esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 4 Real-Time Energy Management Strategy
### PMP-Based Solution  
According to the aforementioned objective function, PMP can convert the global optimum problem into a local one by a series of constrained conditions. The following Hamiltonian function [15] expresses the objective function in Equation (8):  
\[H\left(x,u,\gamma,\varphi,t\right)=\dot{m}_{f}+\left[\gamma\left(t\right)+ \varphi\left(t\right)\right]\cdot\dot{x}\left(t\right). \tag{8}\]  
In Equation (8), \(\gamma(t)\) is the co-state and \(\varphi(t)\) is the working mode coefficient of the battery. The specific expression is listed as follows:  
\[\varphi\left(t\right)=\left\{\begin{array}{l}\varphi_{B}:\qquad SoC\geq SoC_ {H}\\ -\varphi_{B}:\qquad SoC\leq SoC_{L}\\ 0:\qquad\qquad\text{else}\end{array}\right., \tag{9}\]where \(SoC_{H}\) is the upper limit of SoC, and \(SoC_{L}\) is the lower limit of SoC for the battery working mode; the working mode coefficient of the battery \(\varphi(t)\) is set to limit SoC within the working mode.  
The power distribution for the minimum fuel consumption rate based on PMP should meet the following conditions [16; 17]:  
1. At the duration [\(t_{0}\), \(t_{f}\)] of a driving cycle, an optimal control variable \(u(t)\in[P_{B\_min},P_{B\_max}]\) exists, which enables the Hamiltonian function \(H(x^{\star}\), \(u^{\star}\), \(\gamma^{\star}\), \(\varphi^{\star}\), \(t)\) to be a global optimal solution.
2. Meanwhile, \(x^{\star}(t)\) and \(\gamma^{\star}(t)\) should satisfy the following canonical equations: \[\dot{x}^{\star}(t)=\frac{\partial H^{\star}}{\partial\gamma},\] (10) \[\dot{\gamma}^{\star}(t)=-\frac{\partial H^{\star}}{\partial x}.\] (11)  
Therefore, \(H(x^{\star}\), \(u^{\star}\), \(\gamma^{\star}\), \(\varphi^{\star}\), \(t)=\min H[x\), \(u\), \(\gamma\), \(\varphi\), \(t]\) and \(u(t)\in[P_{B\_min},P_{B\_max}]\). The optimal control variable is \(u^{\star}\) = arg min \(H[x\), \(u\), \(\gamma\), \(\varphi\), \(t]\), and the desired \(u^{\star}\) is the optimal power distributed by the battery. Hence, the alternator power should be the difference between the load power and the battery power.
## 2 System Architecture and Methods
### System Setup Details  
The system primarily comprises a collaborative robot arm, the Ufactory Lite 6, with a vacuum gripper to manipulate objects. A ZED Mini camera (Stereo Labs, San Francisco, CA, USA), mounted on an aluminum frame, monitors the robot's workspace for scene observation, distance measurement, and object detection. The Samson XPS2 microphone captures (Samson AG, Frankfurt, Germany) voice commands, allowing user interaction with the robot. The processing unit is a PC equipped with an NVIDIA RTX3080 Ti graphics card (NVIDIA, Santa Clara, CA, USA), capable of running deep learning models. The robot and computer communicate via high-speed HTTP messages over an AX9000 router (Xiaomi, Pekin, China).
# A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
## 3 Network Architecture and System Model
### Network Architecture
To better understand it, two curves are marked in red and brown as the working path and the backup path, respectively. It is worth noting that there are no penetrated nodes to relay, amplify, and strengthen the optical signals in the optical networks owing to the absence of a relationship with the IP nodes. In consideration of the compatibility between the existing infrastructure and the upgrades and new installations, we proposed the weak centralized control mode to shield the differences of in the underlying hardware. In the traditional hardware-dependent distributed network, each node needs to be configured as an individual logical processing module, which brings about high construction and maintenance. By contrast, this weak centralized architecture can save expenditure on hardware and software integration in the evolution of architecture upgrades, which is beneficial to accelerate its application in the real network. At the same time, it enables the realization of faster routing convergence and could better meet the needs of EI communication services. Here, the total communication architecture is made up of the energy subsystem and the information communication subsystem. The former is at the bottom of the layered system including different energy provisions, such as power plant and energy storage. There are two parts in the information communication subsystem: the forwarding plane and the control plane which correspond to the optical layer and the IP layer, respectively. The forwarding plane mainly consists of a variety of devices to achieve data transmission, like BVT and BV-OXC. The top of the forwarding plane includes SDN switches and routers. The control plane consists of an SDN controller in the IP/Optical layer, and a coordinated controller to achieve path calculation and cooperative control.
## 6 Seismic Response Characteristics by Nonlinear Dynamic Analyses
### Analysis of the Variations of the Natural Frequency  
This study also evaluated the changes in the natural frequency of bridges before and after ground motions. The natural frequency of the bridge tended to decrease in some ground motions but did not change in other ground motions. The decrease in the natural frequency was larger at near-fault motions than at far-fault ground motions. Forward-directional near-fault ground motion decreased the natural frequency by approximately 32.8%. For near-fault ground motions with the fling-step effect, the decrease in the natural frequency of the bridge ranged from approximately 9.6 to 41.0%. The far-fault ground motion showed a minimal decrease in the natural frequency of approximately 3.4 to 5.3% only when an EQ3_FF earthquake occurred. Therefore, the proposed \(DR\) was used to assess the relationship between the characteristics of ground motion and the decrease in the natural frequency of the bridge.  
Figure 15 plots the reduction of the natural frequency in terms of \(DR\). Both the near-fault and far-fault ground motions showed no decrease in the natural frequency when the \(DR\) of the bridge was less than 12.5%. Therefore, this study investigated the relationship between the \(DR\) and the decrease in the natural frequency for a \(DR\) larger than 12.5%. Linear regression analysis showed that the decrease in the natural frequency was associated with the increase in the \(DR\), as shown in Figure 15. Compared with the far-fault ground motion, the near-fault ground motion with forward-directivity and fling-step pulses significantly increased the DR and reduced the natural frequency. The linear regression line shows a relatively larger dispersion in the near-fault ground motion than in the far-fault ground motion, as shown in Figure 15. The rate of decrease in the natural frequency due to the far-fault ground motions was similar to that due to the near-fault ground motions. The rate of decrease in the natural frequency was approximately 2.25% for the near-fault ground motions and 2.54% for the far-fault ground motions as the \(DR\) increased.  
Figure 14: Relationship between the maximum moment and the dynamic response ratio (\(DR\)).
## 4 Experimental Results  
### Experimental Setup  
To evaluate the performance of the developed facial expression recognition system, we equiped a computer with an Intel Core i5-9500 processor and 16 GB of RAM as the hardware platform for conducting our experiments. The FER-2013 dataset was adopted to evaluate the proposed system. The FER-2013 dataset is a dataset designed for expression recognition. It comprises seven emotion classes, namely Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. The FER-2013 dataset is divided into 28,709 images for training and 7178 images for testing (see Table 1). The size of the images in the FER-2013 dataset is 48 \(\times\) 48 pixels. However, the FER-2013 dataset still faces the challenge of data sparsity. To address this issue, data augmentation techniques were employed to augment the FER-2013 dataset, mitigating the impact of data sparsity.  
The metrics in facial expression recognition tasks are accuracy and time complexity. The recognition result is divided into four parts: true positive (_TP_), true negative (_TN_), false positive (_FP_), and false negative (_FN_). Accuracy is defined as follows:  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN}\times 100\% \tag{6}\]  
We adopted the methodology proposed by [PERSON] et al. [28] for calculating the time complexity of CNNs. Time complexity is estimated based on the input channel number, kernel size, number of filters, and length of output feature map for each layer of the CNN model, as shown in Equation (7). For the Haar cascade classifier, time complexity estimation was determined by the input image's size and the number of channels in the features, as shown in Equation (8).  
\[complexity(CNN)\equiv\mathrm{O}(\sum_{n=1}^{d}k_{n-1}\cdot\cdot 2_{n}^{2} \cdot f_{n}\cdot l_{n}^{2}) \tag{7}\]  
\[complexity(Haar)\equiv O(l_{w}\cdot I_{h}\cdot n) \tag{8}\]  
where \(d\) is the depth of the convolutional layer, \(l_{n}\) is the length of the output feature map, \(f_{n}\) is the number of filters in the layer, \(S_{n}\) is the length of the filter, and \(k_{n-1}\) defines the number of input channels in the \(l\) layer. \(I_{w}\) is the width of the input image, \(I_{h}\) is the height of the input image, and \(n\) is the number of the feature dimension.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Angry** & **Disgust** & **Fear** & **Happy** & **Sad** & **Surprise** & **Neutral** & **Total** \\ \hline Training & 3995 & 436 & 4097 & 7215 & 4830 & 3171 & 4965 & 28,709 \\ \hline Testing & 958 & 111 & 1024 & 1774 & 1247 & 831 & 1233 & 7178 \\ \hline \end{tabular}
\end{table}
Table 1: Dataset distribution.  
Figure 11: Flatten layer diagram.
# Recovery Behavior of Fe-Based Shape Memory Alloys under Different Restraints
###### Abstract  
This paper presents the experimental results of an evaluation of the recovery behavior of Fe-based shape memory alloys (Fe-SMAs) under different restraints. For the study, three types of Fe-SMA (FSMA-A, FSMA-B, FSMA-C) were produced. As a result of the direct tensile test, the yield strength of the FSMA-A specimen was nearly 34% higher than the strength of FSMA-B and FSMA-C. Under free restraint, the recovery strains are 0.00956, 0.01445, and 0.01977 for FSMA-A, FSMA-B and FSMA-C specimens, respectively, after activation when the pre-strain is 0.04, and the heating temperature 200 \({}^{\circ}\)C. Under rigid restraint, the final recovery stresses are 518, 391 and 401 MPa for FSMA-A, FSMA-B, FSMA-C specimens after activation when a pre-strain of 0.04 and heating temperature 200 \({}^{\circ}\)C. Additionally, under the rigid restraint, the effect of pre-strain on the final recovery stress was insignificant, whereas the final recovery stress increased as the heating temperature increased. When Fe-SMA was constrained during cooling, the recovery stress is 50% lower than under rigid restraint. Hence, in order to develop a large recovery stress, Fe-SMA must be constrained during heating. In addition, a method for calculating the effective confining stress of the Fe-SMA coupler for pipe joining was proposed based on the experimental results.  
Fe-based shape memory alloy (Fe-SMA); recovery stress; activation; restraint; pre-strain; heating temperature; pipe joining +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Materials  
Sylgard 184 (Sylgard) (SYLGARD 184, Dow Corning Corporation), a polydimethylsiloxane was used as precursor of the polymeric matrix and mixed 10:1 with the curing agent. Three different types of fillers were added to Sylgard matrix: metal fillers, ceramic fillers, and carbon fillers. Concerning metal fillers, two copper (Cu) particles different in shape and size (spheroidal powder, 10-25 \(\upmu\)m, purity of 98%, Sigma-Aldrich; dendritic powder, <45 \(\upmu\)m, purity of 99.7%, Sigma Aldrich), and two nickel (Ni) particles different in size (powder, 3-7 \(\upmu\)m, purity of 99.8%, Sigma Aldrich; powder, <45 \(\upmu\)m, purity of 99.8%, Sigma Aldrich). Boron nitride (BN) nanoparticles (size < 150 nm, purity of 99%, Sigma Aldrich) was selected as ceramic filler. Carbon nanotubes (CNT) (NC7000, purity of 90%, Nanocyl SA) and carbon black (CB) (carbon, mesoporous nanopowder, particle size (DLS) < 500 nm, Sigma Aldrich) were selected as carbon filler. The fillers were used as received.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.4 Comparison with Other Methods  
To compare the proposed algorithm against similar approaches, key space, entropy of the encrypted signal, and correlation between the original and encrypted signals were taken into account (Table 1).  
In the proposed scheme, the size of the key was the largest of the compared methods, which allowed a greater computational requirement for brute force attacks. In turn, the entropy was very close to the ideal value, which guaranteed a high uncertainty in the behavior of the data. Likewise, the proposed scheme presented a null correlation between the original and encrypted signal, and consequently the scheme presented a low probability against statistical attacks.  
Figure 4: Computational cost analysis. Confidence range charts (95%) for encryption (CC) and decryption execution times.  
Figure 3: Similarity analysis. Confidence range charts (95%) for correlation coefficient (CC) and spectral distortion (\(S_{p}D\)).  
In terms of computational cost in the recovery process, the proposed approach was compared to a similar method using variable length codes obtained through a binarization process based on the Collatz Conjecture [12]. For this comparison, all 85 test signals were used, coded, and then decoded. The execution time of the recovery process was measured and the relationship between the recovery time and signal duration was evaluated. The PC used for these tests was the same as the one used in Section 4.3.3. The results showed that the recovery time in the proposed method was 5.38% of the duration of the original signal, while in the method based on variable length codes was 8.82%. This means that, in the proposed method, the recovery time was reduced by approximately 39% compared to the method based on variable length codes. The reduction of the recovery time of the proposed method was mainly based on the use of fixed length codes. This allows data to be structured in matrices, and operations to be performed with vectorized implementation.
## 4 Conclusions  
The proposed system makes use of existing display screen and surveillance cameras facilities. This reduces the overhead cost and need for additional components. It provides a robust, secure MIMO communication services without distorting the quality of the cover image displayed on the screen. Spatial coverage quality of a single camera enables invisible simultaneous visual MIMO communication and object monitoring services. Hough transform is used to extract ROI of stego image, and object tracking is achieved by a Kalman filter-based template matching technique. An IWT-based ARC-LSB embedding technique has been adopted for image hiding and extraction. Color independent visual MIMO is employed, which provides a high data rate. A combination of 32,4001 \(\times\) 8 is required to recover the data image, which illustrates the robustness of the proposed embedding algorithm.  
Performance of the proposed system is analyzed in terms of imperceptibility and accuracy at the transmitter and receiver terminals. Experiments results at the transmitter illustrate a significant improvement in imperceptibility as compared with existing techniques. Similarly, at the reception terminal for the received data image, experimental results show a promising accuracy of 97.6%, while for object tracking, accuracy of 95% is observed.  
Further work will include an investigation of the performance assessment of the proposed system in the presence of distortions in light illumination, relative distance, and orientation. Usage of randomization algorithms for data images created to provide additional security is also a future initiative associated with this present work. This work can also be extended to control violence against civilians by providing 24/7 hidden security services in all public gathering places by implementing suspicious-movement detection and multi-object tracking algorithms in the proposed system.  
**Author Contributions:** All the authors equally participated in technical discussion, design, implementation, testing, performance measurement and writing the article.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We would like to thank god for giving us an opportunity, encouragement, and strength to finish this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Material and Methods
### Phenotyping: NIRS (Near-Infrared Spectroscopy) and Chemical Analysis
#### 4.2.4 Gas Chromatography-Mass Spectrometry (GC-MS) Measurements of Volatile Compounds (VOCs)  
Solid Phase Micro Extraction (Arrow)--Gas Chromatography/Mass Spectrometry (SPME (Arrow)-GC/MS) was used to extract and analyse the volatile compounds in the headspace of the ground faba beans. Each sample (0.1 g powder) was mixed with 1 g NaCl (Merck), 0.15 mL 1M sodium phosphate monobasic and sodium phosphate dibasic buffer (Merck), and 5 mL demineralized water (final pH 7.1) in a 20 mL ND20 headspace glass vial (BGB(r)). The internal standard solution containing deuterated hexanal (0.5 ug/L) was added for quality control. A blank sample was prepared similarly. All samples were kept at 4 \({}^{\circ}\)C for at least 2 h to enable equilibrium.  
Samples were incubated at 40 \({}^{\circ}\)C for 12 min with agitation (1200 rpm). Then, volatile extraction from the headspace was performed using a Restek PAL SPME-Arrow fiber DVB/C-WR/PDMS (120 um \(\times\) 20 mm) for 10 min at 40 \({}^{\circ}\)C with agitation (1200 rpm) via a TriPlus RSH autosampler (Thermo Scientific, Waltham, MA, USA). Volatiles were transferred to a Trace1300 gas chromatograph (Thermo Scientific, USA) coupled to an ISQ7000 single quadrupole mass spectrometer (Thermo Scientific, USA) by thermal desorption in a GC cryogenic cold trap (CryoFocus-4, GL Sciences, Eindhoven, The Netherlands) in split-less mode under constant hydrogen flow (2.17 mL/min). A Rxi-5 SIL MS column (30 m \(\times\) 0.25 mm ID \(\times\) 1.0 um film thickness) was used (Restek, USA). The GC oven temperature was started at 40 \({}^{\circ}\)C for 5 min, ramped up to 30 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, and held for 2 min. The column effluent was ionized by electron impact (70 eV) with a scan range of \(m/z\) 25-250, and the MS interface was set at 260 \({}^{\circ}\)C.  
Raw data were processed and analysed using Chromeleon 7.3.1 (Thermo Scientific, USA) and MsXelerator(tm) software 4.9.6 (MS Metrix, Utrecht, The Netherlands). Volatile compound identification was performed by matching mass spectra with the NIST17 Spectral library. Integrated peak areas were adjusted for changes in MS detection sensitivity using the internal standards. Hexanal was quantified in ppm.
## 4 Suggestion and Verification of Optimal Mixing Ratio for Waterproofing Performance
### Verification of Waterproofing Performance  
In order to verify the effectiveness of the optimal superabsorbent polymer mixing ratio to ensure waterproofing performance under deterioration conditions, the waterproofing performance evaluation was carried out again in accordance with the deterioration conditions presented in the Section 3.2.1 Swelling Rate Test Method and Results and the test methods presented in Section 3.1.2 Leakage Test Methods and Results. However, for the evaluation, the specimens were fabricated using a mixing ratio of 14%, the highest superabsorbent polymer mixing ratio, so that they could respond to all deterioration conditions. The evaluation results are shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Division** & \begin{tabular}{c} **Non-** \\ **Treatment** \\ \end{tabular} & \begin{tabular}{c} **Hydrochloric** \\ **Acid** \\ \end{tabular} & **Nitic Acid** & **Sulfuric Acid** & **Alkali** & \begin{tabular}{c} **Sodium** \\ **Chloride** \\ \end{tabular} & **Freeze-Thaw** & \begin{tabular}{c} **Repeated Wet-Dry** \\ **Treatment** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Mixing ratio \\ (\%) \\ \end{tabular} & More than 20 & More than 14 & More than 14 & More than 14 & More than 14 &
\begin{tabular}{c} More than \\ 13 \\ \end{tabular} & More than 13 & More than 12 & More than 12 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The optimal superabsorbent polymer mixing ratio for water-expandable polyurethane to secure waterproofing performance according to deterioration conditions.  
As shown in the above evaluation results, the specimen fabricated by setting the superabsorbent polymer mixing ratio to 14% exhibited waterproofing performance under all deterioration conditions. Based on this result, the present study proposes a mixing ratio of 14% as the optimal superabsorbent polymer mixing ratio for water-expandable polyurethane to exhibit the appropriate swelling ratio and waterproofing performance.
# State Feedback Control Based Seamless Switch Control for Microgrid Inverter
## 3 Proposed Control  
For the battery inverter system in Figure 2, a control frame supporting the seamless GC-to-SA transition is designed in this paper, as shown in Figure 3. Complex variables are adopted to facilitate the system analysis and design, as a result, the double-line arrows in Figure 3 represent the information streams of complex variables. We define the complex variables \(\mathbf{i}=i_{\alpha}+ji_{\beta}\), \(\mathbf{u}=u_{\alpha}+ju_{\beta}\), and \(u_{g}=u_{g\alpha}+ju_{g\beta}\) to represent the inductance current, the output capacitance voltage of the battery inverter, and the grid voltage at the point of common coupling (PCC), respectively, where symbol '\(j\)' denotes the imaginary unit. It is obvious that \(\mathbf{u}\approx\mathbf{u_{g}}\) in the GC mode for the small simple MG where the impedance of the line can be ignored. Moreover, the magnitudes of the complex variables are calculated as \(|\mathbf{u}|=\sqrt{u_{\alpha}^{2}+u_{\beta}^{2}}\).  
Figure 1: Structure of the simple MG.  
Figure 2: Circuit of the battery invert system in the simple MG.  
The control strategy in Figure 3 includes the generalized control objective (GCO), instantaneous current tracking control (ICTC), instantaneous voltage tracking control (IVTC) and FLL-based reference calculation (RC). The GCO is actually a state feedback system of the original plant which can improve the dynamic response. The IVTC and ICTC constitute to the voltage and current control for the inverter to track the sinusoidal voltage reference. The IVTC and ICTC use one dynamic controller \(G_{c}\) to ensure the successive control input for the GCO. This is critical for the smooth switch. The FLL based RC realize the continuous reference at the instance of the switch.