esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring
## 2 Materials and Methods
### LEACH Protocol  
LEACH is a hierarchical protocol in which nodes transmit data to CHs, and then they forward data to the base station (sink) [16; 30]. The main idea of the LEACH protocol is to divide the whole WSN into several clusters [3; 13; 19; 23]. LEACH randomly selects a few sensor nodes as CHs and rotates this role to distribute the energy load among the sensors in the network [14; 31; 32; 33]. The CH node is randomly selected, and each node can be selected as a CH node [3; 21; 34; 35]. LEACH protocol runs for a predetermined number of rounds and each round contains two states: cluster setup state and steady state [10; 11; 12; 20; 26]. In the cluster setup state, it forms a cluster in the self-adaptive mode; in the steady state, it transfers data [1; 5; 25; 26; 31]. The time elapsed in the second state is usually longer than the time elapsed in the first state for saving the protocol payload. Figure 1 shows the flowchart of the LEACH operation.  
CH election in LEACH is based according to a certain energy threshold value [31]. If the remaining energy is lower than a threshold, the node becomes a CH for the current round [35]. Nodes that have been CHs cannot become CHs again for P rounds, where P is the desired percentage of CHs. Thereafter, each node has a 1/P probability of becoming a CH in each round [2]. At the end of each round, each node that is not a CH selects the closest CH and joins that cluster [5; 9; 10; 11; 22; 24; 25; 26; 27; 31; 32; 36]. The threshold is set as shown in (1):  
\[\mathrm{T(n)}=\left\{\begin{array}{c}\frac{\mathrm{P}}{1-\mathrm{P}\times \left(\mathrm{r}\times\mathrm{mod}\,\mathrm{d}\right)}\mathrm{if}\;\mathrm{n} \in G\\ 0\;\mathrm{else}\end{array}\right\} \tag{1}\]  
where P is the desired percentage of CHs, r is the current round, and G is the set of nodes that have not been CHs in the last 1/p rounds [10; 33].  
Using this threshold, each node will be a CH at some point within 1/p rounds [35]. Nodes that have been CH cannot become CHs for a second time for 1/p - 1 rounds [31]. The CHs combine and compress the data and forward them to the BS; therefore, it extends the lifespan of major nodes [6; 12; 24; 25]. However, the main challenge in LEACH is the non-uniform distribution of CH nodes in the network, which makes it inapplicable in large regions [3; 20].
# Parallel Improvements of the Jaya Optimization Algorithm
## 2 The Jaya Algorithm  
We review here some different studies of the Jaya algorithm and summarize their conclusions. The author of in [3] tested the performance of the Jaya algorithm, by means of a series of experiments on 24 constrained benchmark problems. The goal of the algorithm was to get closer to the best solution, but in so doing it also moves away from the worst solution. Results obtained using the Jaya algorithm were compared with results obtained by other optimization algorithms such as GA, ABC, TLBO, and a few others. The superiority of Jaya was shown by means of two statistical tests: the Friedman rank test and the Holm-Sidak test. The Jaya algorithm came first in the case of the best and mean solutions for all considered benchmark functions, while TLBO came second. With regard to the results of the Friedman rank test for the Success Rate solutions obtained, Jaya again came first followed by TLBO. The Holm-Sidak test provided a difference index related to the results obtained by Jaya and the other algorithms. This test showed a maximal difference between Jaya on the one hand and GA and BBO on the other, and a minimal difference with TLBO.  
In the same study, Jaya performance was tested further on 30 unconstrained benchmark functions that are well known in the literature on optimization. Results obtained using Jaya were compared with results obtained using other optimization algorithms such as GA, PSO, DE, ABC, and TLBO. Mean results obtained were compared with other algorithms. Jaya obtained better results in terms of best, mean, and worst values of each objective function and standard deviation.  
The authors of [32] applied Jaya to 21 benchmark problems related to constrained design optimization. In addition to these problems, the algorithm's performance was studied over four constrained mechanical design problems. An analysis of the results revealed that Jaya was superior to, or could compete with, the others when applied to the problems in question. The authors of [33] showed that Jaya is applicable to data clustering problems. Results demonstrated that the algorithm exhibited better performance in most of the considered real-time datasets and was able to cluster appropriate partitions. We want to emphasize that our parallel algorithms do not modify the behavior of the Jaya algorithm. Moreover, in [15], the authors explore the use of advanced optimization algorithms for determining optimum parameters for grating based sensors; in particular, Cuckoo search, PSO, TLBO, and Jaya algorithms were evaluated. The best performance was obtained using the Java algorithm, good results were also obtained using the Cuckoo search algorithm, but it should be noted that the latter requires tuning the specific parameters of the algorithm to find the global optimum value.
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 3 Progress in Remote Sensing of Drought from a Climatological and Ecological Perspective
### Remote Sensing of Soil Moisture in Drought
Using time-series Sentinel-1 C-band images with NDVI data derived from the optical Sentinel 2-data satellite, it is achievable to derive information about soil moisture in different areas using WCM [168; 171; 176]. Comparing with the models which require in situ data for calibration, method change detection can be used for SSM estimation without this requirement. To detect changes in backscattered signal observed on two consecutive dates of Sentinel-1, method change detection is often applied in research studies. It is based on the assumptions that the change in vegetation is small between two days and that the difference in backscattered signals depends on the change in SSM [177]. This method requires data such as NDVI from optical satellites, and Sentinel-2 provides that opportunity with high spatial and temporal resolution. According to [PERSON] et al. [172], the change detection method enables the global application for SSM detection and can be applied to any vegetation area where the images from Sentinel-1 and Sentinel-2 are available [172]. [PERSON] et al. [172] also showed that with this approach, SSM can be retrieved for 100 m (or even better) resolution. [PERSON] et al. [65] analyzed the potential of SSM estimation for drought monitoring in the savanna ecosystem by applying a change detection method to VV-polarized Sentinel-1 C-band. He found good agreement between SurfMI derived from Sentinel-1 in a combination with Sentinel-2 and Landsat-8 data and in situ soil moisture data. They concluded that deriving data about SSM in different land cover classes can be suitable for drought monitoring.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 3 Results and Discussion
### BDS/GPS PPP Performance
#### 3.3.1 Static Mode  
This section gives four BDS/GPS static PPP models for positioning. Figure 7 shows the static positioning error curves of stations CUSV, DGAR, GAMG, MIZU, SEYG and MOBS on DOY121. The positioning accuracy of the TF-F model for the CUSV and DGAR stations is less than their median, while it is greater than their median for the GAMG, MIZU, SEYG and MOBS stations. It can be observed that the DF and TF-F models perform slightly worse in the convergence process compared to the other three frequency models. After convergence in the E, N and U directions, the positioning error curves of the four PPP models basically coincide.  
Figure 8 shows that, except for the TF-F model, the positioning accuracy of the stations does not improve significantly with the addition of the third frequency [34], but the convergence time is slightly reduced. Table 6 demonstrates that the positioning accuracy is better than 1.0 cm, except for the TF-F model's U direction, and better than 1.5 cm for the TF-F model's U direction. In terms of convergence time, the convergence time of the TF-CF model is improved by 18.1%, 9.7% and 12.1% in the E, N and U directions, respectively. The convergence time of TF-C model is the shortest among the triple-frequency PPP models. It is 28.43, 11.61 and 16.68 min in the E, N and U directions with improvements of 23.5%, 13.6% and 19.8%, respectively.  
In contrast to the experiment above, in the dual-system static PPP experiment, ignoring the GPS PIFCB had a slight effect on the PPP results; this is because the BDS plays a major role in the PPP process, as the BDS can ignore the PIFCB [43].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Convergence Accuracy (CM)**} & \multicolumn{3}{c}{**Convergence Time (Min)**} \\ \cline{2-7}  & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline DF & 0.56 & 0.38 & 1.00 & 37.18 & 13.43 & 20.80 \\ TF-F & 0.81 & 0.49 & 1.39 & 35.18 & 15.04 & 19.54 \\ TF-C & 0.54 & 0.39 & 0.93 & 28.43 & 11.61 & 16.68 \\ TF-CF & 0.56 & 0.39 & 0.93 & 30.46 & 12.12 & 18.29 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics on the positioning results of the four BDS/GPS static PPP models.  
Figure 6: Positioning accuracy and convergence time distribution of the four BDS static PPP models. (**a**) Convergence accuracy. (**b**) Convergence time.  
Figure 8: Positioning accuracy and convergence time distribution of the four BDS/GPS static PPP models. (**a**) Convergence accuracy. (**b**) Convergence time.  
Figure 7: Positioning error of the four BDS/GPS static PPP models (DOY121, 2022). The stations are (**a**) CUSV, (**b**) DGAR, (**c**) GAMG, (**d**) MIZU (**e**) SEYG and (**f**) MOBS, respectively.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 3 Outdoor Measurements and Wireless Link Characterization
### Communication Range Outdoor Measurements
#### 3.2.3 Experimental Results  
Figure 12 shows the results of the measurements done in the urban and suburban areas. From these measurement results, it can be seen that increasing the bandwidth, \(B\), in the urban area to achieve a higher data rate has a more pronounced impact on range (i.e., \(BER\leq 0.1\)) when compared with the suburban area, which can be explained by the previously measured higher noise density. The noise power in a given bandwidth is expressed by  
\[P_{\text{NOISE}}=N_{A}+10\;\log(B) \tag{11}\]  
Figure 11: Google satellite image of the field measurement setup in the urban area.  
Figure 12: _Cont._It was previously mentioned that using high data rates would result in a better energy consumption, as depicted in Figure 6. However, the achievable communication range was yet unknown. Figure 12 shows that, at 0.1% BER as required by Bluetooth applications, similar communication ranges can be achieved by increasing the output power and data rate (i.e., 500 Kbps and 0 dBm) or decreasing them (i.e., 50 kbps and \(-\)10 dBm). However, Figure 6 shows that the energy consumption per measurement, when the first configuration is used, is three times less than when using the second. Therefore, the previously made assumption of the need to increase the data rate in order to achieve a better energy efficiency while covering the desired communication range is judged to be logical and valid.
# Relational Action Bank with Semantic-Visual Attention for Few-Shot Action Recognition
## 5 Conclusions  
In this paper, we proposed a novel relational action bank with a semantic-visual attention module for few example video action recognition. The proposed relational action bank can use existing or past actions for current few-shot action recognition. Furthermore, we introduce a semantic and visual attention mechanism to exploit the relational action bank. The extended experiments demonstrate that embedding our proposed RABVSA module can obtain state-of-the-art performance on a common kinetics dataset. In particular, we achieved a 6.2% improvement in top-1 accuracy compared to the current second-best method.  
**Author Contributions:** Methodology, J.D. and H.L.; Investigation, H.Z.; Data curation, B.H.; Writing-original draft, H.L.; software, H.L.; Visualization, Y.M.; Funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Science and Technology Planning Project of Gansu Province, China (Grant No. 20 JR10 RA185).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **1-Shot** & **2-Shot** & **3-Shot** & **4-Shot** & **5-Shot** & **AVG** \\ \hline Matching Net [4] & 53.3 & 64.3 & 69.2 & 71.8 & 74.6 & 66.6 \\ MAML [85] & 54.2 & 65.5 & 70.0 & 72.1 & 75.3 & 67.4 \\ CMN [6] & 60.5 & 70.0 & 75.6 & 77.3 & 78.9 & 72.5 \\ TARN [8] & 66.6 & 74.6 & 77.3 & 78.9 & 80.7 & 75.6 \\ OTAM [86] & **73.0** & - & - & - & 85.8 & - \\ TRX [87] & - & - & - & - & 85.9 & - \\ \hline RABSVA-C3D (Ours) & 71.0 & **79.0** & **85.8** & **85.4** & **87.8** & **81.8** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The top-1 accuracies of C3D embedding our RABSVA module compared with state-of-the-art methods for 1-shot to 5-shot, the average of which is listed in AVG.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design
### System Overview  
The system is built on the ICN scheme, which relies on name-based lookup for content discovery, similar to MobilityFirst. In this framework, devices, data, and services are considered as Named Data Objects with globally unique Entity Identifiers (EID). Network Addresses (NA) serve as locators, similar to IP addresses in IP networks. The Name Resolution System (NRS) maintains dynamic mappings between identifiers and locators. Multicast services are treated as entities and assigned a globally unique Multicast Service Identifier (Multicast ID or MID).  
Figure 2 illustrates a multicast system comprising routers, multicast sources, receivers, and a Name Resolution System (NRS). Initially, the receiver node consults the NRS to obtain the list of NAs associated with the MID. Select the appropriate NA and send a JOIN message to it. During the propagation of JOIN messages to tree nodes (TN), network status is collected. Upon receiving a JOIN message, the tree node constructs multicast routes to receiver nodes based on locally maintained information and the details recorded in the JOIN message. During multicast tree construction, routers create Multicast Forwarding Table (MFT) entries for the MID. The MFT maintained by the multicast TN records the MID, its corresponding predecessor node, and a list of forwarding nodes. Upon receiving multicast data packets, TN retrieves MFT entries using the recorded MID in the packet header, obtaining the forwarding nodes' list and forwarding the packets accordingly.
## 4 Discussion
### Plant Hormone Signal Transduction Played a Key Role in Rosemary Suspension Cells Responding to MeJA  
Plant hormones play an important role in the synthesis of secondary metabolites, and the regulatory process involves a variety of signal transduction and interaction factors [59]. In _Arabidopsis_ _arf6_ and _arf8_ single mutants and sesquimutants, _ARF6_ and _ARF8_ gene dosage affected the accumulation of JA and the expression of _MYB_[60]. Over-expression _miR393_ could stabilize _ARF1_ and _ARF9_ to increase glucosinolate and decrease canlexin in _Arabidopsis_[61]. Exogenous cytokinin and ethylene up-regulated the alkaloid production through independent pathways in perifiwinkle suspension cells [62]. Exogenous ABA, GA and ethylene increased the levels of phenolic acids by activating the PAL and TAT in _Salvia milliorrhiza_ hairy roots [63]. DELLA would inhibit the transcriptional activation of downstream target genes by binding to PIF3 and PIF4 [64; 65; 66]. In _Triptergium wilfordii_ suspension cells, the contents of triptolide and triptolide increased significantly with exogenous ABA after 10 days [67], and the expression of _AACT_, _MCT_, _CMK_ and _CYP450_ in terpenoids biosynthesis increased significantly by exogenous MeJA [68]. The content of total essential oil was significantly increased by exogenous BR in _Mentha canadensis_[69]. Under MeJA treatment, the expression of _ODC_, _ADC_, _PMT_, _QPRT_ and _bHLH_ in nicotine biosynthesis were up-regulated, which affected the accumulation of nicotine and other pyridine organisms in _Nicotiana tabacum_[70]. _JAZ8_ participated in the biosynthesis of phenolic acids in _Salvia milliorrhiza_ under MeJA treatment [71]. Over-expression _ORCA3_ enhanced the expression of _Tdc_, _Str_, and _D4h_ in terpenoid biosynthetic genes increased the accumulation of terpenoid indole alkaloids in _Catharantthus roseus_[72; 73]. MeJA induced the expression of JA biosynthesis and signaling pathway genes, and the transcription factor _MYC2_ was released from JAZ to transcribe and activate the down-stream response genes in _Arabidopsis_[74; 75; 76], _Heva brasiliensis_[77], _Nicotiana tabucam_[78], _Catharanthus roseus_[79], _Chrysanthemum indicium_[80] and _Vitis viinfera_[81]. In our study, many genes showed differential expressions under MeJA treatment, e.g., _ARF, DELLA, EIN3, JAZ, MYC2_, further study of these genes is required. Thus, 100 uM MeJA could significantly induce the genes of JA biosynthesis and signal transduction and inhibit certain hormone signal transduction, especially SA signal transduction. MeJA could regulate the biosynthesis of secondary metabolites via the complex network of plant hormone signal transduction in roseamy suspension cells, such as terpenoids, flavonoids.
# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 1 Introduction
In this approach, some of the computations are performed on classical deep neural networks, while critical computations are performed on quantum circuits. In this way, the potential of quantum computing is exploited, despite the limitations of existing QNNs [23]. HQCDNNs are particularly advantageous in handling large and complex datasets where classical methods face limitations such as reduced generalization capacity and prolonged processing times [20,24]. By leveraging quantum computing's parallel processing capabilities, superposition, and entanglement, HQCDNNs can model intricate relationships within data more efficiently [20,23]. For instance, in the security analysis of smart contracts, the complexity of EVM opcode structures and the need for applicability on resource-constrained devices highlight the significant advantages of the hybrid design of HQCDNNs. This approach provides enhanced performance and efficiency in such scenarios [18]. This paper examines the incorporation of HQCDNN methods for identifying security weaknesses in smart contracts deployed on the Ethereum blockchain network. Our objective is to unite recent quantum computing breakthroughs with real-world security solutions in blockchain technology. By adapting hybrid quantum algorithms to analyze smart contract code within the EVM environment, we seek to improve detection accuracy and processing efficiency compared to classical approaches. Our contributions are threefold. First, we conduct an in-depth survey of current techniques for uncovering security flaws in Ethereum smart contracts, highlighting their shortcomings. Second, we introduce a framework for applying hybrid classical-QNN algorithms to this problem domain, incorporating advanced feature extraction techniques suitable for the EVM's opcode structure. Third, we share experimental findings that highlight the potential benefits of our hybrid approach, accompanied by an examination of the obstacles and future research paths in this domain.
# A Review on Two-Phase Volumetric Expanders and Their Applications
###### Abstract
Thermal equilibrium between the liquid and vapor phases during the expansion process is required to determine the high performance of two-phase volumetric expanders. Due to the short time related to the two-phase expansion process, the thermal equilibrium exists if the heat exchange surface between the two phases is high. Thus, the working fluid in the two-phase expanders needs to be constituted by fine liquid particles uniformly dispersed in the vapor phase [2]. Another drawback of the two-phase volumetric expanders is the high heat exchange coefficient between the two-phase fluid and the machine's stator, surfaces resulting in large thermal losses. Thus, suitable thermal insulation of the two-phase volumetric expanders is required to ensure their adiabaticity. The interest in two-phase expansion initiated to increase at the beginning of the 2000s. Indeed, at the 4 th IIR-Gustav Lorentzian Conference on Natural Working Fluids, several authors [3; 4; 5; 6] proposed different solutions to increase the COP of the transcritical carbon dioxide vapor compression refrigeration cycle by using two-phase expanders. In particular, [PERSON] et al. [3] developed a combined compression-expansion machine suitable for this application whose advantages and drawbacks were described in a following work [6]. A prior review about the advances of two-phase expanders was published by [PERSON] et al. to summarize the performances of machines for transcritical CO\({}_{2}\) vapor compression refrigeration cycles. In particular, the review was related to volumetric machines prototypes, such as piston, screw, scroll, and vane expanders, and turbomachinery [7]. Moreover,experimental investigations ([PERSON] et al. [8]) showed that the screw, scroll, and root expanders are more suitable for two-phase expansion than piston expanders. On the other hand, some authors proposed to develop two-phase volumetric expanders modifying commercialized single-phase volumetric compressors [9], from which the commercialized single-phase volumetric expanders are also derived [10; 11; 12]. Alternatively, volumetric single-phase expanders and pumps represent other potential machines from which to derive two-phase volumetric expanders, providing that proper geometry modifications are adopted. Despite the high potentiality of the two-phase volumetric expanders for profitable implementation in several applications, the detailed contributions in the scientific literature related to the overview of the state-of-the-art aspects of this technology are absent. For this reason, the present work discussed the potentialities and drawbacks of the available technologies of volumetric expanders operating with a single component working fluid. The aim was to go beyond previous efforts in the literature by analyzing a summary of leading studies in this field, comparing the performances of the available two-phase volumetric expanders to provide an overview of their advantages and drawbacks.