esa-sceva/satcom-chunk-collection huggingface dataset:
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 2 Materials and Methods
### Methods
#### 2.3.3 Partial Correlation Analysis  
Pearson correlation analysis (R) is widely used to measure the degree of correlation between two variables, with a range between \(-1\) and \(1\). This study used Pearson correlation analysis to measure the correlation between vegetation and drought pixel by pixel. The calculation formula is as follows:  
\[R_{(x,y)}=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt {\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{ y})^{2}}} \tag{7}\]  
where \(n\) is the length of the time series, \(x_{i}\) is the NDVI or SIF standardized anomaly at the pixel, \(y_{i}\) is the multi-scale SPEI at the pixel, \(\overline{x}\) and \(\overline{y}\) are the average values of the corresponding research object from 2001 to 2020. \(R_{(x,y)}\) is used to quantify the sensitivity of vegetation to drought. The greater the \(R_{(x,y)}\), the higher the sensitivity, and vice versa.
## 3 Decentralized Treatment Systems
### Rain and Stormwater Harvesting and Treatment
The combination with an additional treatment reducing the COD can make the collected water suitable for multiple domestic, non-drinkable purposes [151]. Urban stormwater is generally of low quality, carrying sediments, chemicals, bacteria, viruses and other pollutants. Interesting information that could aid in the correct adoption of stormwater reuse schemes can be found in the paper by [PERSON] et al. that reports the results of a fortnightly sampling campaign performed over 1 year to characterize the water quality in two stormwater harvesting system tanks from two multiplex units located in a high-income neighborhood in Rio de Janeiro. The total coliforms, _E. coli_, enteric viruses (HAdV, JCPyV, RVA) and norovirus GI and GII were quantified in the harvested water. The 93.8% of samples containing viruses were compliant as per fecal indicator bacteria (FIB) according to Brazilian standards for rainwater reuse and US EPA Guidelines for Water Reuse, suggesting that viruses or specific pathogen monitoring should complement the study of bacterial indicators to assess the actual microbiological quality of alternative water sources, such as stormwater and harvested rainwater [152]. The modular bioretention tree system (MBT) is a treatment device that consists of a tree planted in soil media in a compact module that can be applied to manage polluted stormwater in a highly urbanized tropical environment. The performance of an MBT was monitored both on pilot and field scale to obtain key insights for the pollutant removal capabilities by employing 1 m depth engineered soil, _Talipariti tiliaceum_ and _Sterculia macrophylla_ trees in the pilot test and _Talipariti tiliaceum_ for the field test. The field study, lasting 15 months, produced a very clean effluent quality, with average TSS, TP and TN effluent EMC of 4.8 mg/L, 0.04 mg/L and 0.27 mg/L, respectively. Both MBTs could effectively remove total suspended solids (TSSs), total phosphorus (TP), zinc, copper, cadmium and lead, with removal efficiencies greater than 90%. The performance can vary according to the \"wet\" or \"dry\" period [153].
# Low Delay Inter-Packet Coding in Vehicular Networks
## 1 Introduction
### Contributions  
The new contributions of the paper are the following:  
* New low-complexity low-delay decoding algorithm for erasure correction by the Wyner-Ash code applied in V2R scenario.
* Erasure-correcting performance analysis for Wyner-Ash and Reed-Solomon convolutional codes.
* Comparative analysis of suggested codes and decoding algorithms for: (i) memoryless channels; (ii) channels with memory described by Gilbert-Elliott model; and (iii) real-life VANET provided by AstaZero facility.
# Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load
## 2 Materials and Methods
### Methods
#### 2.3.3 Material Modeling Through RVE and TPMS  
In the virtual validation stage, the Material Designer module from the ANSYS suite was employed to homogenize the material and obtain its effective properties. These properties were subsequently used in a numerical vibration simulation, whose results were compared with both experimental data and the analytical predictions (Figure 7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Equation** & **W** \\ \hline Volume & \(V=L\cdot W\cdot H\) & (6) \\ Total mass & \(M_{Total}=\rho_{AL}\cdot V\) & (7) \\ Foam mass & \(M_{Foam}=M_{Total}\cdot(1-F_{Air})\) & (8) \\ Foam density & \(\rho_{Foam}=\rho_{AL}\cdot(1-F_{Air})+(F_{Air}\cdot\rho_{air})\) & (9) \\ Foam elastic modulus & \(E_{Foam}=E_{AL}\cdot(\frac{\rho_{Foam}}{\rho_{AL}})^{2}\) & (10) \\ Moment of inertia & \(I_{z}=\frac{W\cdot H}{12}\) & (11) \\ Resonance frequency & \(f_{n}=\frac{18\sqrt{2}\cdot 9}{2\cdot\pi}\cdot\sqrt{\frac{E_{Foam}\cdot I_{z}}{ \rho_{Foam}\cdot W\cdot H\cdot L^{2}}}\) & (12) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of analytical equations.  
Figure 6: Cantilever beam.  
Figure 7: Material Designer schematization.  
In the case of the investigated material, two distinct methods were employed:  
A. The Random Particle Method--as shown in Figure 8, where the first step involved selecting the constituent materials (aluminum and air), and the second step included defining parameters such as  
* Particle volume fraction (obtained from CT data);
* Particle diameter distribution;
* Mean particle diameter;
* Standard deviation of particle diameter;
* Particle wall thickness;
* Size ratio (defined as the ratio between the particle diameter and the side length of the representative cube).  
B. TPMS Method: The second modeling approach employed the TPMS (Triply Periodic Minimal Surface) function. As with the previous method, the final model included the same two constituent materials: aluminum and air. However, in this case, the modeling parameters focused on the gyroid surface type and the solid volume fraction. This fraction represents the proportion of solid material within the representative cube--specifically, in a 10 mm \(\times\) 10 mm \(\times\) 10 mm cube, 29% of the volume consists of solid material, while the remaining 71% is void (air). The size parameter previously discussed was also applied in this configuration (Figure 9).
# Simulation-Based Evaluation of V2X System with Variable Computational Infrastructure
## 3 Materials and Methods  
The primary components of a V2X system are the onboard unit and the roadside unit. The OBU is a device installed in a vehicle that is responsible for communication with other vehicles (V2V, vehicle-to-vehicle) and infrastructure elements (V2I, vehicle-to-infrastructure). The OBU collects and transmits data necessary for enhancing road safety, informing the driver, and supporting various applications such as navigation and collision warning systems. The RSU is a stationary device installed along roads or on other infrastructure elements. RSUs facilitate communication with OBUs (V2I) and among themselves (I2I, infrastructure-to-infrastructure). The primary function of an RSU is to collect, process, and transmit data received from vehicles, as well as provide access to information necessary for the correct operation of transportation applications and the improvement in traffic coordination. Mobile RSUs are increasingly utilized to organize communication between OBUs and RSUs. Their purpose is to move across the map and address tasks directly near the user, i.e., the OBU.  
Furthermore, different variations may include Edge Computing (EC). EC servers provide computational resources at the network edge, enabling data processing closer to its source, i.e., the vehicles. EC servers allow for real-time processing of data received from OBUs, minimizing latency and ensuring faster responses to events. EC can interact with both OBUs and RSUs, providing additional computational power and data storage capabilities. Within this task's framework, EC that any RSU can access with a guaranteed latency for data synchronization. Figure 2 illustrates the interaction of V2X system elements.  
When simulating a system involving vehicles, roads or lanes are required. For simplification, we assume all roads have one main lane and one opposite lane. The set of road connections can be interpreted as a graph. By mapping the graph onto a graphical interface, we obtain a map of the terrain where the vehicles are moving. For model simplification,  
Figure 2: Interaction of V2X system elements.  
intersections will not be further burdened with traffic lights or pedestrian crossings. Therefore, it is necessary to create the following components: RSU, OBU (vehicle), and road (lane).  
The well-established mathematical framework detailed in [43] can be employed to organize vehicle movement across the created map. Using a greedy algorithm [44] for appropriate device selection has proven effective, but it has drawbacks in high-load networks. In such cases, we utilize the Hungarian algorithm, which solves the assignment problem in polynomial time.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 5 ALOHA Software, Strengths, Limitations, and Need for Improvements
### Opportunities for Technological Enhancements  
The opportunities for improving ALOHA lie primarily in integrating it with modern technologies, such as AI, GIS, and drones, to enhance its real-time capabilities. Drones, for example, could collect atmospheric and environmental data from an affected area, feeding these data into ALOHA to refine predictions on the spread of hazardous chemicals. The use of Artificial Intelligence (AI) would further improve ALOHA's ability to process large volumes of data and adjust simulations dynamically. The study by [PERSON] et al. (2024) emphasised that integrating ALOHA with real-time atmospheric data would significantly improve its predictive power and response accuracy during emergencies [20; 25].  
Furthermore, the research by [PERSON] et al. (2021) supports the idea of integrating AI and GIS systems into ALOHA to enhance its accuracy in complex terrain, particularly where diverse geographical factors could influence the spread of hazardous materials. Such integration would address ALOHA's current limitations in dynamic real-time hazard modelling, providing a more adaptive approach to emergency management [17; 26].  
Finally, combining ALOHA with tools such as PHAST, which excels in quantitative risk assessments (QRA), could improve the overall framework for long-term risk analysis. ALOHA's quick, real-time predictions could be complemented by PHAST's ability to analyse chemical behaviours and scenario planning, creating a more comprehensive system for managing industrial hazards over both short- and long-term periods [22; 27].
# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
## 2 Materials and Methods
### Data and Statistical Analysis
Boosted regression analysis was carried out to analyze factors (lake area, shore length and development, total lake volume, lake average depth, water residence time, lake elevation, watershed area, longitude, latitude, ratio catchment area/lake area, mean ratio BA/A, ratio BA/A level 0-4, ratio total BA/A, maximum BA, annual SPI mean, min, and max, total annual rainfall) determining the timing of the first peak and maximum peak of Chl-a and turbidity after a fire [47]. Analysis was carried out on 75% of the data with 25% used for model evaluation by calculating the Root Mean Square Error (RMSE). Analysis was carried out in R package 'gbm' [48]. To determine short-term effects of fire on lake Chl-a and turbidity, we compared mean concentrations over a six-month period after a maximum fire occurrence with mean concentrations over a six-month period either without or with smaller fires using R (package dplyr [49]). Multinomial logistic regression with \"nnet\" R package [50] was performed in order to investigate the role of geographical factors (i.e., altitude, latitude), land cover types, maximum fire, and total annual rainfall in determining changes in water quality (i.e., Chl-a and turbidity). Finally, lake responses to typological factors and in-lake interactions were analyzed using a graphical analysis [51] which allows for a comparison of the timeseries data of different parameters: fire occurrence and intensity (BA), an indicator of precipitation/drought (SPI), lake surface water temperature (LSWT), and lake water quality parameters such as Chl-a concentration (smoothed) and turbidity concentration (smoothed). These timeseries graphs allow for the investigation of the temporal succession of events in a lake catchment and the identification of lake case studies to support statistical analysis results.
## 4 Experimental Results
### Integrated Detection Algorithm
#### 4.2.3 Effectiveness of IDA Detection Algorithm against Other Attacks  
The CP attack is a target attack. However, due to the large-scale change it produces in the targeted dataset, it is used here to illustrate the performance of the IDA detection algorithm in detecting \"other forms of attack\" (see Section 3.2.2). The experiments considered 50 CP attacks, 25,000 Gaussian attacks, and 50,000 normal data items. As described in Section 3.2.2, the IDA algorithm detects \"other attacks\" based on an inspection of the reconstruction error. Table 9 shows the mean and standard deviation values of the reconstruction error for the present detection experiments. It is apparent that the CP attack produces a discernible mean reconstruction error between the normal data and the abnormal data. Table 10 shows the corresponding detection results obtained when using a reconstruction error threshold value of 2.5. It is evident that the IDA algorithm achieves a good detection performance for both attacks, i.e., CP and Gaussian attacks.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 1 Introduction
### Security Requirements for Smart Cards  
Authentication, non-repudiation, confidentiality, and integrity are the fundamental security properties that must be satisfied by any system. However, by studying [PERSON] [3] and [PERSON] et al. [4], it can be figured out that in addition to these four fundamental security properties, forward security, along with availability, must also be included in the implementation in order to secure the smart cards. The security attributes satisfied by smart card-based systems are briefly described below:  
* Confidentiality--This property ensures that the information or data transmitted and received from the smart card are in an incomprehensible form, so that an unauthorized party cannot understand it.
* Authentication--The smart card must authenticate the other party before sending any message or information to it.
* Non-repudiation--The smart card and the other legitimate party cannot repudiate after sending or receiving the transmitted message.
* Integrity--The data sent or received by the smart card must not be rehabilitated in transit.
* Availability--The smart card must be capable to produce the required data as and when needed by the user.
* Forward Security--This attribute ensures that an adversary is not be able to obtain the past messages, even if they are in the possession of a secret session key for the long term.
# Biological and Technical Challenges Related to Cultured Meat Production
## 4 Technical Challenges in Cultured Meat Production
### Alternatives to Scaffold Fabrication
#### 4.2.2 Microcarrier Cultures  
Microcarriers are beads with typical dimensions between 100 and 200 \(\upmu\)m, and they represent a possible solution because mammalian cells require a surface on which they can grow [17]. For tissue engineering applications, especially for food products, microcarrier-based systems are retained as the major culturing system to achieve a high volume of cells because they can provide a large surface area per unit volume of medium [88]. [PERSON] et al. proposed a myoblast cell production system based on microcarriers suspended within cells and medium inside a stirred tank bioreactor, achieving promising results in terms of cell growth for efficient and cost-effective development of cultured meat [88]. [PERSON] et al. provided three scenarios based on cultures with microcarriers: temporary microcarriers culture for proliferation, non-edible but degradable microcarriers, and edible microcarriers embedded in the final product. According to the author, the third is the most promising for cultured meat production [89]. Beyond the great possibilities, the main drawback of the use of microcarriers or aggregates is that the cells may form clusters that do not proliferate in the correct manner, because of which, if not modified, the cell proliferation phase would be difficult to control [17].
# Stability Improvement Method for Embankment Dam with Respect to Conduit Cracks
## 3 Results and Discussion
### Pore Water Pressure Distribution: Model Experiment  
The pore water pressure gauge was located at a height of 20 cm from the foundation, and when the measurement started, the model embankment was saturated. Therefore, this measurement result reveals a condition in which negative pore water pressure may occur,  
Figure 5: Crack conditions in the improved model (Case 2—Crack): (**a**) modest loss of soil particles in the conduit, (**b**) sand and geotextile layers in the downstream slope, and (**c**) minimal soil loss near the conduit-filling zone.  
Figure 6: Reinforced conditions of improvement model (Case 2—Reinforcement): (**a**) zero loss of soil particles due to reinforced geomembrane, (**b**) reduced wet area in the downstream slope, and (**c**) stability against erosion in the filling zone near the conduit.  
depending on the fluctuation condition of the water level. Figure 7 shows the change in pore water pressure in the current model and the improvement model when a crack occurs in the conduit on the downstream slope.  
In Case 1--Crack, the upstream pore water pressure (P1) shows a continuous decrease, which indicates that the crack in the conduit was affected by leakage. The change in the downstream pore water pressure (P3) was insignificant because the leak from the conduit crack escaped to the lower part of the point where the pore water pressure gauge was installed, and the effect of the water pressure was not observed. This trend provided the same measurement results in all cases. In addition, the water pressure change was small at the center pore water pressure (P2). The results show that leakage was extensive in the downstream conduit cracks, and thus, the seepage water did not reach the pore pressure gauge located in the center of the embankment zone. The embankment cross-section with these conditions suggests that the risk of internal erosion around the downstream conduit crack area may increase because the seepage water proceeds at the conduit crack area.  
In Case 2--Crack, the pore water pressure trends on both the upstream side (P1) and downstream side (P3) were similar to those of Case 1--Crack, but only the pore water pressure (P2) at the center side increased. The increase in the central pore water pressure (P2) indicates that water leakage is induced by the sand layer and geotextile installed on the downstream slope.  
These results indicate that the sand layer and geotextile disperse the water pressure acting intensively on the conduit crack area and show that these additional layers are effective in reducing the risk of internal erosion by delaying the leakage's velocity.
## 3 Results and Discussion
### The LOI of the PET/CF Composite Boards  
For the PET/CF composite boards, the LOI decreases as a result of the addition of the CF woven fabrics. FR matrices are flame-retardant as their LOI is 30. However, the PET/CF composite boards have a LOI of 26. During the test, the composite boards can be set alight, but do not exhibit shrinkage or deformation, and the fire does not last once the samples are no longer in contact with flames. Theoretically, CF woven fabrics that are composed of carbon filaments cannot catch fire. However, the filaments can cause abrasion to the machine and thus are coated with an oiling agent before being fabricated into woven fabrics in order to decrease the friction force between carbon fiber and the machine. During the combustion process, the outer layers of the FR matrices are the first point that the flame contacts, and they are melted into liquid that covers the contact surface between them and the flame, as indicated in Figure 4a. The melt of the FR matrices then puts out the fire and stops the combustion. However, when the flame is continuously present and the flame temperature increases, the residual oil of the CF woven fabric is a flammable substance, and FR matrices thus cannot completely extinguish the combustion and form a carbonized layer over the PET/CF composite boards. This carbonized layer raises the temperature of the second ignition point, thereby preventing the sample from burning again and preserving the morphology of the PET/CF composite boards.