculated using the results of the investigation of [PERSON] et al. Figure 13 shows the comparison between two SPT correlations and the tensile tests to assess whether the choice of fitting parameters from a reference without Inconel 718 is acceptable. \"SPT fit 1\" corresponds to the estimation of mechanical properties obtained by SPT in [23], \"SPT fit 2\" corresponds to the estimation of mechanical properties obtained by SPT in [23] but applying the correlation parameters \(\beta_{Rm}\) = 0.277 and \(\beta\) = 0.476, and \"Tensile\" corresponds to the tensile test performed in [23]. The notation of the original paper has been kept in the figure, LPBF-V and LPBF-H, where LPBF stands for PBF-LB, V stands for the vertical building direction, and H for the horizontal building direction. It can be observed in Figure 13 that both correlations can be applied for the calculation of \(R_{m}\) but for \(R_{p0.2}\), the \"SPT fit 2\" is a better estimation even though the correlation does not include the exact analyzed material, the Inconel 718. Thus, the fitting coefficients employed in this work are considered valid. Furthermore, the results in [23] allow comparing DED-LB and PBF-LB for Inconel 718. The PBF-LB tests showed strength levels over 30% higher than DED-LB. This observation fits with the presence of Laves phases in the DED-LB micrographs.
# An Investigation on the Synthesis of Alkali Activated Materials from Thermally Modified Clays
## 3 Results and Discussion
### Precursor Characterization
#### 3.2.2 Influence of SF Incorporation  
Given the insufficient availability of Si in the composition of some clay precursors, the addition of secondary sources of silica is often reported as necessary to deliver competent alkali-activated materials. The provision of extra silicon can be delivered by modifying the concentration and dosage of silicate solutions (discussed in Section 3.2.3) and/or by introducing amorphous Si-rich powdered materials (e.g., silica fume) in partial lieu of clay precursors. However, optimal replacement levels vary according to the characteristics of the systems examined, thus requiring dedicated investigations as performed here.  
Figure 4 shows the effects of MK replacement by SF on CS and AD. The results show that all binder formulations increased in CS and reduced in AD with curing, regardless of the MK to SF mass ratio (CC/SF ratio). After 28 days of curing, binders with the lowest CC/SF ratio (CS3.00) recorded the lowest CS values (10.1 \(\pm\) 0.9 MPa), whereas the CS4.00 formulations showed the highest CS (12.5 \(\pm\) 1.1 MPa). [PERSON] et al. [57] reported increases in CS up to 18 wt.% SF, but decreases for higher dosages. Previous studies have shown that excessive SF can be deleterious for CS development due to its volume expansion [58; 59]. The authors reported that SF contents over 10 wt.% led to the formation of large voids and concomitant reductions in CS. However, residual modifications on AD, OP, and WA were imposed by the CC/SF ratio (Table 5), suggesting similar porous structures being formed in all binders. The highest early strength of the CS3.00 samples (8.2 \(\pm\) 1.5 MPa; CS3.50 = 5.8 \(\pm\) 0.7 MPa; CS4.00 = 7.1 \(\pm\) 1.1 MPa) may indicate that SF promotes early age polymerization but hinders the later dissolution of clay precursors and/or the diffusion of dissolved species. However, increases in early CS can be an interesting technological feature if one is prepared to partially sacrifice strength development at later ages. Economic and environmental benefits can also accrue from the partial replacement of calcined clay by other Si-rich waste-based materials such as among others, rice husk ash, and the versatility and resilience introduced into the manufacturing process should also not be neglected here. Therefore, 3.00 was selected as the CC/SF ratio for the further examination of the MK and CRC binders.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Water Absorption (WA)** & **Open Porosity (OP)** \\ \cline{2-3}  & \% & \% \\ \hline SL0.90 & 15.68 \(\pm\) 0.16 & 24.18 \(\pm\) 0.17 \\ SL1.00 & 14.34 \(\pm\) 0.07 & 22.48 \(\pm\) 0.15 \\ SL1.10 & 14.96 \(\pm\) 0.66 & 24.23
## Nomenclature  
PMPentene poly (4-methyl-1-pentene)  
PMPentyne poly (4-methyl-2-pentyne)  
Tg glass transition temperature (\"C)  
Tm melting temperature (\"C)  
FFV free volume fraction  
Xc crystallinity degree  
\(\theta\) time-lag (s)  
\(p\) permeability coefficient (Barrer); 1 Barrer = 10\({}^{-10}\).[(cm\({}^{3}\)(STP)\)-cm)/(cm\({}^{2}\)s\(<\)cmHg)]  
\(V\) rate of the gas carrier with the penetrant (cm\({}^{3}\) (STP) s\({}^{-1}\))  
\(A\) membrane area (cm\({}^{2}\))  
\(p_{1}\) the feed pressure (cmHg)  
\(p_{2}\) the permeate pressure (cmHg)  
\(c_{i}\) the concentration of the penetrant in the feed flow (vol%)  
\(c_{i}\) the concentration of the penetrant in the gas carrier stream in the permeate (vol%)  
\(l\) membrane (film) thickness (cm)  
\(\alpha\) separation selectivity  
\(D\) diffusion coefficient (cm\({}^{2}\)/s)  
\(D_{a}\) diffusion coefficient in the amorphous phase in PMPentene (cm\({}^{2}\)/s)  
\(D_{c}\) diffusion coefficient in the crystalline phase in PMPentene (cm\({}^{2}\)/s)  
\(X_{a}\) the amorphous phase fractions of PMPentene  
\(X_{c}\) the crystalline phase fractions of PMPentene  
\(A_{i}\) the shape parameter of crystallites  
\(Z\) the coefficient introduced for concentrated dispersions  
\(K_{H}\) Henry constant (cmHg)  
\(p\)\({}^{*}\) the dissolved gas partial pressure  
\(\overline{G}_{i}\) partial molar free energy of interaction of the solute and solvent molecules (kJ/mol)  
\(\overline{G}_{c}\) partial molar free energy of the cavity creation (kJ/mol)  
\(V_{1}\) partial molar volume of the solvent (polymer) (cm\({}^{3}\)/mol)  
\(T\) Kelvin temperature (K)  
\(R\) universal gas constant (J/K-mol)  
\(S\) solubility coefficient [cm\({}^{3}\)gas(STP)/(cm\({}^{3}\)polymer-cmHg)]  
\(V_{m}\) molar gas volume (cm\({}^{3}\)/mol)  
\(\Delta H_{S}\) heat of gas solution (kJ/mol)  
\(\overline{H}_{c}\) partial molar free enthalpy of cavity creation (kJ/mol)  
\(\overline{H}_{i}\) partial molar free enthalpy of interaction of solute and solvent molecules (kJ/mol)  
\(\alpha_{p}\) coefficient of thermal expansion of the solvent (polymer) (I/K)  
\(K_{i}\) the functions of the density, temperature, pressure and molecular sphere diameter  
characterising the thermodynamic data for cavity formation  
effective molecular diameter (nm)  
\(\varepsilon_{i}\)_k_ Lennard-Jones additive potential (K)  
\(E_{p}\) apparent permeability activation energies (kJ/mol)  
diffusion activation energies (kJ/mol)
## 7 Risk Analysis
### Gps  
GPS is a relevant component for understanding the location of a user when an accident happens and consequentially for enhancing the efficiency of ERS. Despite being a critical component in both architectures, Figure 8 shows that the [PERSON] et al. [28] architecture is vulnerable to more threats due to the absence of communication redundancy and the low-level communication protocol used.  
Repudiation  
1. **Component** _Security risk_: A critical risk lies in the potential compromise of non-repudiation within the GPS component of the ACN system, with implications that extend far beyond mere data manipulation. This vulnerability threatens the accuracy and reliability of crash event notifications, potentially leading to delayed or inadequate emergency responses and legal complications.  
_Security measures_: To mitigate this, the integration of robust cryptographic signatures, secure time synchronization, and immutable logs, combined with the use of hardware security modules (HSMs) and secure time synchronization protocols, bolsters the accuracy and veracity of GPS data. Digital certificates and public key infrastructure (PKI) facilitate trusted sender authentication, while anomaly detection and IPS monitor data streams for irregularities. Implementing redundant GPS sensors, frequent auditing, real-time data validation, and secure cross-referencing aligns with the security enhancements proposed in the system's architecture. These measures collectively ensure data integrity, origin verification, and effective operation of the ACN system while preserving user safety and privacy.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 2 Related Works
[32] introduced a conditional generative adversarial neural network-based algorithm to optimize network coverage in similar situations. Their simulations showed that the algorithm converges to a quasi-optimal solution. Recently, drone network-based disaster recovery has attained even wider acceptance. [PERSON] and [PERSON] [33] discussed the various aspects of using drones for disaster risk mitigation and crisis response, especially in Japanese contexts. They explain the need for drones in disaster risk reduction, the different technologies applicable to this use case, and a variety of such cases in Japan. There have been studies focusing on the communication aspects as well; [PERSON] and [PERSON] [34] proposed Reconfigurable Intelligent Surface (RIS) technology to support communications in post-disaster areas. In [35], the authors explained how a drone equipped with RIS can be used to cover users with an mmWave base station. An energy-aware multi-armed bandit algorithm is used to cover several high-capacity network hotspots while minimizing the energy consumption. In [36], the authors proposed the use of access and gateway drones to support communication over disaster-affected areas. An access drone collects information from its assigned area and flies to the nearby gateway drone to transfer the information. In order to avoid two access drones targeting the same gateway, the authors employ two multi-armed bandit-based algorithms, namely, the Kullback-Leibler upper confidence bound (KLUCB) and minimax optimal stochastic strategy (MOSS) algorithms. However, frequent flying can drain the energy of drones very quickly in this case. The above works show that a drone-based network is a good option for establishing communication in a post-disaster area. However, there are gaps in the current research with respect to ensuring complete coverage while the minimum number of drones. In the above-mentioned drone deployment algorithms, the terrain is assumed to be a planar region, which can create bottlenecks in real-world implementation scenarios. Furthermore, maximization of average leftover energy in the system is another area that needs to be addressed in order to enable system operations over sufficiently long durations. Hence, in this study we concentrate on the development of a terrain-aware, deterministic, and energy efficient region-filling algorithm that uses the minimum number of drones to cover the entire disaster-affected area with no network voids.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Field Data Collection  
GPS survey ground data was obtained in June 2018 and used Google Earth maps to extrapolate backwards and construct ground truth data for 1984. The period was chosen as it is difficult to find cloud-free images necessary for land surface analysis during the main growing season. Similar periods were chosen for both 1984 and 2018 to avoid the effect of differences in seasons on mapping accuracy and change detection. The experience of 2018 was used to identify similar LULC classes in the same season as 1984 using high-resolution Google Earth imagery. Table 1 describes the LULC classes that were predominant in the study area and were used in this study. Field data were split into 70% for training LULC mapping and 30% for post-classification accuracy assessment. Daily rainfall data for the study area were downloaded from the NASA website ([[https://power.larc.nasa.gov/data-access-viewer/](https://power.larc.nasa.gov/data-access-viewer/)]([https://power.larc.nasa.gov/data-access-viewer/](https://power.larc.nasa.gov/data-access-viewer/)), accessed on 13 September 2022) for the period from 1984 to 2020. This data was used in order to cover the same period of analysis as the land cover maps.
# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 3 Results

Table 4: Comparison of Random Forest against other machine learning models. Figure 7: Statistical test for the experiment with different numbers of trees using the Random Forest technique. Also, a statistical comparison for the previously presented results is outlined in Figure 8.  
In Table 4, we observe that the RBF10 model generally exhibits higher error rates compared to the other models, with greater variability from year to year. Its highest error occurs in 2021 at 18.17%, while its lowest is in 2016 at 7.25%. The MLP10-BFGS model, although showing some fluctuations, is more stable than RBF10 and has lower error rates in most years. Its lowest error is recorded in 2016 at 4.36%, while the highest is in 2021 at 14.12%. The MLP10-LBFGS model generally performs better than MLP10-BFGS, with slightly lower error rates in most years. Its lowest error is 3.61% in 2016, while the highest is in 2021 at 12.71%. Finally, the Random Forest model shows the best overall performance, with the lowest error recorded in 2016 (3%) and the highest in 2021 (11.92%). Overall, Random Forest consistently maintains lower error rates compared to the other models in nearly all years. Comparing the models using the t-test (Figure 8), the statistical analysis shows that the differences between the models are statistically significant, especially when comparing RBF10, MLP10-BFGS, and MLP10-LBFGS to Random Forest. The \(p\)-value is lower than the specified threshold (\(p<0.05\)), confirming that Random Forest statistically outperforms the other models. Furthermore, to measure the effectiveness of the artificial neural network on the proposed datasets, one more experiment was conducted, where the BFGS method was used to train a neural network, where the number of processing nodes was in the range \([2,20]\). The results from this experiment are outlined in Table 5.  
Figure 8: Statistical comparison for using the methods MLP, RBF, and Random Forest to predict the duration of fires. From the generated results, it is evident that the artificial neural network model shows improved performance when the nodes increase from 2 to 5, or to 10, but from this point onwards there is no noticeable difference.