esa-sceva/satcom-chunk-collection huggingface dataset:
# The Perspective of Large-Scale Production of Algae Biodiesel
## 4 Results and Discussion
### Opportunities
#### 4.3.3 Economical  
As mentioned above, the use of algal biodiesel as a fuel is at the forefront of the challenge of lowering the price of more economical biofuels. To increase the economic value of algae, it is also necessary to review the utilization of by-products produced from algal biodiesel production. Sometimes we ignore the economic value of this by-product, but often this by-product can provide a far higher value than the main product itself, the biodiesel. Moreover, much non-energy or high-value products can be extracted from algae, which are considered commercially valuable [97]. The economic viability of an algae biodiesel industry, which has not been promising, might become feasible if it takes into account the economic value of this by-product. Valorization of many by-products in algal biodiesel production could improve production economics. However, it is significant that plenty of companies with biofuel production shifted to producing high-value products from algae, such as food supplements and cosmetics.  
Algae are composed of three main compounds, namely protein, carbohydrates, and lipids. These three compounds have been proven to be used as high-value biomaterials in the industry. Algae can produce various products, depending on the processing method. In the chlorination process, green algae that live in rivers and lakes can be used as precursors in making halobenzquinone (HBQ) disinfectants [98].  
In the process of biodiesel production, crude glycerol is formed in a significant amount from 8% to 10%. It can be applied in the production of hydrogen, biopolymers, ethanol, and fuel additives through the process of gasification and pyrolysis [99].  
In the last few years, a bioreinery system has been created that produces biodiesel fuel and various other products. The application of modern technology that uses supercritical extraction can provide more results, lower costs, and ease of operation [100]. The feasibility of a bioreinery that produces multi-products will undoubtedly increase the feasibility of using algae as biodiesel fuel.  
Algae need nutrients (mainly nitrogen and phosphorus) for their growth that are also found in wastewater. The use of wastewater for algae farming provides a nutrient for algae, and at the same time, wastewater treatment is performed. By combining these two processes, it is possible to reduce costs and thus make algae production more competitive. Nevertheless, it should be remembered that the composition of nutrients from wastewater change over time, making it difficult to use [101].
## 3 Chitosan Modification and Functionalization
### Chitosan Chemistry
#### 3.1.4 Cross-Linked Chitosan Derivatives  
Making cross-linked CS requires the use of specific chemical agents for linking the chains together and thus creating a three-dimensional macromolecular network [1; 2; 9]. CS is most often crosslinked by covalent bonds in the presence of aldehyde derivatives, such as glyoxal, formalin, or glutaraldehydehyde, in acid or basic medium to generate CS-based hydrogel [9]. As a rule, the cross-linking reaction with CS consists of forming a Schiff base (imine) [2; 4; 9]. Glutaraldehyde (GTA) is the most studied crosslinking agent. It is synthetic, available, and inexpensive [1; 9]. The reaction is a condensation reaction between the aldehyde function and a primary amine group from the CS chain in the presence of labile hydrogen [6; 9; 16]. However, GTA is toxic and natural alternatives to GTA are being studied to produce CS hydrogel, such as genipin [9], citric acid [53], and inorganic phosphate [54]. For example, [PERSON] et al. [53] reported the use of citric acid as a cross-linking agent for the preparation of a CS/PVA membrane. This cross-link strategy was generally investigated to produce biomaterial as hemodialysis membranes. The cross-linking between citric acid and CS was expected to incorporate carboxylate groups (COO\({}^{-}\)) into biomaterial in order to increase the bioactive sites on the CS membrane for transporting biomolecules (urea, creatinine, etc.). Polyvinyl alcohol (PVA) was used to increase the mechanical efficiency and hydrophobicity of the cross-linked CS membrane [53]. In Figure 5, the main cross-linking CS strategies are presented.  
Figure 4: Environmentally friendly oxidation of CS _via_ TEMPO/laccase system (adapted from [52]).
## 2 Materials and Methods  
### Study Area and Materials  
The air quality dataset employed in this study originates from the dataset provided by [PERSON] et al. (2018), which is readily accessible for download from the University of California, Irvine (UCI) Machine Learning Repository page. This dataset encompasses air quality records spanning the period from 2013 to 2017, comprising data collected from 12 distinct Guokong monitoring sites situated in and around Beijing. Specifically, the monitoring sites include Aotizhongxin, Changping, Dingling, Dongsi, Guyuan, Gucheng, Huairou, Nongzhanguan, Shunyi, Tiantan, Wanliu, and Wanshougong. To facilitate the description, we number the above-mentioned sites, with Aotizhongxin noted as S1, Changping as S2, Dingling as S3, and so on.  
The individual sites are represented by raw sensor data obtained at hourly intervals, spanning 1461 days with a total of 35,064 data samples. The dataset for each site contains 12 variables. For the purposes of this study, however, only the PM2.5 data series from each site is utilized.  
Due to subjective and objective reasons such as instrument damage and human factors ([PERSON] et al., 2018), there is a certain percentage of missing data in this dataset. Within this study, the missing data are supplied employing the random forest interpolation algorithm ([PERSON] et al., 2018). In order to eliminate the different dimensions between the features and speed up the data convergence, we normalize and scale the dataset to [0, 1]. In this case, Equation (1) illustrates the Min-Max normalization method.  
\[X=\frac{x-min}{max-min} \tag{1}\]  
where \(X\) denotes the normalized value, \(x\) denotes the original value, and \(min\) and \(max\) denote the minimum and maximum values in the dataset, respectively.
## 4 Performance Evaluation
### Simulations
#### 4.2.2 Analysis on Processing Capability of Nodes Using _HeaLow_  
In our second simulation, we set up an environment where multiple smartphones process intense amount of workload. The concept is that higher resolution brings finer grained information. Hence, it is better to do as much work as possible if no other issue is considered. More detailed explanation on how the precision and accuracy is affected is discussed below.  
As explained before, a buffer hazard incident occurs because the processing rate intermittently drops down to a tenth of average processing time. Therefore, even when a device is capable of handling the acoustic sensor input, workloads are dropped unless the capability is a few times greater than the input data. Figure 6 shows such result. Y-axis shows the successful processing ratio, and x-axis is the processing power relative to the generated workload. For example, if the processing rate is 0.5, the device is capable of processing only half of its generated workload. In a single device scenario, 5% of workloads were dropped even when the value of processing power over generated workload is 1. As the number of devices grows, the amount of processing power and generated workload grows proportionally. Therefore, the scenario of three devices completing higher ratio of workloads when there is sufficient computing power shows that _HeaLow_ stabilizes performance when there is enough processing power in the entire system.  
Figure 5: The successfully processed and offloaded amount in a sensor network environment.  
However, when any of the devices is not capable of extra workload, as shown in (0.75, 0.85) range, _HealLow_ cannot give much benefit. In addition, it is worth noting that the success ratio hikes as \(\times\) gets close to 1. This is because the cost of queue management significantly drops when the device can process as much as it generates.
## 3 Materials and Methods
### Detailed Description of the Routing Algorithm  
In this paper, the entire LEO satellite optical interconnect network is graphically depicted as  
\[G(V,E) \tag{1}\]  
where \(V\) is the set of satellite nodes equipped with TSN forwarding switches and \(E\) is the set of directed edges connecting two nodes, representing full-duplex optical interconnection physical connection paths between two nodes. Traditional inter-satellite links (ISLs) use microwave communication, while laser inter-satellite links (LISLs) utilize high-frequency lasers as carriers. The reason for choosing laser links in this paper is that the laser carrier frequency is 3 to 5 orders of magnitude higher than that of microwaves, allowing it to carry more information. Therefore, LISLs have higher signal bandwidth and antenna gain. Additionally, laser wavelengths are smaller than microwave wavelengths, and the optical antennas and other devices required for laser communication are lighter and smaller compared to those needed for microwaves, meeting the development requirements of satellite miniaturization and low orbit deployment [29].  
Figure 4: The whole process of the model.  
Based on the team's previous research [4], the process of dividing optical interconnection parallel subnets can be briefly outlined as follows. Obtain the IP addresses of each satellite and the laser connection information table. According to the laser connection information table, use the satellite's logical address to find the preceding and succeeding satellites in the same orbit and neighboring satellites to the left and right. Connect the satellite node to these four adjacent satellites to complete the initialization. Repeat this process for each satellite to establish satellite connections with equal initial costs. Initiate connections between adjacent satellite nodes and plan routes using the traditional Dijkstra path algorithm. After selecting important links based on a set threshold, reduce the graph based on traffic weights and strong connectivity algorithms to divide the entire satellite constellation into several parallel subnets. Use cross-links between parallel subnets and strong connections within each subnet. Repeat the above steps to convert inter-subnet connections into long-distance connections to ensure specific services. Perform route replanning; use the original routing method for services within each subnet, and calculate link costs within the subnet; for inter-subnet services, use long-distance links to connect satellite nodes across the two subnets. After the partitioning is complete, the topological structure of a portion of the satellite network is transformed into Figure 5.
## 3 Results
### Inter-Batch Variability  
As highlighted in Table 3, the differences in torque values were statistically different among the three batches in four of the twelve systems: SYNTHESIS(r), NU-EDGE(r), FLI TWIN(r), and SCAPE(r). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Bracket System** & **N** & **Mean (\(\pm\)SD)** & **Min** & **Max** & **MD** & \(\mathbf{Z_{\text{W}}}\) & \(\mathbf{p}\) \\ \hline SYNTHESIS & 30 & \(5.03\) (\(\pm 0.095\)) & \(4.85\) & \(5.28\) & \(0.029\) & \(-1.611\) & \(0.107\) \\ \hline NU-EDGE & 30 & \(4.64\) (\(\pm 0.272\)) & \(4.11\) & \(5.00\) & \(-0.363\) & \(-4.782\) & \(<\)0.001 *** \\ \hline DISCOVERY & 30 & \(4.04\) (\(\pm 0.118\)) & \(3.83\) & \(4.43\) & \(0.036\) & \(-1.525\) & \(0.127\) \\ \hline FLI TWIN & 30 & \(3.92\) (\(\pm 0.378\)) & \(2.81\) & \(4.59\) & \(-0.078\) & \(-0.422\) & \(0.673\) \\ \hline MINI SPRINT & 30 & \(4.30\) (\(\pm 0.205\)) & \(3.91\) & \(4.82\) & \(0.301\) & \(-4.680\) & \(<\)0.001 *** \\ \hline OMNIARCH + & 30 & \(3.97\) (\(\pm 0.139\)) & \(3.56\) & \(4.19\) & \(-0.029\) & \(-0.782\) & \(0.434\) \\ \hline SCAPE & 30 & \(3.94\) (\(\pm 0.204\)) & \(3.18\) & \(4.29\) & \(-0.060\) & \(-1.718\) & \(0.086\) \\ \hline MINI-TWIN & 30 & \(4.08\) (\(\pm 0.136\)) & \(3.82\) & \(4.43\) & \(0.077\) & \(-2.478\) & \(0.013\) * \\ \hline JAZZ & 30 & \(4.71\) (\(\pm 0.343\)) & \(3.99\) & \(5.50\) & \(0.712\) & \(-4.762\) & \(<\)0.001 *** \\ \hline MINI MASTER & 30 & \(4.16\) (\(\pm 0.172\)) & \(3.92\) & \(4.76\) & \(0.162\) & \(-4.247\) & \(<\)0.001 *** \\ \hline MINIPREVAIL & 30 & \(4.09\) (\(\pm 0.186\)) & \(3.88\) & \(4.85\) & \(0.093\) & \(-2.509\) & \(0.012\) * \\ \hline VICTORY & 30 & \(4.11\) (\(\pm 0.180\)) & \(3.90\) & \(4.60\) & \(0.110\) & \(-2.887\) & \(0.004\) ** \\ \hline \hline \end{tabular} SD: Standard deviation; Min: Minimum angular solution value; Max: Maximum angular solution value; MD: Mean difference between nominal and actual values; Z\({}_{\text{W}}\): Wilcoxon standardized rank; Wilcoxon test * \(p\leq 0.05\); ** \(p<0.01\); *** \(p<0.001\).  
\end{table} 
Table 2: Slot angulation (tip) in the analyzed systems. Figure 5: Boxplot reporting the median and interquartile ranges for bracket angulation. Outliers (O) and extremes (*) are reported. The dotted red line shows the declared values of slot angulation for NU-EDGE® and SYNTHESIS® (5°), and the dashed blue line shows the 4° of tip declared by the rest of the systems. In seven systems, all the batches presented torque values that did not outreach the tolerance limits reported by the norm. NU-EDGE(r), FLI TWIN(r), and SCAPE(r) were the systems with a higher inter-batch variability, with SCAPE(r) presenting a batch with all torque values out of the tolerance limits. MINI SPRINT(r) and VICTORY(r) presented the lowest variability between batches (Figure 6).
## 3 Results and Discussions
### Simulation Results  
Figure 3 shows the simulation BER versus SNR for 4 CAP-IM and 4 CAP-DM achieving the same SE of 2.5 bits/s/Hz over the additive white Gaussian noise (AWGN) channel. It can be clearly seen that 4 CAP-DM requires a lower SNR to reach the 7% forward error correction (FEC) coding limit of BER = \(3.8\times 10^{-3}\) in comparison to 4 CAP-IM. More specifically, the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of subbands (\(m\)) & 4 \\ Number of subbands in each subblock (\(N\)) & 2 \\ Number of activated subbands in each & 1 \\ subblock (\(k\)) & 0.15 \\ Roll-off factor of RRCF & 0.15 \\ Upsampling factor & 12 \\ Spectral efficiency (SE) & 2.5 bits/s/Hz \\ Simulation channel & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 2: Schematic diagrams of (**a**) PWC encoding and (**b**) PWC decoding.  
required SNRs for 4 CAP-IM and 4 CAP-DM to reach the BER threshold are 10.8 and 9.2 dB, respectively, indicating that 4 CAP-DM outperforms 4 CAP-IM by an SNR gain of 1.6 dB.
## 3 Results
### Quantitation of Aroma-Active Compounds

Table 2: Concentrations of aroma compounds in four different grades of FJ. Figure 1: Osme value comparison of four different grades of FJ. Notations: The number on the peak corresponds to Table 1, the abscissa represents the retention index of the compounds on the DB-FFAP chromatographic column, and the ordinate represents the aroma intensity of the compounds. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**No. **} & \multicolumn{1}{c}{**Aroma Compound**} & \multicolumn{1}{c}{**F00**} & \multicolumn{1}{c}{**F10**} & \multicolumn{1}{c}{**F20**} & \multicolumn{1}{c}{**F30**} \\ \hline  
henylethanol & 3627.27 \(\pm\) 485.36 b & 5290.1 \(\pm\) 690.47 a & 4995.13 \(\pm\) 230.03 a & 5948.54 \(\pm\) 460.99 a \\ Acids & & & & & \\
37 & Acetic acid * & 464.55 \(\pm\) 24.48 c & 497.28 \(\pm\) 10.93 c & 558.76 \(\pm\) 33.68 b & 655.99 \(\pm\) 27.11 a \\
49 & Propanoic acid & 5551.89 \(\pm\) 30.24 b & 5507.75 \(\pm\) 36.97 b & 5590.53 \(\pm\) 41.86 b & 5765.54 \(\pm\) 54.67 a \\
51 & 2-Methylpropanoic acid & 1564.50 \(\pm\) 6.06 c & 1689.79 \(\pm\) 36.05 b & 1748.42 \(\pm\) 9.81 a & 1671.23 \(\pm\) 12.23 b \\
56 & Butanoic acid & 3233.10 \(\pm\) 34.30 a & 3203.36 \(\pm\) 14.03 a & 3409.23 \(\pm\) 215.26 a & 3275.12 \(\pm\) 8.66 a \\
62 & 2-Methylbutanoic acid & 632.55 \(\pm\) 9.76 b & 656.37 \(\pm\) 8.79 b & 656.00 \(\pm\) 12.06 b & 688.52 \(\pm\) 16.15 a \\
63 & 3-Methylbutanoic acid & 12559.58 \(\pm\) 18.10 a & 1227.21 \(\pm\) 10.64 b & 1207.21 \(\pm\) 8.06 b & 1227.39 \(\pm\) 6.40 b \\
67 & Pentanoic acid & 1097.81 \(\pm\) 7.71 a & 1102.06 \(\pm\) 12.26 a & 1103.19 \(\pm\) 14.53 a & 1096.73 \(\pm\) 3.32 a \\
74 &
# On Hybrid Energy Utilization in Wireless Sensor Networks
###### Abstract  
In a wireless sensor network (WSN), many applications have limited energy resources for data transmission. In order to accomplish a better green communication for WSN, a hybrid energy scheme can supply a more reliable energy source. In this article, hybrid energy utilization--which consists of constant energy source and solar harvested energy--is considered for WSN. To minimize constant energy usage from the hybrid source, a Markov decision process (MDP) is designed to find the optimal transmission policy. With a finite packet buffer and a finite battery size, an MDP model is presented to define the states, actions, state transition probabilities, and the cost function including the cost values for all actions. A weighted sum of constant energy source consumption and a packet dropping probability (PDP) are adopted as the cost value, enabling us to find the optimal solution for balancing the minimization of the constant energy source utilization and the PDP using a value iteration algorithm. As shown in the simulation results, the performance of optimal solution using MDP achieves a significant improvement compared to solution without its use.  
M * [1]Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu 30012, Taiwan; [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (K.-T.F.) Department of Electronics Engineering, Chung Hua University, Hsinchu 30012, Taiwan; [EMAIL_ADDRESS] Department of Communication Engineering, National Central University, Taoyuan 32001, Taiwan  
* [1]Correspondence: [EMAIL_ADDRESS]; Tel.: +886-3-4227151 (ext. 35512)  
* [1]These authors are the members of IEEE.  
* [2]This author is the senior member of IEEE.
## 5 Numerical Examples
### Specific Examples  
The examples are divided based on their underlying scenarios: examples 1 and 2 demonstrate the fundamental behavior in non-periodic situations, while examples 3 and 4 focus on periodic situations. For all the examples presented here, we have consistently used the following initial condition:  
\[(S_{0},I_{0},U_{0},W_{0})=(0.1,0.1,0.1,0.1).\]
## 2 Materials and Methods
### Transcript Assembly and Annotation  
Clean reads were assembled using Trinity v2.11.0 [34]. Subsequently, CD-HIT v4.8.1 [35] was used for clustering (95% similarity threshold) to remove redundant transcripts and obtain unigenes. Functional annotation of unigenes was then performed against the Nr, Pfam, Uniprot, KEGG, GO, and KOG/COG databases.
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 2 Mathematical Model of Non-Contact Excitation Motor  
### The Working Principle of Non-Contact Excitation Motor  
Conventional electrically excited motors are excited by brushes and slip rings. On some special occasions, the wear of the meetings will produce carbon powder and cause damage to the electrical equipment. To this end, a new motor excitation method is proposed, which transfers energy through electromagnetic coupling, replacing traditional brushes and slip rings, making the motor run more safely and environmentally friendly. As shown in Figure 1, the motor's structure, the motor's stator, has a primary coil, and the rotor has a secondary coil. The DC power supply provides DC power, transmits the DC power to the full-bridge inverter module, and converts the DC power to AC power. The primary coil resonates, and the parameters of the primary coil are equal to the resonant frequency of the second wave. The primary coil transfers energy to the secondary coil without contact, and the secondary coil also resonates. The alternating current generated in the secondary coil continues to be connected to the full-bridge rectifier module. After the full-bridge rectifier module, since the stability of the output voltage needs to be controlled after the full-bridge rectifier circuit, the DC circuit conversion link should be added. Therefore, the BUCK circuit is added to stabilize the output voltage.  
The system assembly diagram is shown in Figure 2: one is the transmitter subsystem, and the other is the receiver subsystem. The sending system is mounted on the stator and remains stationary during motor operation, while the receiving subsystem rotates with the motor rotor [43]. The size of the interval between the transmitter coil and the receiver coil will affect the transmission efficiency of the motor, so it is necessary to ensure that the size between the transmitter coil and the receiver coil is 2 mm.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 3 Proposed Method
### Scenario for the Proposed Method
#### 3.2.1 MD2 MD Content Delivery  
There is an MD2 MD content delivery scenario which uses CCNx and the previously proposed method. Using the proposed method, effective content delivery between mobile devices is possible.  
Figure 5: Proposed process for connection between devices.  
The device consists of a content transmission module, a Multi-RAT module that controls the interfaces, and a Bluetooth module that transmits and receives Bluetooth Beacon. In this scenario, it is assumed that a connection is established between the devices by using WLAN. The methods of content delivery are divided into Transmit/Receive and Request/Response.  
First, the Transmit/Receive method involves one device sharing its owned content and the other device receiving that content. As shown in Figure 6, the transmitter sends a Transmit Beacon including interface information for communicating and the content to be shared. The receiver receiving a Transmit Beacon including the desired content information will connect to the transmitter and then receive the content. With this method, the main agent providing the content delivery is the transmitter. This method can also be a Broadcast/Receive method since it is shared by the transmitter. The operation process of the Transmit/Receive method is shown in Figure 7. In this process, it will transfer and receive content using ccaputfile and ccapetfile of CCNx.  
Figure 6: Content delivery process of the Transmit/Receive method.  
Figure 7: Operation process of the Transmit/Receive method.  
The Request/Response method is that whereby one side issues a request to share specific content and the other side transfers the requested content. As shown in Figure 8, the requester sends a Request Beacon including information related to the content request. The responder receives the Request Beacon and sends a Transmit Beacon in response. The next process is similar to the aforementioned Transmit/Receive method. In this method, the main agent responsible for the content delivery is the requester. The operation process of the Request/Receive method is shown in Figure 9.  
Figure 8: Content delivery process of the Request/Response method.  
Figure 9: Operation process of the Request/Response method.  
The Transmit/Receive method is used only for public sharing because it is a unilateral sharing method. The Request/Response method can be used for private sharing among specific users as well as for public sharing since message exchange is possible.