esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 1 Introduction  
Global fish stocks in every ocean have been declining for decades, primarily due to overfishing [1, 2]. Changes in the marine environment from human pollution have also brought about further reductions in fish populations and the bioaccumulation of pollutants such as heavy metals and microplastics in wild fish [3, 4]. Since 1990, fish consumption has doubled, while capture fishery production has remained constant. The rising demand has been met by the explosive growth of aquaculture, which has grown exponentially over the last few decades, producing more fish than capture fisheries for the first time in 2012 [5].  
Aquaculture is a labor-intensive activity, with the most burdensome task being maintaining appropriate water quality in the water body. Of particular importance is the level of dissolved oxygen (DO), which, if mismanaged, will result in sudden collapse of a fish pond. Fish farmers typically measure DO by attaching a sensor to a vehicle which is manually driven to each pond. This process is repeated multiple times a day and is particularly important to measure at night, when DO depletion typically occurs due to the lack of photosynthesis and subsequent plant respiration. Because DO measurement is a repetitive and relatively simple task, it is a prime target for automation.
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 2 Basic Knowledge and Literature Review
### Task Assignment Model
The total range of all UAVs is  
\[J_{1}=\sum\limits_{i=1}^{N_{V}}\sum\limits_{m=0}^{N_{T}}\sum\limits_{n=1}^{N_ {T}}x_{m,n}^{i}\cdot Range_{m,n}^{i} \tag{2}\]  
The time to complete all tasks refers to the maximum time taken by any UAV to complete its own task sequence, and is expressed as  
\[J_{2}=\underset{i\in N_{V}}{\max}Time_{i} \tag{3}\]  
where \(Time_{i}\) is the time of UAV \(V_{i}\) to finish its all tasks and can be equivalent to the total range of UAV \(V_{i}\), so the time to complete all tasks can be represented as  
\[J_{2}^{*}=\underset{i\in N_{V}}{\max}\sum\limits_{m=0}^{N_{T}}\sum\limits_{n =1}^{N_{T}}x_{m,n}^{i}\cdot Range_{m,n}^{i} \tag{4}\]  
To keep the values of the two costs within the same order of magnitude, the total range of all UAVs is equivalent to the average range, represented as  
\[J_{1}^{*}=\frac{\sum\limits_{i=1}^{N_{V}}\sum\limits_{m=0}^{N_{T}}\sum\limits_ {n=1}^{N_{T}}x_{m,n}^{i}\cdot Range_{m,n}^{i}}{N_{V}} \tag{5}\]  
The optimization goal is to minimize both the total range of all UAVs and the maximum range among all UAVs. Thus, the cost function of task assignment is  
\[\min J=\omega_{1}J_{1}^{*}+\omega_{2}J_{2}^{*}=\omega_{1}\frac{\sum\limits_{i =1}^{N_{V}}\sum\limits_{m=0}^{N_{T}}\sum\limits_{n=1}^{N_{T}}x_{m,n}^{i}\cdot Range _{m,n}^{i}}{N_{V}}+\omega_{2}\underset{i\in N_{V}}{\max}\sum\limits_{m=0}^{N_ {T}}\sum\limits_{n=1}^{N_{T}}x_{m,n}^{i}\cdot Range_{m,n}^{i} \tag{6}\]  
\[ \begin{array}{ccc}s.t.&(1)&\sum\limits_{i=1}^{N_{V}}\sum\limits_{m=0}^{N_{ T}}x_{m,n}^{i}=1\\ &(2)&\sum\limits_{i=1}^{N_{V}}\sum\limits_{m=0}^{N_{T}}\sum\limits_{n=1}^{N_ {T}}x_{m,n}^{i}=N_{T}\end{array}  \tag{7}\]  
where \(\omega_{1}\) and \(\omega_{2}\) are weighting factors reflecting the importance of each performance criterion, decided by the commander, and \(\omega_{1}+\omega_{2}=1\). Equation (7) indicates constraints: each target can be attacked only once, and all targets must be attacked.
# Fractional Order Viscoelastic Model for Stress Relaxation of Polyvinyl Chloride Geomembranes
## 2 Materials and Test Methods
### Specimens Preparation  
Previous research [34; 35] has demonstrated that the mechanical properties of PVC GMBs vary in the transverse and the longitudinal direction. To enhance the reliability of test results, the specimens in both directions, denoted by symbols T and L in this work, respectively, were taken from single rolls of the product and cut with the long dimension parallel to the machine direction. A special GMB cutter and punching machine were used to cut the specimens along the longitudinal and transverse directions, and the prepared specimens were 200 mm wide and 180 mm long. A marker was used to draw a 200 mm marked line along the width direction with 40 mm away from both ends of the length direction. The distance between two parallel marked lines, namely the gauge length of the test specimen, was 100 mm. To assess the effects of tensile rates and initial strains on the stress relaxation properties, three tensile rates were selected, 2.5, 5.0, and 10.0 mm/min, and three initial strains were investigated, 40.55%, 58.78%, and 81.09% to simulate the PVC GMBs at the peripheral joints during construction and initial impoundment of the reservoir. According to the tensile rates and initial strains, 18 groups of tests were designed (nine for transverse specimens and nine for longitudinal specimens), and five specimens were prepared for each group.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Property** & **Method** & **Value (T)** & **Value (L)** & **Unit** \\ Thickness & ASTM D5199-12 [31] & 2.0 \(\pm\) 0.2 & 2.0 \(\pm\) 0.2 & mm \\ Mass per unit area & ASTM D5261-10 [32] & 1.77 & 1.77 & g/cm\({}^{2}\) \\ Break strength & & 30.23 & 36.41 & MPa \\ \(>\)Break strain & & 450.52 & 421.76 & \% \\ Yield strength & & 21.76 & 23.36 & MPa \\ Yield strain & & 30.68 & 31.57 & \% \\ \hline \end{tabular} Note: T represents the transverse specimen, and L represents the longitudinal specimen.  
\end{table}
Table 1: Index properties of PVC GMBs.  
Figure 2: Diagram of stress relaxation test setup.
## 4 Results and Discussion
### Acquisition B021_0 (222 DE), Fan End Bearing Featuring Ball Fault, Non Periodic Impulses  
Finally, Acquisition B021_0 (222 DE) selected as the accelerometric signal is of a highly non-stationary nature, showing many large impulses. Regarding this, apart from \(MP\), \(L1\)/\(L2\), and \(P\), all the other band indicators reported in Table 3 seem to be able to find a demodulation band which can produce good diagnostic results. From Figure 6c, it can be seen that in a large band (i.e., roughly 2000-4000 Hz) the envelope demodulation could lead to almost optimal results. Nevertheless, the best indicators (\(AK\), \(K\), and \(SGI\)) point to a suboptimal frequency range in the high-frequency portion of the spectrum. The result is suboptimal, but nonetheless acceptable from a diagnostic point of view.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _Acquisition:_ & **176 FE** & **275 DE** & **177 FE** & **222 DE** & **Avg** \\ \hline dE & 0.71 & 0.59 & 0.58 & 0.18 & - \\ \hline \multirow{4}{*}{iE} & \(K\) & 0.88 & 0.58 & 0.31 & 0.645 & 0.604 \\  & \(P\) & 0.07 & 0.79 & 0.615 & 0.235 & 0.428 \\  & \(L2/L1\) & 0.07 & 0.26 & 0.615 & 0.235 & 0.295 \\  & \(SGI\) & 0.67 & 0.26 & 0.06 & 0.645 & 0.409 \\  & \(I\) & 0.88 & 0.725 & 0.59 & 0.585 & 0.695 \\  & \(AK\) & 0.88 & 0.845 & 0.59 & 0.645 & 0.740 \\  & \(MP\) & 0.07 & 0.79 & 0.295 & 0.235 & 0.348 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the results in terms of demodulation and indicator effectiveness.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Results Analysis  
#### 4.2.1 Training Dynamics  
Figures 5 and 6 depict the training loss curves for the Fog Generator and dehazing models, respectively. The Fog Generator exhibits a steady decline in loss, converging after approximately 80 epochs, indicating effective learning of fog synthesis patterns. Similarly, the dehazing models show consistent loss reduction, with ModelU achieving the lowest final loss, suggesting that training on GridNet-generated fog enhances dehazing optimization.
# A Smart Decision System for Digital Farming
###### Abstract
VRI is a new technique that allows optimization of irrigation application, considering the status of field and weather. In most cases, these systems are implemented by using a center pivot irrigation system that  
Figure 1: The cycle of smart farming data management. can reduce the total irrigation water volume required to grow field crops. According to Reference [8], this kind of system is able to reduce irrigation water use by 8% compared to uniform irrigation application. Considering this idea, we designed [9], developed, and tested an intelligent system for irrigation control of urban lawns. The system is based on the use of a multisensor node composed by humidity and temperature for soil and air, as well as a rain sensor. The multisensor node is controlled by an intelligent algorithm that takes into account the real time values provided by the sensors and the stored data of previous measurements. The intelligent algorithm is responsible for analyzing the data and deciding on the amount of water to be used in a given area. Using actual annual meteorological values of a city with different water requirements, the system's operation was simulated. The results showed that for a landscaped area of about 140 ha, we could save around 6% of the irrigation water, which would be equivalent to about 22 million liters of water. Finally, the complete system behaves as a collaborative platform, taking into account the experience of other users and previous events introduced into the system. A system with these features can help farmers to enhance the activity sustainability, at the same time, that a more controlled use of chemical products and natural resources will protect our environment and groundwater resources. This study is organized as follows. Section 2 will present the reasoning behind the study and related works. Section 3 will describe the individual steps of the preparation process of a classification rule set. Section 4 will then detail the software components of the engine and, in Section 5, the web platform and the processing of open data will be outlined. Finally, in Section 6, the conclusions will present the advantages of the demonstrated model and future avenues of work will be suggested.
# Variable Curvature Displays: Optical Designs and Applications for VR/AR/MR Headsets
## 4 Optical Design Example
### Design Description  
A known triplet design [16] was used as the starting point. It was scaled and optimised to match the above given parameters. An overview of the resulting **\"CV\"** design is shown in Figure 3. Note that this is an example design and some additional technological optimisation is required for its practical implementation.  
The figure clearly shows the difference in the image plane position and curvature between the configuration with object at 1.5 m (\"1\" index) and at infinity (\"2\" index). The design contains three Q-type aspheres at the first, third and sixth surfaces with six aspheric coefficients each. The maximum surface slope deviation is 0.163, 0.147 and 0.107, respectively.
# LDM-Ex-FDM: A Novel Multi-Service Transmission Scheme for the ATSC 3.0 System
## 4 Simulation Results and Analysis
### Further Discussions on the LDM-Ex-FDM Scheme  
Based on the above simulation results and the BICM capacity analysis in Section 3, it is shown that the LDM-Ex-FDM scheme has a higher \(EL\) capacity and the overall capacity than the traditional \(LDM\) scheme. Compared to the traditional method, i.e., to increase the \(EL\) power by simply changing the \(LDM\) power injection level, the proposed solution is able to improve the decoding performance of the fixed service without decreasing the required SNR threshold for mobile services. This can be explained by the effective use of the puncturing strategies at the transmitter with which part of the \(EL\) services can be demodulated with less signal interference and a higher receiving SNR at the receiver side, so as to get more frequency diversity gain. It is noted that this capacity gain comes at the cost of increased complexity at the transmitter side. Therefore, a DIC detection method is developed to further reduce the overall computational complexity. The technical comparison between the proposed LDM-Ex-FDM scheme and several typical \(LDM\) schemes is illustrated in Table 4.  
In fact, our solution is helpful for some practical application scenarios. Take the system coverage and networking into consideration, \(LDM\) can achieve the same coverage requirements of different services by reasonably selecting the coding and modulation parameters and the injection level of both layers. However, in actual network deployment, when the antenna gain and installation height are not completely matched with the planned parameters and other non-ideal conditions, the effective coverage of the transmission service will be reduced, and in particular the coverage performance of the \(EL\) will be worse as a result of a higher demodulation SNR threshold. In this case, multiple \(EL\) holes will  
Figure 20: BER performance of fixed service in the LDM-Ex-FDM scheme with DIC and SIC with perfect channel impulse response (CIR) under the multipath channel, where the parameters of the ATSC 3.0 system were set to \(\Delta=10\) dB, 12 dB, 15 dB, the \(CL\) constellation was set to QPSK, the \(EL\) constellation was set to NU\(-\)64 QAM, and the puncturing rate was set to 1/12.  
be generated in the network coverage area. This phenomenon is more common in urban environments. The proposed LDM-Ex-FDM scheme is capable of improving the reception performance of the _EL_ services without degrading the reception SNR threshold of the _CL_ services, and further increase the overall system coverage performance. In a sense, the _EL_ coverage hole can be filled effectively.
## 3 Tracing the Real and Reactive Power Flow
### Load Contribution Tracing  
Now, we have a mathematical formulation for not only the generator's active power tracing but also the reactive power. Besides, with load converted into a branch equivalent circuit, the contribution of the \(g^{th}\) generator to the designated \(j^{th}\) load can be found by:  
\[\begin{array}{ll}P_{load,j}^{g}&=\sum\limits_{m\in\Omega}P_{m}^{g}\\ &=\sum\limits_{m\in\Omega_{f}}(d_{1,mg}^{f}\cdot p_{g}+d_{2,mg}^{f}\cdot q_{g}) +\sum\limits_{m\in\Omega_{b}}(d_{1,mg}^{b}\cdot p_{g}+d_{2,mg}^{b}\cdot q_{g}) \\ \\ Q_{load,j}^{g}&=\sum\limits_{m\in\Omega_{f}}Q_{m}^{g}\\ &=\sum\limits_{m\in\Omega_{f}}(d_{3,mg}^{f}\cdot p_{g}+d_{4,mg}^{f}\cdot q_{g}) +\sum\limits_{m\in\Omega_{b}}(d_{3,mg}^{b}\cdot p_{g}+d_{4,mg}^{b}\cdot q_{g}) \\ \end{array} \tag{27}\]  
where \(\Omega\) is the set of all line sections connected to the \(j^{th}\) load bus. It can be divided into \(\Omega_{f}\) and \(\Omega_{b}\) for the forward and backward line contributions, respectively.
# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 3 Motivation and Methodology for Empirical Models of MAC Protocol Performance
### Network Metrics as a Function of Scenario  
In [19], we presented vehicular simulations to explore the behaviour of relevant network performance metrics, namely the rate of collisions and the ratio of medium accesses during which the medium was busy (after [24]), with the three aforementioned protocols, i.e., CSMA/CA, PLEEX-slotted and RA-TDMAp. The studied scenario encompassed 16 platoons travelling side-by-side in four parallel lanes at the same speed. This situation generates persistent interference among all vehicles, leading to a stationary process. The only parameter we varied was the platoon size, \(N\), from 2 to 16 vehicles.  
The respective results are shown in Figure 5, with 2 nd order fitting curves superimposed over the measurement points. An initial observation is the effectiveness of RA-TDMAp to allow platoons to escape from the interference of each other adjusting the phase of their cycles. A second and more meaningful conclusion is that, despite the considerable differences in the operation of the three protocols, we observe a consistent relationship between the network performance metrics and a wireless network topological feature, namely the number of vehicles in the platoon.  
This leads us to hypothesize that relevant performance metrics (e.g., collisions rate and channel busy time ratio) may be related to a network topology parameter that represent a variety of physical and scenario parameters, such as the platoons sizes and physical layout, and the RF transmit power. In this paper, we study the number of RF neighbours in the vicinity of a particular vehicle as such network topology parameter. In fact, this number seems to depend directly on platoons size by means of the leader/follower ratio, but also on platoons physical arrangement either serialized in a single lane or side-by-side in multiple lanes, and also on the effective RF power received from each source, since higher power implies larger range. Moreover, the number of RF neighbours is easily measurable by each vehicle without need for specialized support from either network hardware or device-driver as it would be the case for measuring collisions rate or channel busy time ratio directly. The approach we propose opens the way to an online quantitative assessment of the channel quality for the platooning application using COTS IEEE 802.11p network interfaces.  
To evaluate this hypothesis, we propose to model the performance metrics as a function of the number of RF neighbours in an empirical fashion (i.e., by carrying out extensive parameter-space exploration and extract fitting model parameters), and investigating if the relationship is kept as other parameters are varied.
## 2 Signal Processing Principles
### CDR Matrix Synchronization Operations to Obtain Synchronization Moments  
To improve the stability of the synchronization signal, reduce the interference of transient noise, and minimize the decoding error, we low-pass-filtered the frequency correlation detection output \(P_{i}(t)\) to obtain a smooth envelope signal.  
\[y_{i}(t)=P_{i}(t)\times h(t) \tag{6}\]  
where \(h(t)\) is the filter response function; this step can effectively suppress high-frequency noise and improve clock synchronization robustness.  
The key to frame synchronization lies in the matching between the data received at the receiver and the local link frame header. The locally stored link symbol synchronization frame header matrix \(B\) is structured as follows:  
\[B=\begin{bmatrix}B_{1}(1)&B_{1}(2)&\cdots&B_{1}(L)\\ B_{2}(1)&B_{2}(2)&\cdots&B_{2}(L)\\ B_{3}(1)&B_{3}(2)&\cdots&B_{3}(L)\\ B_{4}(1)&B_{4}(2)&\cdots&B_{4}(L)\end{bmatrix} \tag{7}\]  
\(B_{c}(i)\) represents the preset link synchronization symbol pattern, and \(L\) is the length of the frame synchronization stored locally. At time \(t\), we compute the second-order correlation between the current data window \(Y(t)\) and the local link frame header \(B\) by matrix matching.  
\[Z(t)=Y(t)\cdot B^{T} \tag{8}\]  
\(Y(t)\) represents the cross-correlation matching result matrix obtained at time \(t\), derived from the combination of the filter output curves. The matrix inner product operation allows fast calculation of symbol synchronization matching at different time points. The synchronization moment is determined by identifying the maximum value of \(Z(t)\).  
\[t_{CDR}=\text{argmax}_{i}Z(t) \tag{9}\]  
This process employs matrix operations for batch processing to obtain the optimal synchronization position. Meanwhile, data corresponding to successful synchronization of the link frame header is stored in memory.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{6}{c}{**November**} & \multicolumn{6}{c}{**December**} \\ \cline{2-10}  & **22** & **23** & **24** & **25** & **26** & **27** & **28** & **29** & **30** & **1** & **2** & **3** \\ \hline \multirow{2}{*}{2011} & \multirow{2}{*}{EN} & \multirow{2}{*}{SIC} & \multirow{2}{*}{EN} & \multirow{2}{*}{EN} & \(\mathcal{S}\) & \multirow{2}{*}{EN} & \multirow{2}{*}{EN} & \multirow{2}{*}{\(\mathcal{E}\)} & \multirow{2}{*}{\(\mathcal{E}\)} \\  & & & & & & & & & & & \\ \hline \multirow{2}{*}{2012} & \multirow{2}{*}{
\begin{tabular}{c} SIC \\ MOD \\ \end{tabular} } & \multirow{2}{*}{MOD} & \multirow{2}{*}{MOD} & \multirow{2}{*}{S} & \multirow{2}{*}{S} & \multirow{2}{*}{\(\mathcal{E}\)} \\  & & & & & & & & & \\ \hline \multirow{2}{*}{2013} & \multirow{2}{*}{MOD} & \multirow{2}{*}{SIC} & \multirow{2}{*}{MOD} & \multirow{2}{*}{MOD} & RS & \multirow{2}{*}{MOD} & RS & \multirow{2}{*}{\(\mathcal{E}\)} \\  & & & & & & MOD & & MOD & \\ \hline \multicolumn{10}{l}{\(\mathcal{S}\): start journey in ice cover; E: end journey; SIC: sea ice concentration maps; EN: Envisat ASAR images; MOD: MODIS images; RS: Radarsat-2 images.} \\ \end{tabular}
\end{table} 
Table 3: Key dates of RV _Xuelong_ navigation in the ice covered water in Prydz Bay and acquisition dates of the satellite and SIC map data _Question 5: How can a remote-sensing-driven route be evaluated against possible adjustments based on ship observations of the surrounding ice?_  
According to ship-based observations of surrounding ice, the planned route may have to be adjusted while moving through sea ice as in the cases of CHINARE2011 and CHINARE2013 expeditions (Figures 7 and 9, respectively). The maximum deviation of the actual route from the planned waypoints was nearly 7 km and 3.5 km with standard deviation of 2.8 km and 3.0 km in 2011 and 2013, respectively. These numbers are justified based on typical values of ice motion (2-6 km per day), when the observations used to develop the proposed nautical route is one or days lagging behind the actual operation time. Higher deviation from the planned route is expected when the vessel encounters a highly dynamic ice regime with intensive spatio-temporal variation of ice cover.