esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
Figure 2 depicts the overall approach to define and characterize the rock massifs, the techniques applied, and the laboratory experiments, which are described in the second article [23] to establish the potential for mineral carbonation.  
A first screening of mafic and ultramafic geological formations located in the study area, the Alentejo region (Figure 1a), was made, using the geological map of Portugal (scales: 1/50,000, 1/200,000 and 1/500,000), an analysis of published information, and publicly available maps and reports.  
A first field trip was made to outcrops of the screened rock massifs and samples were collected for laboratory characterization, comprising thin section production, mineral identification, textural description, and evaluation of the porosity by optical and conventional microscopy.  
Next we used the mineralogical characterization of those samples, as well as other data resulting from desk studies, to rank the different ultramafic and mafic rock massifs in terms of their theoretical suitability for mineral carbonation. The criteria adopted in the ranking procedures are further described in Section 2.1.  
For the two highest-ranking rock massifs, we conducted detailed fieldwork and experiments to identify the relevant fracture patterns at outcrop, along scanlines, and in boreholes. Representative samples were submitted to a battery of petrographic, mineral chemistry, and geochemistry techniques to fully characterize the samples. Those same techniques were applied to study the changes imposed by a set of experimental runs in which the rock samples were put in contact with a brine saturated in CO2 under pressure and temperature conditions that simulated the supercritical injection of CO2. The techniques utilized for the characterization of the rock samples are described in Section 2.2, while the results of the mineral carbonation experiments are addressed in Moita et al. [23].  
Figure 2: Diagram illustrating the methodology employed to screen, rank, and characterize the rock massifs and rock samples. The samples were subsequently submitted to mineral carbonation experiments in the laboratory, the results of which are the subject of a second article [23].
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 3 Biofilms in Food Industry
### Health Aspects  
Biofilm formation in food processing environments can be a source of various kinds of microorganisms, including pathogens [94; 95] and toxigenic bacteria [96]. Campylobacter-rioiss was the most prevalent zoonosis in the EU in 2017, representing nearly 70% of all reported cases [97]. Hospitalization ranged from 30.5% to 42.5% for _Campylobacter_, _Yersinia_, and _Salmonella_ infection diseases, with around 150 reported deaths. Strong-evidence outbreaks have been caused by food vehicles, such as milk, broiler meat, eggs, and bakery  
Figure 2: The effects of material properties in complex environments on reversible bacterial adhesion.  
products. Obviously, food-borne bacteria can be transferred into the food via contact with unclean solid surfaces. Although there is no direct evidence that biofilm-associated bacteria were responsible for these particular outbreaks, such bacteria were consequently screened for their ability to form biofilm. Some studies showed that the majority of _Salmonella enteritidis_ SE86 isolates (96.3%) from outbreaks produced biofilm, which was like those isolated from poultry [98]. On the other hand, one of 14 _Yersinia pseudotuberculosis_s outbreak strains had characteristics favorable for the formation of biofilm in vitro [99] which was in accordance with the literature data [100], where only one _Salmonella_ outbreak strain had biofilm-forming potential. However, the enhanced ability to attach to the polystyrene plates at 37 \({}^{\circ}\)C was recognized as an adaptive advantage of cells under adverse conditions, and no evidence of increased virulence was found in their research. The _Y. pseudotuberculosis_s outbreak strain that formed biofilm contained both a biofilm-associated gene and virulence plasmid [99]. An interesting outcome has been published by [PERSON] in 2020 [101], who had found that the same outbreak type of _Campylobacter_ isolated in two different Finnish dairy farms (milk tank) demonstrated the opposite biofilm-forming ability. Similar findings were also obtained for _E. coli_ O157:H7 outbreak strains which did not form biofilm in vitro [102]. It may suggest that there is no simple relation between an outbreak and biofilm-forming abilities. Despite this fact, the presence of biofilm in food processing environments (devices, pipeline, membranes etc.) represents a serious problem with all its financial consequences and health implications.
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 3 Cybersecurity Attacks in Wireless Sensor Networks
### Clone Node Attack (Replication Attack) in Static Wireless Sensor Networks
#### 3.3.2 Clone Node Attack Detection Techniques in Static Wireless Sensor Networks
Another factor of disadvantage is that most memory limitations are not taken into account, which impacts the performance of the network when the memory is being over-used. While reconfiguration is easy to implement, doing so requires more network resources and a high energy cost. Because of the huge amount of data generated in the network zone, the network does not support a high density of nodes. Additionally, the communication in the network is not always guaranteed since the communication can be made with a selective amount of nodes instead of the entire network; this is based on the application in question, which also determines the robustness and reliability features. When a situation of fixing the network nodes arises, it can be a hassle if the principal device (usually called central device) is broken, which means the whole network is broken, because the principal device is responsible for the recovery and fixing a failure. Advantages and Drawbacks of Distributed Techniques  
Usually one resorts to the distributed technique when there is a necessity to have and maintain redundancy and reliability of the information. It mostly happens when the application has to control a lot of information. The primary advantages of the distributed techniques can be summarized as follows:  
The information is local; that is to say, a node keeps the information of its neighbor. Since the nodes are autonomous, any solution that is to be given to a node is made according to its activities and location. Therefore, the reconfiguration is made locally only on the affected node. Another vibrant aspect is that, when a node dies, the performance of the network does is not affected, and the whole network will remain in operation. This decentralized technique, and hence distributed, is not impacted when dealing with noisy environments and obstacles. The power consumption is decreased considerably by every node; the routing starts working whether an event is discovered (detected) or there is a path to follow; this strategy signifies that there is no useless exhaustion of energy before the routing starts. There are some constraints that the distributed techniques share with the centralized ones, for example, \"the communication in the network is not always guaranteed\", because nodes only hold local information. When there is only a single base station node, and where the transmission is made by multi-hop, accessed on 14 September 2021, it can be a hassle because the network will stop. Finally, the nodes' mobility necessitates more energy.
## 3 Results
### Finite Element Analysis Results  
Considering all design models and the derived experimental results, DM1 and DM5 are the most interesting for analyzing and comparing with the numerical model to determine whether the FE model verifies the experimental results. Verification by the FE model is crucial concerning experimental testing since the influence of the quality of 3D printing on the structure's mechanical response and how much imperfection in 3D printing affects the experimentally obtained results should be assessed. The established parameterized FE model is used to analyze DM1 and DM5, assuming loading conditions across time steps that adjust the load incrementing throughout the solution. This means the curve's flow is obtained from smaller increments to better adjust tangents to the force-deflection curve at a particular point in the load path, resulting in more precise numerical charts and data. DM1 and DM5 were selected to compare since they delivered the most featured results.  
Experimental testing data showed that all specimens deformed at least 1 mm without any failure, so the numerical FE model used 1 mm in negative \(Z\) for displacement load condition as a referent point for comparing results. Figure 13 shows the results of numerical analysis in the case of model DM1. It can be seen that in the case of the FE analysis of Seed 1 (DM1-S1), the force needed for 1 mm displacement is 650 N (average experimental force-1022.66 N), and in the case of Seed 2 (DM1-S2), the force is 680 N (average experimental force-872.51). In the case of model DM5 (tessellation based on a parabolic surface distribution \(p=0.5h_{v}\)), the FE analysis (Figure 14) of the Seed 1 model (DM5-S1) showed a force of 881 N, lower than the average in experimental data (1205.98 N). In the case of the Seed 2 model (DM5-S2), the force is 770 N, lower again than the average in experimental data  
Figure 12: Force-Displacement charts including mean values of each design (DM) in the Seed 2 Design model.  
(893.63 N). The difference in Seed values between the FE-obtained forces of DM5 and DM1 is significantly less than those of experimental-obtained forces.
# Ultimate Compressive Strains and Reserves of Bearing Capacity of Short RC Columns with Basalt Fiber
## 4 Results of Testing Short Columns
### Load Bearing Capacity
An increase in the relative eccentricity of the external force in all cases led to a decrease in the columns' bearing capacity. At the same time, the degree of reduction in the bearing capacity slightly depended on the type of concrete, and was 79%. Analysis of the results of testing short column-pillars for axial compression (T-1 and TF-2, K-3 and KF-4, KK-0 and KKF-0) ograssiushowed (Table 8) that, other things being equal, specimens with combined reinforcement had peak load values on average 11-12% higher than pillars with conventional, traditional reinforcement. Simultaneously, the degree of increase in the bearing capacity of the test racks did not significantly depend on the type of concrete. To compare the experimental and theoretical values of the bearing capacity of short columns, the latter were calculated according to the norms' methodology, taking into account the actual strength of concrete and reinforcement, determined empirically. To determine the theoretical bearing capacity, the actual stress-strain diagrams for compressed concrete were used, shown in Figure 5. The calculation results showed that the discrepancy between the experimental and theoretical values of the peak load is quite large, and the maximum ratio reaches 23.0% for all tested elements. Thus, the experimental peak load in expanded clay-reinforced concrete specimens with the KKF-0 code exceeded the theoretical one, calculated according to the current standards, considering the actual characteristics of materials by 17%. In heavy concrete (samples T-1 and TF-2), the indicated difference between the peak load's experimental and theoretical values was 20 and 18%, respectively. In our opinion, the main reason for this is because the norms of the limiting compressive stresses in the reinforcement of the compressed zone are not considered, which increase as a result of the fiber reinforcement of the samples. The specimens crushing without fiber was transient: when the peak value was reached on the force meter, a rapid decrease in the bearing capacity in the specimens was observed without a significant increase in the ultimate longitudinal strains. In specimens with fiber, especially from constructive expanded clay BFRC, the picture of crushing has changed dramatically. In particular, when the peak load in expanded clay BFRC samples with the code KF-4, KKF-0, KKF-0.15 was reached, the process of destruction seemed to stretch over time: the load on the force meter remained practically unchanged (decreasing or increasing within 1.5-2%), while reinforcement strains increased at a tremendous rate. The absolute values of strains in such a prolonged stage of fracture were approximately 2-2.5 times greater than the strains recorded at the moment of reaching the peak load.
## 3 Data Collection to Cloud: Automation and Real-Time Processing
### Data Transmission: Edge Computing and Fog Computing
#### 3.2.2 Challenges and Solutions in Real-Time Data Transmission for IoT-Based Irrigation  
Real-time data transmission in automated irrigation management systems faces significant challenges due to environmental factors, technical limitations, and data security and privacy concerns. Agricultural environments are particularly challenging due to adverse weather conditions such as heavy rain, fog, and strong winds, which can attenuate or block radio signals, leading to data loss [84]. Dense vegetation and uneven terrain further complicate signal propagation [85]. Additionally, the limited range of traditional wireless technologies and network congestion caused by numerous sensors transmitting concurrently can lead to delays and data loss, hindering real-time decision-making.  
To address these challenges, researchers have explored various advanced networking technologies. Cognitive radio networks (CRNs) and dynamic spectrum access (DSA) are promising solutions for optimizing spectrum utilization and reducing interference. CRNs enable devices to sense and adapt to the surrounding radio environment, dynamically adjusting transmission parameters to avoid interference [86]. DSA complements CRN technology by dynamically allocating unused spectrum, enhancing spectrum utilization, and reducing congestion. Integrating CRNs and DSA into the IoT network architecture requires careful consideration of spectrum sensing techniques, network topology, and data security [86].  
Furthermore, implementing robust and adaptive communication protocols such as LoRa or ZigBee with suitable range and penetration capabilities is crucial for overcoming the interference and signal degradation caused by weather conditions and physical obstacles. Techniques like frequency hopping and error correction codes improve communication resilience and mitigate data loss. Optimizing network architecture, including deploying a distributed network with edge and fog computing capabilities, enhances data processing and transmission efficiency. Data optimization techniques such as compression, aggregation, and filtering are also vital in improving transmission efficiency.  
Lastly, data security and privacy are paramount concerns, necessitating robust security measures to prevent unauthorized access and data breaches [66]. Effectively addressing these challenges requires a multifaceted approach that combines advanced networking technologies, robust communication protocols, and comprehensive security measures.
## 3 Results and Discussion
### Evaluation of \(S_{\text{eff}}\) and Cloud Droplet Properties
This could be caused by the seasonality of prevailing winds in East Asia: winds come from the Pacific under the Pacific high-pressure system during summer, but in other seasons, winds come mostly from the Asia continent. It is considered that the period (summer) in which we conducted our observations was the season with the lowest \(N_{\rm CD}\) of the year in the region. The normalized size distributions of \(N_{\rm CD}\) were illustrated in Figure 9. The shapes of size distributions resembled each other, but the CDs larger than 20 \(\upmu\)m were more in the Pacific airmass. The calculated \(R_{\rm eff}\) were 6.5 and 9.3 \(\upmu\)m, respectively, in the Continental  
Figure 8: Relationship between \(N_{\rm CD}\) and \(N_{\rm CCN}\) under 0.21% SS. \begin{table}
\begin{tabular}{c c c c c} \hline  & & & **Air Mass Origin** & \\  & **Entire Period** & **Continental** & **Pacific** & **Others** \\ \hline \(SS_{\rm eff}\) **(\%)** & 0.34 (0.34) & 0.24 (0.14) & 0.44 (0.41) & 0.26 (0.26) \\ \(w\) **(m s\({}^{-1}\))** & 1.29 (1.21) & 1.03 (1.32) & 1.40 (0.83) & 1.37 (1.63) \\ LWC **(g m\({}^{-3}\))** & 0.05 (0.05) & 0.08 (0.06) & 0.03 (0.02) & 0.06 (0.05) \\ \(N_{\rm CD}\) **(cm\({}^{-3}\))** & 78 (78) & 144 (92) & 36 (32) & 86 (65) \\ \(R_{\rm eff}\) **(\(\upmu\)m)** & 8.1 (2.5) & 6.5 (1.7) & 9.3 (2.5) & 7.6 (2.1) \\ \hline \end{tabular}
\end{table} 
Table 3: Mean values of \(SS_{\rm eff}\), \(w\), \(N_{\rm CD}\), _LWC_ and \(R_{\rm eff}\) for clouds at the MFRS classified according to air mass origin. Standard deviations are indicated in parentheses. and Pacific airmasses. If the water content is constant in the atmosphere, \(R_{\text{eff}}\) of cloud particles decreases as \(N_{\text{CD}}\) increases. This results in a change of the optical properties of the cloud and is known as the Twomey effect [43]. In this study, _LWC_ of Continental airmasses is larger than that of Pacific airmass. This could be due to the higher \(N_{\text{CD}}\) of Continental air masses, because _LWC_ was a function of both number and size. Although the values of LWC differed, the inversely proportional relationship between \(N_{\text{CD}}\) and \(R_{\text{eff}}\) was still observed between Continental and Pacific airmasses.
## 4 Weighting Factor Estimation Method from Scheduling Results
### Feature Extraction
#### 4.2.2 Large Scale Problems with 2 Objectives Optimization Problems  
Then, the features for large-scale problems are proposed. We try to extract effective features when the objective function is different. The sum of delivery delay and the sum of setup costs is used as the objective function. The objective function of the scheduling problem is expressed in Equation (5).  
\[\min_{\{\delta_{jk}\}}\quad w_{1}\sum_{k=1}^{M}\sum_{i=1}^{N}L_{ik}+w_{2}\sum _{k=1}^{M}\sum_{i=1}^{N}\delta_{ijk}S_{ij} \tag{5}\]  
where \(L_{ik}\) represent a delay in delivery of job \(i\) at machine \(k\). Since the objective function has changed from small-scale problems, it is necessary to change the features used. Features that are not related to the weighting factor do not contribute to the improvement of estimation accuracy, so it is not preferable to use the same features. We considered the following features: 5. The values of the sum of delivery delay, 2. The values of the sum of setup costs, 6. The variance of delivery time setting for each machine, 7. The sum of completion time of each machine, and 8. [PERSON]'s rank correlation coefficients (\(\tau_{s,i}\) and \(\tau_{d,i}\)). For the problem of minimizing the sum of delivery delays in the case of one machine, the optimal schedule can be obtained by processing in the order of earliest delivery time. It is effective even in the case of multiple machines. In that case, there is no difference in the delivery date set by each machine. The variance of delivery time setting for each machine \(\sigma_{d}^{2}\) is represented by Equation (6).  
\[\sigma_{d}^{2}=\frac{1}{M}\sum_{k=1}^{M}\left(D_{k}-\overline{D}\right)^{2} \tag{6}\]  
where \(D_{k}\) represents total delivery time of job processed of machine \(k\), \(\overline{D}\) represents mean of the delivery time of job processed by each machine. If the sum of delivery delay is minimized, the job needs to be completed in less time. It is considered that the sum of delivery delay and the total completion time are related. The sum of completion time of each machine \(U\) is represented by Equation (7).  
\[U=\sum_{k=1}^{M}\sum_{i=1}^{N}y_{ik}p_{ik} \tag{7}\]  
where \(y_{ik}\) represents 1 when job \(i\) is processed at machine \(k\), 0 otherwise, \(p_{i,k}\) represents processing time at job \(i\) at machine \(k\). \(\tau_{d,i}\) represents delivery time order of job \(i\) in ascending order. Since the objective function has changed from the maximum completion time to the sum of delivery delays, a rank correlation is generated from the processing sequence of the job and the job delivery time order. It is represented by Equation (4). \(X\) is \(\tau_{s,i}\), \(Y\) is \(\tau_{d,i}\).
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 3 Consideration of Local Scour
### Scour Characteristics  
#### 3.2.1 Range of Scour  
The surveying range is 35 m in the center radius of the monopole. In the center radius of 17 m\(\sim\)35 m, the topographic fluctuation changes are very small (\(<\)1 m), and the average altitude is very close to the original designed mudline altitude (\(<\)0.1 m). The erosion or deposition are mainly concentrated in the range of 17 m from the center radius of monopile foundations, about 3D. The volume varies between \(-\)4307 m\({}^{3}\) and 199 m\({}^{3}\), and the average value is \(-\)1983 m\({}^{3}\). Except for the positive values of pile 7# and 65#, that is, deposition, the value of other monopile foundations is negative, that is, erosion. Figure 9a is the normal probability plot of erosion and deposition volume.  
Figure 8: Investigation equipment: (**a**) POSMV positioning system; (**b**) SeaBat T50-P multibeam bathymetric system.  
According to the scour depth obtained from 68 monopiles, the minimum is 2.24 m (0.41D), the maximum is 9.18 m (1.67D), and the average is 5.67 m (1.03D). The scouring depth is mainly concentrated in the range of 4 m\(\sim\)7 m, accounting for about 70% of the total number of monopiles. Figure 9b shows the normal probability of the distribution of maximum scour depth.
# Laser Technology in Photonic Applications for Space
## 3 Lasers in Space  
### Science  
Although recent applications have been focused towards industrial usage of space technologies, several under way deployments are still catering to scientific purposes, aiming at increasing the knowledge regarding the history of the universe and the discovery of traces of potential life. In the following section, we will be addressing scientific objectives, before discussing more pragmatic matters, such as industrial application of space technologies.