esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Impermeable Surface Extraction  
#### 3.1.1 Impervious Surface Extraction Results  
Figure 3 shows the variation in extraction queues and the corresponding impervious surface patch perimeters for each dataset under different geomorphological partitions. The curves in the figure demonstrate that the polygon perimeter of the urban area gradually decreases as the segmentation threshold increases gradually. When the threshold increases to a certain queue value, the polygon perimeter starts to increase and then decreases gradually. This queue value is the optimal threshold point for extracting the impervious surface of the city. Figure 4 shows the impermeable surfaces extracted from six different geomorphological divisions in the Karst Mountains based on Luojia1-01, NPP-VIIRS, and Flint nighttime light images, using the queues shown in Figure 3. The extracted surfaces are compared with the real impermeable surfaces. Overall, the results extracted using the three nighttime light images exhibit similar spatial patterns. Figure 3: _Cont._  
A comparison of the impermeable surfaces extracted from different data sources shows that the impermeable surfaces extracted from Luojia1-01 for the landform types other than the Karst fracture basins overlap more closely with the actual impermeable surfaces. The NPP-VIIRS images have higher spatial similarity to the Flint images. However, the impermeable surface patches extracted from the former type of images are highly separated. Furthermore, the extracted impermeable surface shows a poor spatial overlap with respect to the actual impermeable surface. The processed Flint dataset can effectively complement the detailed and missing information within the nighttime lighting data. Consequently, the impervious surface patches extracted from the Flint data are complete and highly aggregated but have blurred boundaries.
## 2 Materials and Methods
### Statistical Method  
As noted in Section 2.3, 320 double-leg drop-jump landings were analysed. A series of 10 \(\times\) 10-fold cross validation experiments were applied on full (320 videos) and balanced (153 videos, \(\leq\) 20 videos per LESS score) to predict the scores using random forest for regression, linear regression, and dummy regression (ZeroR) models in WEKA [20]. To assess the effectiveness of the automated cropping algorithm in the context of the overall system, we additionally ran the entire pipeline with crops generated by the clinician. Mean absolute error and Pearson correlation coefficient (\(r\)) were calculated to assess the accuracy of the predictions. Predictions were then converted to a binary category and sensitivity-specificity for categorising individuals at high risk of non-contact ACL injury (LESS \(\geq\) 5 errors [12]) were assessed for each method. The outcomes of the models were compared using paired corrected \(t\)-tests in WEKA [20], and the timestamps of the key frames IC and \(\text{KF}_{\text{max}}\) respectively compared between manual (clinician) and automated (OpenPose) cropping methods using unpaired \(t\)-tests assuming homoscedasticity. Since the LESS score was treated as a regression problem, actual (clinical LESS) versus predicted (automated LESS) and Bland-Altman [21] plots were used to allow for a visual inspection of the models. Statistical significance was set at \(p\leq 0.05\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Key Frames and Views** & \begin{tabular}{c} **Measurement** \\ **(OpenPose Numbers \({}^{\text{a}}\))** \\ \end{tabular} & **Kinematic Features** \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} **All four key frames** \\ (two frontal key frames and two \\ lateral key frames) \\ \end{tabular} } & Angle (9,8,1) & Right knee angle \\  & Angle (2,1,8) & Right trunk angle \\  & Angle (3,2,1) & Right shoulder angle \\  & Angle (4,3,2) & Right elbow angle \\  & Angle (2,1,0) & Right neck angle \\ \hline \multirow{6}{*}{
\begin{tabular}{c} **Two key frames** \\ (two frontal key frames) \\ \end{tabular} } & Angle (11,12,13) & Left knee angle \\  & Angle (1,11,12) & Left hip angle \\  & Angle (11,1,5) & Left trunk angle \\  & Angle (1,5,6) & Left shoulder angle \\  & Angle (5,6,7) & Left elbow angle \\  & Distance (9,12) & Knee distance \\  & Distance (2,5) & Shoulder distance \\ \cline{1-1}  & Distance (9,12)/Distance (2,5) & Knee distance/Shoulder distance \\ \hline \hline \end{tabular} _Notes._ Key frames are: (i) initial contact, (ii) maximal knee flexion. \({}^{\text{a}}\) Refer to Figure 3 for keypoints.  
\end{table}
Table 3: Measurements extracted from key frames and used as kinematic features.
## 3 Experimental Results
### The Second-Order Zeeman Shift Evaluation  
The DISC method can also be applied in the closed-loop operation of optical lattice clocks. For removing the first-order Zeeman shift, the lattice light vector shift and line  
Figure 3: (**a**) The collisional shift of unit fluorescence intensity (\(\Delta_{\rm uI}\)) at different excitation fraction. The error bars are given by the last point of the self-comparison instability. (**b**) The measurements of \(\Delta_{\rm uI}\) when the excitation fraction is about 0.4. The measurement uncertainty is also given by the last point of the self-comparison instability. The solid line indicates the weighted average of the ten measurements of which the value is same as the corresponding data in (**a**) where \(P_{\rm e}\) is about 0.4. The dashed lines represent the \(1\sigma\) standard deviation of the average value that has been multiplied by the square root of the reduced-chi-square \(\chi^{2}_{\rm red}=2.06\).  
pulling shift, the clock laser frequency of the \({}^{87}\)Sr optical lattice clock is usually stabilized to the average frequency of the transitions of \(m_{\rm F}=+9/2\)\(\rightarrow\)\(m_{\rm F}=+9/2\) and \(m_{\rm F}=-9/2\)\(\rightarrow\)\(m_{\rm F}=-9/2\). The magnitude of the bias magnetic field, which defines the magnetic field quantization axis, can be accurately extracted from the frequency gap between the two transitions. Benefiting from the DISC method, this frequency gap can be precisely extracted without the frequency offset caused by the clock laser frequency drift. Figure 4 shows the frequency gap of the \(m_{\rm F}=+9/2\) and \(m_{\rm F}=-9/2\) during the closed-loop operation of our clock with the DISC operation. Herein, \(Pana.1\) and \(Pana.2\) correspond to the \(m_{\rm F}=+9/2\)\(\rightarrow\)\(m_{\rm F}=+9/2\) and \(m_{\rm F}=-9/2\)\(\rightarrow\)\(m_{\rm F}=-9/2\) transitions, respectively, and the clock detection cycle is 0.6 s (the duration of the clock laser is 0.15 s). The average frequency gap is 297.8(8) Hz, where the uncertainty indicates the 95% confidence interval of the mean, and the corresponding magnetic field intensity is 305.2(8) mG [36]. As the second-order Zeeman coefficient is \(-23.37(3)\) MHz/T\({}^{2}\)[37], the fractional second-order Zeeman shift is 5.06(3) \(\times\) 10\({}^{-17}\).
## 5 Conclusions  
This study investigated the effect of the confined granular soil on the efficiency of the _PZT_ patches under various exciting frequencies. The dynamic response of experimental data were analysed using fast Fourier transformation (_FFT_). The peak pick method was used to analyse the natural frequency of the system. An analysis was also performed of the influence of excitation frequency and embedment depth on the voltage output of _PZT_ patches. Further, a set of static cone penetration tests were carried out to monitor the effect of confinement under various excitation frequencies. The significant findings are summarized as follows:  
1. The voltage output from the _PZT_ patch depends on the excitation frequency and placement of the _PZT_ patch. For the same embedment depth, the peak voltage output in vertical direction is observed at 50 Hz, while in the transverse direction, it is obtained at 10 Hz.
2. The depth of the compressive and tensile regions of the embedded _PZT_ patch should be considered to obtain the maximum output voltage. For the same excitation frequency (\(f\) = 10-20 Hz), the maximum output voltage was obtained at a thickness of 20 cm for vertical vibration. For the transverse vibration, the maximum output voltage was obtained at a thickness of 30 cm.
3. The cone resistance at various penetration depths indicates that voltage output increased proportionally with the cone resistance after excitation at the natural frequency. For excitation frequencies (\(f\) = 40-50 Hz), the voltage output increased from 22-45% for cone resistance in the 9-10% range. This variation indicates that the densification of fill and consequent increase in the cone resistance significantly impact improving the efficiency of the _PZT_ patch.  
Figure 8: Output voltage for _PZT_ patches embedded at varying depths in the transverse direction.  
* The natural frequency and damping loss factor significantly influence the voltage output of the _PZT_ patch.  
Conceptualization, N.K. and A.T.; methodology, N.K. and A.T.; formal analysis, N.K.; investigation, N.K.; resources, N.K. and A.T.; data curation, N.K.; writing--original draft preparation, N.K.; writing--review and editing N.K.; visualization, N.K. and A.T.; supervision, A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data availability Statement: Data is contained within the article.  
The first author is thankful to [PERSON], Washington state university, Pullman, for the constructive and valuable suggestion for writing the manuscript. The authors acknowledge resources and support from Delhi technological university, Delhi.  
The authors declare no potential conflict of interest with respect to the research, authorship, and/or publication of this article.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 3 The Proposed Image Encryption Scheme
### The Encryption Process  
Each round of the encryption process consists of the following three stages: (1) generating a modified chaotic sequence; (2) encryption processing row by row; (3) column-by-column encryption processing. The following describes the operation algorithm of each stage, respectively.
# Analysis of the Sugarane Biomass Use to Produce Green Hydrogen: Brazilian Case Study
## 5 Environmental Impacts and Sustainable Solutions
### Second-Generation Ethanol as a Sustainable Pathway for Green Hydrogen
#### 5.2.1 Green Hydrogen Production from 2G Ethanol  
Second-generation ethanol can be an excellent raw material to produce green hydrogen, since it is produced from the fermentation of sugarcane biomass and is a zero-emission method. However, despite substantial research efforts, no economically viable method for converting ethanol into green hydrogen has been achieved. The primary challenge lies in the formation of coke, which leads to catalyst deactivation during the process.  
Ongoing studies aim to address this issue by exploring methods to mitigate catalyst deactivation caused by coke formation. One example is a study published in the _International Journal of Hydrogen Energy_[79], which investigates the electrochemical synthesis of Ni-Cu bilayer hollow microtubes for the electrocatalytic reforming of ethanol. In this method, external potential facilitates the oxidation of ethanol at the anode and the reduction of protons at the cathode, resulting in hydrogen production. Hydrogen, as an efficient energy carrier, can subsequently be used in fuel cells to generate electricity [80]. The study focuses on understanding the synthesis processes of Ni and Cu hollow tubes (NC-HT) and evaluating their effectiveness as electrocatalysts in hydrogen evolution reactions from water-ethanol solutions.  
According to a study involving efficient plasma technology to produce green hydrogen from ethanol and water, the use of excess water reduces coke, but requires more energy to heat water, making the hydrogen production process unprofitable [81]. This research proposed an alternative approach, utilizing a plasma reactor driven by spark discharge, which is resistant to coke formation (Reaction (3)). The spark reactor demonstrated high operational flexibility and the capability to supply hydrogen to fuel cells, even under varying energy demands.  
Reaction (3):  
\[C_{2}H_{5}OH^{*}+H_{2}O^{*}\to 2 CO+4H_{2} \tag{3}\]
## 4 Methodology
### Measures
#### Decision quality measures  
The decision quality of the participants was only measured for one scenario (i.e., the first scenario). A predetermined optimal assortment was provided by JDA for each condition of this scenario. JDA was not able to provide predetermined optimal assortments for both scenarios. Therefore, participants, with the help of the scenario's guidelines and all the information made available to them, were led, for each task, to a predetermined optimal assortment of products. The assortment of products selected by each participant for each condition was compared to the predetermined optimal assortment, thus evaluating the decision accuracy (i.e., decision quality) of the participants. A decision quality score was then created, for each task and each participant, by awarding one point per selected product included in the predetermined optimal assortment. This decision quality score was then transformed in a percentage for each task and each participant.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 2 State-of-The-Art FC-AE-1553 Space Network
### Traffic Flow in FC-AE-1553 Space Network  
The NC of the backbone FC-AE-1553 network functions as the central controller for the entire single multi-hierarchical indoor and outdoor FC-AE-1553 network. It is responsible for managing the allocation of multiple tasks and schedules within the network. The backbone NC controls the connectivity and communication between different NTs within the backbone FC-AE-1553 network.  
In the backbone FC-AE-1553 network, one NT can establish a connection with another NT through the backbone switch, all under the control of the backbone NC. This enables communication between different NTs within the backbone network.  
Similarly, in the access FC-AE-1553 network, one NT can connect with another NT from a different access FC-AE-1553 network. This is achieved through the use of a backbone switch and two optical splitters. These components facilitate communication between NTs in different access networks.  
The system supports four typical types of traffic flows:  
1. Communication between NTs within the backbone network (Figure 2a);
2. Communication between NTs within the same access network (Figure 2b);  
Figure 1: Multi-hierarchical FC-AE-1553 space network.  
3 Communication between NTs in the backbone network and NTs in the access network (Figure 2c);
4 Communication between NTs across different access networks (Figure 2d).
# Performance Optimal PI controller Tuning Based on Integrating Plus Time Delay Models
## 2 Preliminary Theory  
### Definitions  
Given a PI controller  
\[H_{c}(s)=K_{p}\frac{T_{i}s+1}{T_{i}s}, \tag{4}\]  
where \(K_{p}\) is the proportional constant and \(T_{i}\) is the integral time constant.  
Consider the standard feedback system with disturbances as illustrated in Figure 2. To compare the different controllers, we consider indices such as defined in [12, 14, 15]. Performance is measured in a feedback system by  
\[\text{IAE}=\int_{0}^{\infty}|\,e\,|dt. \tag{5}\]  
Furthermore, the following is defined.  
* IAE\({}_{\text{\tiny{$\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{ \mathrm{ \mathrm{    }}}}}}}}}}}}}}}}\) evaluates the performance in case of a step input disturbance (\(H_{p}(s)=H_{p}(s)\)), \(v=1\) (default), with the reference, \(r=0\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(s_{r}\) & **0** & **0.1** & **0.25** & **0.5** & **0.75** & **1** \\ \hline \(c\) & 2.4 & 2.5 & 2.6 & 2.7 & 3.7 & \(\infty\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The table shows the recommended MP parameters \(c\) if one wants to minimise the main performance objective \(V_{M}\) (Equation (42)) for different servo-regulator parameters \(s_{r}\) in Equation (41). The optimal \(c\) values as indicated are almost constant in the interval \(\delta\in[1.1,3.4]\) ([2]).  
* IAE\({}_{xy}\) evaluates the performance in case of a step output disturbance (\(H_{v}(s)=1\)), \(v=1\) (default), with the reference, \(r=0\).
* IAE\({}_{r}\) evaluates the performance in case of a reference unit step, \(r=1\), with the disturbance, \(v=0\).  
Similarly, we define the Integrated Time-weighted Absolute Error (ITAE), Integrated Square Error (ISE) and Integrated Time-weighted Square Error (ITSE) and Total input Value (TV) as the following i.e.,  
\[\mathrm{ITAE} = \int_{0}^{\infty}t\,|\,e\,|\,dt, \tag{6}\] \[\mathrm{ISE} = \int_{0}^{\infty}e^{2}\,dt,\] (7) \[\mathrm{ITSE} = \int_{0}^{\infty}te^{2}\,dt,\] (8) \[\mathrm{TV} = \int_{0}^{\infty}|\,\Delta u_{k}\,|\,dt, \tag{9}\]  
where \(\Delta u_{k}=u_{k}-u_{k-1}\).  
Robustness is quantified according to the maximum sensitivity peak  
\[M_{s} = \max_{0\leq\omega<\infty}|S(j\omega)|=||S(j\omega)||_{\infty}, \tag{10}\]  
where, \(S(j\omega)=\frac{1}{1+H_{p}(j\omega)\,H_{c}(j\omega)}\), and \(||\cdot||_{\infty}\) is the \(\mathcal{H}_{\infty}\)-norm.
## 3 The Proposed Approach  
The bridge of a ship is a unique environment, with several drivers operating the vessel simultaneously. As illustrated in Figure 1, this paper proposes a cross-modal behavioral intelligence recognition approach.  
Figure 1: The framework of the multi-person behavior recognition algorithm.  
The procedure for this approach is as follows: Firstly, the YOLOv7 model is utilized for the detection of individuals and feature objects. Subsequently, the ByteTrack algorithm is introduced for the tracking of multiple drivers, with an improved TSM method applied for the preliminary recognition of target behaviors. To address the challenge in regard to differentiating between two types of abnormal driver behaviors, namely smoking and playing on a phone, an optimization approach aided by feature objects is proposed based on RS. This involves the development of personnel object spatiotemporal correlation rules and the implementation of a joint reasoning engine for actions and feature objects, leading to enhanced identification accuracy in terms of these similar behaviors.  
The Multi-person behavior recognition algorithm is given as Algorithm 1:  
Input: Real-time video stream from ship bridge cameras  
Output: Anomaly alerts with behavior classification  
1: Initialize YOLOv7 model, ByteTrack tracker, Improved TSM algorithm, Decision Optimization Engine  
2: FOR each frame in video_stream DO  
3: Perform Personnel and Feature Object Detection (frame)  
4: Perform Personnel and Feature Object Localization (frame, YOLOv7)  
5: IF Personnel detected THEN  
6: Perform Personnel Tracking Support (frame, ByteTrack)  
7: Assign Feature Object Scores (frame, YOLOv7)  
8: ENDIF  
9: Perform Spatiotemporal Alignment Module (frame)  
10: Associate Personnel-Feature Object (frame, tracking results, feature scores)  
11: Perform Behavior Recognition (frame, Improved TSM)  
12: Calculate Action Score (behavior recognition results)  
13: Generate Anomaly Alert (action score, Decision Optimization Engine)  
14: END FOR  
15: RETURN Anomaly Alerts  
**Algorithm 1** Multi-person behavior recognition algorithm
# A Review on Styrene Substitutes in Thermosets and Their Composites
## 2 Traditionally Crosslinkable Monomers
### Other Monomers  
Tung oil is a drying oil obtained by pressing the seed from the nut of tung tree. Tung oil hardens upon exposure to air and has been mostly used for coatings [40; 41]. The fatty acids of tung oil consist of eleosetaxic, palmitic, oleic, linoleic acids and others (Figure 2). Due to the conjugated double bonds in eleosetaxic acid, tung oil can directly participate in the crosslinking of some thermosetting resins. It was reported by [PERSON] et al. [42] that tung oil was cationically copolymerized with AESO, methyl ester of tung oil, and styrene to produce vegetable oil-based thermosets. The fully tung oil-based resins presented comparable dynamic mechanical properties with styrene analogues. The addition of low concentrations of DVB (5-10 wt%) in the resins increased their rigidity and \(T_{\text{g}}\).  
[PERSON] et al. used 20 wt% tung oil to copolymerize with maleated castor oil (MACO) for fabricating jute fibers reinforced composites [43]. The prepared MACO composites had 42% higher impact strength than that of jute fibers reinforced UPE composites. The flexural modulus of MACO composites was almost similar to that of UPE composites. Besides, the MACO composites exhibited higher damping behavior at room temperature and high temperature. Such composites can be explored further as environmentally friendly and high damping materials over a wide range of temperature [43].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Monomer** & \
\begin{tabular}{c} **Molecular Weight** \\ **(g/cm\({}^{3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Boiling** \\ **Point (\({}^{\circ}\)C)** \\ \end{tabular} & **Toxicity** & **Chemical Structure** \\ \hline Butyl methacrylate (BMA) & 140.20 & 162–165 & acute toxicity \\ Hydroxypropylacrylate (HPA) & 130.14 & 77 & acute toxicity \\
2-Hydroxymethac-rylate (HEMA) & 130.14 & 85 & danger \\ Isobornyl methacrylate (IBOMA) & 222.32 & 127–129 & environmental hazards \\ Methyl methacrylate (MMA) & 100.12 & 100 & acute toxicity \\ Trimethylopro-panetri-acrylate (TMPTA) & 296.32 & - & acute toxicity \\ Trimethylopropane trimethacrylate (TMPTMA) & 338.40 & - & environmental hazards \\