esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Contact Tilapia Mass Estimation Method Based on Underwater Binocular Vision
## 1 Introduction
This study utilized binocular stereoscopic vision technology to capture images of tilapia, eliminated the influence of uneven illumination on underwater images using the Retinex image enhancement algorithm, conducted stereo matching, segmented fish bodies using a segmentation platform, marked key point coordinates, and estimated the length of the tilapia using three-dimensional reconstruction technology. Five tilapia length-mass prediction models were established, and the one with the smallest error was selected through analysis and comparison, providing technical support for rapidly estimating the length and mass of freely swimming fish underwater. The main contributions of the work in this paper are as follows:  
1. Firstly, a binocular image dataset of underwater tilapia is established, which can be used for binocular image stereo matching in combination with underwater image enhancement algorithm. It provides an effective basis for the three-dimensional reconstruction of tilapia. 2. Aiming at the problem of non-contact tilapia length estimation based on underwater binocular stereo vision, this paper proposes a tilapia length estimation method based on binocular vision, image segmentation, and key pixel marking, which is aimed at realizing efficient and low-cost non-contact tilapia length estimation. 3. A regression model for predicting the mass of tilapia body length-body mass relationship was developed. The body length was obtained by the body length estimation algorithm, and the mass of tilapia could be estimated by inputting the model. The experimental results showed that the method was highly reliable and could be used for non-contact mass estimation of underwater tilapia.
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 2 Dynamic Similarity Metrics  
In the case of link prediction in static networks, there are predominantly three types of topological similarity indices [3]; (i) local, (ii) global, and (iii) quasi-local. Local similarity indices are constructed using neighbourhood-related structural information, whereas global similarity indices use the whole network topological information to compute the similarity between actor pairs. Notwithstanding, many real-world networks are longitudinal in nature, involve dynamic processes, and evolve temporally. With this temporal evolution of networks, actors simultaneously experience altering topological properties that make these similarity indices incompetent in dynamic link prediction. Researchers have used time series of these topological similarity indices to emulate the evolutionary aspect of dynamic networks. A given dynamic network can, therefore, be defined as a time series of network snapshots where each snapshot represents the corresponding network state at a particular time, known as a short interval network (SIN). Actors change their link structure, neighbourhood, and network positions in every SIN over time that, in turn, also represent 'actor dynamicity' [38]. The term actor dynamicity refers to the variability in the involvement of individual actors in dynamic social networks over time. Based on the temporal patterns and evolutionary processes taking place in dynamic networks, firstly, this study attempts to define two types of actor-dynamicity, namely (i) structural dynamicity, and (ii) neighbourhood dynamicity. Secondly, temporal similarity and correlation measures over this actor-level dynamicity are used to measure the similarity and/or proximity between non-connected actor pairs. In the following sections, different techniques are described which were used to build dynamic similarity metrics in this study.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 5 Case Study
### Optimization Result Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Whether to Start at the Same** & **Start-Up Sequence** \\  & **Time** & \\ \hline
1 & Payloads B, D, E, H and I & Disorderly start \\  & start simultaneously & The payload start-up sequence is \\
2 & No & I-D-H-B-E-C-G \\
3 & No & The payload start-up sequence is \\  & & I-D-H-B-E-C-G \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Scheduling results of payload start-up in Schemes 1 to 3. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **The Sum of Task** & \multirow{2}{*}{**Average State of Charge**} & **The Total Number of** \\  & **Priorities** & & **Tasks Starts** \\ \hline
2 & 1486 & 0.7456 & 23 \\
3 & 1460 & 0.7564 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Task priority and reliability indicators of Schemes 2 and 3.  
index values are all low, and the average state of charge is lower than 75%. Scheme 3 comprehensively considers task value and reliability. The state of charge of the battery is relatively high throughout the cycle and is 1.45% higher than that of Scheme 2, indicating that the energy storage level of the battery is relatively high under this scheduling scheme. There can be more energy backup to undertake the power supply of the rest of the satellite or to deal with emergencies, so the reliability of the CubeSat power distribution system is higher. In Scheme 2, all payloads are started 23 times in one orbital period, and in Scheme 3 after optimization, all payloads are started 20 times in one orbital period, which effectively reduces the number of switching operations and possible surge currents, saves the extra time and energy consumed by frequent start-up, and improves the stability and reliability of the system. Overall, Scheme 1 without considering the task start-up problem and does not meet the normal operation requirements of the CubeSat mission, so this scheduling scheme is not considered, and its energy profile does not need to be analyzed. Scheme 2 with improvements in the task start-up problem can meet the requirements of normal operation. Scheme 2 uses the task priority as an optimization objective and obtains the highest mission value, but the battery energy presents a low level, and the payload starts frequently, which affects the system reliability. Scheme 3 with the improvement in the mission start-up problem is the same as Scheme 2, but the reliability-related indexes are taken into account in the optimization direction, and the battery energy level is improved, so the optimization result is conducive to the safe and reliable operation of the CubeSat.
## 2 Passive Wake-Up Radio
### Discussion of the Passive Wake-Up Radio Parameters
#### 2.4.3 Sensitivity  
Receiver sensitivity is a measure of how well the receiver performs and is defined as the power of the weakest signal the receiver can detect in \(-\)dBm. Higher sensitivities result in longer wake-up range. However, higher receiver sensitivity also translates to increased power consumption. Since power consumption, sensitivity and wake-up range are important features for effective passive wake-up radio performance, the trade-off between these factors need to be considered during the circuit design. However, from Table 1, the values in the dissipated power column are not changing relative to the sensitivity due to several design factors. The design in [90], for example has a low sensitivity of \(-\)17 dBm but a relatively high power dissipation of 2.64 uW, as a result of other parameters, such as the modulation scheme. However, in [84] the wake-up radio has relative high sensitivities of \(-\)56.5 dBm and \(-\)39 dBm, and the power consumption is measured as only 236 nW and 104 nW, respectively. Although the power dissipation is higher when the sensitivity value is higher, the very low power consumption can be explained by the efficiency of the advanced design techniques utilized in the protocol, such as the back-channel communication and Bluetooth low energy (BLE). The BLE offers the lowest average radio power that can communicate directly to a mobile device [86]. Back-channel modulation controls the BLE standard compliant radio to produce signals that a passive wake-up receiver can demodulate to recover the back-channel information.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 5 Mesh Networking with IEEE 802.15.4
### Applications: Comparison between ZigBee Pro and LWMesh Protocols
#### 5.3.3 Self-healing Capabilities  
Looking to the critical and essential feature of being able, for a network, to react to disruptions in an autonomous way, the results presented in [73] highlight how deterministic and well controllable is self-healing in LWMesh. Owing to this, LWMesh outperforms ZigBee in terms of self-healing latency. However, LWMesh requires a larger amount of data during the rejoin procedure with respect to Zigbee, even though LWMesh rejoins quickly than ZigBee Pro. However, it is likely that inefficiency in routing in Zigbee-based networks does not come from the ZigBee Pro technology itself (during a self-healing operation, in terms of massive data overhead).  
In conclusion, ZigBee Pro is more stable and interoperable, less complex, and easy to use, than LWMesh, which, in turn, is more appropriate for scenarios in which time is crucial and extra characteristics (not natively provided by ZigBee Pro) are required.
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 5 Experiments and Results
### Pixel-Label Extraction Phase Experiments  
#### 5.3.1 Effect of Splitting Touching Components  
The pixel-label extraction phase uses an energy minimization framework that is formulated at the connected component level. It is possible to formulate the energy function at the pixel level; however, it would be computationally costly. On the contrary, component level formulation is unable to split the components that touch several blob lines. Therefore, the baseline configuration splits a touching component \(c\) by labeling each pixel inside the component as the blob line closest to it. We show the effect of splitting touching components in the second column of Table 4. For all similarity assumptions, splitting the touching characters results in better performance. The improvement is larger on the LIU metric than on the PIU metric. Correcting the split of a touching component might rehabilitate a complete text line that was oversegmented, whereas it increases the number of correctly segmented pixels insignificantly relative to the number of all pixels (Figure 10).  
Figure 9: Effect of various similarity assumptions on the training time and qualitative results. The pseudo-RGB image and accuracy results of validation and training sets are shown for the similarity assumptions. We observe that it takes the network longer to reach high accuracy as additional assumptions are added.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 2 Proposed Method
### Structured Spike and Slab for Modulated Signals
#### 2.4.2 Strong Assumptions on In-Block Symmetry and Inter-Block Similarity  
One would set \(z_{q,b}=z_{-q,b}\) in order to assure a perfect symmetry of the support around a given carrier harmonic \(b.f_{c}\). In fact, if a non-zero coefficient is related to a modulating signal, then there is a high probability that its symmetric with respect to the harmonic of the carrier frequency in this block is also nonzero. Furthermore, in the ideal setting, the modulations tend to have a similar magnitude, so that one can assume \(\lambda_{q,b}=\lambda_{-q,b}\). We further aim to obtain a perfect similarity of the support across active blocks. One way to do this may be is to set \(z_{q,b}=z_{q,b^{\prime}}\), \(b^{\prime}\in\{1,\,\ldots,\,B\}\), so that the position of non-zero elements remains always the same for all groups. Under these strong prior assumptions, we propose the following spike-and-slab model (S&S):  
\[\forall q\in\{1,\,\ldots,\,Q\}, \quad\mathbf{x}_{q}\sim(1-z_{q})\delta_{\emptyset}+z_{q}\mathcal{ CN}\big{(}\mathbf{0},\mathbf{\Lambda}_{q}\big{)} \tag{6}\] \[z_{q}\sim B(\phi(g_{q}))\] \[\mathbf{g}\sim\mathcal{N}(\mathbf{m},\mathbf{R})\]  
where \(\mathcal{B}(.)\) stands for the Bernoulli distribution, \(\mathbf{\Lambda}_{q}\) is the diagonal covariance matrix formed by the slab variable \(\lambda_{k}\) of coefficients on position \(q\), \(\phi\) is the cumulative function of the standard normal distribution ensuring a result on \((0,1)\) and the vector \(\mathbf{g}\) is a Gaussian latent variable with mean \(\mathbf{m}\) and covariance matrix \(\mathbf{R}\). Regarding the latent variable, the mean value \(\mathbf{m}\) controls the expected degree of sparsity inside the block and the covariance matrix \(\mathbf{R}\) expresses the prior correlation regarding the support. For example, by choosing a covariance matrix \(\mathbf{R}\) that has a non-zero positive correlation between two neighboring positions \(q\) and \(q^{\prime}\), we promote slightly wide peaks in the spectrum (i.e., peaks over more than one frequency bin in the spectrum). Such a latent variable for the prior probability of the slab component is highly related to the latent Gaussian process in [56]. It may be also used to promote group-sparse vector recovery, similarly to [55].  
It can be noted that letting \(\lambda_{q,b}=\gamma_{q}\psi_{b}\), where \(\gamma_{q}\) and \(\psi_{b}\) can be defined as in (5), coefficients at symmetrical and same positions in the spectrum are encouraged to have relatively close contributions to the energy of the block where they lie. Therein, model (6) can be seen as a multivariate Bernoulli-block sparse model that promotes perfectly symmetrical spectrum. The advantage of (6) compared to (5) is that it offers a strong level of sparsity, so that group of coefficients that do not belong to modulations are all set explicitly to be exactly zero, rather than small values.
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 2 Operation Mode and Coordination Control of Microgrid in Forest Area
### Operation Mode of the Forest Microgrid  
#### 2.2.1 Free Mode  
Being different from a grid-connected microgrid, the forest microgrid is isolated from the utility grid and is an energy self-sufficient system. In free mode, the system operates normally under small power disturbance, with DC voltage of the microgrid being in the interval of [0.98, 1.02] pu (per-unit value). The wind turbine unit carries out maximum power point tracking, while the biomass power generation unit takes in charge of suppressing the power disturbance by regulating its output power. Furthermore, the supercapacitor performs high frequency power smoothing and the battery is charged with a small constant current when the voltage is above its rated value.
## 5 Numerical Results with Adaptive Time-Step Sizes
### Experiment 1 with Adaptive Solvers  
In this experiment, the parameters listed in Equation (19) are used. The errors as a function of the running time are presented in a log-log diagram in Figure 11. From the figure, it is evident that the adaptive LNe3 controllers and the adaptive CLL controllers are significantly faster than the RKF and RKCK when the desired accuracy is not very high. The RKF and RKCK can achieve the same accuracy as the adaptive LNe and the adaptive CLL families with the same running time, only when the error is \(1.4\times 10^{-6}\). However, none of the adaptive controllers can go beyond this accuracy due to the space discretization error. It does indeed look like the error, in the case of the RKF and RKCK, is relatively independent of the running time. According to our previous experience, this is not uncommon behavior in the case of explicit adaptive solvers, if the method used for designing the controller is only conditionally stable, such as some of the built-in ODE solvers of MATLAB [45].
## 4 Light Attenuation
### Angle Attenuation  
The cosine efficiency measures the loss of radiation (energy) due to radiation absorption and scattering effects resulting from the angle between the heliostat normal vector (\(n\)) and the incident sun ray (s) [24]:  
\[\eta_{\text{cos}}=\frac{\overset{\rightarrow}{n\cdot\overset{\rightarrow}{ s}}}{|n||s|} \tag{8}\]
## 2 Materials and Methods  
To achieve the objectives, the paper develops an integrated leak detection method using GIS and remote sensing. For this reason, the methodology is deconstructed into two phases. In the first phase, the use of GIS helps identify potential leak locations using different sets of hydraulic and water quality data from a real WDN that may indicate potential leaks. That is, sudden drops in pressure, flow, and water quality can be shown on spatial variability maps generated by GIS. At the end of the first phase, either no leaks were found in certain locations or candidate leak locations were identified. Once the candidate leak locations are found, the second phase begins with the use of remote sensing to capture and process images that can be utilized at the candidate leak location.
## 3 Research Method
### Cbam_Ista-Net\({}^{+}\) Compression and Reconstruction  
To achieve the compression and reconstruction of images and spectrograms, we proposed the CBAM_ISTA-Net\({}^{+}\). It is an Iterative Shrinkage-Thresholding Algorithm (ISTA) network based on attention mechanisms, which can optimize the general L1 regularized compressive sensing model. A total of 20 types of bird images, 15 kinds of Mel spectrograms, and 10 kinds of wavelet transform spectrograms are input, and CBAM_ISTA-Net\({}^{+}\) is used to compress and reconstruct the images and spectrograms.
## 4 Discussion
### Future Research
#### 4.4.2 Design of a Speaker for High Accuracy Measurements and Auralization  
Results from this study showed that a single directional loudspeaker can be used for acoustic measurements. However our study revealed certain limitations, such as that in the high frequency range the directivity of the two-way speaker design has a negative effect on the results. Future research might help to suggest several courses of action in order to solve this problem and lead to a specific design of a speaker for measurements with better results compared with a dodecahedron speaker. This could hypothetically point to a method with even better results for omnidirectionality than the current dodecahedron speakers. This approach could be utilized for auralization purposes since high accuracy impulse responses could be obtained. Research into solving this problem is already underway. We hope that our research will serve as a base for future studies since this would be a fruitful area for further work.
# Time-Delay Vibration Reduction Control of 3-DOF Vehicle Model with Vehicle Seat
## 6 Conclusions  
In this paper, the 3-DOF quarter vehicle active suspension is employed as the control object, and the vehicle ride comfort is optimized as the target. A linear function equivalent excitation method is proposed to establish the objective function, and the optimal parameter time-delay control active suspension is designed. The adaptive weighted particle-swarm optimization algorithm is used to obtain the optimal delay feedback control parameters. The feedback control parameters are obtained by stability analysis and time-domain simulation as follows:  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Sinusoidal Excitation** & **Passive Suspension** & **Active Suspension with Backstepping Control** & **Active Suspension with Time-Delay** & **Optimized Percentage Compared to Passive Suspension** & **Optimized Percentage Compared to** **Consumption** & **Optimized Percentage Compared to** **Consumption** \\ \hline Passenger acceleration (m/s\({}^{2}\)) & \(0.2181\) & \(0.1768\) & \(0.1527\) & \(29.99\%\) & \(13.63\%\) \\ Body acceleration (m/s\({}^{2}\)) & \(0.2812\) & \(0.2072\) & \(0.1484\) & \(47.23\%\) & \(28.38\%\) \\ suspension dynamic displacement (m) & \(0.00325\) & \(0.00360\) & \(0.00687\) & \(-111.38\%\) & \(-90.83\%\) \\ Tire dynamic load (N) & \(112.50\) & \(68.18\) & \(67.21\) & \(40.26\%\) & \(1.42\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Suspension performance root mean square value comparison table.  
In this paper, the stability switching method is used for the first time to analyze the stability interval of 3-DOF time-delay controlled active suspension, which deduces the system stability conditions related to the Strum sequence and the feedback control parameters. The results show that the time-delay control parameters can change the stability of the system.  
A linear excitation function is equivalent to excitation cup design. It can estimate the disturbance in real time to improve the control accuracy.  
Through simulation analysis, the time-delay control algorithm designed in this paper is used to compare the inverse control and the passive suspension control. In the time domain of different working conditions, especially in harmonic excitation, the damping performance of vehicle acceleration and passenger acceleration is obviously improved. This can provide a useful reference for further experimental investigation.  
Writing--original draft, K.W.; software K.W., Y.C.; Writing--review and editing, K.W. and C.R. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Natural Science Foundation of China (Grant No. 51275280).  
Not applicable.  
The authors declare no conflict of interest.