esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this work, a novel clustering routing protocol called YSGAP was proposed. The method finds the optimal network configuration to reduce the energy consumption in every round and prolong the network lifetime. In its operation, the YSGAP protocol automatically determines the number of clusters heads and selects the sensor nodes that will play the role of cluster heads in every round. Our method was compared with some of the most popular clustering routing protocols in the literature, such as DEEC, LEACH, and SEP. Experimental results proved that the proposed strategy outperforms the methods in comparison. The evaluation of the proposed routing protocol was carried out, demonstrating that our approach manages to considerably increase the lifetime of the network, while also providing robustness in communications, fault tolerance, and time-bounded response questions.  
As feature work, the proposed protocol can be enhanced by including more terms in the cost function, such as considering uniform clusters sizes for a better load balancing. Additionally, the  
Figure 6: Energy deviation.  
Figure 7: Throughput: total number of packets sent to the BS.  
encoded candidate solutions might be arranged differently so that the dimensionality of the optimization problem can be considerably reduced. This change can increase the scalability of the sensor network. Furthermore, the off-line execution of the protocol can consider the main drawback of the proposed method. Therefore, a significant improvement can be its development in parallel to speed up the process of finding the optimal cluster configuration. This improvement will allow the protocol to run online in real time applications.  
**Author Contributions:** Relatively to the present manuscript, [PERSON] developed the energy algorithm, prepared, and executed the scenario and test-bed, interpreted and analyzed the results, and designed the methodology. [PERSON] was involved in the algorithm design, she supervised the research methodology and the approach of this work, she performed the formal analysis and interpreted the results. [PERSON] reviewed, interpreted, and drafted the simulation results, he reviewed the methodology and the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Results
### Ground Measured NDVI Time-Series vs. Smoothed Satellite NDVI Time-Series
In conclusion, this experiment showed that applying smoothing methods to MODIS NDVI reduced differences with ground-measured NDVI. The local filtering methods (SG and LO) had better performance in representing ground NDVI from MODIS NDVI with optimized setting parameters, but the function fitting methods (AG and DL) were more robust in the sense that they generated less varying results depending on how the settings were chosen. CV for automatically choosing smoothing parameters generated better-than-average fits for the majority of SG, LO and SP runs, however it failed in a few cases.
## 4 Data Pre-Processing  
### Data Collection  
The first step involved the collection of past MTSA performance data. This included gathering available datasets from the Korea Maritime Transportation Safety Authority(KOMSA) regarding MTSA implementation from 2010 to 2014, before the scope of the MTSA system was reduced in accordance with the marine development project cycle mentioned in Section 3.1. During this period, 75 MTSAs were implemented. The data columns are structured as shown in Table 3.  
The second step involved the collection of information on coastal passenger ships and marine accidents. Data on the sailing of coastal passenger ships from 2017 to 2022 were collected from open data provided by the Korea Shipping Association (KSA). In addition, marine accident occurrence data for coastal passenger ships from 2017 to 2022 were collected from KOMSA. The major data column structures are listed in Table 4 below.
# Task Migration with Partitioning for Load Balancing in Collaborative Edge Computing
## 3 Task Partitioning and Migration Methods
### Task Partitioning Method  
The following describes a task partitioning that divides tasks into a set of subtasks, i.e., clusters. The task is divided into a set of subtasks, and offloading them increases the parallelism of task execution, minimizing latency [24]. Therefore, we will apply a scenario in which the set of subtasks can be migrated to other MECSs, which can balance the loads among MECSs in the MEC system. In addition, the subtasks are executed collaboratively by MECSs in the system, which can improve the QoS requirements of tasks by minimizing the delay for computing and improving system throughput.  
We assume that the task can be divided into a set of subtasks. We define the set of clusters as \(C\), which consists of the subclusters \(C_{i}\), e.g., \(C=\{C_{2},\ C_{3}\}\) (Figure 3). Cluster \(C_{i}\) is a set that consists of subtasks \(i\) and all its successors in the task graph, except for the last subtask. For example, we have \(C_{2}=\{2,4,5,8,9,10\}\) and \(C_{3}=\{3,6,7\}\). We also define cluster \(C_{i}\), consisting of subcluster \(C_{j}\), e.g., \(C_{5}\) is the subcluster of \(C_{2}\) (\(C_{5}\subset C_{2}\)). At this time,subtask \(j\) is the direct successor of subtask \(i\). As the clusters in \(C\) are not related to each other, each cluster can be migrated independently.  
\(C\) may have more than one cluster; therefore, the order of clusters' migration should be determined. We select the order of clusters with large computational workloads, which have a large number of CPU cycles to compute the subtasks [20]. We represent the CPU cycles required to complete the set of subtasks in a task as \(l_{i}^{c}\), which is calculated as \(\eta_{n,i}/\left(T_{n}^{max}-T_{n}^{trans}\right)\). To balance the load among MECSs, particularly between overloaded and underloaded MECSs, the amount of workload of clusters is migrated using the load difference of MECSs. By migrating them in descending order cluster size, the load of the overloaded MECS and underloaded MECS can be narrowed. If the condition that the cluster workload is lower than the load difference between the overloaded and underloaded MECS is not satisfied, the large cluster will be split into smaller ones.
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
###### Abstract  
Random numbers are widely employed in cryptography and security applications. If the generation process is weak, the whole chain of security can be compromised: these weaknesses could be exploited by an attacker to retrieve the information, breaking even the most robust implementation of a cipher. Due to their intrinsic close relationship with analogue parameters of the circuit, True Random Number Generators are usually tailored on specific silicon technology and are not easily scalable on programmable hardware, without affecting their entropy. On the other hand, programmable hardware and programmable System on Chip are gaining large adoption rate, also in security critical application, where high quality random number generation is mandatory. The work presented herein describes the design and the validation of a digital True Random Number Generator for cryptographically secure applications on Field Programmable Gate Array. After a preliminary study of literature and standards specifying requirements for random number generation, the design flow is illustrated, from specifications definition to the synthesis phase. Several solutions have been studied to assess their performances on a Field Programmable Gate Array device, with the aim to select the highest performance architecture. The proposed designs have been tested and validated, employing official test suites released by NIST standardization body, assessing the independence from the place and route and the randomness degree of the generated output. An architecture derived from the Fibonacci-Galois Ring Oscillator has been selected and synthesized on Intel Stratix IV, supporting throughput up to 400 Mbps. The achieved entropy in the best configuration is greater than 0.995.  
random; number; generator; TRNG; FPGA; entropy; NIST; Fibonacci; Galois; FiGaRO
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 3 Experiment Results  
### Experiment Data  
#### 3.1.1 xBD Dataset  
In our study, the xBD dataset [48] is used to validate the performance of the proposed method. The xBD dataset is a large-scale public building segmentation and damage assessment dataset with high-quality building annotations from high-resolution satellite images before and after 19 different natural disasters (e.g., earthquakes, volcanic eruptions, hurricanes, and floods). It is sourced from the Maxar/DigitalGlobe Open Data Program [52], where high-resolution images from many disparate regions of the worlds are available. The dataset consists of pairs of pre-disaster and post-disaster 1024 \(\times\) 1024 satellite images. The images are below the 0.8 m ground sample distance (GSD) mark. The split of train, validation, and test sets is shown in Table 1.  
The dataset provides 4-level damage labels, including no damage, minor damage, major damage and destroyed. The number of damage annotations of each level is shown in Table 2. It should be noted that the distribution of each damage level is imbalanced.
# FnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm
## 4 Experimental Design and Result Analysis
### OS-ELM Mixed with RNN  
On the IMDb dataset, we compare three commonly used algorithms without network structure, namely LR, Multinomial NB, as well as SDG, and three algorithms with network structure, i.e., LSTM, RNN, as well as fnnmOS-ELM. The comparison results are shown in Table 3. For enhancing the contrast effect of the fnnmOS-ELM and improving the accuracy, we introduce the TF-IDF statistical method [47] to the three algorithms without network structure after processing the data using the embedded layer [48]. The first three algorithms have advantages in training time and trainable parameters. After 20-epoch training, the accuracy of the pre-trained RNN (Figure 3(b)) reaches 0.8254, which is worse than LR, Multinomial NB, SGD, and LSTM. Then, we access the OS-ELM after the RNN layer (\(i\) = 4, Figure 3(c)), the hidden nodes were set to 10, the batch size was set to 100, and the training epoch was set to 40. From Figure 3(a), it can be seen that the accuracy reaches 0.9925 (\(\pm\)0.005), which surpasses other algorithms tested on the same dataset, and the result enters the top 3 in the Kaggle leaderboard. Compared with the pre-trained RNN, the training time is reduced from 16.72 s to 1.15 s, and the trainable parameter size in the classifier is reduced to 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Method** & **Training Time (s)** & **Trainable Parameters** \\ \hline \multirow{8}{*}{**Iris**} & SVM & \textless{}0.1 & - \\  & LR & \textless{}0.1 & - \\  & Decision Tree & \textless{}0.1 & - \\  & KNN & \textless{}0.1 & - \\  & Simple NN & 16.7213 & 131 \\  & fnnmOS-ELM & \(i\) = 3 & 0.5004 & 15 \\  & \(i\) = 4 & 0.5084 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training time and parameters on the Iris dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Method**} & \multicolumn{2}{c}{**Training Time (s)**} & **Trainable Parameters** \\ \hline \multirow{8}{*}{**IMdb**} & \multirow{4}{*}{TF-IDF} & LR & \textless{}0.1 & - \\  & & Multinomial & \textless{}0.1 & - \\  & & NB & \textless{}0.1 & - \\  & & SGD & \textless{}0.1 & - \\ \cline{1-1} \cline{2-5}  & LSTM & 1178 & 438,045 \\  & RNN & 16.72 & 126,993 \\ \cline{1-1} \cline{2-5}  & fnnmOS-ELM & \(i\) = 4 & 1.15 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training time and parameters on the IMDb dataset.
## 2 Study Area and Data
### Data
#### 2.2.2 Climate Data  
We used climate data from the China Meteorological Data Network [44] to investigate the climatic variations in this region. There are nine meteorological stations in our study area (Figure 1b). Daily mean air temperature, daily mean precipitation, and daily mean air relative humidity at the nine meteorological stations in the years from 2000 to 2016 were used. The identification numbers, land cover types, and elevations of the nine stations are listed in Table 1.  
Figure 2: Spatial pattern of the annual precipitation in the study area. The spatial pattern of vegetation greenness generally agrees with the spatial pattern of precipitation.  
As for the spatial data of precipitation, we used the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) [45]. CHIRPS daily precipitation data in the years from 2000 to 2019 were used. As for the spatial data of VPD, we used the ERA5-Land data [46]. Specifically, the daily air temperature and dew point temperature from ERA5-Land were used in our study.
## 2 Background
### Related Work
However, since both DNSMap and DBod require the observation of an enormous quantity of extensive DNS traffic, their utility is limited to large-scale networks, such as Internet service providers (ISPs). [PERSON] et al. [32] revealed the DGA landscape by reverse-engineering 43 malware families, and [PERSON] et al. [33] provided a mature dataset with analysis results for over 30 million domains generated by 50 malware families. Based on those results, other researchers have attempted to distinguish between benign and malicious domains using only their character strings in manners similar to our approach. For example, [PERSON] et al. [4] proposed a technique that learns and predicts character patterns using bigram models with supervised learning algorithms, and [PERSON] et al. [5] extended this technique using character-level models with LSTM networks. Meanwhile, [PERSON] et al. [34] combined LSTM networks with attention mechanisms to assign appropriate weight values to the characters in domain names. In addition, [PERSON] et al. [35] compared the performance of various machine learning (ML) and deep learning (DL) algorithms when detecting DGA-generated malicious domains. These techniques are based on the existence of discernible bias in the rules for generating malicious domain names and thus must learn the bias in advance by analyzing both benign and malicious datasets. [PERSON] et al. [36] proposed a method for detecting dict-DGA malware based solely on domain strings. This method has two functions: Estimating the dictionary used by dict-DGA malware from their callbacks and identifying malicious domain names using the estimated dictionary. However, this method is extremely simple in that malicious domain names are identified by whether the number of words constituting the character string exceeds a given threshold in the estimated dictionary. As a result, numerous false positives are caused by the inability to consider benign domain names. In contrast, our work focuses on using ML to characterize the differences between benign and malicious domain names and thus produces better results than can be achieved by the method in [36].
# Susceptibility of _Staphylococcus epidermidis_ to Argon Cold Plasma Jet by Oxygen Admixture
## 2 Materials and Experimental Setup  
### Non-Thermal APPJ System  
The non-thermal APPJ system consisted of an alumina ceramic (Al\({}_{2}\)O\({}_{3}\)) insulator tube with 150 mm length and having a 3 and 4.8 mm inner and outer diameter, respectively (Figure 1). The alumina tube has a purity of 99.7%. The tube was surrounded by two identical copper rings with a 6 mm width and 0.48 mm thickness. The two rings were separated by 10.2 mm. The lower ring was far from the tube nozzle by 5 mm and connected to the ground. A sinusoidal AC high-voltage signal was applied to the upper upper ring, which was acting as the high-voltage electrode. Argon and oxygen gases with purity 99.999% were blown through the alumina tube to form a plasma jet when the AC high voltage was applied between its electrodes. The gas flow was controlled and measured using mass flow meters (AlicatScientific MC-5 SLPM (standard liter per minute)-D and MC-20 SLPM-D).  
The emission spectra of non-thermal atmospheric pressure plasma jets (APPI) in the air were characterized by optical emission spectroscopy using an Acton SP-2356 imaging spectrograph with a single-leg-fiber optic bundle (LG-455-020-3). It is a triple grating 500 mm focal length spectrograph. Two gratings were used throughout this work. The first grating has 3600 gr/mm, which is blazed at 253.65 nm and sensitive in the range 200-450 nm, the second grating has 1800 gr/mm, which is blazed at 500 nm, and its sensitivity range is 450-850 nm. The third grating--which was not used--has 150 gr/mm and is blazed at 1000 nm with a sensitive range of 615-1800 nm. The spectrograph was equipped with a built-in high sensitive photomultiplier detector with a sensitivity range from 190 to 900 nm (model ARC-P2, Princeton instrument).  
The gas temperature of the non-thermal APPJ was measured by evaluating the rotational (0,0) band of the second positive N\({}_{2}\) system. The recorded spectrum was observed side-on, from the nitrogen second positive system N\({}_{2}\) (C\({}^{3}\Pi_{u}\)-B\({}^{3}\Pi_{g}\)), and compared to a simulated spectrum. Then, the temperature was attained by matching the two spectra. Moreover, the vibrational temperature (T\({}_{vib}\)) was measured by comparing the experimental emission spectra of the argon non-thermal APPJ with the simulated one.
This process further leads to an increased extraction yield [3]. Both the UAE and MAE methods are considered green extraction methods. The _Quercus_ genus (L.) belongs to the Fagaceae family and Fagales order and contains trees that are spread throughout the world with different morphology in terms of chemical compounds [6]. One of the most well-known species is _Quercus robur_ L. (syn _Quercus pedunculata_ [PERSON]), known as common oak or pedunculate oak. It is found spontaneously in deciduous forests and is used in the wood industry and folk medicine [6; 7] _Q. robur_ bark also contains hydrolysable and condensed tannins. [PERSON] et al. [6] revealed several phenolic compounds in the composition of _Q. robur_ bark, including catechin, ellagic acid, protocatechuic acid, gallic acid and vanillic acid. The bioactivity assay revealed antioxidant, antibacterial, antidiabetic and anticancer activity in the _Q. robur_ bark extracts [6; 8]. According to the European Pharmacopoeia [9], oak bark can be obtained from 10 to 15-year-old oak trees (_Quercus robur_, _Q. petraea_ and _Q. pubescens_). However, these trees are not of interest to the wood processing industry, since their diameter is too small. Therefore, a question arises: what happens to the bark of the oak trees used in the forestry industry? According to the FAO (Food and Agriculture Organization), global wood production was about 5157 million m\({}^{3}\) in 2020 (FAO, 2022), generating over 103 million m\({}^{3}\) of wood residue [10]. In the wood industry, the bark is considered a waste and it is mainly used as fuel. Thus, this study evaluated (1) the histo-anatomical structure of older oak bark; (2) the polyphenic and tannins content of the aqueous and hydroalcoholic extracts that were obtained using MAE and UAE; and (3) the biological potential of older oak bark extracts (antioxidant capacity, antibacterial activity, enzyme inhibitory activity). The novelty of the study consists of the valorization of old pedunculate oak bark (forest waste) by recommending it as a source for phenolic compounds with potential antidiabetic, antioxidant and antimicrobial biological actions.
## 4 Results and Discussion
### Parameters' Optimization of the Extreme Learning Machine
For BERs below the FEC threshold (a successfully communication), the RF linewidth decreases by a factor close to 2 and 20 times from BPSK to QPSK and from QPSK to 16 QAM, respectively. These relationships tend to reduce as the BER reaches the FEC limit, which can be attributed to the ELM behavior as the constellation alphabet and phase-noise level augment. Because the system performance of phase-uncorrelated OFDM signals depends on the relationship between the RF linewidth and OFDM symbol period for a certain subcarrier modulation format [2; 3; 7], this outcome and the rest of observations along the paper are useful for the design and implementation of CO-OFDM schemes. Figure 5: For the different modulations: (**a**) BPSK, (**b**) QPSK, and (**c**) 16 QAM, the system performance as a function of the RF linewidth with the activation function as parameter. Figure 6: For (**i**) SIG, (**ii**) COS, (**iii**) HL, (**iv**) TB, (**v**) RB, and (**vi**) HT activation functions, histogram constellations together EVM values at training and testing stages. The subcarrier modulation format and combined linewidth correspond to 16 QAM and 100 kHz, respectively.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 5 Titanium-Based Nanomaterials
### Nanoparticles and Microspheres  
The most common TiO\({}_{2}\) photocatalysts for air purification are zero-dimensional structures such as nanoparticles and microspheres. This kind of structure allows a high photocatalytic efficiency due to its small particle size that causes a high specific surface area and high pore volume. The high specific surface area enhances the exposure of the photocatalyst to radiation and enhances the quantum efficiency. Moreover, the use of nanoparticles implies short charge carrier diffusion distances which reduce the charge carrier's recombination [113]. Therefore, this enhances the adsorption of the target pollutant, the light-harvesting capacity, and the photocatalytic degradation. Despite this, an important problem that influences its photocatalytic activity is the agglomeration of TiO\({}_{2}\) nanoparticles with high Gibbs free energy into larger aggregates leading to a decrease in the active sites of the photocatalyst [114]. Photocatalytic activity is enhanced by the homogeneous distribution of the TiO\({}_{2}\) nanoparticles [115]. To obtain separate nanoparticles is often needed a deagglomeration step [111]. To overcome this limitation, for instance, it has been studied the use of clay mineral-TiO\({}_{2}\) nanocomposites that allow better dispersion of TiO\({}_{2}\) nanoparticles [114].  
Particle size significantly influences photocatalytic efficiency since it directly affects the specific surface area of the photocatalyst. It has been demonstrated that there is an optimum particle size of TiO\({}_{2}\) that corresponds to about 10 nm [116]. A smaller radius can reduce the efficiency of the photocatalytic reactions due to the quantum size effect. This term indicates the phenomenon whereby reducing the particle size to a certain value, these particles have discontinuous energy levels of conduction and valence bands resulting in increasing the energy band gap of the material [113].  
Mesoporous and porous TiO\({}_{2}\) spheres have been widely studied and presented high photocatalytic efficiency due to the high pore volume and pore size, high specific surface area, and good crystallinity [86; 117]. Also, hollow nanospheres have been studied since these structures exploit the multiple scattering of incident light inside the particles increasing the photocatalytic efficiency.