esa-sceva/satcom-chunk-collection huggingface dataset:
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 4 Biobased Monomers for Polyester and Polyamide Synthesis
### Biobased Aliphatic Diamines  
1,4-Butanediamine (1,4-BDA, putrescine) is naturally produced by decomposition of amino acids in living and dead organisms, which is used for the production of engineering plastics and high performance materials such as nylon 4,6, nylon 4,10 and PA 4,T. It is produced industrially via chemical synthesis approaches starting from petrol-based 1,4-dichloro-2-butene or 1,4-dihalobutane, or succinodinitrile [113; 114]. It is also possible to synthesize biobased 1,4-BDA via chemical conversion of biomass-derived succinic acid [18], or via fermentation of sugars using engineered _E. coli_ strains [113].  
1,5-Pentanediamine (1,5-PDA, cadaverine) is a naturally occurring compound which is produced by hydrolysis of protein during the tissue putrefraction of animals, the same as 1,4-BDA. 1,5-PDA can be used for the production of nylon 5,6 and nylon 5,10. The industrial production of 1,5-PDA is similar to that of 1,4-BDA, using petrol-based 1,5-dichloropentane, glutarodinitrile, or glutaraldehyde as the starting material [114]. Moreover, the biosynthesis of 1,5-PDA is well established, by decarboxylation of lysine using several microorganisms [115; 116]. It is also promising to produce biobased 1,5-PDA via fermentation of sugars by metabolic engineering. Currently, biobased 1,5-PDA has been produced in industrial scale by Cathay Industrial Biotech (Shanghai, China) [29]. In addition, Ajinomoto (Tokyo, Japan) is working on the industrial production of biobased 1,5-PDA by decarbonating of lysine via an enzymatic process.  
1,6-Hexanediamine (1,6-HDA) is a raw material for synthesis of nylon 6,6, nylon 6,10 and PA 6,T, which is currently produced industrially from petrol-based butadiene or propylene. Recent developments show that biobased 1,6-HDA can be produced by chemical-catalytic conversion of adipic acid [117] or 1,6-hexanediol [110] derived from carbohydrates, or by a fermentation route [31]. The commercial production of biobased 1,6-HDA is already in preparation stage [31].  
1,8-Octanediamine (1,8-ODA) can be potentially derived from biomass. It can be produced by animation of suberic acid which can be converted from plant oils [118].  
1,10-Decanediamine (1,10-DDA) can be chemically converted from sebacic acid derived from castor oils. They are interesting biobased monomers for the synthesis of fully biobased nylon 10,10 which have already been commercially available in the market [31].
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 3 Method  
In order to resolve the different design objectives, we propose the CRMOP algorithm based on the penalty-based boundary intersection (PBI) approach to combine the original object functions formulated as [41]  
\[\min_{{\mathbf{u}},{\mathbf{p}}}d^{pbi}({\mathbf{l}}_{{\mathbf{U}}},{\mathbf{p}}|(\mathbf{{w}},{\mathbf{z}}^{*}))=g_{1}+\theta g_{2} \tag{12a}\] \[\text{s.t.}\ P_{\min}\leq p_{k}\leq P_{\max},\ \forall k\in\mathcal{K},\] (12b) \[\frac{p_{k}h_{{\mathbf{U}}k}}{\sum_{k=k+1}^{K}p_{i}h_{{\mathbf{U}}i}}\geq P _{\text{SIC}},\ \forall k\in\mathcal{K},\] (12c) \[R_{k}\geq R_{\text{thr}},\ \forall k\in\mathcal{K},\] (12d) \[\min(l_{x})\leq x\leq\max(l_{x}),\] (12e) \[\min(l_{y})\leq y\leq\max(l_{y}),\] (12f) \[||{\mathbf{l}}_{{\mathbf{U}}}-{\mathbf{l}}_{{\mathbf{R}}}||^{2}\geq D_{\min^{ \prime}}^{2}, \tag{12g}\]  
where  
\[g_{1}=\frac{||((-F_{1},F_{2})^{T}-z^{*})^{T}{\mathbf{w}}||}{||{\mathbf{w}}||}, \tag{13}\]  
\[g_{2}=\left|\left|((-F_{1},F_{2})^{T}-(z^{*}+g_{1}\frac{{\mathbf{w}}}{||{\mathbf{w}}|| })\right|\right|, \tag{14}\]  
where \({\mathbf{w}}\) is the weighted vector, \(z^{*}=(z_{1}^{*},z_{2}^{*})^{T}\) is the ideal optimal vector with \(z_{1}^{*}<min(-F_{1})\) and \(z_{2}^{*}<min(F_{2})\), \(\theta\geq 0\) is a self-defined penalty indicator which controls the bias of \(g_{1}\) and \(g_{2}\). Compared to the traditional approach [51], the method employed in (12) balances the convergence and diversity in which \(g_{1}\) is used to assess the convergence of each parameter toward the efficient front and \(g_{2}\) is the measurement of population diversity. We aim to decrease the object value as small as possible so that it can reach the optimal boundary. The proposed algorithm can effectively combine the two objectives and ensure the diversity of the population results, and at the same time has a better performance in dealing with constraints.
## 4 Conclusions  
In this paper, we propose a multi-teacher knowledge distillation GAN for underwater image enhancement (MTUW-GAN). By employing a multi-teacher network to simultaneously instruct a student network, we achieve a comparable performance to image fusion methods while addressing color distortion and detail loss issues in underwater images. The student model of MTUW-GAN acquires rich intermediate-layer feature information from the teacher model through channel distillation, enhancing image color and feature details without increasing the parameters or computational requirements. Additionally, our proposed underwater image enhancement function effectively removes noise and color distortion caused by underwater light scattering, restoring image quality. Compared to typical fusion networks, MTUW-GAN eliminates multiplexed branching and fusion modules, using online knowledge distillation for model compression, significantly reducing the computational requirements for the lightweight student model. Numerous experiments demonstrate that MTUW-GAN achieves a state-of-the-art performance in terms of evaluation metrics and visual quality in underwater image enhancement. It has low computational requirements and real-time performance, making it viable for future applications in AUVs for operational tasks like underwater mineral resource exploration and biological resource investigation. In addition, we plan to file a patent application based on the method presented in this paper.  
While MTUW-GAN offers notable advantages, there is still room for improvement. Firstly, the setup of the multi-structured teacher network results in a large number of overall parameters within the framework, potentially leading to slower training. Secondly, the student model in our algorithm struggles to effectively recover certain specialized underwater images. To address these issues, we intend to conduct further research in our future work. Our plan involves exploring several more streamlined and effective teacher networks to optimize the framework's training process. Additionally, we aim to identify the root cause of the problem by utilizing statistics such as image histograms, with the goal of narrowing the gap between the outputs of the student model and the teacher model.  
Conceptualization, T.Z. and Y.L.; methodology, T.Z. and Y.L.; software, Y.L.; validation, Y.L.; formal analysis, T.Z.; investigation, T.Z. and Y.L.; resources, T.Z.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, T.Z. and Y.L.; visualization, Y.L.; supervision, T.Z.; project administration, T.Z.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grant 52001039.  
Not applicable.  
Figure 5: Example of control group results from an ablation study.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Homing Attack  
In a homing attack, malicious attacker may analyze the traffic to identify and target nodes that have special responsibilities, such as cluster heads or sinkhole node. Moreover, the attacker may launch other DoS attacks to jam or destroy these special nodes.  
Header encryption is a common countermeasure, but it does not completely prevent analyzing traffic. It might be enough to identify the location of these special nodes by simply analyzing the volume of traffic in various portions of the network. In [55], an anti-traffic analysis strategy is proposed to help disguise the location of the base station from eavesdroppers by using \"dummy packets\". Unfortunately, these dummy packets significantly consume nodes' energy, especially for UWSNs. Hence, it can be used only when it is of utmost importance to prevent traffic analyzing.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 3 System Model
### Dynamic Topology Discovery of Existing Paths  
In this section, Algorithm 2 is proposed for TBRL to check the affected paths through dynamic topology. Initially, all of the paths from source to different sinks are taken as an input parameter. All of these paths had been created by different nodes. At step 2, each path's nodes has been extracted and saved in an array. Multiple nodes are placed inside the array, after that, each node's speed has been calculated. At speed determining step, \(g\) is a gravitational force that is caused by water, \"lambda\" is the wavelength of wave determined in Equation (21), and \(h\) is the depth of the node. TBRL is a positioned based routing technique; therefore, each node knows its location and depth level. At step 6, the node's new position is saved in another array. After the completion of outer loop, all new paths have been taken out and passed in \"Paths\". These paths are further assigned to the source node, and then IDFS will be called and it will select the path that have maximum number of intermediate nodes from source to sink node.  
```
1:All Paths from source to sink nodes extracted through IDFS
2:Affected Paths due to topology Initialization:
3:for\(i=1\) to All Pathsdo
4: Path_Nodes [ ] = i // Nodes that make a path
5:for\(j=0\) to Path_ Nodesdo
6:\(speed=\sqrt{(((g*\lambda)/(2*\pi))*tanh(2*\pi*h_{j}/\lambda))}\)
7:\(move=speed/4\)
8:\(node_{ii}=node_{j}\;((X\underline{+}move),Y)\) // Topology Changed
9:\(node_{j}=node_{ii}\)
10:\(new\_Path\_Node\;[\;]=node_{j}\)
11:endfor
12:\(Path_{i}=new\_Path\_Node\;[\;]\)
13:endfor
14:return\(Path_{i}\) // Paths received from step 12 will be entertained in given steps
15:for\(m=1 to\)Pathsdo
16: Call IDFS(m)
17: Compare Number of Nodes Participating
18: Select path having higher nodes participation
19:endfor
```  
**Algorithm 2** Dynamic Topology Discovery of Existing Paths
## 4 Results and Discussions
### Effect of Slider Geometry under Different Submergence Conditions
#### 4.3.2 Cases of Partial Subaerial Landslides  
The ratio of the long axis to the short axis of slider (\(R\)) are set as 6, 8, 10, 12, 14 and 16, respectively, and the submergence condition of the landslides is partially subaerial (the falling height is \(-\)0.2 m). When the falling angles are 6, 10 and 14, the temporal evolution of water elevation in the G\({}_{1}\) and G\({}_{2}\) point are shown in Figure 17. When the wave train propagates from G\({}_{1}\) to G\({}_{2}\) (from the solid lines to the dotted lines in Figure 17), the first peak and the first trough have some attenuation, while the second peak increases. As the ratio increases, the first peak and the second peak in the wave series at G\({}_{1}\) decay only slightly. By contrast, that of the first trough decays more sharply as the ratio increases. Similarly, the first crest in the wave train at G\({}_{2}\) has some slight attenuation as the ratio increases. However, the second peak and the first trough attenuate obviously when the ratio changes from 6 to 10 and attenuate slightly when the ratio changes from 10 to 14, which indicates that the increase in the ratio has a greater influence on the trough in the wave train than the peak and that on the second peak is greater than the first peak (especially from the 6 and 10 of the ratio).  
Figure 16: The temporal evolution of the water elevation in G\({}_{1}\) and G\({}_{2}\) with the subaerial landslide: (a) r = 6; (b) r = 10; and (c) r = 14.
## 4 Dynamic Response Analysis of the RPC Frames Under Coupled Fire and Explosion
### Comparative Analysis of RPC Framework and RC Framework Damage  
Both the RC framework and the RPC framework comprise the same model size and building process. When the explosives are placed in different positions, the degree of damage to RC frames and RPC frames varies. The frame damage map at t = 200 ms was chosen for comparative analysis.  
For the location of the explosion load at the bottom of the frame corner post with a 45\({}^{\circ}\) diagonal direction of 1.5 m and 1 m from the ground, the damage diagrams of the RC frame and RPC frame structure are shown in Figure 18. The bottom-floor corner columns of the RC frame, together with the neighboring beams and panels, were all damaged. Additionally, the tops of the beams adjacent to the explosion location also suffered tensile damage due to the explosion. In contrast, the beam-column joints of beams and panels in the RPC frame did not sustain damage. The results show that RPC frames experience significantly less strain and damage due to explosions than RC frames.  
Figure 17: Explosion load located at the top of a beam on the first floor.  
For the location of the explosion load in front of the frame of the ground floor side columns at a distance of 1.5 m, 1 m from the ground position, the damage diagrams of the RC frame and RPC frame structures are shown in Figure 19. From the figure, it can be seen that the bottom side columns of the RC frame and the connected beams and slabs were all damaged. It can be seen that the tops of the beams above the explosion location were damaged by tensile forces, and the neighboring beams lost almost all of their load-bearing capacity. It can also be seen that the beams and slabs of the RPC frame connected with the bottom side columns were all damaged. However, the extent of damage to the RPC frame was significantly less than that of the RC frame.  
For the location of the explosion load 1 m from the center of the bottom beam, the damage diagrams of the RC frame and RPC frame structures are shown in Figure 20. From the figure, it can be seen that the first- and second-floor slabs of the RC frame were destroyed, and the three beams perpendicular to the location of the explosives were damaged due to the tensile damage of the upper part. The slab of the RPC frame near the bottom beams lost its load-bearing capacity, and the beams connected to the slabs were plastically deformed. It can be observed that the overall damage degree of the RPC frame was significantly lower than that of the RC frame.  
Figure 19: Diagram of frame damage when explosion load located on the first layer edge pillar.  
Figure 18: Diagram of frame damage when explosion load located on a corner pillar on the first floor.
# Applications of Water Jet Cutting Technology in Agricultural Engineering: A Review
## 3 Applications of Water Jet Cutting Technology on Different Agricultural Materials
### Meats
#### 3.3.2 Improving Cutting Ability  
To improve the jet cutting ability and complete cutting operation of bone or larger sections of meat, adding jet media (i.e., abrasives and liquids) to the working fluid is studied.  
Typical hard abrasives such as garnet are not suitable for meat processing, so salt, sugar, bone meal, and ice particles are used as abrasives [37]. [PERSON] et al. [59] concluded that AWJ using sugar and salt as abrasives could be successfully applied to beef and pork cutting. [PERSON] et al. [60] studied the cutting effect of salt abrasive water jets on beef, pork, and lamb meat. It was shown that AWJ cutting could deepen the depth of boneless meat compared with pure water jet cutting, and with more salt particles hittingthe meat, the depth of the cut was deeper. Meanwhile, the freezing or chilling costs could be eliminated because AWJ cutting was completed at room temperature. [PERSON] et al. [61] compared the qualities of bovine femur bones obtained by pure water jet cutting, bone meal abrasive water jet machining, and sugar abrasive water jet machining (350 MPa pressure, 50 mm/min transverse speed). The results showed that bone meal and sugar biocompatible abrasive particles improved the surface roughness and cut quality compared to pure water jets. In addition, sugar particles had better properties than bone meal because of a lower density and a more uniform aggregation. However, salt and sugar as abrasives can have a certain impact on the taste of the final products, so ice particles are proposed as a substitute. [PERSON] [37] found that ice particle water jets were 40% deeper into bones than pure water jets, which meant that ice particles might be applicable in the primary cutting of meats and bones in abattoirs. Ice particles were also suitable for cutting frozen and soft foods. When ice particles were used as abrasives with 0.15-0.3 mm diameters, properties such as the hardness, flowability, and elastic modulus have temperature dependence. Therefore, ice particles should be generated below \(-20\)\({}^{\circ}\)C, and injecting ice abrasives with cooling water was recommended [62; 63].  
Polyethylene oxide (PEO) is a safe polymer commonly used in the food industry as a thickener or flocculant [64]. [PERSON] et al. [39] used PEO solution mixed with water as the working fluid to perform liquid jet cutting tests on varying meats (i.e., chicken fillet, hake fish, beef, and pork). The experiments were carried out at temperatures of \(-7\) and \(-25\)\({}^{\circ}\)C, with pressures changing from 50 to 150 MPa and cutting speeds ranging from 0.015 to 0.1 m/s. They showed that with the increasing PEO molecular mass and concentration, the cutting depth of frozen food increased rapidly and then decreased, which could reach a maximum at some optimal concentrations. When the cutting depth was the same, the required PEO solution jet pressure was only 45-65% of the pure water jet.
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 3 Proposed Matrix Completion Based Localization
### Matrix Completion: Optimization over GD and Its Variants
#### 3.2.2 Nesterov Accelerated Gradient (NAG)  
A second commonly used variant is the acceleration of Nesterov, that has been published in [37] and fits in the same vein as the idea of the Momentum [38]. It has the same intuition using the gradient history but it calculates the gradient with respect to an approximate future values of our parameters instead of the current parameters. To update \(\mathbf{W}^{(t)}\) with NAG, we use the following equation.  
\[\mathbf{W}^{(t)}=\mu\times\mathbf{W}^{(t-1)}+\lambda\times\
abla(l(\mathbf{\hat {X}}^{(t)}-\mu\times\mathbf{W}^{(t-1)})), \tag{17}\]  
where  
\[\mathbf{W}^{(t-1)}=\
abla(l(\mathbf{\hat{X}}^{(t-1)}))  
Suppose that  
\[\mathbf{\hat{Y}}^{(t)}=\mathbf{\hat{X}}^{(t)}-\mu\times(1-\mathbf{H})\odot( \mathbf{\hat{X}}^{(t-1)}\cdot((\mathbf{\hat{X}}^{(t-1)})^{T}\cdot\mathbf{\hat{X }}^{(t-1)}+\epsilon\times\mathbf{I})^{-0.5}), \tag{20}\]  
Then,  
\[\
abla(l(\mathbf{\hat{Y}}^{(t)})=(1-\mathbf{H})\odot(\mathbf{\hat{Y}}^{(t)} \cdot((\mathbf{\hat{Y}}^{(t-1)})^{T}\cdot\mathbf{\hat{Y}}^{(t-1)}+\epsilon \times\mathbf{I})^{-0.5}), \tag{21}\]  
We obtain  
\[\mathbf{W}^{(t)}=\mu\times(1-\mathbf{H})\odot(\mathbf{\hat{X}}^{ (t-1)}\cdot((\mathbf{\hat{X}}^{(t-1)})^{T}\cdot\mathbf{\hat{X}}^{(t-1)}+ \epsilon\times\mathbf{I})^{-0.5})\\ +\lambda\times(1-\mathbf{H})\odot(\mathbf{\hat{Y}}^{(t)}\cdot(( \mathbf{\hat{Y}}^{(t-1)})^{T}\cdot\mathbf{\hat{Y}}^{(t-1)}+\epsilon\times \mathbf{I})^{-0.5}), \tag{22}\]  
The main advantage of the NAG method compared to the GD one is related to the fact that an anticipatory update prevents us from going too fast and results in increased responsiveness to the landscape of the loss function [36].
## 5 Field Testing and Validation
### Hardware Testing  
Hardware testing of the AIoT device included the assessment of the data transmission, power management, resistance to water, and hardware health. A summary of each of the hardware tests is listed as follows:  
* The alerts were successfully generated by the AIoT device during the field tests and went through a Portal, forwarding the data and alerts to the user interface. The LoRaWAN data transmission was reliable for the tests.
* In order for the AIoT device to remain operational when powered using batteries (12 V, 12 Ah) and the solar panel (10 W), the device was configured to be in hibernation mode and to wake up every 15 min to monitor the area in its field of view. The wakeup, detection, and hibernation operational flow for the device was successfully validated during the tests.
* The AIoT device was designed to be waterproof; however, minor condensation appeared on rare occasions specifically on days with large temperature variations.
* The temperature, humidity, and battery life sensors of the AIoT device were regularly transmitted via LoRaWAN messages to monitor the device health. The payload of the message contains the temperature in Celsius, the relative humidity in percentage, and the battery voltage. The field tests reported no unusual behavior from the sensors.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 3 Data Description and Analysis
### Data Sets for Application of Machine Learning Models  
Based on the early work provided by [PERSON] et al. [47], the information used to create the classification models focuses on features that give a high correlation with our target classification set. Two sets of data were referenced to run the different models [47]: \"lane detection\" with a set of 214,151 sample points and \"traffic status\" with a set of 46,542 sample points corresponding to the motorway class. Using the available data, two different feature sets were constructed, in which the first one, lane detection, focused on the position and orientation of the car of interest. The second feature set, traffic status, related to the car's environment. Both features sets are described as follows:  
1. Lane detection (a) Timestamp (seconds); (b) Position of car relative to lane center (X) (meters); (c) Orientation of car relative to lane curvature (phi) (degrees); (d) Lane width (Width_road) (meters); (e) Driver state of mind (status) (0 = normal, 1 = aggressive, 2 = drowsy).
2. Traffic status (a) Timestamp (seconds); (b) Distance to car ahead in nearest lane (Dist_vehicle) (meters); (c) Time for impact with nearest car (Impact_time) (seconds); (d) Number of vehicles in the same frame (Num_vehicles); (e) GPS speed (GPS_speed) (km/h); (f) Driver state of mind (0 = normal, 1 = aggressive, 2 = drowsy).  
The data were collected from six different drivers [47] for each of the above sets and corresponding to the motorway class to compare the results from each of the models and search for consistency in the results. Then, the data were randomized before analyzing and applying any machine learning classifiers. The initial data that were in the order of \"normal\", \"drowsy\" and \"aggressive\" sets were shuffled to ensure that the models provided a good representation of their performance for more practical, varied test sets.