esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process
### Commercially Available Adsorbents  
#### 6.9.1 Graphene  
Due to the large surface area, improved active sites and functional groups nanomaterials are effective adsorbents for removing heavy metals from wastewater. Graphene is a two-dimensional carbon-based nanomaterial with a large specific surface area and excellent chemical stability. Due to its relatively large specific area, rich functional groups, and outstanding mechanical strength, graphene oxide (GO), one of the most notable graphene derivatives, has been shown to be a promising material for the preconcentration of heavy metal ions [246] GO, in particular, has a significant advantage in removing heavy metal ions from aqueous solutions, including lead (Pb (II)) [247], copper (Cu (II)) [248], cobalt (Co (II)) [249], cadmium (Cd (II)) [250], chromium [251]. [PERSON] et al. used chitosan-gelatin and GO to create ordered porous composites for Cu (II) and Pb (II) adsorption, and the composite had a very high ability to adsorb both metal ions [252]. Adsorption of high amounts of Pb (II) and Cd (II) onto GO was attributed to the GO's higher surface area and oxygen functions [253]. Whereas, the adsorption of Cu (II) on GO was studied by [PERSON] et al. and fit the experimental results to a Freundlich model with a maximum adsorption capacity of 117.5 mg/g for Cu (II). The adsorption of Cu (II) on GO was attributed to complexation, ion exchange, and electrostatic attraction [254].  
[PERSON] et al. investigated the use of graphene as an adsorbent for the removal of Sb (III) in aqueous solutions. Batch adsorption studies were carried out to investigate the impacts of operating conditions on Sb (III) adsorption, such as starting concentration, pH, and temperature. The Freundlich isotherm model appears to be followed rather than the Langmuir isotherm model. The adsorption capacity of graphene for Sb (III) was determined to be 10.92 mg/g under optimal circumstances [255]. [PERSON] et al. investigated the Cu (II) and Pb (II) adsorption mechanisms on a graphene-MnO\({}_{2}\) nanosheet. Adsorption data fit a Langmuir isotherm model well, indicating monolayer adsorption. With a 3 h equilibration time, the optimal adsorption capacities for Cu (II) and Pb (II) ions were found to be 1620 and 781 mol/g, respectively. The data were found to follow a pseudo-second-order kinetic model, indicating a chemisorption process [256]. [PERSON] et al. used SiO\({}_{2}\)-graphene to study the adsorption of Pb (II) ions. The greatest percentage removal was 98.82% at pH 6 with a contact period of 30 min, and the optimal adsorption capacity was found to be 113.6 mg/g at 25 \({}^{\circ}\)C. The kinetics of adsorption followed a pseudo-second-order model. The Langmuir isotherm model fit the adsorption equilibrium data nicely [257]. [PERSON] et al. investigated the ability of GO to remove Cu (II) ions from aqueous solutions by adsorption. Cu adsorption capability was found to be 117.5 mg/g in the GO (II) [258].
# Improving the Energy Efficiency of the Modular Buildings
## 4 Results and Discussion
### Drying Room Setup in Wincham
#### 4.3.2 Effect of Drying Temperature on Drying Time  
Figure 20 shows the effect of different combinations of a heater, air mover, and dehumidifier on moisture content in wet garments in function of time. In all four tests, garment weight was measured every hour during the drying process and converted into moisture content using Equation (1). In Figure 20, temperature has an increasing trend in all cases, and the higher the temperature, the lower the moisture content becomes. However, it is worth noting that within a mere 2 h timeframe, the temperature surpassed 35 degrees in the last scenario featuring the operation of a heater, air mover, and dehumidifier. In contrast, in Figure 20a,c, even with the heater activated, the temperature took approximately 6 h to reach similar levels. This disparity underscores the pivotal role of air circulation in effectively distributing hot air throughout the room, thereby expediting the drying of garments. Furthermore, it is noteworthy that despite reaching 30 \({}^{\circ}\)C in the case of Figure 20a where only the heater was active, the moisture content remained notably high in all the tested garments. Contrastingly, with the introduction of an air mover in Figure 20c, the moisture content was lower compared to Figure 20a, underscoring the significant impact of enhanced air circulation in accelerating the evaporation process. The addition of a dehumidifier alongside the heater and air mover further amplified the drying process, yielding commendable results within just two hours. This multifaceted analysis underscores the crucial role of combining temperature, air circulation, and humidity control in optimising the efficiency of the drying system instead of relying only on heat by raising the temperature of the drying environment as is the case for the current drying room, which is the major aim of this research.  
Therefore, at the same temperature and a higher air velocity induced by the circulation fan, the moisture content in wet garments is lower than in the case of the heater only, and the moisture content is reduced in a shorter time.  
Figure 20: Moisture content in garments tested and temperature variation in four tests: (**a**) heater ON, (**b**) dehumidifier ON and air mover ON, (**c**) heater and air mover ON, (**d**) dehumidifier, air mover, and heater ON.
## 4 Results and Discussion
### Principal Component Analysis (PCA)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Latent** & **Observed Variable** & **Variable** & **Loading** & **Variance** & **Cronbach’s** \\
**Variable** & **Coder** & **Factor** & **Explaned** & **Alpha** \\ \hline \multirow{7}{*}{Risk Perception} & Frequently changing lane to overtake the vehicle in front. & Y1 & 0.577 & & \\  & Speeding up and suddenly braking. & Y2 & & & \\  & Exceeding speed limit even feeling unsafe. & Y3 & 0.79 & & \\  & Riding fast on the curve. & Y4 & & & \\  & Continine riding although feeling sleepy. & Y5 & 0.829 & & \\  & Taking alcohol before riding. & Y6 & & 60.13\% & 0.945 \\  & Run a red light. & Y7 & 0.864 & & \\  & Racing with other vehicles. & Y8 & & & \\  & Overtaking/turning without using signal lights. & Y11 & 0.869 & & \\  & Riding without wearing crash helmet. & Y12 & & & \\  & Using mobile phone while riding. & Y14 & 0.900 & & \\ \hline \multirow{7}{*}{Positive Affect} & Taking alcohol before riding. & Z6 & 0.574 & & \\  & Run a red light. & Z7 & 0.509 & & \\ \cline{1-1}  & Overtaking/turning without using signal lights. & Z11 & 0.698 & 41.40\% & 0.889 \\ \cline{1-1}  & Riding without wearing crash helmet. & Z12 & 0.781 & & \\ \cline{1-1}  & Crossing a stop-junction without fully stopping. & Z13 & 0.693 & & \\ \cline{1-1}  & Using mobile phone while riding. & Z14 & 0.654 & & \\ \hline \multirow{7}{*}{Risk Behavior} & Frequently changing lane to overtake the vehicle in front. & X1 & 0.580 & & \\ \cline{1-1}  & Exceeding speed limit even feeling unsafe. & X3 & 0.653 & & \\ \cline{1-1}  & Riding fast on the curve. & X4 & 0.669 & 31.49\% & 0.833 \\ \cline{1-1}  & Racing with other vehicles. & X8 & 0.651 & & \\ \cline{1-1}  & Using mobile phone while riding. & X14 & 0.556 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variables and reliability analysis of latent and observed variables.
## 3 Environment Characterization  
Environment characterization is a critical step in AMI applications, as it ensures that a given communication technology is optimized under specific conditions of the environment, providing reliable and accurate data for smart metering and energy management. Communication between the SM and DC covers a very large geographical area, with its characteristics strongly affecting the transmitted signal for wired and wireless technologies. In fact, dense obstructions, such as buildings, floors, walls, etc., or new constructions or tree growth heavily attenuate wireless signals. However, the PLC signal depends only on the electric network topology and any modification or extension of such network may damage the existing communication. Therefore, to efficiently extend the environment limitation and transmit data to the destination through two different characteristics of communication technologies, wired and wireless, the authors classify areas based on two criteria: the density of loads and the density of obstructions. This section defines the three environments, namely rural, urban, and industrial.
## 5 Experiments
### Results and Discussion
#### 5.2.3 Comparison with Benchmark Beamforming Methods  
To further illustrate the superiority of the proposed method in generating broadband frequency-invariant beampatterns, we compared the proposed method with traditional beamforming methods. We selected two classical beamformers as our benchmark methods, namely the Delay and Sum (DS) method, which is derived by maximizing the WNG [7], and the Minimum Variance Distortionless Response (MVDR) method, which aims to minimize the output power of the array while maintaining a distortionless response in the desired direction [33]. The MVDR method requires a regularization parameter during the solving process. We set the regularization parameter to 0.01, which is consistent with the regularization parameter added in the proposed method. Figure 13 gives the measured broadband radiation patterns of the benchmark methods. Comparing Figure 13 with Figure 12d, it can be found that the main lobe of the beampattern generated by the DAS and MVDR methods becomes narrower as the frequency increases. Meanwhile, the main lobe of the beampattern synthesized by the proposed method remains constant throughout the entire evaluation frequency range, even at high frequencies.  
Figure 12: Comparisons of the measured beampattern generated by MM-JAD and the proposed method with the ideal beampattern at (**a**) 250 Hz, (**b**) 500 Hz, (**c**) 750 Hz, (**d**) 1 kHz.
## 4 Challenges
### Link Quality  
WSNs often operate in unattended and open environments, where the quality of the radio transmission can be affected by factors such as weather conditions. If the quality of the link between the evaluating node and the evaluated node is poor or unstable, packet loss may occur. This can result in calculating a low trust value for a non-malicious node. Assessing link quality ahead of trust value calculations may address the issue as the trust evaluation can be put on hold if the link quality is compromised and reattempted later when the link becomes stable and reliable again [14; 18; 28].  
In wireless networks, the link quality indicator (LQI) is used as a measure of communication link quality. In the case of the most widely used radiofrequency, CC2420, the LQI value is embedded in the received data packet; it varies from 0 to 255. An active node assesses the quality of a link between itself and a target node, gathering the LQI for a period of time and calculating the average. The average is compared to the threshold, e.g., 220, to determine if the link is stable or not [14].  
However, using the LQI in trust evaluation models may not be reliable due to the assumption of uniform network conditions. In environments where natural conditions may weaken the radio signal, this may lead to assigning low trust values to affected nodes that are otherwise trustworthy. Second, the continuous monitoring of link quality indicators is a significant energy overhead. For example, [PERSON] et al. [18] and [PERSON] et al. [28] used a triangle model for link quality assessment based on the packet received rate (PRR), the signal-to-noise ratio (SNR), and the LQI for assessing the quality of the link between two nodes: A and B. As the model was designed for underwater environments in which signal attenuation is high, it may not be generalizable to terrestrial WSNs. Second, the SNR and PRR calculations rely on real-time measurements, which may be delayed or may be prone to errors in noisy environments.  
Further research may consider incorporating adaptive thresholds for the LQI or SNR based on historical data or on environmental factors to reduce reliance on static thresholds and to enhance model adaptability. The continuous monitoring of links could be replaced with periodic sampling to improve energy efficiency.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Silver-Based NPs
#### 4.1.1 Impact on Plant Growth  
Beneficial effect of AgNP seed germination characteristics of _Thymus kotschyanus_ was reported by [PERSON] et al. [179]. Moreover, treatment with 40 mg/kg AgNPs during the germination process effectively improved germination and growth of _Ocimum basilicum_ L. plants and their resistance to salinity stress [180]. By application of 0.2 mg AgNPs (8-21 nm) per fenugreek seedling, the root and shoot length of seedlings was approximately doubled and wet weight of treated plants was even 3-fold higher compared to the control [115]. Increasing concentrations of AgNPs (27.5 \(\pm\) 4.8 nm) up to 10 mg/L showed beneficial impact on growth and development of _Lavandula angusifolia_ propagated in vitro, which was reflected in improved formation of shoots, increased plant weight, and longer roots compared to control. Increasing AgNPs concentrations reduced the number of secretory trichomes; the greatest diameter of trichomes on the adaal surface of the leaf blade was observed at exposure to 2 mg/L AgNPs and the smallest one at 5 mg/L AgNPs. The largest trichome diameter on the abaxial surface was formed at treatment with 1 mg/L and the lowest one using 5 mg/L AgNPs [181].  
Spraying of borago (_Borago officinalis_ L.) plants at the seed growth stage with AgNPs using doses 20, 40, and 60 ppm resulted in improved seed yield compared to the control; this beneficial impact increased with increasing AgNPs concentration, although content of polyphenols in treated plants showed a decrease [182]. Spraying of shoots of borago plants at the onset of the flowering stage (65 days after cultivation) and maintained until flowering (98 days after cultivation) with solutions containing 0.2, 0.4, and 0.6 mM AgNPs resulted in considerable increase of leaf numbers, plant, and inflorescency dry weights, and petal abscission [183].  
For vanilla (_Vanilla planifolia_) micropropagated in a semi-solid Murashige and Skoog (MS) medium treatment, 25 mg/L and 50 mg/L AgNPs with a mean diameter of 35 \(\pm\) 15 nm was found to be the most favorable for shoot multiplication. The greatest lengths, number of shoots, and number of leaves were obtained with 25 mg/L and 50 mg/L AgNPs, and such treatment effectively reduced the contamination during micropropagation of _V. planifolia_[184].  
Ag-SiO\({}_{2}\) core-shell NPs (101.8 \(\pm\) 8.9 nm) applied at 900 mg/L for 3 d to the HR cultures of _Artemisia annua_ promoted their dry weight, which was 71% higher than that of the control [164]. AgNPs and AgNO\({}_{3}\) did not affect the in vitro multiplication of _Campomanesia rufa_ at doses 0.385 and 0.77 mg/L, respectively, while treatments with 15.4 mg/L showed toxic effects reflected in the reduced number of shoots [185].
## 3 Results
### System Distributability
#### 3.3.1 Runtime Performance  
Figure 8 shows interaction run times for 1250 agents on multiple node configurations. Runtime initially increases when using both two and four nodes, with testing showing this is largely due to network transfer penalties associated with frequent writes to the networked storage, as agents traverse nodes. However, nine nodes outperform a single node, with continued runtime improvements when moving to 16 nodes. Figure 9 shows the same node configuration but with 5000 agents participating. There is an increase in overall runtime, with runtime analysis indicating that CPU-bound tasks are the primary cause. The network transfer time takes a similar amount of absolute time in both the 1250 and 5000 agent configurations, indicating there is some initial time penalty. However, we do see a decrease in runtime moving from one node to two nodes, with further decreases in runtime as more nodes are included. Without additional hardware it is difficult to determine how many additional nodes will continue to improve runtime performance.
## 3 Results and Discussion
### Analysis of Controllability and Resulting Performance
Even though the imposed intake temperatures are 20-40 \({}^{\circ}\)C lower than that required for the conventional HCCI combustion at the same engine load, it is clear that, due to the higher reactivity of the pre-chamber mixture when compared to the mixture in the main chamber, the autoignition in the pre-chamber would be inevitable if the mixture is not consumed by the spark-initiated flame propagation prior to that point. The spark timing of 2 \({}^{\circ}\)CA  
Figure 12: The comparison of pressure profiles with reference HCCI pressure profile (reference point R13, denoted by the dotted lines) at the optimal combustion phasing achieved with different control strategies at each of the investigated intake temperatures: (**a**) intake temperature = 80 \({}^{\circ}\)C; (**b**) intake temperature = 90 \({}^{\circ}\)C; (**c**) intake temperature = 100 \({}^{\circ}\)C.  
bTDC seems to be the limit. With higher reactivity of the PC mixture (OP36 and OP37), the ignition delay is lower and the spark-initiated combustion occurs prior to the point of autoignition. However, as there is an evident increase in average MAPO values for all operating points at ST = 2 \({}^{\circ}\)C a bTDC, it can be safely assumed that the autoignition still occurs in the pre-chamber but not until a fair amount of mixture is already consumed by the flame propagation. Finally, it can be concluded that further reduction in mixture reactivity affected the flame kernel formation in such a way that the autoignition occurred first and then caused the initiation of HCCI combustion in the MC as well, and did so earlier due to a much higher burn rate, evident from the high peak pressure in the pre-chamber. Based on all the presented results, a conclusion could be made that the intake temperature of 90 \({}^{\circ}\)C offers the highest flexibility and most reliable control of combustion phasing, but the overall efficiency is much more dependent on the CA50 values than at the intake temperature of 100 \({}^{\circ}\)C. The intake temperatures of 80 \({}^{\circ}\)C and 100 \({}^{\circ}\)C are once again confirmed as the lower and upper bounds for the PC-HCCI combustion at the compression ratio of 17.5 and the IMEP of 3.5 bars. At 80 \({}^{\circ}\)C and the optimal combustion phasing, the operating points are either at the limit of knock intensity or at risk of unstable combustion. The intake temperature of 100 \({}^{\circ}\)C, on the other hand, offers a much narrower range of possible combustion phasing adjustments, limited on one side by knocking intensity and on the other side by the risk of undesired and uncontrolled autoignition in the pre-chamber.
## 4 Experiments and Performance Analysis
### Parameter Settings
Figure 6: The performance curves of CNMF (\(\square\)) and the proposed JSMV-CNMF (\(\circ\)), in terms of (**a**) reconstruction signal-to-noise ratio (RSNR) and (**b**) spectral angle mapper (SAM), with respect to the number of endmembers \(N\) for the two datasets; i.e., (top row) Pavia University and (bottom row) Moffett datasets, respectively.
### Objective and Contributions  
In addressing the evolving security landscape of CAVs, our current study introduces a novel dual-layer pairing methodology. Our current research extends our previous work documented in [8], which concentrated on evaluating the reliability of sensor data from IoT nodes through various machine learning techniques and inventive sensor pairing methods. Our research is driven by the hypothesis that employing this dual-layer approach will not only ensure accurate data validation, but also improve power efficiency and computational effectiveness, without imposing additional memory or network burdens on ECUs. This major contributions of this work are outlined below:  
1. **Innovative ECU and vehicle pairing approaches**: introducing a dual-layer methodology that notably strengthens security by securing communication among ECUs within a vehicle and across vehicles, while also safeguarding sensor data from spoofing, injection, and similar attacks.
2. **The practical use and effectiveness of TinyML in embedded systems within vehicles**: Our research further contributes to the field by demonstrating the practicality and effectiveness of TinyML in embedded systems within vehicles. This implementation improves the accuracy and efficiency of security measures in CAVs, providing insights into power utilization and resource optimization. It highlights the practical value of TinyML in environments constrained by resources.
3. **Comprehensive evaluation**: we undertake comprehensive experiments to evaluate the effectiveness of our proposed method in mitigating various security threats across CAVs environments.
4. **Scalability and efficiency**: the study explores particular difficulties presented by limited resources in CAVs, providing insights into implementing security solutions customized for these settings, including strategies and considerations for optimizing memory usage.  
By addressing the current deficiencies in security research for CAVs, this study aims to advance the development of automotive systems that are more secure, safe, and dependable, thereby enhancing operational integrity.
# Use of Social Media Data in Disaster Management: A Survey
## 4 Data Sources for Social Media Data Analysis
### Social Media Platforms  
Generally, social media data are directly accessible on social media platforms (e.g., Facebook, Twitter, and Instagram). These platforms are considered as major data sources for social media data analytics in disaster management. Most social media platforms provide HTTP-based Application Programming Interfaces (APIs) for data consumers to access their social media services (e.g., data service and analytics services). Data consumers can use their tools to communicate with the respective APIs to collect and store social media data for their purposes [28]. For example, Twitter provides search APIs that enable consumers to find historical or real-time data by using keywords or hashtags. Much research on using social media data for disaster management utilises such APIs to access social media data directly from the social media platforms [22; 23; 24; 25]. Due to the unstructured characteristics of social media data and the indeterminacy of the sources, the quality and trustworthiness of the collected social media data become significant issues [20]. Based on this, additional processes (e.g., data filtering, data classification, and data extraction) for data preparation are required. Due to privacy concerns, some of the social media platforms (e.g., Facebook and Twitter) have put several restrictions on data access.
## 2 State of the Art
### Algorithm Performance Evaluation in Other Fields
#### 2.5.1 Data Compression  
Data compression aims to reduce the volume of data while preserving the quality, and it can be classified as either lossy or lossless compression. In lossy and lossless compression, the goal is to maintain quality by using the least amount of data to represent the information. In lossless data compression, the original data can be obtained. However, in lossy compression, some information is lost.  
A corpus, in data compression, is a collection of representative files to evaluate the effectiveness of the compression ratio [49]. Calgary [50] and Canterbury [51] are corpuses used in lossless data compression.  
Using a corpus to evaluate compression algorithms reduces bias and facilitates the experiments' reproducibility. Furthermore, using a corpus creates compression benchmarks, a standard compression ratio that other algorithms may be compared to. Nowadays, the criteria regarding the corpus are widely accepted in the compression field.