esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
###### Abstract  
Due to the efficiency of self-attention mechanisms in encoding spatial information, Transformer-based models have recently taken a dominant position among semantic segmentation methods. However, Transformer-based models have the disadvantages of requiring a large amount of computation and lacking attention to detail, so we look back to the CNN model. In this paper, we propose a multi-path semantic segmentation network with convolutional attention guidance (dubbed MCAG). It has a multi-path architecture, and feature guidance from the main path is used in other paths, which forces the model to focus on the object's boundaries and details. It also explores multi-scale convolutional features through spatial attention. Finally, it captures both local and global contexts in spatial and channel dimensions in an adaptive manner. Extensive experiments were conducted on popular benchmarks, and it was found that MCAG surpasses other SOTA methods by achieving 47.7%, 82.51% and 43.6% mIoU on ADE20K, Cityscapes and COCO-Stuff, respectively. Specifically, the experimental results prove that the proposed model has high segmentation precision for small objects, which demonstrates the effectiveness of convolutional attention mechanisms and multi-path strategies. The results show that the CNN model can achieve good segmentation effects with a lower amount of calculation.  
convolutional attention; deep learning; feature guidance; multi-path; semantic segmentation Article
# A Machine Learning Approach to Determine Oyster Vessel Behavior
## 2 Related Research  
Various attempts have been made to track fishing behavior using VMS data. [PERSON] et al. [3] used VMS data to track fishing vessel behavior using speed as a single criterion ranging from around 1.4 knots to 4.5 knots. They compared their VMS based approach to the logbook based approach which reports catches of target (and other commercial) species by the fishers that are subject to potential biases in reporting [3]. Another group of researchers, studied frequency distribution of points with relation to speed to track fishing range and produced a similar result of 1.5 to 5.5 knots [4]. Both papers stated that due to the lack of metadata, such as the size of the vessel or the type of gear used, the ranges had to be broadened to account for equipment variances. These tracking methods rely heavily on the error-prone human elements and are more labor-intensive than an automated method, such as the one described here.  
Likewise, numerous efforts have been made to mine and classify different type of trajectory data. One such effort is the mining and classification of transportation data in which movement data is classified according to transportation mode used by humans (e.g., bicycle, on foot, car and train) [10]. Similarly, [PERSON] et al. mined GPS tracking data from smartphone to classify human activity patterns using fuzzy classification scheme [11]. In addition, the knowledge discovery of trajectories involves segmentation of trajectory into sub-trajectories. Methods for both segmentation and classification can be divided into many categories such as rule-based [10], clustering-based [12], kernel-based [11] and cost-function based [13] approaches. In a seminal work by [PERSON] et al. [13], a trajectory data of fishing vessels operating in Brazil's northeast coast was collected. Each data point in the trajectory consisted of latitude, longitude and the timestamp. Furthermore, each data point was labelled as either fishing or non-fishing. Next, two trajectory features including mean speed and direction variation were computed for all the points in this dataset. The mean speed between two points was computed by dividing the distance in meters by the time spent between these points in seconds. Additionally, the direction variation was computed using the azimuth direction between two points. Subsequently, the two trajectory features were used to generate segments or sub-trajectories using an unsupervised greedy randomized adaptive search procedure [13]. Unlike this method, we collected oyster vessels trajectory data from the VMS module installed in the oyster vessels and segmented the trajectory into sub-trajectories (paths) based on the speed of the vessel.  
Moreover, in the initial work of [PERSON] [6], the behavior of a vessel was predicted using the data from the installed VMS module. A rule-based classifier was developed, which deterministically groups the samples into likeliest behavior based on the ranges of speed and net speed determined from the correlation between speed and net speed of all the samples in the dataset.
## 5 Results and Discussion
### Wondering Point  
We will require this circuit to transfer the value 1 to the next output with each rising edge given to clk. At the beginning of the system operation, only the value 1 should be at output 0, then after the first rising edge given on clk it should be transferred to output 1, then to output 2. Finally, after 7 rising edges, the value 1 should appear at output no. 7. We assume that the way the system will behave after the next edges given on clk does not matter. A table describing such an operation is shown in Table 10. The input of the system is a constant with the value 0. As a result of the experiments (\(number\_columnsCP=20\), set of functions: JK, D, T, AND, OR, NOT, NAND, NOR), e.g., genotype was obtained: ([1]7 0 0), *([2]1 1 0), *([3]4 0 2), *([4]6 2 1), *([5]0 4 3), *([6]1 5 4), ([7]3 0 3), ([8]4 3 6), ([9]5 6 4), ([10]5 2 9), ([11]2 5 4), *([12]1 6 9), ([13]0 0 10), *([14]1 12 7), ([15]1 5 3), ([16]5 12 15), *([17]1 14 13), *([18]1 17 3), ([19]5 8 6), *([20]1 18 5), 4 5 6 12 14 17 18 20 which represents the system shown in Figure 17. It can be seen that the resulting system resembles a series-parallel register, except that a constant value of 1 is given to the input of the D 02 flip-flop. The D flip-flop in the diagrams will be represented as a JK with a small NOT gate going from the J input to the K input. Therefore, after starting the system, the Q output of this flip-flop has the value 0, and after the first rising edge given to clk, it permanently takes the value 1. The output of the NAND gate 04 is the negation of the Q output of the D 02 flip-flop and constitutes the OUTPUT0 output. This value is shifted in turn by the flip-flops 05, 06, 12, 14, 17, 18, and 20 after each rising edge on clk.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 4 Results and Discussion
### Refrigerator  
Unlike the two previous home appliances, the use of the refrigerator does not depend on the room occupancy status. Rather, the scheduling of the refrigerator was based on the internal and surrounding temperatures. Figure 21 shows the effect of the HEM system use on the power consumption of the refrigerator and the internal temperature variation of the refrigerator.  
Figure 20: (**a**) Energy consumptions and (**b**) bills incurred by the AC unit without and with the use of the HEM system.  
The power consumption of the refrigerator was reduced by decreasing the time during which the refrigerator was switched on. The refrigerator was turned off during normal pricing hours when the lower preset temperature limit was reached, and more often during the maximum pricing time when the internal temperature was within the preset range. This can be clearly observed from the second graph of Figure 20b, where the on cycle while using the HEM system is narrower than that when not using the system during the maximum pricing hours. However, the HEM algorithm effectively managed the internal temperature within the desired range, regardless of the reduced power consumption and on time of the refrigerator. The benefit of the HEM algorithm can be assessed by comparison of the hourly energy consumptions and bills without and with the use of the proposed system, as illustrated in Figure 22.  
Figure 21: (**a**) Power consumptions and (**b**) internal temperatures of the refrigerator without and with the use of the HEM system.  
The system afforded reductions during non-peak pricing hours although the energy consumptions and bills during minimum pricing hours without and with the use of the HEM system are similar. The savings were particularly substantial during maximum pricing hours. Overall, the daily energy and bill savings that were achieved by the system were 0.098 kWh and $0.0116, respectively.
# Low-Complexity Online Calibration for Large-Scale Multi-Beam Antennas
## Appendix A Expectation and Variance for \(y_{k}\)
### Expression of \(\phi_{k,1}^{RMSE}\)  
Similar to \(A_{k,1}^{\text{RMSE}}\), the RMSE of the relative phase in (21) can be rewritten as  
\[\phi_{k,1}^{\text{RMSE}}=\frac{180}{\pi}\sqrt{\left\{\text{E}(\widehat{\varphi} _{k}-\varphi_{k})+\text{E}(\widehat{\varphi}_{1}-\varphi_{1})\right\}^{2}+ \text{V}(\widehat{\varphi}_{k}-\varphi_{k})+\text{V}(\widehat{\varphi}_{1}- \varphi_{1})}. \tag{11}\]  
The derivation problem of \(\phi_{k,1}^{\text{RMSE}}\) can be transformed into estimating the statistical characteristics of \((\widehat{\varphi}_{k}-\varphi_{k})\). Substituting \(\widehat{\omega}_{k}=\omega_{k}+y_{k}\) into \((\widehat{\varphi}_{k}-\varphi_{k})\) and separating the real part from the imaginary part, \((\widehat{\varphi}_{k}-\varphi_{k})\) can be given by  
\[\widehat{\varphi}_{k}-\varphi_{k} =\arctan\biggl{(}\frac{y_{k\text{Q}}\cos\varphi_{k}-y_{k\text{I}} \sin\varphi_{k}}{a_{k}+y_{k\text{I}}\cos\varphi_{k}+y_{k\text{Q}}\sin\varphi_{ k}}\biggr{)}\] \[\approx\frac{y_{k\text{Q}}\cos\varphi_{k}-y_{k\text{I}}\sin \varphi_{k}}{a_{k}+y_{k\text{I}}\cos\varphi_{k}+y_{k\text{Q}}\sin\varphi_{k}}, \tag{12}\]  
where \(\widehat{\varphi}_{k}\to\varphi_{k}\). The denominator of (12) follows the distribution \(\mathcal{N}(a_{k},\sigma^{2}/2)\) and the numerator follows \(\mathcal{N}(0,\sigma^{2}/2)\). The ratio of two Gaussian random variables, \(\widehat{\varphi}_{k}-\varphi_{k}\), approximately follows \(\mathcal{N}(0,\sigma^{2}/2/a_{k}^{2})\)[27; 28]. Finally, the RMSE of the relative phase (in degree) can be derived as  
\[\phi_{k,1}^{\text{RMSE}}\approx\frac{180}{\pi}\sqrt{\frac{\sigma^{2}}{2a_{k }^{2}}+\frac{\sigma^{2}}{2a_{1}^{2}}}. \tag{13}\]
# Technology Trends for Massive MIMO towards 6G
## 3 Localization and Sensing Using Metasurface-Enabled Massive MIMO
The metasurface, which could customize the propagation environments, is believed to be an add-on enabler for future massive MIMO to facilitate such integration. In essence, the metasurface-enabled massive MIMO systems bring forth new dimensions to the wireless communication field, not only by overcoming the scalability and energy consumption issues with traditional massive MIMO, but also by extending their utility to the realms of localization and sensing. Thus, these systems are likely to play a central role in the future massive MIMO technology, serving as key enablers for multifunctional services integrating communication, localization, and sensing. However, to unlock the full potential of these systems, we must explore frequency bands that can deliver higher capacity and improved sensing capabilities. One promising area of exploration is in the realm of THz frequencies. The move towards THz frequencies will undoubtedly present new challenges that must be addressed, but it also offers exciting possibilities for the future of massive MIMO.
# Thermo-Optic Numerical Research on Segmented Circular LD Arrays Side-Pumping a Nd:YAG Laser Rod
## 2 Materials and Methods  
### Side-Pumping Configurations  
Figure 1 shows a segmented circular LD array side-pumping configuration model [8].  
Figure 1: Segmented circular laser diode (LD) array side-pumping configuration.  
In Figure 1, the upper and lower heat sink has a comb shape with four rod holders that are responsible for supporting and cooling the rod. An Nd:YAG rod in the middle of the circular structure is clamped by the holders in the upper and lower heat sink. Each LD array mounted on the upper and lower heat sink has a semicircular structure, and the LD bars are arranged along the axis of the Nd:YAG rod. The face-to-face configuration of the upper and lower semicircular LD arrays forms a circular periodical side-pumping geometry. The heat produced by the LD arrays and Nd:YAG crystal is conducted to the heat sinks cooled by thermoelectric coolers (TEs). Two fans are used to generate airflows that pass over the outside heat exchangers to dissipate the heat into the surrounding air [8].  
For comparative research of the temperature distributions and thermo-optic effects in laser rods, we considered the annular liquid cooling and the compensated semicircular LD array side-pumping configuration models, as shown in Figure 2.  
In the annular liquid cooling scheme as shown in Figure 2a, a laser rod is immersed into the fluid of the transparent coolant jacket. The laser rod is surrounded by the six outside semicircular LD arrays and has circular pump geometry. The LD arrays are placed along the length of the laser rod, and the pump light at wavelength 808 nm is transverse to the direction of the 1064 nm laser beam. In addition, the liquid passages in heat sinks are designed to cool LD arrays.  
In the compensated semicircular LD array side-pumping scheme as shown in Figure 2b, three semicircular LD arrays are mounted on a heat sink with one holder. The pump and holders have a face-to-face arrangement. On the left side of Figure 2b, the Nd:YAG rod is pumped from the upper semicircular LD arrays and bonded to the lower holder on the heat sink. Conversely, the Nd:YAG rod is pumped from the lower semicircular LD arrays and bonded to the upper holder in the right of Figure 2b. Therefore, the two same semicircular LD pump and holder structures form a compensated side-pumping scheme. The temperature of heat sinks is controlled by both the TEs and outside fans.
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 4 Numerical Simulations
### Simulation Method  
The simulation process followed the sequence of phases used in the field, accurately mirroring the effects of excavation on both the enclosure structure and the surrounding environment. This simulation was designed to match actual conditions closely. In line with the project's real-world situation, fluid-solid coupling was considered. Before each excavation phase, a dewatering step was included. Dewatering was achieved by adjusting the liquid head. The simulated working conditions and their corresponding durations are detailed in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stage** & **Simulation Step** & **Construction Sequence** & **Construction Date** \\ \hline
1 & Generate the initial stress field & & \\ \hline
2 & Create an adjacent building model & & \\ \hline
3 & Clear displacement; create the enclosure structure and column piles & Construction of diaphragm walls and column piles & 2020.12.28–2021.3.14 \\ \hline
4 & Dewatering to BGS 2.5 m (level 1), and excavating to BGS 1.5 m & Dewatering to BGS 1.5 m & 2021.3.15–2021.3.27 \\ \hline
5 & Activate the first level of horizontal support at BGS 1.5 m & First floor support set up & 2021.3.31–2021.4.11 \\ \hline
6 & Dewatering to BGS 8 m (level 2), and excavating to BGS 7 m & Dewatering to BGS 8 m (level 2), and excavating to BGS 7 m & 2021.4.22–2021.5.20 \\ \hline
7 & Activate the second level of horizontal support at BGS 7 m & 2 nd level support set up & 2021.5.22–2021.6.1 \\ \hline
8 & Dewatering to BGS 14.5 m (level 3), and excavating to BGS 13.5 m & Dewatering to BGS 14.5 m (level 3), and excavating to BGS 13.5 m & 2021.6.2–2021.6.10 \\ \hline
9 & Activate the third level support at BGS 13.5 m & 3 rd-level support set up & 2021.6.11–2021.6.22 \\ \hline
10 & Dewatering to BGS 19.5 m (level 4), and excavating to BGS 18.5 m & Dewatering to BGS 19.5 m (level 4), and excavating to BGS 18.5 m & 2021.6.25–2021.7.12 \\ \hline
11 & Activate the fourth level support at BGS 18.5 m & 4 th-level support set up & 2021.7.14–2021.7.25 \\ \hline
12 & Dewatering to BGS 26 m (level 5), and excavating to BGS 25 m & Dewatering to BGS 26 m (level 5), and excavating to BGS 25 m & 2021.7.27–2021.8.16 \\ \hline
13 & Activate the bottom plate & Pouring bottom plate & 2021.8.16–2021.8.30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key working conditions and construction periods.
## 2 Dispersion-Managed Link and WDM System Modelling
### Dispersion-Managed Link
In all 16 dispersion maps analyzed in this paper, the deployment order of SMF and DCF is opposite to each other with respect to the midway-OPC, as can be seen in the transmission link shown of Figure 1. That is, all dispersion maps are obtained from a structure in which DCF is always placed before SMF in all fiber spans of the first half-link, whereas DCF is always placed after SMF in all fiber spans of the latter half-link. The most fundamental reason for reversing the arrangement order of each fiber is to further strengthen the symmetry of the dispersion profile with respect to the middle of the entire transmission link, which is the basic condition of applying optical phase conjugation into an optical link. And this deployment of optical fibers is to help achieve another purpose. [PERSON] et al. have reported that the best compensation of a distorted optical signal in a pseudo-linear system over a dispersion-managed link is that the total residual dispersion (TRD) is not 0 ps/nm but near 0 ps/nm, and the authors' previous studies have confirmed this result. All of the dispersion maps proposed in this study have a structure in which the overall dispersion profile is obtained by successive combinations of the unit dispersion profiles made by each of the four FSGs in each half-link. Since the cumulative dispersion of these four FSGs is 0 ps/nm, the TRD in the half-link also becomes 0 ps/nm, making it difficult to achieve the best compensation condition for the distorted WDM channel. Therefore, in the proposed dispersion-managed link of Figure 1, the TRD should be set to a value other than 0 ps/nm by using any one fiber span. We make the first fiber span in the first half-link or the last fiber span in the second half-link play this role. To be more precise, the variation of the TRD in the overall link for searching the optimal dispersion is set by correspondingly adjusting only the DCF length of the first or last fiber span under the respective circumstances where the TRD in the second or first half-link is set to 0 ps/nm. We define the methods for adjusting the TRD only with the DCF lengths of the first and last fiber span as pre-DC (dispersion calibrator) and post-DC, respectively.
# Advanced Scheme to Generate MHz, Fully Coherent FEL Pulses at nm Wavelength
## 3 Resonator Considerations
### Optical Klystron
In addition, it increases the gain both in the positive gain regime and in the zero net gain regime as \(R_{56,1}\
eq 0\) as well. This relaxes significantly the requirements in mirror reflectivity in the XUV range. Moreover, the optical klystron could be used as an active tuning tool to adjust the gain per pass and absorb different sources of jitter which contribute to gain changes. Concerning technical requirements, a chicane consisting of fast kickers for this purpose should be able to change the \(R_{56}\) by several \(\upmu\)m and with a MHz repetition rate. Stripline fast kickers are already standard technology and are, for instance, used at the European XFEL for extracting individual electron bunches with up to 4.5 MHz repetition rate [63; 64]. Let us assume that a change of 10 \(\upmu\)m is sufficient to transition from positive net gain to zero net gain. The longitudinal dispersion of the chicane is approximately \(R_{56}\approx L\theta^{2}\), where \(L\) is the distance between the first and second dipole of a chicane and \(\theta\) is the bending angle of the first dipole. A kicker adds an angle  
\[\Delta\theta[\upmu\text{rad}]=L_{kicker}[\text{cm}]B_{kicker}[\text{Gauss}]/E_{b}[\text{GeV}],\]  
with \(L_{kicker}\) and \(B_{kicker}\) being the length and field of the kicker and \(E_{b}\) the electron beam energy. With these kickers, a kick angle of 0.6 mrad can be achieved with \(E_{b}=1\,\text{GeV}\) and the change of \(R_{56}\) shown in Figure 10b would be possible within 1 \(\upmu\)s. It is important to ensure that implementing this change in \(R_{56}\) will not affect the stability of the system. Using the kickers only in the build-up regime would ensure stability during the steady-state regime. For the build-up regime, the stability is not so important, as long as the peak power is reached, since during these passes no seeded radiation is generated. Figure 10: (**a**) Changing the \(R_{56,1}\) of the chicane affects drastically the gain in power. Here we show the net power gain for selected set reflectivities \(R_{set}\), between 0.4% and 1%. The horizontal line shows the zero net gain. (**b**) With a reflectivity \(R_{set}=0.38\%\), it is possible to transition from positive gain to zero gain by adjusting the longitudinal dispersion of chicane1 as shown in Figure 8, from \(R_{56,1}=42.5\,\upmu\)m to \(R_{56,1}=30\,\upmu\)m, respectively.
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 3 Results
### Simulations in Complex Environment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & \(p_{n}\) & \(v_{n_{k}}\) [**kn**] & \(R_{n_{k}}\) [**nm**] & **Situation** & **Ranges to TS [**nm**] & **Own Ship Alters** & **DCPA [**nm**] \\  & & & & & & **Course to** & \\ \hline TS1 & \((-0.5,0.3)\) & \((2,1)\) & 0.05 & overtaken & 1.7 & starboard & 0.3 \\ TS2 & \((60,2.1)\) & \((-11.0,-0.5)\) & 0.1 & crossing A & \(>\)4 & starboard & 1.6 \\ TS3 & \((4.5,4)\) & \((0,-2)\) & 0.15 & crossing B & / & keep course & 1.0 \\ TS4 & \((10.3,8.7)\) & \((-2.8,-2.8)\) & 0.2 & head-on & 4.6 & starboard & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Initial inputs and simulation results for dynamic TSs in a complex environment. Figure 16: **(a)** When heading to WPT2, the own ship encounters TS4 in a head-on situation. It is noted that TS4 does not alter her course to starboard; hence, in the range of 4.6 nm to TS4, the own ship takes a substantial right turn 35\({}^{\circ}\) to pass on the port side of TS4. **(b)** The own ship takes a new course 065\({}^{\circ}\), but there is a static obstacle Obs4 in front of her. Therefore, the own ship must avoid Obs4 and consider the influence of TS4. Finally, the own ship selects an optimal course to port and passes abaft the TS4 at a range of 0.6 nm due to the sudden course change 45\({}^{\circ}\) to starboard by TS4. In addition, the passing distance between the own ship and Obs4 is safe, approximately 1.0 nm. Figure 17: **(a)** After clearing Obs4 and TS4, the own ship heads for WPT2 and then reaches WPT2 and the final goal. Although the goal is very close to Obs5, the own ship can still reach the goal and keep a safe passing distance of 0.57 nm from the point obstacle Obs5. **(b)** The above figure shows the distance between the own ship and each TS at every time step. The below figure shows the heading changes of the own ship at each time step, where \(-50^{\circ}\) means 310\({}^{\circ}\) in circular notation. Although the heading angle of the own ship has some small fluctuations, the trajectory direction reflects substantial alterations of course when avoiding TSs, which is in line with Rule 16 of COLREGS. In addition to the above snapshots, this simulation shows that the total trajectory length is 19.38 nm, and the total time steps are 315, which corresponds to 4725 s in reality. This simulation result is unique in 10 more repeats due to this deterministic algorithm. Only the total running time of the simulation that is in the interval of 0.804-0.833 s has a small difference. Furthermore, the real-time manipulation data obtained by a PD controller are shown in Figure 18a-d.