esa-sceva/satcom-chunk-collection huggingface dataset:
# Magnetic-Responsive Microparticles that Switch Shape at 37 \({}^{\circ}\)[PERSON]
## 3 Results
### Magnetic-Responsive Capture/Release of Microrparticles  
Figure 4a shows SEM images of the MNP-loaded PCL microparticles after removing the large size of particles by sedimentation technique. The average diameter after purification was approximately 4.1 (\(\pm\)2.4) \(\upmu\)m. Both morphology and size before the separation by sedimentation were similar to the sample without MNPs. This implies that incorporation of MNPs does not affect particle formation and crosslinking. To investigate the response of the MNP-loaded particles to a magnetic field, a neodymium magnet was kept near a vial in which particles were dispersed in water. Figure 4b shows the photographs of the capture/release experiment. The particles were quickly attracted by the magnet within 60 s. When the applied field was removed, on the other hand, the captured particles were released back into the solution within 10 s by simple stirring or pipetting. Transmittance change was observed to assess the magnetophoretic mobility of the particles. Initially, the transmittance of the solution was zero, meaning that the solution was turbid. The optical transmittance for the particles without applying the magnetic field hardly changed during 10 min, suggesting that the particles have high dispersivity. When the magnetic field was applied, on the other hand, the transmittance sharply increased as shown in Figure 4c. Repeated on and off cycles of magnetic field was also conducted (Figure 4d). The particles were first captured on the wall (on), and then released (off). This magnetic-responsive behavior enables remote manipulation of particles under a controlled magnetic field.  
Figure 3: SEM images of thermally-induced shape recovery of shape-memory PCL microparticles with acceleration voltage of 5 kV (**top**) and 10 kV (**bottom**). Highly strained temporary shape (**left**) was returned to original permanent shape (**right**) by heating at 37 \({}^{\circ}\)C for 15 min. Images shows the shape recovery of particles for two different sets (**top** and **bottom**). Scale bars, 200 \(\upmu\)m (**top**) and 1 mm (**bottom**).
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
###### Abstract  
The increasing demands for real-time marine monitoring call for the wide deployment of Marine Monitoring Networks (MMNs). The low-rate underwater communications over a long distance, long propagation delay of underwater acoustic channel, and high deployment costs of marine sensors in a large-scale three-dimensional space bring great challenges in the network deployment and management of MMN. In this paper, we first propose a multitier, hierarchical network architecture of MMN with the support of edge computing (HMMN-EC) to enable efficient monitoring services in a harsh marine environment, taking into consideration the salient features of marine communications. Specifically, HMMN-EC is composed of three subnetworks, i.e., underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork, with a diversity of network nodes with different capabilities. We then jointly investigate the deployment diverse network nodes with various constraints in different subnetworks of HMMN-EC. To this end, we formulate a Multiobjective Optimization (MO) problem to minimize the network deployment cost while achieving the maximal network lifetime, subject to the limited energy of different marine nodes and the complex deployment environment. To solve the formulated problem, we present an Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm to find the optimal locations of nodes in different subnetworks of MMN in a large-scale deployment. The time complexity of the proposed algorithm is also analyzed. Finally, extensive simulations are carried out to validate the superior performance of the proposed algorithm compared with some existing solutions.  
marie monitoring networks; multiobjective optimization; network deployment; ant colony algorithm; gurobi +
Footnote †: journal: Accepted: 3 August 2020; Published: 11 August 2020
## 3 Continuous-Time Spline Representation for Fast Motion Estimation
### Cubic B-Spline Representation for Continuous-Time Motion Estimation  
Estimated velocity and acceleration need a C\({}^{2}\) continuity, a good approximation of minimal torque trajectories and a parameterization of rigid-body motion devoid of singularities [25; 26]. A cubic B-spline representation can satisfy these conditions. We update the 3D position to SE(3), i.e.,  
\[T_{j}=\left[\begin{array}{cc}R_{w}^{j}&PW_{j}\\ 0&1\end{array}\right]T_{j}\in SE(3),R_{w}^{j}\in SO(3) \tag{16}\]  
where \(R_{w}^{j}\) is the rotation between the world coordinate and the fragment coordinate at time stamp \(j\). When we treat the fragment as a single point, \(R_{w}^{j}=I_{3}\). For cubic B-spline, Equation (14) can be updated by using Equation (16), i.e.,  
\[T(t)=exp\big{(}B_{0,A}(t)log(T_{j-1})\big{)}\prod_{k=1}^{3}exp\Big{(}B_{k,4}( t)log(T_{k-1}^{-1}T_{k})\Big{)} \tag{17}\]  
We define time fraction as: \(u(t)=\frac{t-t_{j}}{t_{j+1}-t_{j}},u\in[0,1)\). By using lie algebra of SE(3) and the related propriety, we give the analytical result for motion estimation of the position (Equation (18)), the velocity (Equation (19)), and the acceleration (Equation (20)) at time \(u(t)\) during time \(t_{j}\) and \(t_{j+1}\).  
\[T(u(t))=T_{j-1}A_{0}A_{1}A_{2} \tag{18}\]  
\[\dot{T}(u(t))=T_{j-1}(\dot{A}_{0}A_{1}A_{2}+A_{0}\dot{A}_{1}A_{2}+A_{0}A_{1} \dot{A}_{2}) \tag{19}\]\[\begin{array}{ll}\ddot{T}(u(t))=&T_{j-1}(\ddot{A}_{0}A_{1}A_{2}+A_{0}\ddot{A}_{1} A_{2}+A_{0}A_{1}\dddot{A}_{2}\\ &+2A_{0}\dot{A}_{1}A_{2}+2A_{0}\dot{A}_{1}\dot{A}_{2}+2\dot{A}_{0}A_{1}\dot{A}_{ 2}\end{array} \tag{20}\]  
We define \(\Omega_{k}^{k-1}=log(T_{k-1}^{-1}T_{k})\), the parameters \(A_{k}\) are under these formulas:  
\[A_{k}=exp(B_{k}(u(t))\Omega_{i+k}^{i-1+k}) \tag{21}\]  
\[\dot{A}_{k}=\dot{B}_{k}(u(t))\dot{\Omega}_{i+k}^{i-1+k}A_{k} \tag{22}\]  
\[\ddot{A}_{k}=\dot{B}_{k}(u(t))\dot{\Omega}_{i+k}^{i-1+k}\dot{A}_{k}+\ddot{B}_{ k}(u(t))\dot{\Omega}_{i+k}^{i-1+k}A_{k} \tag{23}\]
## 5 Discussion  
Both the simulation and experimental results demonstrate that, compared to traditional algorithms, the SAIOR algorithm effectively eliminates single/multi-component PFM interference by leveraging the periodicity of PFM interference signals and the characteristics of LEO constellation signals. It significantly improves interference suppression performance under conditions such as narrow bandwidth and high power, demonstrating a notable enhancement in countering PFM interference for LEO satellite positioning scenarios, which highlights the algorithm's value. The following aspects warrant further discussion:  
1. Applicability: This paper proposes a multiple PFM interference suppression algorithm in LEO satellites SOP positioning for single-antenna receivers that requires no prior knowledge. By exploiting the periodicity of PFM interference signals and LEO constellation signal characteristics, the algorithm eliminates the influence of LEO satellite signals in the input signal and concentrates the PFM interference energy--originally spread over a wide bandwidth--into a few frequency bins in the resampled data through optimized sampling. This effectively mitigates single/multi-component PFM interference while minimizing damage to the desired signal during suppression, over  
Figure 12: Comparison of positioning results of scene two. (**a**) Positioning result after anti-jamming ()SR is 15 dB). (**b**) Positioning result without anti-jamming algorithm ()SR is 15 dB). (**c**) Positioning result after anti-jamming ()SR is 25 dB). (**d**) Positioning result without anti-jamming algorithm ()SR is 25 dB).  
coming the limitations of traditional PFM interference suppression methods under single-antenna LEO signal reception conditions. Thus, the algorithm is suitable for single-antenna-based LEO satellites SOP positioning.
2. Limitations: Since optimized sampling was employed, a larger amount of sampled data were required to obtain sufficient resampled data for interference detection and suppression, mitigating the impact of spectral leakage during frequency-domain blanking. The algorithm demands high volumes of original sampled data and future improvements could focus on reducing this requirement.
3. Potential Enhancements: Another improvement approach involves establishing a background interference-type database, integrating artificial intelligence for automatic detection and identification, reconstructing interference signals in the background, and then canceling them from the received signal to suppress interference. This could effectively address complex interference forms or multiple interference sources, potentially achieving zero loss to the desired signal under ideal conditions.
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## Design of the Improved MPPT Control Method
### Regulation of Optimal Collective Blade-Pitch Angle  
As shown in Figure 4, the optimal collective blade-pitch angle varies with the instantaneous TSR. Interpolation function is used to establish the lookup table between the optimal collective blade-pitch angle and the instantaneous TSR based on the results from AeroDyn:  
\[\beta^{*}=\text{lookup}(\hat{\lambda}) \tag{44}\]  
where lookup represents one-dimensional interpolation function with the instantaneous TSR serving as input and the optimal setting value of the collective blade-pitch angle serving as output.  
The collective blade-pitch angle is saturated to a maximum of 2\({}^{\circ}\), and a minimum of \(-2^{\circ}\) in the MPPT region since too wide blade-pitch angle regulation may cost too much energy from the variable-pitch system and bring in more extra fatigue loads to VSWTs.  
Under assumption 10, the mathematical model of the blade-pitch actuator is:  
\[\frac{\beta(s)}{\beta^{*}(s)}=\frac{1}{1+3s} \tag{45}\]
## 4 Numerical Examples  
This section evaluates the effectiveness of the proposed up/down-going wave separation method, which is based on target signal knowledge representation, through numerical experiments. Two velocity models were selected for the experiments: a simple horizontally layered geological model and the two-dimensional (2D) elastic Marmousi model. We conducted ablation studies and comparative experiments on the horizontally layered velocity model and also plotted comparison curves showing single-trace separation results from different methods. On the Marmousi model, comparative tests were performed against the traditional f-k method to validate the effectiveness of knowledge representation in the process of separating up- and down-going waves. All numerical simulations and model training were completed under a unified hardware environment, utilizing an NVIDIA A6000 GPU computing platform and implemented using the TensorFlow deep learning framework.
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 3 A Topology Change Aware Routing Protocol Choosing Scheme (TARCS)
### Description  
The topology change aware routing protocol choosing scheme (TARCS) includes two procedures: the periodically topology change aware (PTCA) and the adaptive route choosing scheme (ARCS). Accurate perception of topology changes is a prerequisite for routing protocol adjustment and reselection. The routing protocol selection is based on a preset topology change threshold/interval value.  
Traditional routing protocols either maintain routing paths during node movement (active routing) or start routing discovery when nodes need to communicate (passive routing). Neither active routing nor passive routing pays close attention to the surrounding topology. This is completely sufficient for low-speed nodes, but for high-speed nodes the topology changes so fast that nodes cannot respond to the rapidly changing environment and adjust strategy if they lack the knowledge of the external environment. Obviously, the consequence is that the nodes are not well adapted  
Figure 2: The mobility aware methods and the subsequent processing methods.  
to the topology changes, the network performance is severely constrained, and the task cannot be successfully completed.  
The advantage of TARCS is that:  
* It can monitor the topology changes around the nodes in real time.
* Nodes will respond to the perceived results in time and adjust to select the appropriate routing protocol.
* It will make the current routing protocol more suitable for node mobility mode and topology change requirements.  
Assume that node A (\(n_{\text{a}}\)) will send information to node B (\(n_{\text{b}}\)) in a FANET. Figure 3 shows the comparison of communication mode from \(n_{\text{a}}\) to \(n_{\text{b}}\) with and without the TARCS. Figure 3a shows that \(n_{\text{a}}\) communicates with \(n_{\text{b}}\) directly through a routing protocol in the traditional manner. \(n_{\text{a}}\) first establishes a valid path with \(n_{\text{b}}\) through route discovery, then sends data and maintains the route. Figure 3b shows the communication mode from \(n_{\text{a}}\) to \(n_{\text{b}}\) after using the TARCS.  
Figure 4: Process flow of the TARCS.  
Figure 3: Communication mode comparison between two nodes without and with the topology change aware routing protocol choosing scheme (TARCS). (**a**) Traditional communication mode between nodes without the TARCS; (**b**) communication mode between nodes with the TARCS.  
Figure 4 shows the basic workflow of the TARCS. The TARCS is divided into two phases. The left region represents the periodic topology change aware (PTCA), and the right region represents the adaptive routing protocol selection (ARCS).
# BioShare: An Open Framework for Trusted Biometric Authentication under User Control
## 3 BioShare Design
### Trusted Application (TA)  
The trusted application is a module running in terminal TEE that receives calls from the UA and conducts biometric operations through the standard interfaces listed in Table 1. Because all actions are performed in the terminal TEE and all private data are stored in tamper-resistant secure storage, biometric services from TA are secure and trusted.  
**Biometric Data Acquisition:** TA captures biometric raw data through biometric hardware integrated into mobile terminals, such as fingerprint devices, cameras, and other devices. TA calls the hardware driver to control the biometric sensor, the sensor captures raw data and writes it to a memory buffer, and finally, the raw data are copied to secure memory.  
**Biometric Recognition:** Biometric recognition transmutes biometric raw data into feature data in the terminal TEE. A typical process of biometric recognition consists of three steps: a. Data processing. TA processes the raw data with specified algorithms,such as biometric detection algorithms. b. Liveness detection. As an option, TA performs liveness detection with infrared images, RGB images, or depth images, etc. c. Feature extraction. TA extracts feature data from raw data using a specified model. Taking face recognition as an example, the typical process includes face detection, liveness detection, and facial feature extraction.  
**Secure Data Storage:** Biometric feature data are stored in secure storage in terminal TEEs and are protected by hardware isolation to ensure privacy protection, and only trusted applications (TAs) can obtain permissions to access biometric data in TEE mode. When biometric data are moved from secure storage to insecure environments, they are encrypted by the SCM module to ensure data security, as in Section 3.6.  
**Biometric Comparison:** TA conducts a biometric comparison by calculating the similarity between source feature values and the target feature values. If the similarity score exceeds the threshold value, TA returns a success, otherwise it returns a failure. To ensure security, private biometric data are transmitted to TA in encrypted formats, and are decrypted in the terminal TEE before biometric comparison.  
**Constraints:** Terminal TEE is a restricted zone in mobile CPU platforms with limited capabilities, and the limitations include CPU speed, maximum RAM, and maximum secure storage, etc. Table 2 lists the TEE capabilities of typical CPUs. We therefore chose algorithms and models with low computing and memory costs in the implementation of biometric recognition and comparison.
## 4 Attack Surface Analysis
### Gateway to Internet Service Provider
#### 4.3.4 Common Wireless Offensive and Defensive Tools  
In real-world WLAN security contexts, various tools have been developed to attack and defend Wi-Fi infrastructures. Understanding these tools is crucial for security assessments and the design of resilient networks.  
Offensive Tools:  
* Airgeddon v11.41 : A multi-use bash script for auditing wireless networks. It supports Evil Twin attacks, handshake capture, captive portal phishing, deauthentication, and channel hopping for attack automation.
* Wifite v2.0: A Python-based automated tool targeting WEP, WPA, WPA2 (PSK), and PMKID attacks. It passively scans networks and captures handshakes for offline brute-force or dictionary cracking.
* Fluxion v4.1: It focuses on social engineering attacks through Evil Twin APs and phishing portals to obtain WPA/WPA2 passphrases without the need for brute force. Defensive Tools:  
\begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attack Surface** & **Relevant ATT\&CK Techniques** & **Description** \\ \hline Radio Access Control & T1078 (valid accounts), T1557 (man-in-the-middle) & Credential access, Wi-Fi spoofing, rogue access point attacks \\ \hline Insider compromise & T1566 (phishing), T1203 (exploitation for client execution) & Initial access via malware or phishing; lateral movement from compromised nodes \\ \hline ISP gateway & T1040 (network sniffing), T1071 (application layer protocol) & Passive monitoring, DNS abuse, and malicious traffic injection \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mapping of WLAN attack surfaces to MITRE ATT&CK techniques.  
* Cisco Wireless Intrusion Prevention System (WIPS): It detects and mitigates rogue APs, spoofing, and deauthentication attacks, and it provides real-time wireless traffic analytics.
* Aruba Wireless IDS/IPS: It offers intrusion detection, tracking of rogue devices, and mitigation of threats such as Evil Twin, MAC spoofing, and unauthorized SSID broadcasting.
* Kismet 2023-07-R2: A passive network detector and sniffer capable of identifying hidden SSIDs, rogue devices, and channel-hopping attackers, useful for WLAN security audits. Tool Coverage Summary: Table 10 summarizes different tools and their capabilities.
## 3 Results  
### Total Polypphenols and Proanthocyanidins in Hydrogel Beads  
The main goal of this study was to estimate the impact of pullulan and/or disaccharide (sucrose and trehalose) addition on the encapsulation of chokeberry polyphenols in alginate hydrogel beads. Alginate is a polymer commonly used as wall material in the encapsulation of polyphenols when encapsulation is performed by ionic gelation. The results of the evaluation of total polyphenols and proanthocyanidins are displayed in Table 1. Chokeberry juice contained 19.98 g/kg of total polyphenols and 3.15 g/kg of proanthocyanidins. Alginate hydrogel beads were used as a control sample, and through the results, it can be perceived that pullulan and/or disaccharides had an impact on the encapsulation of phenolic compounds. The control sample contained 5.54 g/kg of total polyphenols. The addition of pullulan to the encapsulation mixture generally had the most positive effect, so the total polyphenol content of these beads was 8.60 g/kg. A combination of alginate and disaccharides revealed that the type of disaccharide had a significant impact on the encapsulation of phenolic compounds. Sucrose was more effective in encapsulation than trehalose. Consequently, alginate/sucrose beads had 8.17 g/kg of total polyphenols, while alginate/trehalose beads had 4.50 g/kg. The addition of both pullulan and disaccharide to the alginate mixture also affected the encapsulation of phenolic compounds. The combination of alginate/pullulan/trehalose in the encapsulation mixture was more effective than alginate/pullulan/sucrose; thus, the opposite effect of disaccharide type was observed in comparison to hydrogel beads without pullulan. Total polyphenols in alginate/pullulan/trehalose beads were 7.83 g/kg, in contrast to 4.78 g/kg in alginate/pullulan/sucrose beads. So actually, two encapsulation mixtures, alginate/trehalose and alginate/pullulan/sucrose, were less effective than the alginate encapsulation mixture. The same trend was observed for encapsulation of proanthocyanidins. Alginate hydrogel beads contained 1.47 g/kg of proanthocyanidins while alginate/trehalose and alginate/pullulan/sucrose beads had lower amounts, 1.16 g/kg and 1.23 g/kg, respectively. The remaining beads had a higher amount of proanthocyanidins than the control sample. Alginate/pullulan, alginate/sucrose, and alginate/pullulan/trehalose beads contained 2.37 g/kg, 2.27 g/kg, and 2.15 g/kg of proanthocyanidins, respectively.
## 3 Results
### Changes in Apoptosis-Related Bcl-2 and Bax mRNA Expression in Lung Tissue  
In lung tissue of the PM\({}_{2.5}\) control group, apoptosis-related Bcl-2 mRNA expression significantly decreased (\(p<0.01\)), while Bax mRNA expression increased in comparison with the normal vehicle control group. In contrast, Bcl-2 mRNA expression in all three IAP extract groups increased significantly (\(p<0.01\)) and Bax mRNA expression decreased in a dose-dependent manner. Particularly, in the IAP400 group, the changes in PM\({}_{2.5}\)-induced \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Items (Unit)**} & \multicolumn{2}{c}{**Lung Contents (nM/mg Protein)**} & \multicolumn{2}{c}{**Lung Enzyme Activity (U/mg Protein)**} \\ \cline{2-6}
**Groups** & **MDA** & **ROS** & **GSH** & **SOD** & **CAT** \\ \hline Controls & & & & & \\ vehicle & 3.39 \(\pm\) 0.97 & 23.29 \(\pm\) 10.28 & 42.70 \(\pm\) 10.63 & 337.60 \(\pm\) 51.59 & 75.30 \(\pm\) 14.03 \\ PM\({}_{2.5}\) & 21.87 \(\pm\) 4.56 \({}^{\rm c}\) & 96.66 \(\pm\) 11.71 \({}^{\rm a}\) & 6.36 \(\pm\) 2.05 \({}^{\rm c}\) & 60.00 \(\pm\) 18.68 \({}^{\rm cd}\) & 9.40 \(\pm\) 2.01 \({}^{\rm c}\) \\ DEXA & 8.58 \(\pm\) 1.44 \({}^{\rm cd}\) & 43.15 \(\pm\) 8.08 \({}^{\rm ab}\) & 18.84 \(\pm\) 4.56 \({}^{\rm cd}\) & 189.60 \(\pm\) 39.04 \({}^{\rm cd}\) & 30.00 \(\pm\) 7.32 \({}^{\rm cd}\) \\ Test substance-IAP & & & & & \\
400 mg/kg & 8.91 \(\pm\) 2.46 \({}^{\rm cd}\) & 44.30 \(\pm\) 9.58 \({}^{\rm ab}\) & 20.28 \(\pm\) 3.86 \({}^{\rm cd}\) & 197.40 \(\pm\) 34.58 \({}^{\rm cd}\) & 30.80 \(\pm\) 12.45 \({}^{\rm cd}\) \\
200 mg/kg & 12.65 \(\pm\) 2.97 \({}^{\rm cd}\) & 57.72 \(\pm\) 13.11 \({}^{\rm ab}\) & 16.44 \(\pm\) 3.03 \({}^{\rm cd}\) & 157.00 \(\pm\) 23.97 \({}^{\rm cd}\) & 24.90 \(\pm\) 7.17 \({}^{\rm cd}\) \\
100 mg/kg & 14.82 \(\pm\) 1.69 \({}^{\rm cd}\) & 66.53 \(\pm\) 13.73 \({}^{\rm ab}\) & 12.74 \(\pm\) 3.28 \({}^{\rm cd}\) & 129.30 \(\pm\) 14.26 \({}^{\rm cd}\) & 19.60 \(\pm\) 3.10 \({}^{\rm cd}\) \\ \hline \multicolumn{6}{c}{Values are expressed mean \(\pm\) SD. MDA = Malondialdehydedehyde; ROS = Reactive oxygen species; GSH = Glutathione;} \\ \multicolumn{6}{c}{CAT = Catalase; SOD = Superoxide dismutase; TBSD = Tukey’s Honest Significant Difference; DT3 = Dunnett’s T3.} \\ \multicolumn{6}{c}{\({}^{\rm a}\)\(p<0.01\) versus vehicle control by THSD test. \({}^{\rm b}\)\(p<0.01\) versus PM\({}_{2.5}\) control by THSD test. \({}^{\rm c}\)\(p<0.01\) versus vehicle control via DT3 test.} \\ \end{tabular}
\end{table} 
Table 4: Lung lipid peroxidation (MDA Contents), GSH Contents, and CAT and SOD activities in vehicle or PM\({}_{2.5}\)-treated pulmonary injured mice.