esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Feature-Based Approach to Marine Debris Classification
## 5 Discussion
Based on these findings, it would be interesting to investigate further whether the fuzzy-based techniques can enhance the classification performance on the \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Classes** & **Network** & **Problem** & **Reported Result** \\ \hline \hline \multirow{6}{*}{**J-EDI dataset** ([PERSON] et al. [27])} & \multirow{2}{*}{**3 classes:** **Plastic, ROV, Bio} & YOLOv2 FT & object detection & mAP = 47.9 \\ \cline{3-5}  & & Tiny-YOLO & object detection & mAP = 31.6 \\ \cline{3-5}  & & Faster RCNN & object detection & mAP = 81.0 \\ \cline{3-5}  & & (with InceptionV2) & & \\ \cline{3-5}  & & SSD MultiBox & object detection & mAP = 67.4 \\ \cline{3-5}  & & (with MobileNetV2) & & \\ \hline \multirow{2}{*}{**ARIS Explorerx 3000 FLS** **household marine debris dataset** (Valdenegro-Toro [23])} & \multirow{2}{*}{**6 classes:**  Metal, Glass, Cardboard, Rubber, Plastic, Background} & CNN (four layers) & classification & accuracy = 97.1 \\ \cline{3-5}  & & (four layers) & & \\ \cline{3-5}  & & (four layers) & & \\ \hline \multirow{6}{*}{**Underwater Sea Litter dataset** natural and synthetic data ([PERSON] et al. [29])} & \multirow{2}{*}{**5 classes:**  Cardboard, Glass, Paper, Metal, Plastic (threshold 0.75)} & VGG16 & classification & accuracy = 85.0 \\ \cline{3-5}  & & (custom CNN & classification & accuracy = 81.5 \\ \cline{3-5}  & & (custom CNN & classification & accuracy = 81.5 \\ \cline{3-5}  & & (custom CNN & classification & accuracy = 81.5 \\ \cline{3-5}  & & (custom CNN & classification & accuracy = 81.5 \\ \cline{3-5}  & & (custom CNN & classification & accuracy = 81.5 \\ \cline{3-5}  & & (custom CNN & classification & accuracy = 81.5 \\ \cline{3-5}  & & (custom CNN & classification & accuracy = 81.5 \\ \cline{3-5}  & & (custom CNN & classification & accuracy = 81.5 \\ \cline{3-5}  & & (threshold 0.75) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the performance of different approaches to the detection and classification of marine debris presented in the literature. use-case of marine debris identification and classification. However, this falls out of the scope of this paper and is planned for future research.
# A Two-Step High-Order Compact Corrected WENO Scheme
## 4 Error Analysis and Efficiency Comparison  
### Error Analysis  
In addition to the above-mentioned tests based on benchmark cases, this study also conducts error analysis based on discrete Fourier transforms (Fourier stability analysis or von Neumann stability analysis) [35]. It examines the behavior of numerical errors by breaking them down into Fourier modes and evaluating their growth or attenuation over time. This approach is instrumental in assessing whether a given CFD scheme will yield a stable and accurate solution or result in numerical instability and oscillatory behavior.  
Figure 5 illustrates the dissipation (left) and dispersion (phase error, right) characteristics of the WENO scheme compared to the CCWENO and CCWENO2 schemes (positive flux) within a sufficiently smooth region (\(c_{0}=\frac{1}{10}\), \(c_{1}=\frac{3}{5}\), and \(c_{2}=\frac{3}{10}\) are used for the weights). The results clearly demonstrate that the dissipation produced by the CCWENO and CCWENO2 schemes is significantly lower than that of the WENO scheme, highlighting their improved ability to preserve energy and accuracy in smooth flows. Furthermore, the dispersion (phase error) is also notably reduced, as evidenced by the closer alignment of the curves to the ideal reference line (\(y=x\)), indicating better phase accuracy and reduced numerical distortion.  
Figure 6 depicts the error distribution for the three schemes within the shock wave region (the nonlinear weights are \(\omega_{0}=0.45,\ \omega_{1}=0.45,\ \text{and}\ \omega_{2}=0.1\), respectively). The results reveal that, except for the extremely small high wave number region, the dissipation generated by the CCWENO and CCWENO2 schemes is significantly lower compared to the WENO scheme. Additionally, the dispersion (phase error) is noticeably reduced,  
Figure 4: Numerical results for the problem of two interacting blast waves.  
Figure 5: Dissipation and dispersion error in smooth region.  
demonstrating the superior capability of the CCWENO schemes in accurately resolving shock wave dynamics while minimizing numerical distortions.  
Figure 7 illustrates the outcome obtained after further reducing the smoothing weight applied to the third stencil (\(\omega_{0}=0.495\), \(\omega_{1}=0.495\), and \(\omega_{2}=0.01\),respectively). The results indicate that this adjustment yields a solution that is nearly identical to the one presented in Figure 6, demonstrating minimal variation despite the modification.
## 5 SoSOO-Based Architectural Steps and Application Example
### Step 1: Bridge Strategic Domain and Operational Domain by Structural Elements  
This step, which addresses question 2, aims to clarify the structure of the SoS context, where the _OperationalContext_ is a visual connection between high-level operational concepts. Dependencies between capabilities mean the prerequisites that enable other capabilities, and the supporting functions similar to dependencies can be used for designs based on causal semantics [58] or temporal relations [55]. Accordingly, dependency can be used to reason about _OperationalPerformer_ (who) and _OperationalActivity_ (how). The transformation method from _Dependency_ in SoSCO to _OperationalContext_ in SoSO involves four steps:  
1. Use the depth-first search (DFS) algorithm to search for each capability through dependency until the one that does not depend on any other capability is found.
2. Cycle from the deepest to the shallowest capabilities and supplement the context of capabilities by adding the required _OperationalPerformer_ and _OperationalContext_ between performers. Note that the _OperationalContext_ only schematically conveys data/control without an explicit type.
3. When the complement process returns to the outermost capabilities (i.e., those not relied upon by any other capability), list the performers of these capabilities according to the specific operational scenario.
4. Because the _OperationalPerformer_ for different capabilities may overlap, merge the above performers and arbitrary connection flows to obtain a high-level operational concept that can be represented as a picture to facilitate understanding of the SoS context.  
Figure 13: Architectural steps of the operational domain.  
Figure 14 illustrates the context of maritime rescue by showing the SAR operation involving a yacht in distress at sea. The _OperationalContext_ is modeled as a dashed line with an open arrow pointing from flow sources to targets. The diagram shows that the monitoring unit receives the yacht's distress signal and sends it to the C2 center, which coordinates operations between the helicopter, the navy ship, and the rescue boat. Each model element may include a graphical depiction and a brief description of the _OperationalContext_ between the elements. The _OperationalContext_ can represent abstract conceptual relationships and will be refined in subsequent diagrams.
# Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting
## 2 Full-Scale Experimental Blasting of the Combined Blasting Method
### Blasting Vibration Measurement and Analysis Methods  
To measure the vibrations in the upper ground caused by blasting, the following steps were planned: (1) selection of an appropriate vibration meter, (2) securing installation locations, (3) fixing the sensors and testing the connection cables, (4) collecting data during the blast, and (5) analyzing the data using dedicated software. To measure ground vibration at a certain distance from the tunnel face, the Minimate Plus (Mfr: Instantel, Country: Canada, City: Ottawa), a dedicated blasting vibration measurement device, was used. The Minimate Plus is a compact blasting vibration meter with built-in sensors, allowing for vibration measurement without disturbing the terrain or interfering with traffic and pedestrian movement. As shown in Figure 6, in this experiment, two vibration meters were installed at 5 m intervals and 34.0 m directly above the tunnel face for each blast. The data acquisition mode was set to Continuous Mode, and the measurement time was set to record up to 10 s after the blast. To ensure accurate data collection when measuring blasting vibrations on the ground surface, pins were installed on the sensors to prevent errors that may arise from variations in contact with the ground. Table 2 shows the detailed specifications of the equipment and installation setup [19].  
The data collected from each experimental blasting pattern were analyzed using the dedicated program Blastware 10.0. The measured data consist of three components based on the measurement direction: vertical, longitudinal (in the plane direction towards the measurement point from the vibration source), and transverse (perpendicular to both the vertical and longitudinal directions). The data used in this study are PPV (Peak Particle  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Specifications** & **Instrument Installation View** \\ \hline \multirow{2}{*}{Seismic} & Range & 254 mm/s \\ \cline{2-3}  & Trigger Levels & 0.127–254 mm/s \\ \hline Air Linear & Range & 88–148 dB \\ \hline A Weight & Range & 50–110 dB \\ \hline Event Storage & Full Waveform Events & 300 EA \\ \hline Full Waveform & \multirow{2}{*}{Fixed Record Mode} & Manual, Single Shot, continuous \\ Recording & & continuous \\ \hline Battery Life & - & 10 Days \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed specifications and installation of the blasting vibration meter.  
Figure 6: Vibration meter installation points.  
Velocity), which is based on the USBM's research findings that link structural damage to PPV [20; 21; 22].
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 5 Simulation Setup and Results
### Results in Fiber Linear Regime
With 18 xWSSs and PDL of 7.5 dB, splitting the compensation of the steep loss at high frequencies between the Tx and Rx, with minimum mean-square-error at receiver as criteria, reduced the ROSNR by 0.6 dB compared to 0.3 dB when performing pre-compensation only (as shown in Figure 6). The penalty versus center frequency offset, due to the mismatch between the carrier frequency and center of concatenated filters, is highly dependent on the symmetry of the frequency response of the channel, EOs and frequency response of data converters. The reason is that all the frequency responses will be multiplied by the signal, with noise added at different stages of the propagation chain, prior to being processed by the receiver. As expected, and similar results to data presented in [34], the penalty versus frequency offset has minimal around 0 GHz and gets larger on both positive and negative edges. As in other published results [11], compensating for the phase information of the filtering effects did not make a difference in our data. It was noticed that the phase distortion is dealt with properly in the adaptive filters at the receiver. Figure 6: Comparison of different compensation methods, for two links scenarios, in linear regime.
## 6 Conclusions  
This paper presents DEM dynamic analysis results of small (\(d\) = 6.2 m) and super-large (\(D\) = 15.2 m) diameter twin tunnels during a train's vibration at a speed of 120 km/h in T1. Velocity and displacement of T2 walls are discussed, and the sleeper and liner of t2 and T2 are compared. The main conclusions are as follows:  
1. Four walls (Wall_1, Wall_2, Wall_3, and Wall_4) vibrate in horizontal and vertical directions. The \(D_{\rm V}\) and \(V_{\rm V}\) are more significant than the \(D_{\rm H}\) and \(V_{\rm H}\). The horizontal structure (Wall_1 and Wall_2) has the same \(D_{\rm H}\), and the vertical structure (Wall_3 and Wall_4) has the same \(D_{\rm V}\). The stress state of the surroundings around T2 is a decisive factor for \(D_{\rm H}\), and the distance from the vibration point to the measurement point is the decisive factor for the \(D_{\rm V}\).
2. The super-large and small tunnel sleepers will have oscillating displacement and velocity in the horizontal and vertical directions. The resultant displacement and velocity at the left side of t2 and T2 liners are more significant than on the right. The resultant displacement of the right side of the liner is small, and the difference of the resultant displacement of the right side of the liner in the t2 and T2 is significant. In general, the displacement and velocity of the \(d\) tunnel are more significant than those of the \(D\) tunnel. The \(D_{\rm V}\) and \(V_{\rm V}\) of t2 and T2 liners are greater than those of \(D_{\rm H}\) and \(V_{\rm H}\). Therefore, the vertical response of the structure should be paid more attention to during the process of train vibration.
3. The law of the \(V_{\rm H}\) and \(V_{\rm V}\) is similar to that of the \(D_{\rm H}\) and \(D_{\rm V}\), indicating that displacement analysis can supplement dynamic structural analysis. T2 is more stable than t2 from two aspects, namely the time of the tunnel reaches the equilibrium state and the vibration amplitude of the structure's dynamic and static response. This possibility is the result of super-large diameter twin tunnel spacing, high upper water pressure and more stable cross-section.  
Dynamic characteristics of t2 and T2 during train vibration are significantly different, so the dynamic characteristics in \(d\) twin tunnels cannot be applied directly to the \(D\) twin tunnels. The two-dimensional DEM model can explore the dynamic characteristics of small and super-large twin tunnels under train vibration.  
Conceptualization, L.W. and X.Z.; methodology, W.W.; software, L.W.; validation, L.W., W.W., and X.M.; formal analysis, W.W.; investigation, H.G.; resources, H.G.; data curation, L.W.; writing--original draft preparation, L.W.; writing--review and editing, W.W.; visualization, X.M.; supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Cloud of the resultant velocity of liner in t2 and T2: (**a**) Resultant velocity of t2 liner at key time points; (**b**) Resultant velocity of T2 liner at key time points.
## 2 Mmwave 8 \(\times\) 8 AiP Array Platform
### Aip Array Platform Presentation  
As mentioned in the introduction, the mmWave 8 \(\times\) 8 AiP array platform is based on the architecture of the 4 \(\times\) 4 AiP array platform presented in [22]. The photograph and antennas layout of the 8 \(\times\) 8 AiP array module are shown in Figure 1a,b, respectively. The 64 element antennas of the AiP array are arranged in the form of an 8 \(\times\) 8 square. The spacing between the antennas elements is 4.6 mm, which is approximately equal to a half-wavelength (that is 4.67 mm) for 28 GHz. The block diagram of the 8 \(\times\) 8 AiP illustrated in Figure 2, which is adapted from [22]. The 8 \(\times\) 8 AiP consists of:  
1. 64 patch antennas operating at 26.5 to 29.5 GHz.
2. 64 6-bit phase shifters with a phase shift range of 0\({}^{\circ}\) to 359\({}^{\circ}\), and a control resolution of 5.625\({}^{\circ}\).
3. 64 6-bit attenuators with an attenuation range of 0 to 7.5 dB, and a control resolution of 0.5 dB.
4. 32 common attenuators with an attenuation range of 0 to 15 dB, and a control resolution of 1 dB.
5. 32 temperature variable attenuators (TVAs), which provide gain compensation for the disturbance of RF chains due to the variation of the AiP temperature.
6. 64 power amplifiers (PAs), which are tuned when the AiP is in Tx mode, and 64 low noise amplifiers (LNAs), which are tuned with the AiP in Rx mode.
7. 160 switches, which are switched to set the operating mode (i.e., Tx or Rx) of the AiP.
8. 16 ANOKIAWAVE AWMF-0158 RFICs chips. Each RFIC on the AiP board has four control chains, each of which is connected to a patch antenna. A 6-bit phase shifter and a 6-bit attenuator are integrated in each control chain for the phase and amplitude control, respectively. Therefore, the excitation of the patch antenna is fed by controlling the phase shifter and attenuator in the RFIC.
9. One STM32F427 VIT6 chip acts as the microprogrammed control unit (MCU) of the AiP board. The MCU interacts with the external control computer via a universal asynchronous receiver/transmitter (UART). The control of the AiP RFICs chips by the MCU, is achieved via the serial peripheral interphase (SPI).  
Figure 1: (**a**) Photograph and (**b**) chips and antennas layout of the 8 \(\times\) 8 AiP model.  
Based on the hardware platform, the main program of the designed AiP includes the commands to control the aforementioned element phase shifters, common attenuators and element attenuators, and also the following commands:  
1. \(\mathrm{Tx}\)/\(\mathrm{Rx}\) mode switching command for setting the AiP to work in Tx or Rx mode, respectively.
2. Element switch command for controlling each element of the AiP to turn on or off.
3. Chip temperature read command for reading the temperature of each chip of the AiP.
###### Abstract  
The zero-waste biorefinery concept inspired a green oleo-extraction of both natural volatile (e.g., borneol, camphor, \(o\)-cymene, eucalypt, limonene, \(\alpha\)-pinene, and terpinen-4-ol) and non-volatile (e.g., carnosol, carnosol, and rosmarinic acid) bioactive compounds from rosmary leaves with vegetable oils and their amphiphilic derivatives as simple food-grade solvents. It is noteworthy that soybean oil could obtain the highest total phenolic compounds (TPCs) among 12 refined oils including grapeseed, rapeseed, peanut, sunflower, olive, avocado, almond, apricot, corn, wheat germ, and hazelnut oils. Furthermore, the addition of oil derivatives to soybean oils, such as glyceryl monooleate (GMO), glyceryl monostearate (GMS), diglycerides, and soy lecithin in particular, could not only significantly enhance the oleo-extraction of non-volatile antioxidants by 66.7% approximately, but also help to remarkably improve the solvation of volatile aroma compounds (VACs) by 16% in refined soybean oils. These experimental results were in good consistency with their relative solubilities predicted by the more sophisticated COSMO-RS (COnductor like Screening MOdel for Real Solvents) simulation. This simple procedure of using vegetable oils and their derivatives as bio-based solvents for simultaneously improving the extraction yield of natural antioxidants and flavors from rosmary showed its great potential in up-scaling with the integration of green techniques (ultrasound, microwave, etc.) for zero-waste biorefinery from biomass waste to high value-added extracts in future functional food and cosmetic applications.  
green oleo-extraction; zero-waste biorefinery; natural antioxidants and flavors; food-grade solvents; vegetable oils and derivatives; relative solubility simulation +
Footnote †: journal: materials
# A Biologically-Inspired Power Control Algorithm for Energy-Efficient Cellular Networks
## 7 Conclusions  
Inspired by flocking behavior in which birds fly together in a group in an energy-efficient way, we have proposed a distributed TPC algorithm in which each MS tries to match its rate to the average rate of the MSs in its adjacent cells by controlling the transmit power of its serving BS, so that the rates of all of the MSs become equal. We verified that this local rate-averaging algorithm achieves an exponential convergence and maximizes the minimum rate of the MSs with the solidarity property. The results showed that the proposed TPC algorithm effectively reduces the power consumption at the BSs while maintaining a low outage probability, which leads to a significant improvement in energy efficiency. Because the proposed TPC algorithm essentially originates in biological flocking behavior, it is simple and distributed, so that we expect it to be applicable to complex cellular networks.  
In this paper, we considered only one channel for the TPC. However, the proposed TPC algorithm can also be applied to multiple channels, which are allocated to multiple MSs in a cell. Therefore, our final goal in further study would be to apply the proposed TPC algorithm to the situation of multi-channels and multi-users in conjunction with a scheduling algorithm and to optimize the performance of this joint algorithm with respect to both spectral efficiency and energy efficiency throughout the whole network.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0025424), the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2015-H8501-15-1007) supervised by the IITP (Institute for Information & communications Technology Promotion), and the Human Resources Development (No. 20154030200860) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Trade, Industry and Energy.  
**Author Contributions:** [PERSON] contributed by proposing the main idea, deriving the experimental results and writing most of the paper. [PERSON] was responsible for theoretical analysis, derivation, coordination and proof reading.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
## 3 Data Collection and Processing
### Limb Joint Angle  
The upper limbs are comprised of a combination of the left elbow, left shoulder, neck, right shoulder and right elbow. Similarly, the lower limbs have joint angles for the left knee, left hip joint, right hip joint, and right knee. A joint angle can be calculated using three selected points \(p_{1}-p_{3}\), where \(p_{2}\) is the center point for calculation of the vectors \(\stackrel{{\rightarrow}}{{v}}\) and \(\stackrel{{\rightarrow}}{{u}}\), as shown in Equations (6) and (7):  
\[\stackrel{{\rightarrow}}{{v}}=p_{1}-p_{2}; \tag{6}\]  
\[\stackrel{{\rightarrow}}{{u}}=p_{3}-p_{2}. \tag{7}\]  
The angle is calculated with \(arctan2\) and the horizontal axis is from \(-\pi\) to \(\pi\) so the value of \(arctan2\) must be converted from \(-\pi\) to \(0\) to \(\pi\) to \(2\pi\) as in Equation (8):  
\[va(\stackrel{{\rightarrow}}{{s}})=\left\{\begin{array}{c}arctan2( \stackrel{{\rightarrow}}{{s}}),if\;0\geq arctan2(\stackrel{{ \rightarrow}}{{s}})>\pi\\ \pi+arctan2(\stackrel{{\rightarrow}}{{s}}),if\;0>arctan2( \stackrel{{\rightarrow}}{{s}})\geq-\pi\end{array}\right., \tag{8}\]  
where \(\stackrel{{\rightarrow}}{{s}}\) is the input vector and \(arctan2\). The inverse tangent function is used to calculate the angle. Substituting \(\stackrel{{\rightarrow}}{{v}}\) and \(\stackrel{{\rightarrow}}{{u}}\) into Equations (6) and (7) we obtained Equation (8) in order to yield the angles \(i\) and \(j\). The angle \(a(i,j)\) relative to \(\stackrel{{\rightarrow}}{{u}}\) can be found by  
\[a(i,j)=(i-j)\times\frac{180}{\pi}. \tag{9}\]  
The angles calculated above are shown in Figure 2 where \(\theta 1\) to \(\theta 5\) are the upper limb joint angles, and \(\theta 6\) to \(\theta 10\) are the lower limb joint angles.
## 4 Fast Automatic Adjustment Algorithm of Imaging Waveform
### Imaging Waveform Preprocessing  
Before entering the algorithm loop, we gave the initial CMOS exposure time T\({}_{0}\) to obtain the first frame image, and based on it, each subsequent waveform image was preprocessed as follows:  
1. Read the raw data of CMOS imaging waveform, identify the peak (P\({}_{i}\)), and record its position \(x_{p}\).
2. To filter out the edge noise, select 15% of the peak intensity as the threshold, and the waveform whose energy is higher than the threshold is intercepted as the main peak.
3. Apply a median smoothing filter to the main peak obtained by Step 2, taking into account both the filtering effect and speed.