esa-sceva/satcom-chunk-collection huggingface dataset:
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 3 Simulation Results and Discussion
### Optical Power Received (\(P_{rx-los}\))
When setting \(\theta\) = \(40^{\circ}\) and FOV = \(20^{\circ}\) as mentioned in [26], the maximum \(\mathrm{p_{rx}}\) for very clear weather will be \(-22.85\) dBm, which is almost \(-7\) dBm lower than the result when \(\theta\) = \(80^{\circ}\) and FOV = \(10^{\circ}\). This is also obtained at all other attenuation cases. The reason behind this difference is that higher \(\theta\) means more received power, and at the same time lower FOV means a higher gain, which increases the received power. Figure 10 displays the power received under different rain attenuation models. Clearly, \(\mathrm{p_{rx}}\) decreases with rising precipitation levels for all models. All models have a near \(\mathrm{p_{rx}}\) for low distances but vary over a range of 40 m. The maximum \(\mathrm{p_{rx}}\) for the Marshall model varies from \(-16.26\) dBm to  
Figure 9: Optical power received vs. distance for (**a**) very clear weather and 2 mm/h wet, dry snow in the first lane (**b**) very clear weather and 2 mm/h wet, dry snow in the second lane (**c**) 4, 8 mm/h wet, dry snow in the first lane (**d**) 4, 8 mm/h wet, dry snow in the second lane. \(-15.7\) dBm from higher to lower rates. The France design varies from \(-16.66\) dBm to \(-16.06\) dBm, while Japan ranges from \(-16.8\) dBm to \(-16\) dBm. The maximum p\({}_{\text{rx}}\) is measured at \(18\) m for all models. Figure 10: Optical power received vs. distance for (**a**) very clear weather and different rain models at \(25\) mm/h in the first lane, (**b**) very clear weather and different rain models at \(25\) mm/h in the second lane, (**c**) different rain models at \(10\) mm/h in the first lane, (**d**) different rain models at \(10\) mm/h in the second lane, (**e**) different rain models at \(2\) mm/h in the first lane, and (**f**) different rain models at \(2\) mm/h in the second lane.
# Novel Janus Fibrous Membranes with Enhanced Directional Water Vapor Transmission
###### Abstract  
Novel hydrophobic/hydrophilic Janus fibrous membranes, the poly[4,4'-methylenebis (phenylisocyanate)-alt-1,4-butanediol/dipropylene glycol)/plycaprolactone) (PU) fibrous membrane as the hydrophobic layer and cellulose acetate (CA) fibrous membrane as the hydrophilic layer, were fabricated by the so-called \"layer-by-layer\" electrospinning technology. A series of the PU/CA Janus membranes with different electrospinning time of the CA layers by which the thickness of hydrophilic layer can be controlled were also prepared to uncover its influence on the directional water vapor transmission. The results showed that water vapor transmission capability from the hydrophobic side to the hydrophilic side of the PU/CA Janus fibrous membrane was enhanced rather than that from the reverse direction of the same membrane. The optimal water vapor transmission capacity existed when the electrospinning time of CA fibrous membrane reached 15 min. Such enhanced water vapor transmission originated because of the asymmetric wettability of the Janus membrane and the strong force to draw tiny water droplet from the hydrophobic side to the hydrophilic side. The novel understanding is useful for facile designing and fabrication of efficient moisture permeable fabrics and clothing.  
Janus fibrous membrane; hydrophobic; hydrophilic; electrospinning; directional water vapor transmission +
Footnote †: journal: Applied Sciences
## 2 Advanced Oxidation Processes
### Electrochemical Technologies
[115] suggested an easy degradation of the target compound when used in a real water matrix, which is a positive sign for real application purposes. As well as electrochemical oxidation or photoelectrocatalysis methodologies, electro-Fenton and photo-electro Fenton were also used in parabens abatement. [PERSON] et al. [88] removed MP, EP, and PP by photo-electro Fenton with BDD or RuO\({}_{2}\) anode. Regarding the BDD anode, a removal of 65% (360 min), 100% (360 min), and 100% (180 min) was achieved for H\({}_{2}\)O\({}_{2}\), electro-Fenton, and photoelectron-Fenton with solar radiation. Furthermore, the authors studied the influence of the water matrix in the paraben's abatement. When real wastewater was tested, BDD led to total removal in 150 min, while the RuO\({}_{2}\) anode led to a removal higher than 95% in 180 min, using solar light photoelectron-Fenton. This studyproved the good efficiency of electro-Fenton and Photo-electro Fenton, since the removal rate was higher when Fenton's reagent was used compared to single H\({}_{2}\)O\({}_{2}\), and the same conclusion was reached when radiation was added. These results can be explained by an improvement in the generation of hydroxyl radicals. Furthermore, this study also showed high degradation results when real wastewater was use,d which may suggest a good efficiency for these experimental conditions when applied to real treatment approaches. [PERSON] et al. [116] tested the MP abatement for electro-Fenton and anodic oxidation, using BDD/Nb anode, and graphite felt (GF) or Nb as cathode. Results showed that anodic oxidation led to about 60% removal for Ru and GF cathodes, while the electro-Fenton using the same cathodes led to about 78% and 98% removal, respectively, in which the electro-Fenton process led to higher performance when compared to anodic oxidation. The results are related to the different technologies' mechanisms. Table 8 presents the use of electrochemical technologies in parabens removal. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pollutant** & **AOP** & **Material** & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Results and**} \\  & & & **Conditions** & \multicolumn{2}{c}{**Conclusions**} & \multicolumn{1}{c}{**Reference**} \\ \hline \multirow{4}{*}{PP} & Photo- & & \multirow{4}{*}{ \begin{tabular}{c} \(\bullet\) \\ Electrocalis \\ WO\({}_{3}\)-TiO\({}_{2}\)/NT \\ WO\({}_{3}\)-TiO\({}_{2}\)/NT \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} \(\bullet\) \\ electrode. \\ 50 mg/L PP in \\ electrolyte solution of \\ 0.1 mol/L of Na\({}_{2}\)SO\({}_{4}\) in \\ ultrapure water. \\ \end{tabular}  } & \multirow{4}{*}{\ \begin{tabular}{c} 100\% removal in 30 min at \\ pH = 3. \\ The presence of WO\({}_{3}\) \\ improves the photo-activity \\ by 20\%. \\ \end{tabular}  } & \multirow{4}{*}{
\begin{tabular}{c} [36] \\ [36] \\ [36] \\ [36] \\ [36] \\ [36] \\
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Performance of Machine Learning Models
#### 3.1.1 Mean Absolute Error (MAE)  
Mean Absolute Error (MAE) is a measure of prediction accuracy in a regression context. It calculates the average absolute difference between the actual and predicted values, giving an idea of how wrong the predictions were. A lower MAE signifies a better performance of the model as it indicates smaller deviations from the actual values [43; 44].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline MLR & 0.35 & 0.45 & 0.78 \\ ANN & 0.30 & 0.38 & 0.82 \\ RF & 0.25 & 0.30 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of Machine Learning Models.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Hyperparameters** & **Values** \\ \hline Multiple Linear Regression & - & - \\ \hline \multirow{6}{*}{Artificial Neural Network} & Hidden layers & 2 \\  & Neurons per layer & 100 \\  & Activation function & ReLU \\  & Optimizer & Adam \\  & Learning rate & 0.01 \\  & Batch size & 32 \\  & Epochs & 100 \\ \hline \multirow{6}{*}{Random Forest} & Number of trees & 500 \\  & Max depth & None \\ \cline{1-1}  & Min samples split & 2 \\ \cline{1-1}  & Min samples leaf & 1 \\ \cline{1-1}  & Bootstrap & True \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model hyperparameters.  
In our study, the RF model yielded the lowest MAE, indicating that on average, the RF model's predictions deviated least from the actual water-quality indices. While MLR and ANN models also showed relatively small errors, the RF model outperformed them in terms of prediction accuracy.
## 4 Dynamic Power Management System  
We have seen the basic tools that are provided by the hardware for dynamic power control. These include knobs to adjust frequency, supply voltage and hardware resources like number of engines and word length. Work load estimation is another key ingredient of a dynamic power management system. In static estimation schemes, the computation loads are determined a priori via profiling [13, 16]. Further power improvements are possible if dynamic estimation techniques are used. In a queue based approach, a FIFO is used to queue the tasks that need to be worked on. The FIFO depth then gives an indication of the mismatch between the hardware computation rate and the task arrival rate. The power management control system continuously adjusts the frequency (and hence the voltage) to keep the FIFO depth within some bounds [23, 24]. [PERSON] _et al_. use an artificial neural network to estimate the work load for every image frame in a object recognition application. The neural network uses the work load history of the past frames as well as some preprocessed information of the current frame, to estimate the computing load. The estimated load is then used to set the appropriate voltage and frequency value for the engine [25].  
In general we can envision a run-time power management system which continually adapts the power consumption according the work load [26]. The system is modeled to have many power-performance states, where, in each state, the power consumed is related to the performance delivered. For example, in each power domain which is controlled by a power gating transistor, one can identify at least two states: an active state where the domain is connected to the supply and an asleep state where it is disconnected. If clock gating is used, then an intermediate inactive state can be defined where the domain has power, but is idle as clock is stopped. Many different active states are possible, if DVFS is used, each V-F value corresponding to a separate active state. The state transitions of the system incur a time and energy cost and the goal of the run-time power manager is to minimize the power consumption of the system while adapting to the computing load [26].  
\begin{table}
\begin{tabular}{c c c} \hline
**Video Sequence, Resolution,** & **Computations Reduction** & **Power Reduction** \\
**Number of Frames** & & \\ \hline Viper Train, 1920 \(\times\) 1080, 316 & 66.15\% & 78.15\% \\ Riverbed, 1920 \(\times\) 1080, 250 & 93.14\% & 100.67\% \\ Pedestrian, 1920 \(\times\) 1080, 375 & 73.63\% & 86.45\% \\ Sunflower, 1920 \(\times\) 1080, 494 & 84.28\% & 95.81\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of computation and power when using adaptive partitioning instead of fixed \(4\times 4\) partitions.
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 2 The Heterogeneity of the Mature tRNA Pool
### Composition Ratio Difference  
tRNAs are highly diverse and can be classified into isotopes, isoacceptors, and isodo-coders based on specific criteria: isotypes differ in the amino acids they carry, isoacceptors have variations in their anticodon sequences in the same isotype, and isodecoders show sequence differences in the same isoacceptor. Studies have shown that the dwell time of ribosomes at the A site is strongly negatively correlated with the abundance of tRNA isoacceptors [34], illustrating how the abundance of specific tRNA isoacceptors impacts the translation of specific codons. This effect can lead to the regulation of translation rates for messenger RNAs (mRNAs) enriched with particular types of codons. Studies employing next-generation sequencing of tRNAs have revealed significant differences in tRNA expression profiles between different tissues and cancer subtypes [27; 35; 36]. These variations may be associated with tissue-specific development, the maintenance of high translation rates for certain genes, or the severity of cancer [27; 31; 36]. These findings suggest that the composition of tRNA pools could serve as a regulatory node for selective protein translation.
# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
## 2 System Model
### \(\beta\) Reputation System  
By building a reputation system for all the cognitive users in the system, it is possible to effectively identify the hidden malicious users based on their historical data.  
In order to mathematically model the system, we define the cognitive user sending to the fusion center as a dichotomy process with two possible outcomes \(\{x,y\}\), where \(x\) indicates that the cognitive  
Figure 1: Centralized collaborative spectrum sensing system model.  
user sends the true local perceived result to the data fusion center and \(y\) indicates that the cognitive user performs a SSDF attack.  
Considering that the \(\beta\)-probability density function is usually used to describe the probability distribution properties of the two events and the dichotomy process [13], we use this mathematical model to construct the \(\beta\)-reputation system. We supposed that \(r\) represents the number of occurrences of the result \(x\) observed and s represents the number of occurrences of result \(y\). For the probability of \(x\) (\(p_{x}\)) produced by the dichotomy following the \(\beta\) distribution, the probability density function can be expressed as:  
\[f(p_{x}|\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)} p_{x}^{\alpha-1}(1-p_{x})^{\beta-1} \tag{1}\]  
where \(p_{x}\in[0,1]\); \(\Gamma(\cdot)\) represents Gamma function; while \(\beta\)-distribution's parameters are \(\alpha=r+1\), \(\beta=s+1\) and \(r\geq 0\), \(s\geq 0\). The expectation of the \(\beta\)-distribution is:  
\[E[p_{x}]=\frac{\alpha}{\alpha+\beta} \tag{2}\]  
where \(E[p_{x}]\) represents the average probability of the occurrence of the result \(x\) in the future after the number of occurrences of \(r\) and \(s\) of the observed results \(x\) and \(y\) are given. Since the first order variable \(p_{x}\) is continuous, for any given value of \(p_{x}\in[0,1]\), the second order variable \(f(p_{x}|\alpha,\beta)\) is negligible and meaningless. Therefore, the expected \(E[p_{x}]\) of \(p_{x}\) is usually used to calculate the prestige.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 9 Smart Port Use Case
### Methodology
#### 9.5.4 Core Network  
The core network acts as a bridge between data transmission and applications, providing storage, control plane, and data plane functions. After the logical slicing according to the services (AaaS, PaaS, and IaaS), information is provided to applications such as intelligent automation services (mMTC), virtual reality (VR) services (eMBB), and SGV services (URLLC). The performance of our proposed model will be evaluated to manage the resources efficiently.
# Experimental Study on the Influence of Low Temperature on the Gas Permeability of Granite
## 3 Results and Discussion  
### Change in Porosity  
The changes in porosity of individual specimens are analyzed using the rate of change in porosity, which is defined as follows:  
\[\Delta N=\frac{n_{f}-n_{0}}{n_{0}}\times 100\% \tag{8}\]  
where \(\Delta N\) is the rate of change in porosity of the specimen, \(n_{f}\) is the porosity after the freeze-thaw cycle, and \(n_{0}\) is the porosity before the freeze-thaw cycle.  
Figure 6 displays the rate at which the porosity of the two granite specimens changed at various freezing temperatures throughout the freeze-thaw process. The porosity change rate of coarse-grained granite increased from 2.93% to 23.64%, while that of fine-grained granite increased from 2.35% to 27.45% as the temperature dropped from \(-20\)\({}^{\circ}\)C to \(-120\)\({}^{\circ}\)C. Additionally, the rate of porosity change in fine-grained granite was higher than in coarse-grained granite. This difference is primarily due to the lower initial porosity of fine-grained  
Figure 5: Nuclear magnetic resonance test specimens.  
granite, which undergoes a more noticeable shift in porosity following the freeze-thaw cycle. In contrast, coarse-grained granite had an initial porosity of 0.83% and 0.45%.  
When the freezing temperature ranged from \(-60\)\({}^{\circ}\)C to \(-90\)\({}^{\circ}\)C, the porosity of coarse-grained granite increased more slowly. Meanwhile, the porosity of fine-grained granite did not change significantly within the temperature range of \(-40\)\({}^{\circ}\)C to \(-60\)\({}^{\circ}\)C. Since different pore sizes correspond to different freezing points of pore water [38], the freeze-thaw cycle during the initial phase of temperature drop leads to a faster rate of rock damage. Once the water in the larger pores of the rock has completely frozen, the freeze-thaw damage gradually reaches a saturated state. Due to the higher proportion of small pores in fine-grained granite, its freeze-thaw damage reaches saturation earlier. As the temperature continues to decrease, the water in the smaller pores of the granite begins to freeze, resulting in a significantly accelerated increase in porosity for both types of granite.  
In conclusion, the porosity change rate of both coarse-grained and fine-grained granite increases as the freezing temperature decreases. The rate of increase shows notable variations across different temperature intervals, following a pattern of rapid increase, stability, and then a rapid increase once more.
###### Abstract  
The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products' (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused area over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T's causality is evenly distributed amongst all biomes with fractional covers between \(\sim\)10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet's ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (SSP) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.  
vegetation sensitivity; Google Earth Engine; Granger Causality; FAPAR; LAI; SIF; NDVI; ERA5; environmental stress; causality 29 August 2023  
1
## 2 Conventional Power System Security Assessment
### Power System Blackouts-Examples
#### 2.1.2 1200 MW PV Resource Interruption August 2016  
On 16 August 2016, fifteen transmission line faults happened in the Southern California network; four of them lead to loss of solar photovoltaic (PV) generation. In the North American Electric Reliability Cooperation (NERC) report [52], it is mentioned that the PV generation cut because of the wrong frequency detection and in consequence an undesired act of the protection system. Although the system frequency remains constant after the transmission line faults, the inverter PLL detects a frequency drop which is more than 3 Hz. This happens as the PLL transient response is sensitive to its input phase angle and magnitude. A 26\({}^{\circ}\) phase-jump during the faults was detected in the system, which led to a wrong frequency estimation by the PLL. Then, the frequency-sensitive protection system acted and disconnected the 1200 MW PV generation from the system.  
What can be concluded from the Southern California interruption report is that PE-based generation units should be set in order to avoid an undesired unit tripping. On the other hand, a large loss of generation, such as 1200 MW, may cause a cascading trip, which may lead to system instability.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Location** & **Date** & **Duration (hrs)** & **Affected People (million)** & **Causes** \\ \hline India [49] & July 2012 & 15 & 620 & Transmission line overload \\ \hline Bangladesh [50] & November 2014 & 24 & 150 & HVDC station outage \\ \hline South Australia [51] & September 2016 & 6 & 1.7 & Transmission line outage and cascading events \\ \hline The US [52] & August 2016 & 6 & 1.7 & Transmission line outage and cascading events \\ \hline The US [53] & March 2017 & 11 & 21 & Transmission line outage and cascading events \\ \hline The US [54] & September 2017 & 5 & 7.6 & Transmission line trip \\ \hline Brazil [55] & March 2018 & 1 & 10 & Transmission line trip \\ \hline Canada [56] & December 2018 & 4 & 0.6 & Wind reached speed of 100 km/h \\ \hline \end{tabular}
\end{table}
Table 1: Major blackouts in modern power systems with substantial power-electronic-based (PE-based) units.