esa-sceva/satcom-chunk-collection huggingface dataset:
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
## 3 IP Address Lookup Using a Vectored-Bloom Filter
### IP Address Lookup Using VBF  
Algorithm 3 describes the IP address lookup procedure using the VBF in detail. For a given input IP address, the VBF is queried by gradually decreasing the querying length, starting from the longest prefix length.  
If the VBF returns a _negative_ at the current length, the querying is continued at a shorter length (if the current length is not already the shortest).  
If the result of the VBF is a _positive_, the VBF returns a matching output port (BMPport) and the IP address lookup procedure is completed. Note that the VBF can generate a _false positive_ for a substring of the given input even though the substring is not included in the routing set and a false output port can be returned for the input. As will be shown in simulation, the _false port return rate_ converges to zero when the sizing factor of the VBF is larger than two.  
If the VBF returns an _indeterminable_, the off-chip hash table should be accessed because the matching output port at the current length of the given input cannot be determined. In searching the hash table, two hash indexes are used for multi-hashing. If the matching entry is found from the entry pointed by the first hash index, the search is complete. If the matching entry is not found, the procedure continues to search the hash entry pointed by the second hash index. If the matching entry is not found, the procedure continues to search the hash entry pointed by the linked list. The search procedure is completed if the matching entry is found. Otherwise, the search procedure should go back to the VBF.  
```
FunctionSearch(\(DstAdd\)) for\((length=longestLen\) to shortestLen)do if(queryVBF(DstAddr.length)== 0)then continue;//negative elseif(queryVBF(DstAddr.length)!= 0) &(queryVBF(DstAddr.length)!= -1)then //positive  BMPport = queryVBF(\(DstAddr.length\)); break; else //indeterminable  BMPport = searchHT(\(DstAddr.length\)); if(BMPport!= NULL)then break;//nomore Bloom filter access  end end end end  end  return BMPport;  end
```  
**Algorithm 3**IP Address Lookup Procedure Using VBF
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 4 Results and Experiment
### Class Imbalance and Model Performance
#### 4.3.4 Comparative Insights  
Across all three models (Figures 3-5), the significant class imbalance skews predictions heavily towards the \"Very Low\" and \"Very High\" categories:  
* SVM (Figure 4) demonstrates slightly better performance for the \"Low\" and \"Medium\" classes compared to LR and LSTM.
* LSTM (Figure 5) outperforms both LR and SVM for the \"Very High\" class but performs poorly for mid-range classes due to overfitting.  
These findings highlight the need for techniques to address class imbalance, such as oversampling minority classes or employing advanced cost-sensitive learning methods to improve classification performance for underrepresented categories.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 2 Vehicular Named Data Networking (VNDN)
### Producer Vehicle  
The node or vehicle that contains the original data in its depository is the producer in the VNDN. When an interest packet (request) is received at the producer, a data packet is prepared after matching the name or prefix of the content. The data are then forwarded to the consumer through the same path that the interest packet has traversed.
## 2 ARD Framework
### Processing Pipelines  
The batch processing of Sentinel products is accomplished using the Graph Processing Tool of the ESA SNAP toolbox [39]. The following sections present the processing pipeline for Sentinel-1 and Sentinel-2 products, their collocation, patch creation and output generation. The processing graph described here is provided with the ARD framework; however, custom processing graphs can also be defined, in recognition that applications have the need to alter the processing and its parameters. For instance, there may be a need to disable speckle filtering, change the speckle filtering kernel size or modify calibration or collocation procedures and parameters.  
Sentinel-1 products are distributed in the World Geodetic System coordinate reference system, namely the WGS84 (EPSG:4326), whereas the Universal Transverse Mercator (UTM) coordinate system is used by default for Sentinel-2 products. The UTM coordinate system divides the Earth into 60 zones, each 6\({}^{\circ}\) wide in longitude and 8\({}^{\circ}\) in latitude, transforming the latitude and longitude from the spherical coordinate system to specific zone numbers with corresponding x and y coordinates. The UTM system is preserved for Sentinel-2 products and also followed by Sentinel-1 products through a projection from WGS84 to the corresponding UTM zone, thus maintaining consistency between products.
## Materials and Methods
### GC-MS Analysis  
The essential oil of Manuka primarily consists of terpen and terpenoids. They have been usually measured by GC-MS [38].  
The samples were analyzed by a GC-MS-QP2010 Ultra system (Shimadzu) attached to an AOC-5000 plus autosampler (Shimadzu). A HP-5 MS (30 m \(\times\) 0.25 mm, film thickness 0.25 \(\upmu\)m \(\times\) 0.32 mm ID) fused silica capillary column was used.  
Chromatographic conditions were: injector temperature 280.0 \({}^{\circ}\)C, injection mode: split, split ratio: 50.0. The column temperature program was 40 to 100 \({}^{\circ}\)C at a heating rate of 3 \({}^{\circ}\)C/min, then 100 \({}^{\circ}\)C to 115 \({}^{\circ}\)C at a heating rate of 1 \({}^{\circ}\)C/min for 15 min; lastly, from 115 \({}^{\circ}\)C to 200 \({}^{\circ}\)C at a heating rate of 3 \({}^{\circ}\)C/min. The carrier gas was helium. One \(\upmu\)L of the hexane extract solution was injected. Mass spectrometry conditions were as follows: acquisition of mass scanning between 50-800, scan time 0.32 s, and ionization voltage of 70 eV Table 4. Oil constituents were identified by comparing the mass spectra of each component with those values stored in GCMS libraries. Moreover, a solution of C8-C20 alkanes was also injected using the same column under the same conditions described for GCMS analysis to calculate the retention indices of the obtained components and compare it with those available authentic standards.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**GC Conditions** & **MS Conditions** \\ \hline Injection temperature & 280 \({}^{\circ}\)C & Ion source temperature & 250 \({}^{\circ}\)C \\ Injection mode & Split & Interface temperature & 290 \({}^{\circ}\)C \\ Column flow & 4.81 mL/min & Solvent cut time & 2.5 min \\ Split ratio & 50.0 & Start m/z & 50.00 \\  & & End m/z & 800.00 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**Core temperature program** \\ \hline Rate (\({}^{\circ}\)C) & Temperature (\({}^{\circ}\)C) & Hold time (min) \\ \hline - & 40.0 & 5.00 \\
3.00 & 100.0 & 0.00 \\
1.00 & 115.0 & 0.00 \\
3.00 & 200.0 & 5.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gas Chromatography-Mass Spectrometry Conditions.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Experimental Parameters** & **Conditions** \\ \hline Solvent & n-hexane 95\% \\ Sample: solvent ratio, g/mL & 1:20 \\ Extraction time (min) & 0.3, 5.7, 10, 15, 30, 60 min \\ Temperature (\({}^{\circ}\)C) & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters of solvent extraction.  
Figure 1: New Zealand Manuka leaves and their corresponding leaves grounded to different particle size: (**A**) 1400 \(\mu\)m, (**B**) 850 \(\mu\)m, (**C**) 500 \(\mu\)m, (**D**) d \(\geq\) 250 \(\mu\)m, (**E**) d \(\leq\) 250 \(\mu\)m, (**F**) d \(\geq\) 200 \(\mu\)m, (**G**) d \(\leq\) 200 \(\mu\)m, (**H**) 100 \(\mu\)m, (**I**) 68 \(\mu\)m.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 2 Related Works
It is further observed that the clustering protocol is suitable for specific applications, such as temperature monitoring and light event detection. Although researchers have proposed various techniques such as probability basis, fuzzy logic, and residual energy to optimize the energy utilization among the nodes in the cluster, nonetheless, there is still a gap in the literature that focuses on prolonging the network's lifespan while balancing the energy utilization among the cluster nodes. Keeping all these existing works in consideration, the objective of this paper is to prolong the lifespan of the network. This paper proposes a cluster-tree-based routing protocol (CT-RPL) that performs cluster formation process, cluster head selection, and the route establishment process in order to increase the lifespan of the network.
###### Abstract  
This paper reviews key signal processing techniques in structural health monitoring (SHM), focusing on non-parametric time-frequency analysis, adaptive decomposition, and deconvolution methods. It examines the short-time Fourier transform (STFT), wavelet transform (WT), and Wigner-Ville distribution (WVD), highlighting their applications, advantages, and limitations in SHM. The review also explores adaptive techniques like empirical mode decomposition (EMD) and its variants (EEMD, MEEMD), as well as variational mode decomposition (VMD) and its improved versions (SVMD, AVMD), emphasizing their effectiveness in handling nonlinear and non-stationary signals. Additionally, deconvolution methods such as minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) are discussed for mechanical fault diagnosis. The paper aims to provide a comprehensive overview of these techniques, offering insights for future research into SHM signal processing.  
Keywords:signal processing techniques; structural health monitoring; non-parametric time-frequency analysis; adaptive decomposition; deconvolution +
Footnote †: journal: _algorithms_
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 3 RLQR Approach
### Problem Formulation  
In the literature, most of query routing approaches in unstructured P2P systems are based on gossiping mechanisms. As outlined in the related work, within gossiping the forward peer routes the search query to \(k\) relevant neighbors selected among a set of \(m\) neighbors (i.e., \(k<m\)) with respect to their relevance for the query. Indeed, existing approaches relied on specific different scoring functions to select the \(k\) relevant neighbors with the highest score. Our formulation for the K-Neighbors-Selection (K-NS) problem differs from the existing approaches. Indeed, we consider that the natural model for this issue are formalizations from reinforcement learning, in which an agent learns by taking actions that produce rewards. The goal of the agent is to find a selection policy of actions that maximizes the cumulative reward. In the query routing setting, the forwarder peer should run a learning algorithm (e.g., agent) that selects \(k\) neighbors (e.g., actions) for each search query. Each selected neighbor yields a binomial reward which expresses its ability to produce pertinent documents for the query. In fact, the agent must learn a K-NS policy to maximize the cumulative rewards leading to higher user satisfaction. At first glance it appears that modeling the K-NS issue as a Multi-Armed Bandit (MAB) problem, which is considered as one of the classical problems in RL, seems like a natural approach. However, most of the existing MAB algorithms are designed to select one among different actions (e.g., neighbors) while a K-NS algorithm must select, for each search query, \(k\) neighbors (e.g., actions). To address this issue, we introduce in the sequel a generic neighbor selection algorithm that adapts MAB algorithms to the K-NS problem. Figure 1 shows the global architecture of our approach, with P2P query routing terminology in black and corresponding RL terminology in red and italics.  
In the following, we first introduce three MAB-based learning algorithms for selecting one relevant neighbor for each search query, we will refer to these latter as 1-neighbor-Selection (1-NS) algorithms (Section 3.2), then we detail our generic K-NS algorithm (Section 3.3).
## 3 Evolution Towards Next Generation Networks (NGN)
### Service Requirements for a Next Generation Converged Network  
A comparison of the connection models in PSTN and in internet, as well as the network platform parameters, resulted in the following requirements for the new platform--a converged network. The main service requirements for the converged network include:  
* simpler and more flexible introduction of new Information and Communication Technology (ICT) technologies;
* convergence of media (data, voice, video via the same network);
* new voice codec and protocols of QoS support through packet networks;
* development of Voice over IP-based technologies;
* a single network for varied types of services including multimedia;
* simpler and open network architecture;
* quicker introduction of new services.
## 3 Results and Discussion
### Spatial Distribution of Rainfall and Accident Locations
Therefore, the radar in the two-dimensional grid is better able to detect rainfall spatially than the rain gauge, which measures rainfall in a fixed location, thus increasing the reliability of RAR estimation  
Figure 10 shows the mapping results of RAR with fatal and severe injury accidents for the administrative districts of Seoul. The range of RAR values was widened to 8 for fatal and severe injury accidents. It is expected that the district where the severity of traffic accidents increases due to rainfall could be detected based on these results. Figure 9: District-wise RAR distribution based on interpolated AWS rainfall (upper) and from radar-derived rainfall (lower) for all accidents in rain conditions.
# Solving Nuisance Cyanobacteria Eutrophication Through Biotechnology
## 2 Materials and Methods
### Gas Chromatographic (GC-FID) Analysis of Fatty Acid Methyl Esters  
Fatty acid methyl esters were analyzed by a 5890-Series II gas chromatograph (Hewlett Packard, Palo Alto, CA, USA) equipped with a flame ionization detector, capillary column (SGE-BPX 70; 50 m length, 0.22 mm diameter, 0.25 \(\mu\)m film thickness), and a split/splitless capillary inlet (19251 A). Helium was used as carrier and nitrogen as auxiliary gas with a flow rate of 36 mL/min. Flow rates of hydrogen and compressed air for the FID were 30 and 330 mL/min, respectively. Samples were injected in a split mode, using a split ratio of 1:100, and the volume injected was 1 \(\mu\)L. The operating conditions were 320 \({}^{\circ}\)C injection port, 300 \({}^{\circ}\)C flame ionization detector, and column temperature was programmed to hold at 50 \({}^{\circ}\)C for 2 min, then increases linearly, with a rate of 20 \({}^{\circ}\)C/min, to 150 \({}^{\circ}\)C followed by a 1.5 \({}^{\circ}\)C/min increase to 230 \({}^{\circ}\)C with 3 min final time at 230 \({}^{\circ}\)C. For peak identification,solutions of reference substances (Supelco 18919-1 amp FAME mix C4-C24 and FAMEs purchased from Sigma-Aldrich, St. Louis, MO, USA) were analyzed under the same conditions, and their retention times (RT) and chromatograms were compared to those of the samples. The contribution of each identified compound was expressed as a percentage (%) of its peak area to the total area of all peaks eluted in each chromatogram. The precision of the results was always better than \(\pm\)5%.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 2 Review of Related Literature
### QoS Achievement in WBAN/Iot
The drawbacks in this work are limited metrics to select the next hop and not suited to transmit critical data. An optimized health monitoring framework was introduced in [35]. This work consists of entities such as cluster members, cluster heads and core networks. In this work, the cluster head is selected by introducing three optimization algorithms, namely, ant colony optimization, multi objective particle swarm optimization and the comprehensive learning particle algorithm. The selected cluster head acts as a gateway between the core network and members in the clusters. This work employs a cluster head as a gateway between the core network and members in the clusters, which affects the _QoS_ constraints in terms of latency and throughput.
## 4 Materials and Methods
### Pigment Extraction from _E. coli_ and _HPLC Analysis_  
The _E. coli_ BL21 (DE3) cells expressing plasmids constructed in the study were harvested by centrifugation at 12,000\(\times\)\(g\) for 5 min at 4 \({}^{\circ}\)C. The pellet was washed by ddH\({}_{2}\)O twice and dried using  
Figure 6: Structure of plasmid pFZ153 and pFZ153\(\Delta\)E used to confer the function of _HpGGPPS_ genes on astaxanthin biosynthesis in _E. coli_ (BL21 DE3). The plasmid pFZ153 (**a**) was modified to construct the plasmid pFZ153\(\Delta\)E (**b**) by removing the _CrtE_ gene. In this study, _HpGGPPS_ genes were inserted into the plasmid on _EcoRI_/_Hind_III or _EcoRI_/_Not_I site to complete the astaxanthin synthetic gene cluster. The _CrtE_ gene from pFZ153 was served as the positive control.  
a freeze-dryer. Consequently, the pellet was crashed into powder, weighted, and resuspended in 1 mL acetone for pigment extraction. The suspension was then incubated at a water bath at 55 \({}^{\circ}\)C for 15 min with vigorous shaking every 5 min. The supernatant containing pigments was collected by centrifugation at 12,000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C. The pigment was subsequently dried using a vacuum evaporator and dissolved in 0.5 mL solution A (methanol/isopropanol, 8:2, V:V) for the following HPLC analysis.  
The HPLC assay was performed on a Series 1260 system (Agilent Technologies, Santa Clara, CA, USA). Pigments were separated at a flow rate of 0.6 mL/min on a Phenomenex Gemini-NX C18 column (5 \(\upmu\), 150 \(\times\) 3.0 mm, Phenomenex Inc., Aschaffenburg, Germany) using solution A and solution B (ddH\({}_{2}\)O). The gradient elution was 85% A, 15% B at 0 min, followed by linear gradient to 100% A at 10 min, maintained at 100% A to 12 min, returned to initial condition by 12.1 min, and re-equilibrated at initial condition by 18 min. The injection volume was 5 \(\upmu\)L. Column temperature was maintained at 35 \({}^{\circ}\)C. The standard astaxanthin (Aladdin Chemistry Co. Ltd., Shanghai, China) was used to identify the target in the pigment. The detection was carried out by UV absorbance at 475 nm. To determine the concentration of astaxanthin in the testing sample, a calibration curve was constructed using a serial of plots with 500, 200, 100, 50, 20, and 10 \(\upmu\)g/mL standard astaxanthin. The linearity between concentrations of astaxanthin and their corresponding peak areas was figured out to calculate the concentration of each analyte. The final astaxanthin productivity of each constructed plasmid was calculated by the formula, astaxanthin productivity = cal-con. \(\times\) 0.5 mL/DW, where cal-con. refers to the calculated astaxanthin concentration in the analyte from the calibration curve, 0.5 mL comes from the final volume for each pigment extraction, DW comes from the amount of dried cells used for a pigment extraction. All organic solvents (acetone, methanol, and isopropanol) used in this study are HPLC grade.
_Applied_  
_sciences_  
**Improved Tensor-Based Singular Spectrum Analysis**  
**Based on Single Channel Blind Source Separation**  
**Algorithm and Its Application to Fault Diagnosis**  
**[PERSON] 1,2, [PERSON] 1,2,3,*, [PERSON] 1,2,4, [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2,3**  
1 Key Laboratory of Metallurgical Equipment and Control Technology,  
Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; [EMAIL_ADDRESS] (D.Y.); [EMAIL_ADDRESS] (Z.X.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (M.G.); [EMAIL_ADDRESS] (C.L.)
2 Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering,  
Wuhan University of Science and Technology, Wuhan 430081, China  
3 The State Key Laboratory of Refracories and Metallurgy, Wuhan University of Science and Technology,  
Wuhan 430081, China  
4 State Key Lab. of Digital Manufacturing Equipment & Technology,  
Huazhong University of Science and Technology, Wuhan 430081, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-27-6886-2857; Fax: +86-27-6886-2212  
Academic Editor: [PERSON]. [PERSON]  
Received: 9 March 2017; Accepted: 16 April 2017; Published: 20 April 2017  
**Abstract:** To solve the problem of multi-fault blind source separation (BSS) in the case that the observed signals are under-determined, a novel approach for single channel blind source separation (SCBSS) based on the improved tensor-based singular spectrum analysis (TSSA) is proposed. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, TSSA method can be employed to extract the multi-fault features from the measured single-channel vibration signal. However, SCBSS based on TSSA still has some limitations, mainly including unsatisfactory convergence of TSSA in many cases and the number of source signals is hard to accurately estimate. Therefore, the improved TSSA algorithm based on canonical decomposition and parallel factors (CANDECOMP/PARAFAC) weighted optimization, namely CP-WOPT, is proposed in this paper. CP-WOPT algorithm is applied to process the factor matrix using a first-order optimization approach instead of the original least square method in TSSA, so as to improve the convergence of this algorithm. In order to accurately estimate the number of the source signals in BSS, EMD-SVD-BIC (empirical mode decomposition--singular value decomposition--Bayesian information criterion) method, instead of the SVD in the conventional TSSA, is introduced. To validate the proposed method, we applied it to the analysis of the numerical simulation signal and the multi-fault rolling bearing signals.  
**Keywords:** blind source separation (BSS); tensor-based singular spectrum analysis (TSSA); CANDECOMP/PARAFAC weighted optimization (CP-WOPT); fault diagnosis