esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analytical Methods and Laboratory Procedures
### Microbiological Analysis  
Samples were processed by observing them under a microscope, followed by microbial cultivation. In particular, each adhesive tape was divided into small pieces (0.25 cm\({}^{2}\)) and used either for microscope observations or for cultural analysis.  
A detailed observation under an epifluorescent microscope (EFM) Leica DMRE was performed by placing one piece on a glass slide after the deposition of a drop of diluted 1:1 in distilled water and Acridine Orange (AO) fluorescent staining (0.01 mg/mL). For cultural analysis, another piece was streaked (in double for each piece) on the surfaces of the agarized medium Dichlorane Rose Bengal Chloramphenicol, DRBC (Oxoid), for fungi. For the samples taken with a sterile filter paper (60 mm \(\O\)), these were divided into two portions, half of which inoculated on Petri dishes containing agarized Blue Green Medium, BG11, for algae and cyanobacteria, the other half in flasks containing 30 mL of liquid BG11.  
All Petri dishes containing DRBC were incubated in the dark at 25 \({}^{\circ}\)C for one month. Samples inoculated in BG11 (liquid and solid) were incubated in daylight and at room temperature (22 \({}^{\circ}\)C) for 2-3 months.  
Phototrophic microorganisms were discriminated at group level (either eukaryotic algae or prokaryotic cyanobacteria) for their size and morphology, under light and epifluorescence microscopy Leica DMRE.  
Fungi were identified either by using the diagnostic keys of [PERSON] [34], [PERSON] [35], [PERSON] et al. [36] and [PERSON] [37], or by molecular techniques for black fungi as described in detail by [PERSON] [7]. In brief, DNA was extracted and PCR PCR amplified with the universal primers ITS1/ITS4 which are specific for fungal interspacers ITS1, ITS2 and 5.85 rRNA genes. The reaction mixture contained 0.48 mM of each primer, 25 mL of MyTa\({}^{\text{TM}}\) Mix 2x (Boline, London, UK) and 2 mL of template in the total reaction volume of 50 mL. PCR was performed with the following thermocycling program: 5 min denaturation at 95 \({}^{\circ}\)C, followed by 35 cycles of 1 min at 94 \({}^{\circ}\)C, 1 min at 55 \({}^{\circ}\)C and 1 min and 30 s at 72 \({}^{\circ}\)C, and a final extension was run at 72 \({}^{\circ}\)C for 10 min in the thermocyler TPersonal (Biometra, Germany). The PCR products were visualized on 1.5% agarose gel stained with ethidium bromide. Purification and sequencing of PCR products were performed by a commercial facility (Biofab, Rome, Italy). The closest relatives of isolates were determined by comparison with rDNA gene sequences in the NCBI GenBank by BLAST search ([[http://blast.ncbi.nlm.nih.gov/Blast.cgi](http://blast.ncbi.nlm.nih.gov/Blast.cgi)]([http://blast.ncbi.nlm.nih.gov/Blast.cgi](http://blast.ncbi.nlm.nih.gov/Blast.cgi))).  
Cultures allowed us to characterize the main groups of microorganisms involved in the formation of biological patina observed and then later were used to test the effectiveness of some biocides to be applied to slow down the biological colonization.
## Problem Formulation
### Mathematical Description of BESS  
There are two key parameters influencing the optimization capacity of BESS: the investment cost and the cycle life. Researches have shown that BESS will be widely used in electricity market with the investment cost being less than 250 $/kW-h and the cycle life being more than 4000 times [1,2]. At present, the lithium-ion battery is considered to be the most promising energy storage technology for its high rate in the MW-level electrochemical energy storage project [3]. Therefore, this paper chooses the lithium-ion battery as an example to carry out the following research, and the unit investment cost of BESS set as 250 $/kW-h and the cycle life takes 4500 times.
## 6 Method
### Reward Feature Design  
In our active preference learning framework, the reward structure is designed to guide the autonomous vehicle in accomplishing various tasks, with rewards formulated as sub-rewards. The primary goal of the autonomous vehicle is to reach the designated target successfully. We define a successful arrival as when the Euclidean distance between the vehicle and the endpoint is less than 0.5 m, awarding a positive reward of \(r_{success}=+1\). In order to encourage collision-free navigation, a negative reward of \(r_{collision}=-3\) is applied in the event of a collision, which acts as a penalty. Furthermore, to maintain a fixed training episode duration, the episode is terminated, and the agent receives a negative reward of  
Figure 4: The figure displays the simulation model used for navigation, details of the vehicle’s sensor arrangement for capturing forward vision, and the accessible state vectors. These vectors, including velocity, position, and acceleration, can be easily obtained from onboard sensors.  
\(r_{long\_time}=-1\) if it exceeds a predefined length of \(n\). Prolonged periods of inactivity by the vehicle can hinder the training process; hence, if the speed drops below \(0.05\) km/h for 50 consecutive episodes, the training is concluded, and the agent receives a negative reward of \(r_{timeout}=-1\).  
Calculating the distance-based potential reward involves determining the change in the vehicle's proximity to the endpoint between consecutive time steps. This is articulated in Equation (8), where the current and previous distances are compared:  
\[r_{\text{potential}}=|location_{\text{end}}(t)-location_{\text{vehicle}}(t)|-| location_{\text{end}}(t-1)-location_{\text{vehicle}}(t-1)| \tag{8}\]  
As Equation (8) illustrates, a positive reward is attributed when the vehicle approaches the endpoint, while a negative reward signifies increased distance from it.  
Furthermore, to ensure the autonomous vehicle maintains an appropriate speed, a reward mechanism is established to encourage travel within a specific range of 25 to 50 km per hour, as defined in Equation (9):  
\[r_{\text{velocity}}=\begin{cases}v-25,&\text{if }v<25\\ 0,&\text{if }25\leq v\leq 50\\ 50-v,&\text{if }v>50\end{cases} \tag{9}\]  
In Equation (9), \(v\) denotes the vehicle's current speed, with the reward structure designed to incentivize adherence to the target velocity range for autonomous navigation. The reward function \(R(\xi)\) is formulated as a linear combination of carefully chosen features \(\Phi:\Xi\rightarrow\mathbb{R}^{d}\), where \(\Phi(\xi)\) encompasses rewarding aspects such as \(r_{\text{success}}\), \(r_{\text{collision}}\), \(r_{\text{long\_time}}\), \(r_{\text{time\_out}}\), \(r_{\text{potential}}\), and \(r_{\text{velocity}}\). The reward weights \(\omega\) are determined through active preference learning methodologies, confirming a dimensionality of six for these weights, as each feature contributes to the overall reward signal guiding the vehicle's behavior.
###### Abstract  
IoT devices and embedded systems are deployed in critical environments, emphasizing attributes like power efficiency and computational capabilities. However, these constraints stress the paramount importance of device security, stimulating the exploration of lightweight cryptographic mechanisms. This study introduces a lightweight architecture for authenticated encryption tailored to these requirements. The architecture combines the lightweight encryption of the LED block cipher with the authentication of the PHOTON hash function. Leveraging shared internal operations, the integration of these bases optimizes area-performance tradeoffs, resulting in reduced power consumption and a reduced logic footprint. The architecture is synthesized and simulated using Verilog HDL, Quartus II, and ModelSim, and implemented on Cyclone FPGA devices. The results demonstrate a substantial 14% reduction in the logic area and up to a 46.04% decrease in power consumption in contrast to the individual designs of LED and PHOTON. This work highlights the potential for using efficient cryptographic solutions in resource-constrained environments.  
authenticated encryption; FPGA; hardware footprint; hardware security; LED block cipher; lightweight cryptography; logic resources; low power; PHOTON hash function +
Footnote †: journal: Applied sciences
## 4 Experiment and Result Analysis
### Comparison of Results on Datasets

Table 1: Performance on Conll04 dataset. (The highest F1 is shown in bold. metrics: micro-average = \(\ddagger\), macro-average = \(\ddagger\), not stated = \(\ast\)). (without overlap) and 1.20% (with overlap) higher in the named entity recognition, and it is 2.80% (without overlap) and 2.54% (with overlap) higher in the relation extraction. overlap). This result shows that EINET has advantages in extracting both overlapping and non-overlapping entities.
# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 3 Results and Discussion
### Landscape Suitability Assessment
However, the percentage of explained suitability is relatively lower (0.73). In this case, agricultural and natural landscapes produce more directly delivered services to the use regions than urban landscapes (UHI, UMI, and CMI). These results indicate how the scaling method represents a crucial factor determining noticeable quantitative differences in landscape suitability. This evidence is due to their different mathematical meaning: on the supply-based scaling, the result is relative, and for each indicator, there are always a value of 0 and a value of 1 in the study area, while when demand-based scaling is applied, indicators represent the percentage of potential demand that is satisfied. Thus, the two scaling methods give a different meaning to the final suitability indicator: when supply-based scaling is applied, indicators effectively identify the areas with service scarcity, while when demand-based scaling is applied, SUSAM gives informative results about the satisfaction of potential demand. Since many essential services are equally provided across the whole study area, the demand-based  
Figure 5: Graphs of mean suitability values in the various landscape classes (landscape codes are described in Table 1 and scenario codes in Table 2). (**A,B**): economic perspective, (**C,D**): infrastructure perspective. (**A,C**): supply-based suitability, (**B,D**): demand-based suitability. scaling results in a lower level of differentiation between landscape areas, but the resulting suitability is also potentially comparable with the index from other study areas, scaled with the same method. On the contrary, supply-based scaling is more effective in highlighting differences within the area, guiding policy makers more effectively towards comparing the different locations within their administrative areas. The application of SUSAM for identifying the areas where services needing accessibility are concentrated (INF) highlights the crucial role of accessibility for these areas to benefit the supplied services. In fact, accessibility, defined as the potential opportunities that can be reached from a given place by paying a specific generalized and space/time-based cost [61], is a vast and complex concept that needs deep understanding to be adequately quantified also considering its spatial nature. As suggested by [13], accessibility can be used as a proxy for assessing service supply, but its application can limit the use of indicators able to quantify the service supplied. So, further investigation is needed to integrate service quantification with their accessibility assessment.
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Clutter Map Cfar Mixed with the Gabor Transform Algorithm  
#### 2.2.1 Analysis Tools and Investigation Techniques  
Let us consider a metric radar with a pulse width equal to 6 microseconds. The CMAP-CFAR method was first used to detect and determine if there were targets in the scanned cell. After the CMAP-CFAR tool decides on the presence of a target, we use the Gabor transform to verify whether there are several targets inside that cell. The OS-CFAR detector provides the final decision about the number of targets detected. This process can be seen in the following Figure 6, that represent the block diagram of the The joint use of a CFAR detector and a time-frequency technique allows us to address difficult cases (near targets).
# Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions
## 3 Privacy and Security Requirements for Blockchain
**Asymmetric key cryptography**, also called public-key cryptography (e.g., RSA [72]) is efficient for both user identity and transaction data anonymity due to its security properties e.g., confidentiality and authentication on data [61]. It is based on a couple of keys (called public and private) to encrypt and decrypt messages. Only the public key can be shared. It relies on digital signatures to verify that messages come from a particular sender (if encrypted with the sender's private key) or to guarantee that only a specific receiver can read it (if encrypted with the receiver's public key). However, it is computationally more intensive compared to symmetric key cryptography, which makes it less suitable for encrypting large amounts of data. In practice, it typically requires a TTPs to manage user's identities, although key management can be realized individually. **Homomorphic encryption**[73] ensures data confidentiality and provides confidentiality on transaction data, and therefore user anonymity. It encrypts data and allows anyone to perform actions on the encrypted version without accessing the encryption key. However, it depends on TTPs and has a high computation overhead. **Hash Functions** are most often utilized to confirm data integrity over a copy of original data and a signature by comparing with the one generated from the copy without any intermediate party. A hash function is a one-way function that transforms input data into fixed-length data. It has a low computation overhead as compared to other available solutions.
## 4 Discussion
### Temporal Response of Satellite Information to Wet Pulses  
In general, our results about the temporal response of the LSWI and LST to SM show that the LSWI is an integrative indicator of the cumulative SM effect on vegetation status, but it is not an early proxy of that condition, being temporally associated with the EVI. Changes in vegetation water content have frequently been associated with short-term fluctuations in SM over different vegetation types, given its association with leaf water potential and subsequent extraction of water by the roots. Our results suggest that detectable increases in the satellite-derived LSWI in the study area seem to be strongly associated with the vegetation greenness that can be observed through typical vegetation indices like the EVI. Some studies [25; 61] suggest using scatter plots of SWIR/VIs instead of LST/VIs as an indicator of vegetation water condition and SM, given their advantage of better spatial resolution in spectral solar bands. However, considering the observed lags in the response of this spectral index, the use of SWIR over this system should be examined. Although spectral indices associated with changes in vegetation water conditions in the study area should show weekly or daily fluctuations during rainfall wet pulses, sub-daily data can be considered to further test their complementarity with the analyzed temporal scale.  
LST has been widely used as an indicator of SM and subsequent ETa through its relationship with evaporative fraction (the ratio between latent heat flux and available energy at the land surface) [29; 62]. Several works have explained in detail the behavior of LST indicator of sensible heat flux in relation to physiological processes like stomatal regulation according to soil water content in the root zone and atmospheric water demand (e.g., [63]). Results show decreases in LST in the days after the event occurs, maintaining low values during variable temporal windows (Figure 6). The quick response of LST to wet pulses compared to spectral indices suggests that the temporal effect of the evaporative cooling process is more noticeable during the wettest pulses. Although the sensitivity of this savanna ecosystem is not as large as can be expected in agricultural lands [64; 65], where several thermal studies have been conducted, our results suggest that significant decreases in LST during late spring and summer are related to increases in subsurface SM.
## 5 Performance Evaluations
### Results and Discussions  
#### 5.3.1 CH Lifetime  
Figure 5 shows the CH lifetime as a function of the number of nodes in a cluster. The performance of the proposed scheme was analyzed on different cluster sizes (e.g., on average 10, 20, 30, 40 and 50 nodes per cluster) and with two different packet rates (e.g., 5 and 10 pkts/s). As shown in Figure 5, the proposed scheme outperformed SEED protocol in terms of average CH lifetime. It is evident that in SEED protocol, re-clustering happened frequently. The reason is that the SEED protocol only considered the residual energy in the CH declaration process and did not consider other available resources such as storage capacity, and computational capability. Therefore, with increase in nodes density and packet rate, the CH-lifetime decreased. However, the proposed scheme took into account the available resources such as storage capacity and computational capability
## 4 Comparisons of Results for DFE, MLD Algorithms
### Equalization by Radius-Directed Equalizer  
As the RDE algorithm is independent of the carrier offset and is ideal for QAM signals, achieving minimum error when the signal is equalized [45; 46].  
RDE is executed on the distorted data acquired at the OFDM receiver. The constellation diagrams for QPSK and 16-QAM were acquired following equalization at the maximum fiber lengths of 120 km and 100 km, respectively, resulting in an EVM of 11.35% for QPSK and 9.41% for 16-QAM. In our proposed setup, RDE is not effective for higher-order modulation formats such as 64 QAM and 128 QAM.  
Figure 5 shows the constellation diagrams of QPSK and 16-QAM, obtained before and after the equalization at the maximum fiber length of 120 km and 100 km that can be achieved before equalization (a and b) and after equalization (c and d) for the respective modulation formats.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Modulation Scheme**} & **EVM for MLD After** & **EVM for DFE After** & **EVM Improvement (\%)** & **Max Fiber Length (km)** \\  & **Equalization (\%)** & **Equalization (\%)** & **EVM Improvement (\%)** & **Max Fiber Length (km)** \\ \hline QPSK & 17.06 & 16.60 & 0.46 & 120 \\ \hline
16-QAM & 12.40 & 12.32 & 0.08 & 100 \\ \hline
64-QAM & 7.35 & 7.19 & 0.16 & 50 \\ \hline
128-QAM & 4.85 & 4.73 & 0.12 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: EVM improvement of DFE vs MLD across various modulation schemes and fiber lengths.  
Figure 5: Constellation diagrams and EVM measurements for the QPSK and 16-QAM modulation using RDE equalization: (**a,b**) respective constellation diagrams before equalization, and (**c,d**) respective constellation diagrams after RDE equalization.  
The RDE algorithm is based on the equalizer output and the nearest constellation radius. The running time increases linearly with the input size (N symbols). Thus, the complexity is O(N), and for time T, the total time complexity of the algorithm is O(N) \(\times\) T.  
Space complexity measures the amount of memory an algorithm uses as a function of the input size. Since RDE finds the closest constellation point within the decision radius, not in the entire constellation of points, we can say that its space complexity is linear [45].
# Analysis of Seaweeds from South West England as a Biorefinery Feedstock
## 3 Results and Discussion
### Phytolormones  
In the agricultural industry, phytohormones have several commercial uses that are related to plant growth, flowering, ripening and alleviation of abiotic stresses such as drought, heat, cold, light and nutrient stress. Synthetic phytohormones have been used in agriculture for over 50 years with great success, but recent changes in regulation and consumer preferences have created a greater need for natural and sustainable alternatives. Whilst high-purity extracts may or may not prove a viable high-value product in a seaweed biorefinery in their own right, their presence in fractions to be used as agricultural fertilisers, soil conditioners and bio-stimulants could provide an economic premium for such products. Cytokines (including TZR, CZR and 2 iP) are amongst the most valued phytohormones in agriculture as they are directly related to plant growth, flowering and fruit set, and there are currently not many natural sources available in the market.  
The levels of four different bioactive molecules (i.e., abscisic acid (ABA), trans-zeatine riboside (TZR), cis-zeatine riboside (CZR) and N6-(2-isopentyl) adenosine (2 iP)) within the dried seaweed biomass was assessed (Figure 1). The levels of phytohormones were species specific and there was no trend with taxa, and whilst several seaweeds showed strong phytohormone production, four had levels that were extremely low and not of any commercial relevance. The highest level of ABA was seen in _Ulva lactuca_, which also had strong levels of TZR, CZR and 2 iP. _Sargassum muticum_ and _Himanthalia elongata_ both had high levels of 2 iP and TZR, whilst CZR was highest in _Ulva lactuca_ and _Rhodomela confervoides_.
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 3 Fuzzy Sets Theory
### Interval Type-2 Hesitant Fuzzy Set  
In order to simplify the calculation, [29] put forward interval type-2 hesitant fuzzy set based on the interval type-2 fuzzy set. **Definition 7**. : _Assuming that \(X\) is a fixed set, interval type-2 hesitant fuzzy set is a subset of interval type-2 fuzzy set. It can be expressed mathematically as:_  
\[\text{E}=\left\{<x,\widetilde{h}_{E}(x)>\mid\text{x}\in\text{X}\right\}\]  
_where \(\widetilde{h}_{E}(x)\) is the element of [0, 1] and denote the probability of \(\text{x}\in\text{X}\).