esa-sceva/satcom-chunk-collection huggingface dataset:
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 7 Experiment
### Performance of Spatiotemporal Range Query  
Subsequently, we tested the spatiotemporal range query. Firstly, we randomly selected 100 spatiotemporal ranges as the query conditions when the amount of dataset are increased from 40 GB to 200 GB. The spatial range is 1%, and the time range is 24 h, the results of average latency are shown in Figure 6a. It can be seen that as the dataset size increases, the response time of CTDM increases gradually, while THBase and SPDM do not change much. This is because CTDM can only perform pruning and query based on MO, namely, it is impossible to use time conditions to exclude irrelevant trajectory data before querying HBase, and the filter overhead after accessing HBase increases with the increase of dataset. While THBase and SPDM can process pruning and query according to time condition, so the query is less affected when dataset increases and the query time range is unchanged. Among the three schemes, THBase has the highest query performance due to the following reasons: (1) all the index entries and trajectory data of the same MO coreside on same node, so the data transmission overhead in the HBase cluster can be avoided; (2) the distributed indexing scheme can better utilize the parallelism of cluster compared to the centralized index; (3) the orderly data organization for index entries and trajectory points effectively reduces the computing overhead for sorting; (4) the segment-based storage model and index model have a higher pruning precision.  
In addition, we also conducted experiments on our scheme under various spatiotemporal ranges. Experiments are performed under the condition of 200 GB, the spatial range varies from 0.01% to 10%, and the time range varies from 10% to 100% (namely, from 3 days to 31 days). The result is shown in Figure 6b; as the spatiotemporal range increases, the query latency also increases. However, due to the  
Figure 5: Performance of single-object query.  
Figure 6: Performance of spatiotemporal range query: (**a**) result of changing data size, (**b**) result of changing spatiotemporal range.  
nonuniform spatiotemporal distribution of AIS data, the performance is not essentially linear to the change of spatiotemporal range.
## 1 Introduction
As a matter of fact, many devices, for example sensors, are characterized by the EIS technique, therefore the modelling of results is relevant for both the speculative and applicative aspect [22]. Experiments performed with the EIS technique detect a sensitive enhancement of the cell conductance in light [17; 21]. Specifically, it was observed that the charge transfer involves the protein, the quinones in solution and depends on the electrode. Furthermore, a sensitive magnification of conductance is observed in light. CAFM can measure currents from a few to a single biomolecule appropriately bound on a conductive substrate in the presence of significant strong and DC applied electric fields. In this case as well, the experimental setup plays an important role in the result, for example, the specific protein anchoring [12], or the measurement mode (for example, contact or tapping mode) [15; 16; 23]. Photosensitive proteins sandwiched between an electrode and the CAFM tip have been studied under different conditions of illumination showing, in dark, an unexpectedly high conductance [12; 23], which can be further enhanced in the presence of light [15]. Concerning the theoretical interpretation of experimental results, we refer to the proteotronics, a recent branch of molecular electronics developed inside our research group since 2014, that evaluates the electrical properties of a protein by using an impedance network analog based on the protein tertiary structure [9; 24]. By using this approach, we succeeded to reproduce and interpret experimental data performed in neutral or quasi-neutral pH condition and foresee the results in alkaline conditions. In particular, the results of the investigation predict an increase of conductance in analogy to that given by illumination, and that can be useful for applications in sensor devices.
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 3 Data and Results
### Benchmark Functions
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **PSPSO** & **PSOCO** & **CPSO** & **WICPSO** & **APSO** & **ScPSO** & **PSO** \\ \hline \multicolumn{8}{l}{Unconstrained function property} \\ \hline Continuous & 1 & 2 & 3 & 7 & 4 & 6 & 4 \\ Discontinuous & 1 & 1 & 6 & 7 & 4 & 3 & 4 \\ \hline Differentiable & 1 & 2 & 3 & 7 & 5 & 5 & 4 \\ Non-differentiable & 1 & 2 & 5 & 7 & 3 & 4 & 5 \\ \hline Separable & 2 & 1 & 4 & 7 & 6 & 3 & 5 \\ Non-separable & 1 & 2 & 3 & 7 & 4 & 6 & 5 \\ \hline Scalable & 1 & 2 & 3 & 7 & 4 & 6 & 5 \\ Non-scalable & 2 & 1 & 3 & 7 & 6 & 5 & 4 \\ \hline Unimodal & 1 & 2 & 4 & 7 & 3 & 5 & 5 \\ Multimodal & 2 & 1 & 3 & 7 & 6 & 5 & 4 \\ \hline Constrained functions & 1 & 4 & 2 & 7 & 3 & 4 & 6 \\ \hline
**Overall Ranking in Function Evaluation** & 1 & 2 & 3 & 7 & 3 & 5 & 5 \\ \hline
**Overall Ranking in Computational Time** & 3 & 7 & 5 & 6 & 1 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Ranking of each PSO variant (D = 30). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & **PSPSO** & **PSOCO** & **CPSO** & **WICPSO** & **APSO** & **ScPSO** & **PSO** \\ \hline \multirow{3}{*}{c1} & **mean** & \(7.90\times 10^{-28}\) & \(8.25\times 10^{-6}\) & \(2.53\times 10^{-29}\) & \(1.65\times 10^{-3}\) & \(1.00\times 10^{-29}\) & \(2.92\times 10^{-28}\) & \(6.23\times 10^{-28}\) \\  & **  
was just a third, as seen in Table 10. This means that not in every function evaluation was solved the fasted by the PSPSO, and that most of the time APSO or PSO solved a function faster than PSPSO.
## 3 Experiments
### Experimental Results
#### 3.4.2 Effects of Different Combinations of Bi-GRU and CNN  
To investigate the effectiveness of different combinations of Bi-GRU and CNN, we designed a series of models to compare the different effects of different combinations.  
Bi-GRU+CNN: The Bi-GRU+CNN model proposed in the paper.  
CNN+Bi-GRU: Based on Bi-GRU+CNN, the output of the word-embedding layer passes through the deep neural network in the order of the first CNN and the second Bi-GRU.  
CNN-Bi-GRU: On the basis of Bi-GRU+CNN, the output of the word-embedding layer inputs into the Bi-GRU and CNN, respectively, and then the outputs of the networks are concatenated to form a representation of the sentence.  
The results are shown in Table 3. We can see that the performance of CNN-Bi-GRU is mediocre relative to the other two models, but the 6.7% difference between Bi-GRU+CNN and CNN+Bi-GRU is not coincidental. The main reason is that the initial convolutional layer of CNN+Bi-GRU destroyed the sequence structure of the text so that the Bi-GRU layer behind it was just like a fully connected layer, which fails to harness the full capabilities of the Bi-GRU layer. The CNN+Bi-GRU was even worse than a regular LSTM by 0.4%. On the other hand, Bi-GRU+CNN achieved the best performance among the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**MR**} & \multicolumn{2}{c}{**SST**} \\ \cline{2-5}  & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline SVM & 76.4 & 78.8 & 40.7 \# & 42.4 \\ Feature-SVM & 77.3 & 79.4 & 41.5 & 43.3 \\ \hline LSTM & 77.4 \# & 79.6 & 46.4 \# & 47.6 \\ Bi-LSTM & 79.3 \# & 81.0 & 49.1 \# & 50.5 \\ CNN & 81.5 \# & 82.7 & 48.0 \# & 49.3 \\ BLSTM-C & 82.4 & 83.8 & 50.2 \# & 52.0 \\ \hline Tree-LSTM & 80.7 \# & 82.1 & 50.1 \# & 51.8 \\ LR-Bi-LSTM & 82.1 \# & 83.6 & 50.6 \# & 52.3 \\ \hline Self-Attention & 81.7 & 82.9 & 48.9 & 50.1 \\ \hline SDNN & **83.7** & **84.9** & **51.2** & **52.9** \\ SDNN w/o sentiment attention & 82.5 & 84.1 & 50.0 & 51.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results (%) for the MR and SST datasets. The best result for each dataset is in bold. Results marked with # were retrieved from the references (i.e., [20; 22; 26; 27]), while those unmarked with # were obtained either by our own implementation or with the same codes shared by the original authors.  
three variant models. This was because the initial Bi-GRU layer can encode every token in the input text into a vector that contains not only the information of the original token but also the information of the previous token. In this way, the order of each token in the generated text representation is the same as the order in the original text. Afterwards, the CNN layer will find local patterns by using the generated representation, which can further improve the accuracy. This proves that the combination of Bi-GRU and CNN is very sensitive to the improvement of deep neural network performance.
## 3 Results and Discussion
### Adsorption and Release of Diclefonac Sodium
Experimental results showed that at pH 7.4 and 37 \({}^{\circ}\)C, simulating the small intestine environment, the SBA-15(C) sample released 89.4% of diclofenac sodium. [PERSON] et al. [108] applied chitosan and sodium alginate polyelectrolyte complexes for controlled DS liberation at pH 6.8. They reported a linear drug release profile in the first 6 h. [PERSON] and co-workers [109] evaluated surfactant-modified climeptiolite and chabazite as platforms for diclofenac sodium delivery. It was shown that climeptiolite causes an immediate liberation of the drug within 1 h (57%) followed by incremental release during the next 5 h (about 85%) at pH 7.4. [PERSON] et al. [110] synthesized BioMOF-Zn (Zn(II) coordination polymer with 4,4'-biphenyl-dicarboxylic acid (BPDC) and adenine linker) as a diclofenac sodium delivery platform. Approximately 56% of the drug was desorbed from the sample at pH 7.4. The results obtained in the case of functionalized SBA-15 silica, as well as chitosan and sodium alginate polyelectrolyte complexes, are comparable to the release of diclofenac sodium from MIL-88A-1 and MIL-88A-2 materials. In turn, MIL-88A-3 and Zn-based BioMOF were characterized by similar release capabilities. The components of MIL-88A (iron ions and fumaric acid) render it a biocompatible and non-toxic drug carrier. Different kinetic models (zero-order, first-order, Korsmeyer-Peppas, Hixson-Crowell, and Higuchi) were fitted to the experimental data to determine the diclofenac sodium release rate and mechanism. To assess the diffusion kinetics, kinetic values of release constants (k), release exponent (n), and correlation coefficient (R\({}^{2}\)) were estimated and summarized in Table 5. The release profile of the drug from MIL-88A-1 prepared in distilled water at a lower temperature is best fitted to the first-order kinetic model, which is common for a drug release from porous carriers. It is a concentration-dependent drug release mechanism [111]. It implies that the diclofenac sodium release rate diminishes as the concentration gradient of the drug decreases over time [112]. The highest values of R\({}^{2}\) were noted for the Higuchi model in the case of MIL-88A-2, MIL-88A-3, and MIL-88A-4, which  
Figure 10: Diclofenac sodium release profiles for MIL-88A carriers at pH 6.8.  
describes the amount of drug liberated as a function of the square root of time [113]. It is characteristic of the drug release from an insoluble matrix [114].
# Three-Dimensional Division of Visible Light Communication Irradiation Area
## 3 Theoretical Simulation and Analysis of VLC  
### Simulation Model  
In this study, the theoretical model is designed using Optisystem simulation software. Numerical simulation is carried out based on Formulas (1) and (2). Figure 2 shows the entire simulation process.  
Figure 1: Theoretical model. Here \(d\) is the distance between the transmitter and the receiver and \(\phi\) is the angle; \(\psi\) is the field of view (FOV) at the receiver; \(h\) is the distance between the receiver and the reflecting surface; and \(d_{h}\) is the distance between the transmitter and the receiver.  
At the transmitter of Figure 2, a series of binary sequences are generated by a pseudo-random bit sequence generator. This sequence is converted into a non-return-to-zero (NRZ) signal by an NRZ pulse generator. The signal is then modulated by a Mach-Zehnder modulator (the extinction ratio is 30 dB) and transmitted by a single LED (wavelength is 380-780 nm). At the receiver, an optical signal is converted into an electrical signal by a PIN photodetector (the responsivity is 1 A/W and the dark current is 10 nA). After being amplified by a transimpedance amplifier (open-loop voltage gain is 40 dB) and an automatic gain control amplifier (output voltage is 5 V), the electrical signal is filtered in a low-pass Bessel filter. The filtered signal is restored to its original NRZ signal by a 3R generator. Finally, the NRZ signal is analyzed using the BER analyzer. The experimental parameters are set as shown in Table 1.  
We can infer from Table 1 that VLC with a BER of \(4.6\times 10^{-5}\) is achieved at a distance of 1 m, which is lower than the forward error correction (FEC) limit of \(3.8\times 10^{-3}\). The Q factor of this communication system is 3.58, and the eye height is 0.12, which indicates that the communication performance of the system is stable.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Location-Based Routing Schemes  
Location-based routing schemes require location estimates of sensor nodes to determine routes for data forwarding. Location estimation can be achieved using suitable localization services, such as those proposed in [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Table 1 analyzes these protocols based on a number of parameters explained in Section 1. Location-based routing schemes can be categorized based on whether or not they consider node mobility.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 3 Cluster Tree-Based Routing Protocol
### Route Establishment
#### 3.4.1 Routing Metrics  
The queue utilization (\(QU\)) and expected transmission count (\(ETX\)) are two metrics for routing performance. \(QU\) is a proportion between the number of packets occupied in the \(l\)th node's queue \(QU_{occupied}(l)\) and the total number of available packets in the \(i^{\text{th}}\) node's queue \(QU_{total}\)[58]. Its calculation is given in Equation (15).  
\[QU(l)=\frac{QU_{occupied}(i)}{QU_{total}(i)} \tag{15}\]  
\(ETX\) indicates the link quality between the nodes. It is the proportion between the number of data transmissions and the retransmissions that are required to send the data successfully from node \"\(l\)\" to node \"\(p\)\". It is given by:  
\[ETX(l,p)=\frac{1}{F_{d}\times R_{d}} \tag{16}\]  
where \(F_{d}\) indicates the forward data delivery, and \(R_{d}\) indicates the reverse data delivery or acknowledgement from the receiver.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ratio (RER) (\%)** & **Reward (\(R_{k,l}\))** & **Energy Depletion Ratio (EDR) (\%) Penalty (\(P_{k,l}\))** \\ \hline
0–10 & 0 & 0–10 & 100 \\
11–20 & 20 & 11–20 & 80 \\
21–40 & 40 & 21–40 & 60 \\
41–60 & 60 & 41–60 & 40 \\
61–80 & 80 & 61–80 & 20 \\
81–100 & 100 & 81–100 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reward and penalty value for each node.  
\(RER\) denotes the present energy available in the network nodes. The \(RER\) calculation is given in Equation (17).  
\[RER\Big{(}CH_{k,l}\Big{)}=\frac{E_{present}}{E_{initial}}\times 100 \tag{17}\]  
where \(RER(CH_{k,l})\) indicates the \(CH\) node residual energy in \(l\)th node of cluster \(k\), and \(E_{present}\) and \(E_{initial}\) indicate the present energy and initial energy in the node, respectively.  
The cluster with the highest node count (\(CHNC\)) is the ratio between total number of nodes present in the \(i\)th cluster and total number of nodes present in the network. The \(CHNC\) calculation is given in Equation (18).  
\[CNHC(CH_{k})=\frac{T_{c}}{T_{N}} \tag{18}\]  
where \(T_{c}\) and \(T_{N}\) indicate total number of nodes present in the \(k\)th cluster and the total number of nodes in the network, respectively.
# Practical I-Voting on Stellar Blockchain
## 3 System Proposition
### Securing Client App  
Besides widely known best practices for securing web-based apps [48], we also use IPFS (InterPlanetary File System) [49], a content-addressable network, to achieve end-to-end integrity protection. The election organizers publish the following voting meta-data to an IPFS node:  
* Total number of possible vote tokens (number of eligible voters);
* Issuer, distributor, and ballot-box public keys;
* Set of candidates;
* Encryption key, allowing to encrypt the ballot-box.  
In response, the organizers receive a hash of the meta-data. Next, the hash is published to the issuer blockchain account (as memo field in a no-op transaction), achieving a strong coupling between the blockchain and the voting meta-data. Since we trust the blockchain, we can trust the metadatahash published on it, and so we can trust the content fetched from the IPFS node. The integrity is guaranteed because we check the hash of the content received from IPFS and the hash present on a blockchain; in fact, we query the IPFS network by that hash.  
Although the integrity is guaranteed, we still rely on the source from which the Stellar account is received. We believe that a TXT record in DNSSEC is a reasonable place to do it.
# A Machine Learning Predictive Model for Ship Fuel Consumption
## 2 Materials and Methods
### Model Performance Evaluation  
Cross-validation was utilized to partition the sample space into training and testing sets, with the subsets being alternated in each round. The performance of each model was evaluated based on its capacity to identify patterns and predict the target variable, considering the diversity of records in the database. The performance metrics chosen based on the most commonly used criteria and the goals of each metric were: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R\({}^{2}\)).
## 3 Discussion
Their retention at both processes increased with higher pressure and lower temperature, but their concentrations were visible lower than in the initial wine, and in most retentates 4-ethylguaiacol was completely removed. This is a desirable behaviour, because 4-ethylphenol and 4-ethylguaiacol are usually associated with unpleasant aroma of smoke, stable, horse sweat or medicinal aids at higher concentrations [45]. They are products of _Brettanomyces_ yeast metabolism that becomes active after fermentation, mostly during wine storage in wooden barrels or due to inadequate hygiene in wineries. These phenols are mostly associated to red wines and are formed by the decarboxylation and _Brettanomyces_ mediated reduction of the corresponding grape-derived hydroxcinamic acids (\(p\)-coumaric and ferulic acid). Higher concentrations (several hundred \(\mu\)g/L for 4-ethylphenol and 50 to 100 \(\mu\)g/L for 4-ethylguaiacol) negatively affect wine aroma and are considered as wine spoilage [27]. It is reported [26] that reverse osmosis can effectively remove or lower the concentration of these compounds due to their low molecular weight. Reverse osmosis process was used to split the permeate containing 4-ethylphenol and 4-ethylguaiacol from the initial feed. Such obtained permeate is subjected to hydrophobic adsorbent resin treatment in order to remove mentioned compounds and then returned into the retentate. [PERSON] et al. [46] used similar method to remove smoke-derived volatiles, such as guaiacol and 4-methylguaiacol. Therefore, reverse osmosis and nanofiltration can be applied for red wine concentration in order to enhance desirable wine components or to remove compounds that have a negative effect on its sensory properties. Different operating conditions did not affect each compound equally that resulted in different aroma profiles among retentates, according to the PCA analysis. Nanofiltration retentates had very similar aroma profile, while the reverse osmosis ones were significantly different regarding applied pressure and temperature regime. Although reverse osmosis membranes proved to be better in terms of total volatiles retention, the application of nanofiltration is preferred due to higher permeate flux, shorter process duration and lower final retentate temperatures at same pressures than reverse osmosis [37]. Increased temperature causes higher evaporation and thermal degradation of volatiles and other wine components and this is the main reason why cooling should be applied. At both processes, reverse osmosis and nanofiltration, the retention of total volatiles was significantly higher when the retentate temperature was lower. Similar results have been obtained during concentration of grape must [21] and chokeberry juice [33; 38], although juice or grape must have high soluble solids level and the membrane efficiency and working pressure have smaller effect on their concentration comparing to the concentration of wine with lower soluble solids content.
In addition, the AM technology can break through the conventional thinking of deforming and removing materials, realize the new manufacturing concept of \" Net Shape Forming \" [17; 18], and conform to the new tide of \"Green Manufacturing\" [19]. The fabrication of micromixer by AM has been studied. For example, [PERSON]'s team made small continuous flow oscillating baffle reactors with SLA (stereo lithography appearance) technology [20] and [PERSON] team studied a multi-material 3D printed microfluidic reactor with integrated heating [21]. The evaluation results of the study showed that the micro reacting pipe could be manufactured by AM technology to obtain a satisfactory mixing performance, which reflected the potential of AM in the field of micro reacting pipe manufacturing. The micromixers mentioned above are all non-metallic material. But there is also research which is based on metal additive manufacturing such as 316L stainless steel micromixer by micro powder injection molding [22]. [PERSON] et al., proposed bringing the L-PBF method to fabricate the porous metals as catalyst supports to improve the hydrogen production performance of micromixers [23]. But this method also needs to assemble several parts to make a completed micromixer, whose process is relatively complex. [PERSON] et al., investigated optimized wavy microchannels numerically which were manufactured by using L-PBF [24]. Their microchannels had a relatively gentle rolling inner structure. But this structure could not make turbulence as much as possible. In this paper, based on L-PBF technology, a type of internal structure of the micro reacting pipe was designed and fabricated, and its optimal critical dimension was discussed. In addition, the properties of the micro reacting pipe in this paper were compared with the former ordinary micro reacting pipe. To investigate the content above, both the CFD simulation method and chemical experimental method were employed. To study the insight of the micromixer, [PERSON] et al., discussed computational methodology for the development of microdevices and micromixers with ANSYS CFX [25]. At the same time, the chemical experiment was based on the Villermaux-Dushman parallel competition reaction system which used the concentration of I\({}_{3}^{-}\) in the outflowed solution as a probe to evaluate mixing efficiency. On the other hand, [PERSON] et al., researched building direction effects on microchannel tolerance and surface roughness through X-ray computed tomography (CT-scan) [26]. Therefore, this paper will not focus on the dimension deviation and surface roughness.