esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of Setup, Control Mode, and ROV Trajectories for Real Experiments
### Control Scheme  
Figure 14 is the control block diagram of the feeder, which can be controlled using the measured distance between buoys, namely buoy gap on the figure, or using a desired cable length. The control for the feeder consists of a main control loop with proportional controllers, which includes a speed control loop with a proportional-integral controller and anti-windup system. Flex sensor processing, presented in Section 4.1, is used to estimate the inter-buoy distance, which is sent to the feeder by the ROV.  
In normal mode, namely buoy gap control mode, the feeder speed is controlled as the ROV moves to regulate the gap between both buoys. The target buoy gap is defined from the actual ROV speed using the steady-state model that gives the variations in the buoy gap as a function of the ROV speed (see Section 2.3 and Figure 5). The target buoy gap is then compared to the current gap measured by the flex sensor to update the rotation speed of the feeder.  
In length control mode, the cable length can be directly controlled by setting a desired length. The feeder can actually be operated to release the cable when necessary; e.g., when the V-system is stretched, 2 m of cable is uncoiled after a 10 s period until the V-system is released.  
Buoy gap control and length control modes are separately activated as required. Cable length is computed in the speed control loop such that it is accessible even when length  
\begin{table}
\begin{tabular}{l l} \hline \hline Overall buoyancy & Neutral \\ Distance between ballast and buoy & 0.6 m \\ \hline Buoy size & 29 \(\times\) 29 \(\times\) 47 mm\({}^{3}\) \\ Buoy foam density & 288 kg/m\({}^{3}\) \\ \hline Ballast mass & 76 g \\ Ballast diameter & 25 mm \\ \hline Flex sensor model & FS-L-0095-103-ST (Spectra Symbol) \\ Flex sensor size & 110 mm long, 0.5 mm thick \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications and characteristics of the buoy–ballast system adapted for the Fathom Slim tether.  
Figure 14: Control block diagram of the feeder using the steady-state model and length and speed control loops. \(R\) is the radius of the feeder wheel and \(N\) is the number of ticks of the encoder counter.  
control is disabled. The target rotation speed is first bounded to avoid exceeding the motor limits. Embedded in the speed control loop is a proportional-integral corrector, which is applied to the error between the bounded target speed and the measured speed. This corrector has an anti-windup system that limits the sum of past errors to stabilize the system. It outputs a raw PWM signal, which is then bounded and smoothed by a low-pass filter before being transmitted to the motor control board.
## 3 Experiments and Discussion  
The proposed method was tested on our autonomous vehicle \"Intelligent Pioneer\" [19; 20; 21], which is equipped with various sensors, as shown in Figure 10. A Velodyne HDL-64E LIDAR (Velodyne, San Jose, CA, USA), which models the environment by generating a point cloud, is mounted on the top of the vehicle. An IBEO LIDAR (IBEO, Hamburg, Germany) and an SICK LIDAR (SICK, Hamburg, Germany) are mounted in front of the vehicle, and a camera is mounted on the top. A SPAN-CPT  
Figure 9: Collision avoidance: (**a**) collision avoidance in the space dimension; (**b**) collision avoidance in the time dimension.  
and an RTK differential module are used to obtain accurate coordinates of the vehicle and its moving state. The positioning error is less than 5 cm. A computer equipped with a Core i7-3610 CPU is used to process the data.  
A \(750\times 500\) occupancy grid map with a resolution of 0.2 m is generated to represent the local environment of the intelligent vehicle. The intelligent vehicle is located at (250, 500), and the direction of the vehicle is parallel to the y-axis in the positive direction, as shown in Figure 11a. Grids in the map are set as integer variables ranging from zero to 127 to represent different kinds of obstacles. The value of the grid is set to eight if it is occupied by a static obstacle. Dynamic obstacles are represented by a value of 28, and free areas are denoted with zero. A lane-level accurate map is used to recognize the structure of the environment, including road edges, lane marks and crossings. The localization of the intelligent vehicle on the map can be corrected by matching lane marks on the map with the lane marks detected by the on-board camera. The accurate map is shown in Figure 11b. White lines represent lane marks; sidewalks and stop lines red lines are the edges of the roads; and yellow lines are road barriers that divide the road into two parts. The intelligent vehicle is represented by a red rectangle.  
To evaluate the performance of the proposed method, experiments are conducted in an area covering straight roads and winding roads, as shown in Figure 12a. Testing roads are marked in blue and red, including straight roads and winding roads shown in (b) and (c). During the experiments,  
Figure 11: System introduction. (**a**) Local coordinate system; (**b**) road map.  
Figure 10: Intelligent Pioneer. The testing platform is equipped with a variety of sensors including a three-dimensional LIDAR, a two-dimensional LIDAR and a camera.  
the vehicle moved autonomously along the roads and performed evasive maneuvers to avoid potential collisions. Vehicles that were moving in the detection area of the autonomous vehicle could be correctly detected. Then, their moving states, mentioned in [1], and the deviation from lane center \(D\) were recorded and used in the training process. The advantages of the proposed method lie in the accuracy of intent estimation, which increases safety and comfort during collision avoidance.
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 2 Methods
### Decoder  
The decoder's function is to convert the multi-source heterogeneous iris feature information extracted by the encoder into iris semantic information. The decoder performs operations such as upsampling and convolution on the output feature map of the encoder and finally classifies each pixel on the iris image using functions such as SoftMax in order to distinguish pixels in the iris region from those in the non-iris region. Three kinds of upsampling methods are commonly used in decoders for semantic segmentation, unpooling, interpolation, and transposed convolution. Unpooling is a non-linear upsampling method introduced in SegNet, which corresponds to the maxpooling operation used in the encoder for recording the location of the maximum value. Although the calculation time is reduced, a certain amount of memory space is occupied when storing the location information. Interpolation can reduce the amount of computation, but at the same time, the accuracy of segmentation decreases. Since the parameters in the transposed convolution can be learned, the number of parameters and the amount of computation will increase to a certain extent, but the segmentation effect will be more accurate.  
Our decoder adopts four transposed convolution operations, which corresponds to the depthwise convolutions with stride of 2 used in the encoder four times for downsampling. The transposed convolution operation can improve iris segmentation accuracy, and it was combined with the structure of iris-feature module2. Finally, only one skip connection was used to fuse the low-level features to enrich the detailed information, further improving the iris edge segmentation result.  
The network structure of PFSegIris is shown in Figure 5. Table 2 presents the network structure descriptions of PFSegIris.  
Figure 4: The structure of the channel attention module.
## 6 Case Study
### Case 2: Exploring the Moving Semantics of Road Segments
#### 6.2.4 Regional Comparison  
We chose two typical areas to analyze whether cycling infrastructure needed to be built or strengthened in the area. Users could compare different roads by moving and zooming in on the map. Area 1 was around the Fifth Ring Road along the Badaling Expressway,  
Figure 8: Moving semantics profiling. (**a**) The most important topics of velocity. (**b**) The most important topics of acceleration. (**c**) The most important topics of turning angle. (**d**) The topic distribution of a selected road segment. (**e,f**) Grouping of road segments with velocity, acceleration and turning angle in two local areas, respectively.  
where there are mainly residential areas and parks. Area 2 was the western part near the Second Ring Road, with a dense population and concentrated streets.  
**In area 1**, as shown in Figure 8e, the color of the road segments for the velocity was mostly yellow (i.e., the fastest cycling) and orange (i.e., the faster cycling). Taking one road segment colored yellow as an example, as shown in Figure 8d, the distribution probability of this road segment on the four topics was 0.07375018, 0.07389444, 0.77817565 and 0.07417972, respectively. That is, the fastest cycling had a greater impact. Generally, cyclists can enjoy cycling rapidly in that area. For the acceleration, there were more blue (i.e., generally stable cycling) and orange (i.e., relatively smooth cycling) roads in area 1, indicating that sprint and braking rarely happened in the cycling process and the cycling condition was stable. For the turning angle, roads colored blue (i.e., straight cycling) were more common, thus cyclists tended to ride straightly.  
_In conclusion, it is shown in the first row of Figure 9 that the riding in area 1 is good_. Along the Badaling Expressway, there are a lot of frontage roads suitable for cycling. In addition, recreational riding in case 1 also passes through this area. Residents living here can ride conveniently, and the area is also developed into a popular choice for cycling enthusiasts. Hence, the bicycle infrastructures in the area do not need to be strengthened.  
**In area 2**, it is shown in Figure 8f that the velocity grouping was mainly blue (i.e., the slower cycling) and green (i.e., the slowest cycling). For the acceleration, the color of the grouping was mainly yellow (i.e., dangerous cycling) and green (i.e., changeable cycling), indicating that the cycling in area 2 was not stable. For the turning angle, most roads were obviously yellow, revealing that cyclists rode crookedly.  
_It is summarized in the second row of Figure 9 that riding in area 2 is bad_, so the construction of bicycle infrastructure is necessary. Cyclists can bypass the area to avoid degrading their cycling experience. Urban planners can better improve existing streets or build new roads.
## 3 Formulation for Joint Design Problem
### Comprehensive Utility Function  
To guarantee the QoS performance of cross-layer routing, a comprehensive utility function is applied to integrate transmission latency and energy efficiency. A multi-hop network must reduce the transmission latency so that the packet loss rate decreases and the routing stability improves. In addition, the requirement of high power efficiency is also a critical factor for energy-sensitive applications. For instance, grid monitoring and control applications only have limited data to send but demand real-time delivery [22]. Energy-constrained networks that traditionally operate powered by batteries are sensitive to power consumption and thus face an inherent challenge in energy efficiency, but are not sensitive to latency. However, some high-level services such as video and audio demand both real-time data transmission and high power efficiency [23]. Low transmission latency demands high power consumption, whereas excessive energy conservation may cause poor QoS performance and result in a long end-to-end delay. There is an inherent tradeoff between the transmission latency and energy consumption. Therefore, a utility function is designed that jointly captures the end-to-end delay and energy efficiency while making a reasonable tradeoff between the two. The resultant design is as follows:  
\[r_{i}^{t}=-\log_{2}(\alpha\cdot u_{TD,i}+\beta\cdot u_{PCR,i}), \tag{4}\]where \(u_{TD,i}\) accounts for the single-hop transmission latency (TL), and \(u_{PCR,i}\) denotes power consumption ratio (PCR) for \(SU_{i}\), which will be elaborated in following sections; and \(\alpha\) and \(\beta\) are parameters that adjust the tradeoff between transmission delay and energy efficiency, respectively. A larger \(\alpha\) increases the weight of the transmission delay in the utility function, whereas larger \(\beta\) emphasizes the power consumption, and vice versa. The logarithmic operation is used for compressing large values of \(\alpha\cdot u_{TD,i}+\beta\cdot u_{PCR,i}\) to a relatively small range. We can see from Equation (4) that the larger the TL or PCR is, the lower the utility function \(r_{i}^{t}\) becomes. This results in little reward for the agent so that it will explore more efficient actions to achieve minimal latency and energy expenditure.
## 4 Summary and Lessons Learned  
The extensive survey of recent works on the UAV deployment and trajectory in wireless communication networks is summarized in Tables 1-3. It is observed that early studies on the coverage maximization of multiple UAV wireless networks in Table 1 do not consider unique characteristics of UAV wireless networks such as an air-to-ground channel and interferences between UAVs, but only focus on covering a given area [21; 22]. Refs. [23; 24] consider those characteristics with altering the altitude of UAVs to minimize the transmit power at UAVs. On the other hand, most research on the performance enhancement of multiple UAV wireless networks do not consider optimizing the altitude of individual UAVs, but all UAVs at a fixed height. UAV wireless networks at a fixed height could outperform those with the altitude flexibility in some scenarios when the resources are properly managed [33]. However, it is essential to optimize the altitude of the individual UAV to best utilize UAV wireless networks, which is a challenging problem due to a high computational complexity. As in Table 2, single UAV wireless networks, such as UAV BS or RS, mostly consider the variation of the height of UAV to achieve the target performance because it requires much less computational complexity compared with multiple UAV wireless networks. The UAV aggregator in Table 3 selects a flexible trajectory of UAV to maximize the energy efficiency to prolong the lifetime of networks and maintain reliable systems. As introduced in this paper, there have been lots of studies on the deployment and trajectory of UAVs; however, it is still in its infancy and further research on various scenarios should be achieved to satisfy a target performance for their use cases. The optimal deployment or trajectory of UAVs in the literature targets to maximize either the coverage or the throughput of networks with consideration of the altitude flexibility, interferences from UAVs and air-to-ground channels. However, to best utilize UAVs in wireless communication networks, it is essential to jointly consider the resource allocations of transmit power and time at UAVs and ground nodes in the entire networks including scheduling methods and UAV-UEs associations, along with the deployment of UAVs. However, there have been very few studies on optimal resource allocations for UAV wireless networks [44]. Further research on optimal resource allocations for various scenarios should be conducted, and they can be further extended to the joint optimization of UAV deployment and resource allocations to maximize both coverage and throughput of UAV wireless networks. This is challenging, but determines key features on the construction of UAV wireless networks.
## 6 Molecular Imprinting by Phase-Inversion
[PERSON] and his co-workers [183] also described an interesting method for the preparation of hybrid MIM by WPI using a covalently bonded dummy template. A hybrid MIM has the advantage of combining the selectivity of molecular imprinting with the mechanical resistance of the support membrane. It is well known that a membrane presents a great specific surface area and possess a great number of binding sites, at the same time, due to the porous structure that offers a facile access of analyte solutions. In this work, core-shell polymer particles were first produced using 1-naphthol as mimic (dummy) template for CIT (Citrinin). The template was covalently bonded to the matrix, by using \(\alpha\) naphtol methacrylate (NAM) monomer to create a polymeric shell around divinyl benzene (DVB) particles. In the second step, hybrid MIMs were prepared by mixing the resultant core-shell MIP particles with polyether sulphone (PES) in a \(N\)-methyl pyrrolone (NMP) solution. The latter solution was cast on glass plates and transformed into MIMs by WPI in a water coagulation bath. The assessment of CIT was performed by liquid chromatography with fluorescence detection (HPLC-FD), in which case the LOD and LOQ were found to be 0.5 and 1.7 ng g\({}^{-1}\), respectively. The overall results pointed towards sufficient sensitivity of the MIMs for CIT, being recommended for the analysis of CIT in rice [183]. It is important to mention that hybrid membranes can also be prepared using alternative methods. Such hybrid membranes for sensor development are described by [PERSON] and his group [184], where the MIP particles are synthesized by non-covalent complexation of the template with the functional monomers. Herein, the polymeric sensor was prepared using MIP particles, synthesized from MAA and 2-vinyl pyridine as functional monomers, and trimethoprim (TMP) as a template, while the polymer matrix was polyvinylchloride (PVC). Hence, theMIM membrane was obtained without the use of phase inversion. The sensors prepared in this manner displayed a linear behavior against the TMP logarithmic concentration, in a wide concentration range. A very similar method is described in [185], using enrofloxacin as a template. The epitope approach can be applied for MIP preparation not only by surface polymerization but also by phase inversion. An example of the epitope dry phase inversion is described by [PERSON] and his group [186]. EVAL copolymers were dissolved in DMSO containing the following template peptides: SCSGFKKWKDESCEKK (Peptide 2), KSWDTGSPSSANAGYCAS (Peptide 4) and KESSTDDSNVWIG (Peptide 6) of REG1B. The preparation of peptide-MIMs was performed by the dropwise addition of thisprecursor solutionon the gold substrate of a screen-printed electrode. The urine samples from patients with pancreatic cancer revealed higher electrochemical response in comparison with samples from healthy persons, which was consistent with their elevated levels of the marker protein.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
###### Abstract  
The joint radar and communications (JRC) waveform often has a high range sidelobe, which will degrade the target detection performance of an automotive JRC system. To solve this problem, a joint radar and communications complementary waveform group (JRC-CWG) design method is proposed in this paper by exploiting the philosophy of the complementary sequence. In the JRC-CWG, the traditional unimodular communications waveforms, such as the binary phase shift keying (BPSK) waveform, are used to perform the communications function. The sum of the autocorrelation function (SACF) of JRC-CWG is optimized to minimize the sidelobe level. Furthermore, considering that the JRC-CWG has poor Doppler resilience, a Doppler-resilient joint radar and communications complementary waveform (DR-JRC-CWG) design method is proposed to improve the Doppler resilience. Finally, the simulation results show that the proposed JRC-CWG and DR-JRC-CWG have superior radar performances without the degradation in communications performance in terms of the bit error rate (BER).  
joint radar and communications; complementary sequence; joint radar and communications complementary waveform group; Doppler resilience 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Algorithm Performance for Ice-Risk Forecasts
#### 3.2.2 The Case-Study at the Linate Airport  
For the ice alert code forecast, in this second case-study, the algorithm performance is studied by comparing the output initially to the joint distribution of the forecasted air relative humidity and LST and, then, both the observed values. As illustrated in Figure 11, \(2\times 2\) contingency tables are created both for clear and cloudy days and the main statistical indexes are calculated for performance evaluation (Table 13), taking into account only those days where hourly air temperatures are less than 4 \({}^{\circ}\)C.  
The analysis confirms a better accuracy for the ice warning probability forecast when it is applied to cloudy days, as for the Pedemontana case-study. On the contrary, during clear skies, it performs slightly worse with a miss alarm ratio equal to 0.38 and a BIAS score less than 1, however, very few cases of false alarm (1%) are observed.  
This outcome confirms the good performance found in the previous case-study that leads to the final considerations about the differences in the performance in function of the sky cover. By completely distinguishing clear and overcast sky from those days with variable meteorological conditions, an evident variation in the algorithm reliability between cloudy and clear sky days appears. This has two principal explanations: first, the ice risk during cloudy days is less frequent than the risk on clear nights in this geographical area, and few cases have been analyzed; second, the complex relation between the variables involved in the ice formation produces a decrease in the model performance. In particular, the temperature decrease due to radiation loss is very sensitive to the overall meteorological conditions and to the precise location considered. A small difference in wind speed, e.g., can cause a sensible difference in the mixing of the air in the first few centimeters above the ground, resulting in the formation or in the absence of ice.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Accuracy** & **BIAS** & **False Alarm Ratio** & **Miss Alarm Ratio** & **Hit Rate** \\ \hline
**Clear days** & 0.69 & 0.66 & 0.01 & 0.38 & 0.62 \\
**Cloudy days** & 0.86 & 1.00 & 0.20 & 0.20 & 0.80 \\
**Total** & 0.69 & 0.66 & 0.04 & 0.37 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Evaluation of algorithm performance in the ice alert code forecast for Linate Airport forecasted values assumed by the indices.  
Figure 11: Contingency tables for clear (**left**) and cloudy sky days (**right**) for forecasted values.