esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result
### Assessment with AHP for Generating Landslide Susceptibility Mapping  
The LSM map was prepared by integrating all ten layers: drainage density, rainfall, lineament density, slope, geology, LULC, geomorphology, soil, aspect, and earthquake. Each sub-factor of LCF is assigned weights for generating LSM [72]. Table 5 represents the weights of each sub-factor of LCFs. LSM is created by the AHP method in the GIS environment with ten selected LCFs by the formula  
\[\text{LSM}=\sum_{i=1}^{n}\text{w}_{i}\text{*l}_{i} \tag{8}\]  
where l are the individual layers, w are their corresponding weights, and n is the number of layers (Table 6). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sl. No.** & **Class** & **T** & **VIF** \\ \hline
1 & Aspect & 0.751 & 1.331 \\
2 & Soil & 0.711 & 1.406 \\
3 & Rainfall & 0.703 & 1.422 \\
4 & Slope & 0.932 & 1.073 \\
5 & Geology & 0.844 & 1.185 \\
6 & Land use and land cover & 0.972 & 1.029 \\
7 & Lineament Density & 0.703 & 1.423 \\
8 & Geomorphology & 0.797 & 1.255 \\
9 & Drainage Density & 0.762 & 1.312 \\
10 & Earthquake & 0.711 & 1.406 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multi-collinearity analysis of landslide conditioning factors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Weight** & \multicolumn{2}{c}{**Sub-Layer**} & **Rating** \\ \cline{3-4}  & & **Class** & **Weight** & \\ \hline \multirow{4}{*}{Slope (in degree)} & & \textless{}15\({}^{\circ}\) & 0.034 & 1 \\  & & 15–22.45\({}^{\circ}\) & 0.067 & 3 \\  & & 22.46–29.74\({}^{\circ}\) & 0.134 & 5 \\  & & 29.75–37.88\({}^{\circ}\) & 0.260 & 7 \\  & & \textgreater{}37.89\({}^{\circ}\) & 0.502 & 9 \\ \hline \multirow{4}{*}{Rainfall (in mm)} & & \textless{}2038 & 0.034 & 1 \\  & & 2039–2062 & 0.067 & 3 \\ \cline{1-1}  & 0.210 & 2063–2088 & 0.134 & 5 \\ \cline{1-1}  & & 2089–2114 & 0.259 & 7 \\ \cline{1-1}  & & \textgreater{}2115 & 0.503 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LSM weights and rating system of different landslide conditioning factors. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **SL** & **RF** & **EQ** & **LD** & **DD** & **GEO** & **GEM** & **AS** & **LULC** & **SO** & **Weight** \\ \hline Slope & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 9 & 0.288 \\ Rainfall & 1/2 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 0.210 \\ Earthquake & 1/3 & 1/2 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 0.151 \\ Linearment Density & 1/4 & 1/3 & 1/2 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 0.109 \\ Drainage Density & 1/5 & 1/4 & 1/3 & 1/2 & 1 & 2 & 3 & 4 & 5 & 6 & 0.078 \\ Geology & 1/6 & 1/5 & 1/4 & 1/3 & 1/2 & 1 & 2 & 3 & 4 & 5 & 0.056 \\ Geomorphology & 1/7 & 1/6 & 1/5 & 1/4 & 1/3 & 1/2 & 1 & 2 & 3 & 4 & 0.040 \\ Aspect & 1/8 & 1/7 & 1/6 & 1/5 & 1/4 & 1/3 & 1/2 & 1 & 2 & 3 & 0.028 \\ LULC & 1/9 & 1/8 & 1/7 & 1/6 & 1/5 & 1/4 & 1/3 & 1/2 & 1 & 2 & 0.020 \\ Soil & 1/9 & 1/9 & 1/8 & 1/7 & 1/6 & 1/5 & 1/4 & 1/3 & 1/2 & 1 & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The final pair-wise comparison matrix of landslide conditioning factors.
# Improving the Phosphatase-Catalyzed Synthesis of 5'-Nucleotides: A Reaction Engineering Approach
## 2 Materials and Methods  
### Reagents and Equipment  
All solvents and reagents were purchased from Merck (Milano, Italy) and were used without further purification.  
Freeze-drying was performed using a Telstar LyoQuest (Azbil Telstar, Barcelona, Spain) freeze-dryer.  
The expression vector pET28a(+) containing the PhoC-_Mm_ gene was kindly provided by Prof. [PERSON] (University of Graz, Graz, Austria). The protein was purified by fast protein liquid chromatography (FPLC) using a GE AKTA Purifier (Cytiva, Global Life Sciences Solutions, Marlborough, MA, USA) 100 FPLC System w/UPC-900 (HisTrap(tm) High Performance 1 mL pre-packed columns (Cytiva, Global Life Sciences Solutions, Marlborough, MA, USA)).  
Analytical HPLC was performed using a JASCO LC-4000 chromatographer equipped with a UV-4070 UV-Vis detector (JASCO Applied Sciences, Halifax, NS, Canada). Experimental conditions were as follows: column, Jupiter 4 mM Protee 90 A (25 mm \(\times\) 4.6 mm) (Danaher Corporation, Washington, DC, USA); wavelength, \(\lambda\) 254 nm; injection volume, 20 \(\mu\)L; flow rate, 1.00 mL/min; solvents, A = 0.1% TFA in water and B = ACN/0.1% TFA in water water 80:20; gradient, 0-5.00 min: 95% A, 5.01-15.00: from 95% to 60% A, 15.01-15.05: from 60% to 95% A, 15.06-20.00: from 95% A.  
Semi-preparative HPLC was performed using a Dionex Ultimate 3000 chromatographer equipped with a Dionex RS Variable Wavelength detector (Thermo Fisher Scientific Inc., Waltham, MA, USA). Experimental conditions were as follows: column, Atlantis Prep T3 OBDTM 5 um (19 mm \(\times\) 100 mm) (Waters Corporation, Milford, MA, USA); wavelength, \(\lambda\) 254 nm; injection volume, 1 mL; flow rate, 10 mL/min; solvents, A = 0.1% TFA in water and B = ACN/0.1% TFA in water 80:20; gradient, 0-5.00 min: 100% A, 5.01-12.50: from 100% to 85% A, 12.01-15.00: from 85% to 0% A, 15.01-15.05: from 0% to 100% A.  
\({}^{1}\)H, \({}^{13}\)C, and \({}^{31}\)P NMR spectra were recorded at 400, 100, and 161 MHz, respectively, on a Bruker AVANCE 400 spectrometer equipped with a TOPSPIN 3.7 software package (Bruker, Karlsruhe, Germany) at 300 K. D\({}_{2}\)O (Merck) was used as solvent. \({}^{1}\)H and \({}^{13}\)C chemical shifts (\(\delta\)) are given in parts per million (ppm) and referenced to the solvent signals (\(\delta_{\rm H}\) 4.79 ppm from tetramethylsilane (TMS) for D\({}_{2}\)O); \({}^{31}\)P chemical shifts (\(\delta\)) are referred to 85% H\({}_{3}\)PO\({}_{4}\) as external standard (\(\delta_{\rm P}\) 0.00 ppm). Coupling constants (\(J\)) are reported in Hertz (Hz).
## 3 The Asymmetric Hyperchaotic Image Encryption Scheme
### Decryption Process  
In general, the decryption process of the scheme proposed is basically the reverse of the encryption. The input for the decryption process is the receiver's private key u and the PKT.  
1. Split the PKT into the encrypted hash and the encrypted image.
2. Use the private key u to decrypt the encrypted hash.
3. Use the decrypted hash from the last step to generate the hyperchaotic sequences x(i) and y(i), and reshape the x(i) into the two-dimensional sequence x(i, j).
4. Take the inverse diffusion process to obtained the permutated-only image PM from the cipher image C and the use of the sequence x(i, j). The concrete process is as follows:
5. With the combination of two sequences x(i) and y(i), take the reversed operation of the permutation process to obtain five planes which are the divided parts of the original image.
6. Finally, build them all up into one thing and restore it to the decrypted image P, which size is \(M\times N\) as the same of the original plaintext image.  
Figure 9 reveals the flow chart of the decryption procedure.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 7 Open Issues and Future Directions
### Enhancing Communication between Agents in Large Scale MADRL  
Communication plays a critical role in achieving coordination and improving tasks that require synchronization. In large-scale MADRL, the amount of information exchanged between agents is large. Some MADRL algorithms require agents to share actions and observations during training, which performs well in both collaborative (X.2.2) and competitive (X.2.1) environments [89]. However, agents do not generally develop a form of communication based on past experiences, which hinders scalability (O.5) [90]. The frequent information exchange between agents can increase the complexity (O.3) of the MADRL approach. Communication between agents faces the challenges of ultra-densification (C.1) due to the large number of agents in the environment, high dynamicity (C.2) due to the rapid changes in the operating environment, and high dimensionality (C.3) due to the large number of state and action spaces characterizing the operating environment of the large number of agents. Communication can be reduced by enhancing the learning mechanism [76] that normally stores knowledge exchanged with neighboring agents in a memory device [91]. However, a memory device has a limited capacity which is suitable for a small number of agents only. Communication in large-scale MADRL can be enhanced by: (a) Limiting communication between interacting agents. For example, agents know which agents to send their information to, and when to scale down information received from some agents (e.g., far away agents). (b) Enabling agents to remember at least the current and past experience gained from interactions with their operating environment [91]. Enhancing communication enables agents to ensure a higher QoS (P.3) in synchronization and coordination tasks.
## 3 Energy Storage  
Energy storage through batteries entails the conversion of electrical energy into chemical energy, allowing for its storage within the battery's internal components [40]. This stored energy offers a reliable and on-demand power source, with lithium-ion technology being the most prevalent. Battery storage systems play a critical role in conjunction with intermittent renewable energy sources like wind and solar power, facilitating the development of a robust grid infrastructure. They possess the capability to swiftly release stored energy at high ramp-up rates during peak demand periods or grid fluctuations, aiding in the seamless integration of renewable energy generation. Conversely, pumped hydroelectric storage (PHES) offers a time-tested alternative with a slower ramp-up rate but remarkable reliability. In PHES, surplus renewable energy is utilized to pump water  
Figure 6: Pelton turbine general description [34].  
Figure 7: Kaplan turbine general description [34].  
uphill to reservoirs during periods of excess supply. When energy demand surges, the stored water is released downhill, harnessing its potential energy to generate power. PHES functions akin to a large-scale, grid-connected battery, ensuring a stable supply of power to the grid. Compared to battery storage, PHES offers a more resilient and sustainable energy solution, contributing to a more robust energy environment [40].
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 5 Experiments Study
### Network Components  
In this network, there is a **Network Management System** which acts as a monitoring and control center on shore. Its main function is to store and visualize the reported underwater sensor data. It also provides user interfaces for sending various instructions to control the network nodes. One **Surface Gateway Node** and three **Level One Base-Station (L1-BS)** Nodes together form the backbone of the network in the underwater environment. They are deployed in real environments in the form of surface buoys. Especially, the Surface Gateway Node has a radio communication module which is an important relay station between management system and \(uw\)-Cellular network. Ten **Fixed Terminal Network Nodes** were deployed evenly within the coverage area of three L1-BS Nodes. We also deployed six **Mobile Terminal Network Nodes** in this \(uw\)-Cellular network. Three of these are shipboard mobile nodes and the other three are AUV nodes.  
For our job, each underwater network node is equipped with an **Underwater Acoustic Modem (UAM)** we mentioned above. The protocols and SeaLinx network framework are implemented in the Embedded Operating Systems of the UAM.
# Harnessing Agri-Food Waste as a Source of Biopolymers for Agriculture
## 2 Sources of Biopolymers
### Starch: A Promising Biopolymer for Agricultural Applications  
Starch is a promising polysaccharide deriving from abundant renewable sources; for certain applications, it can be a valid replacement of traditional plastics. Starch is synthesized by most plants as an energy reservoir, stored in granular form within the cytosol of plant cells. This biopolymer is mainly composed of amylose and amylopectin; this latter forms the main crystalline parts, while the amorphous regions are formed by branches of both. The starch chemical structure can be seen in Figure 3[62].  
temperature, gelatinization enthalpy, and pasting temperature, making it thermoplastic (Table 3, Entry 4) [66].  
[PERSON] et al. (Table 3, Entry 5) [67] extracted starch rich in phenolic compounds with UAE; the employment of this technology enhanced the water-holding capacity, solubility, oil-holding capacity, and swelling power of starch, when compared to the polysaccharide obtained with conventional extraction.  
Starch can be employed in agriculture for the creation of mulch films showing beneficial characteristics, such as maintaining soil moisture, preventing soil erosion, limiting the growth of weed species, and regulating solar radiation [62; 63; 64; 65; 66; 67; 68]. Additionally, it can be used for the encapsulation of biocontrol bacteria [69]; this encapsulation protects helpful microorganisms that can prevent the cultivated plant from biotic and abiotic stresses. Starch can also be employed in the construction of superabsorbent polymers for irrigation optimization and controlled release of biocides and biofertilizers [70].
## 4 Platform Selection: Polkadot  
The balancing act between decentralisation, security, and scalability is a common challenge faced by developers of blockchain applications and solutions; it is referred to as the \"blockchain trilemma.\" Since striking a balance between these three is extremely difficult, applications are limited to incorporating no more than two of them. For the development process to optimise the performance of the other two, one must be sacrificed. Decentralisation has typically been prioritised over scalability in the development of blockchain services. As a consequence, applications frequently exhibit reduced scalability in comparison to standalone systems [21]. [PERSON] et al. [22] suggested that the issues related to scalability can be overcome by implementing interoperability. Interoperability is defined as a blockchain's ability to perform transactions and process ledgers on other blockchains that are either homogeneous or heterogeneous in nature with the option of verifying transactions on both sides. There are different platforms that provide interoperable blockchain solutions, such as Cosmos [23], Ark [24], Avalanche [25], Cardano [26], and Polkadot [27]. [PERSON] et al. [28] analysed multiple solutions and suggested that Cosmos and Ark could connect up to two heterogeneous blockchains, which is a limitation in situations with more than two heterogeneous platforms and identifies Polkadot as suitable to deal with more than two heterogeneous blockchains.  
Polkadot is a blockchain platform jointly created by the Parity Technologies and Web3 Foundation. It was introduced in 2020 with the intention of facilitating blockchain interoperability. This multi-chain technology enables seamless communication between existing heterogeneous blockchain networks via network bridges while also facilitating the rapid development of new chains [27]. Polkadot is constituted of a number of components and consensus processes to attain interoperability.
## 7 Comparative Analysis
### False Alarm Probabilities  
In comparing the values of the false alarm rate of ASC-RL with those of other schemes, initially, they exhibit the same values but increase with more episodes, as shown in Figure 10. The ASC-RL performs differently, with values decreasing due to frequent state changes based on the reward function and the training of \(A_{g}\). Toward episode 100, ASC-RL achieves a 0.296 false alarm rate with an average of 0.335 for the other three schemes, representing a 10.10% increase. Similarly, upon reaching 500 episodes, the proposed scheme reduces the false alarm rate to 0.112, with an average of 0.1506 for the other schemes. Compared with the average of the three schemes, the proposed scheme accounts for around 25.7%, which shows its ability to learn the ratio of the reward function to decrease false alarm rates. These values are displayed in Table 6.
## 2 System Model
### Performance Metrics
#### 2.4.1 Signal-to-Distortion Ratio (SDR)  
SDR [22] is a widely used metric that quantifies the level of distortion introduced during communication. It measures the ratio of the power of the original speech signal to the power of the distortion components, providing an objective assessment of signal degradation due to semantic compression, channel impairments, and reconstruction errors. SDR is defined as follows:  
\[\text{SDR}=10\log_{10}\biggl{(}\frac{\|s\|^{2}}{\|s-\hat{s}\|^{2}}\biggr{)}\quad \text{(dB)} \tag{13}\]  
where \(s\) denotes the original speech signal, \(\hat{s}\) represents the reconstructed speech signal, \(\|s\|^{2}\) is the total energy of the original signal, and \(\|s-\hat{s}\|^{2}\) quantifies the distortion energy introduced during the communication.  
A higher SDR value indicates lower distortion and better waveform preservation. However, SDR is a purely signal-based metric and does not account for human auditory perception. Therefore, perceptual evaluation methods are necessary to complement SDR and provide a more holistic assessment of speech quality.
## 4 Experimental Setup and Result Analysis
### Ablation Study
#### 4.4.2 Ablation Study on Network Pruning for Wide-Angle Image Correction Deployment  
This experiment compares the differences in model size, image correction quality, and inference speed before and after applying pruning-based optimization strategies to the wide-angle image correction network when deployed on an edge computing platform. The test results are shown in Table 2. The baseline model (denoted as BASE) refers to the original network trained on the server. The parameter \(\omega\) represents the channel retention ratio during pruning; a higher \(\omega\) indicates that more channels are preserved. All experiments are conducted on the Jetson TX2 NX platform with an input image resolution of 1024 \(\times\) 768.  
The experimental results in Table 2 indicate that although the correction network operates in real time on a server, its deployment on an edge computing platform results in an inference time of 104 ms per image frame. When integrated with the image stitching system, this latency fails to meet the real-time requirement of 15 FPS, necessitating pruning optimization of the original BASE network.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**BASE** & **MS Module** & **OF Module** & **PSNR** & **SSIM** & **FLOPs (T)** & **Params (M)** & **Time (ms)** \\ \hline \(\surd\) & & & 22.21 & 0.7382 & 2.15 & 26.85 & 8.1 \\ \(\surd\) & \(\surd\) & & 25.36 & 0.7582 & 2.23 & 30.45 & 8.3 \\ \(\surd\) & & \(\surd\) & 26.97 & 0.7796 & 2.31 & 29.73 & 8.7 \\ \(\surd\) & \(\surd\) & \(\surd\) & 27.43 & 0.8190 & 2.45 & 35.93 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different configurations with and without key modules.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Inference Time/ms** & **Model Size/MB** & **SSIM** & **PSNR/dB** \\ \hline BASE & 104 & 35.93 & 0.6151 & 21.97 \\ BASE \(\omega\) = 85\% & 59 & 21.32 & 0.5332 & 19.26 \\ BASE \(\omega\) = 75\% & 36 & 13.38 & 0.5121 & 17.96 \\ BASE \(\omega\) = 55\% & 31 & 11.01 & 0.4726 & 15.36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of the calibration network after pruning optimization.  
At a pruning rate of \(\omega=55\%\), the inference time is reduced to 31 ms; however, the correction quality deteriorates significantly, leading to poor visual outcomes and limiting its applicability in downstream registration tasks. In contrast, the configuration with \(\omega=75\%\) offers a better balance between accuracy and efficiency. While the PSNR drops by only 4.01 dB compared to the BASE model, the inference time is reduced by 68 ms, and it is also 23 ms faster than the \(\omega=85\%\) configuration. Based on this trade-off, we adopt \(\omega=75\%\) as the final pruning ratio, achieving an optimal balance between performance and runtime efficiency for deployment on the embedded platform.
Pollution transport range depends on emitter characteristics, physical and chemical properties of the pollutant and meteorological conditions [18; 19; 20]. While mineral particles are deposited within the vicinity of the source, aerosols or gaseous emissions can be transported on long distances [8; 21]. Thus, heavy metals bound to and carried by particulate matter [22; 23; 24] enter the environment closer or farther from their source, mainly depending on the aerodynamic diameter of carriers [18; 20]. The environmental load of heavy metals originating in air emissions can be monitored through passive biomonitoring using moss since dense carpets of naturally growing pleurocarpous species are effective traps of pollution present both in airborne particles and precipitation [25; 26]. Moreover, the biomonitoring technique was proven suitable for a long-range pollution transport evaluation [27; 28; 29; 30] as well as for local pollution source assessment (for example: [31; 32; 33; 34; 35; 36; 37; 38; 39; 40]). The objective of this study was to assess the spatial and seasonal variability of heavy metals and other supplementary elements in the moss tissue of _Brachythecium rutabulum_ in the environment surrounding integrated steel mills--the Trinec Iron and Steel Works (Czechia). The concentrations of aluminium (Al), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), antimony (Sb), vanadium (V), zinc (Zn) and nitrogen (N) were analysed (additionally carbon (C) and hydrogen (H)). The survey was carried out following the European norm EN 16,414 Ambient air--Biomonitoring with mosses [41]. Additionally, the relationship between the composition of the moss analysed and modelled seasonal atmospheric particulate matter concentrations (PM\({}_{10}\)) originating in anthropogenic sources were explored. We hypothesised that the element concentrations in moss tissues would be influenced by the heating season, would decrease with the distance from the factory along the prevailing wind direction [33; 42; 43; 44] and would be significantly correlated to the modelled PM\({}_{10}\) concentrations [45; 46].
## 4 Derivation of Energy Efficiency Expression
### The Ratio of PSF  
PSF ratio can be denoted to be the proportion between the association probability of PSF PUE and the sum of the association probability of PSF PUE and that of USF PUE, as shown in Equation (7).  
\[\theta=\frac{A_{pp}}{A_{pp}+A_{up}}=\frac{\lambda_{m}\left(k_{e}^{2}-k_{p}^{2} \right)}{k_{e}^{2}\left(\lambda_{m}+k_{p}^{2}\lambda_{p}\right)}, \tag{7}\]  
where the expressions of \(A_{pp}\) and \(A_{up}\) are obtained according to Equation (5).  
Referring to Equation (7), the PSF ratio versus \(B_{p}\) with different PBS densities \(\lambda_{p}\) is depicted in Figure 4. As shown in Figure 4, with \(B_{p}\) increasing, the pico CRE area will be enlarged. As a result, the PSF ratio will rise. Moreover, with the PBS density \(\lambda_{p}\) increasing, the distance between PBSs will be smaller, which will limit the further expansion of pico CRE area, so that the effect of \(B_{p}\) on \(\theta\) will be weakened with \(\lambda_{p}\) increasing.