esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion and Future Perspectives  
### Development Trends  
1. High degree of intelligence: The interaction system of minimally invasive surgery robots has realized intelligent functions such as surgical space analysis, precise positioning, path planning, and real-time navigation, and has formed intelligent interaction modes such as voice control, eye movement interaction, and tactile feedback. With the innovation and breakthrough of future network technology and control technology, related fields will further deepen the degree of intelligence in operation modes, information analysis and presentation, and evaluation alarms, and improve the safety and usability of the surgical interaction system.
2. Humanization: The current research on the interaction design direction of minimally invasive surgery robots has not fully reflected the user-centered design concept, and the application innovation led by engineering technology is still the mainstream model. This has also led to high learning costs and operation difficulties in the marketization process of related fields. Future research should focus on user needs and form design innovations driven by users.
3. Deepening multimodal interaction: Minimally invasive surgery robots have formed visual, tactile, and auditory interaction methods in the form of interaction. Multimodal interaction provides users with more selectable spaces and participation methods while promoting the completion of surgical tasks. However, the development of related systems is not mature yet, and there is huge potential for deepening and improvement in the design.
# Fast Path Planning for Autonomous Ships in Restricted Waters
###### Abstract  
Presently, there is increasing interest in autonomous ships to reduce human errors and support intelligent navigation, where automatic collision avoidance and path planning is a key problem, especially in restricted waters. To solve this problem, a path-guided hybrid artificial potential field (PGHAPF) method is first proposed in this paper. It is essentially a reactive path-planning algorithm that provides fast feedback in a changeable environment, including dynamic target ships (TSs) and static obstacles, for steering an autonomous ship safely. The proposed strategy, which is a fusion of the potential field and gradient methods, consists of potential-based path planning for arbitrary static obstacles, gradient-based decision-making for dynamic TSs, and their combination with consideration of the prior path and waypoint selection optimization. A three-degree-of-freedom dynamic model of a Mariner class vessel and a low-level controller have been incorporated together in this method to ensure that the vessel's positions are updated at each time step in order to acquire a more applicable and reliable trajectory. Simulations show that the PGHAPF method has the potential to rapidly generate adaptive, collision-free and International Regulations for Preventing Collisions at Sea (COLREGS)-constrained trajectories in restricted waters by deterministic calculations. Furthermore, this method has the potential to perform path planning on an electronic chart platform and to overcome some drawbacks of traditional artificial potential field (APF) methods.  
collision avoidance; restricted water; artificial potential field; autonomous ship; unmanned surface vehicle; path planning Article
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Demodulation
#### 2.4.5 Symbol Decision  
The symbol decision iterates through each frame's symbol space difference sequence \(y_{\text{sym}}\) similarly to (32) to (34) of Section 2.4.4, by separating each frame in two clusters split by the mean symbol amplitude, and estimates the half distance between both clusters' means as a threshold \(y_{\text{fTh}}\) for symbol decision for each channel. The decision equation is, therefore, simply  
\[d_{\text{rmx}}[k]=\begin{cases}1,&\text{for }y_{\text{sym}}[k]>y_{\text{fTh}}\\ 0,&\text{otherwise},\end{cases} \tag{41}\]  
for the \(k\)th symbol of each channel and frame.
## 3 Results and Discussion
### Time-Series Analysis  
The weather forecast model generates forecast results routinely four times per day at UTC 0:00, 6:00, 12:00, and 18:00. Post-correction performance was significantly improved from the original forecasts in almost all forecast processes, as shown in Figure 14 for station 51359. At this station, the RMSE of the raw temperature forecast was around 4 \({}^{\circ}\)C, with a mean of 4.01 \({}^{\circ}\)C and a standard deviation of 1.2 \({}^{\circ}\)C. After post-correction, RMSE decreased significantly, with a mean of 2.1 \({}^{\circ}\)C and a standard deviation of 0.57 \({}^{\circ}\)C. For relative humidity, the RMSE of the raw forecast was relatively large, with a mean of 26% and a standard deviation of 8%. This deviation indicated that raw forecasts have low stability in relation to model performance, which will decrease users' confidence when using their data in applications. Specifically, RMSE decreased to much lower levels, with a mean of 13% and a standard deviation of 3%. The decreased mean RMSE indicated that the forecast accuracies had improved for temperature and relative humidity. Meanwhile, the reduced standard deviation of RMSE indicated an enhanced stability of forecast performance along different forecast processes. Even though post-correction forecasts showed a deteriorating trend along leading hours, a complete circle of forecasts was corrected all at once rather than by each lead hour, as shown in Figure 15. Our approach will also improve public users' confidence in forecasts because the errors are stable.  
Figure 11: Difference between the RMSE values of post-correction forecasts for: (**a**) temperature and (**b**) relative humidity by AutoML model and multiple linear regression MOS method compared to single linear regression method. The deep gray bar and light gray bar indicate raw forecasts and post-processed forecasts.  
Figure 12: _Cont._  
Figure 12: The boxplot of RMSE values by raw forecast, AutoML, SLR, LR, and RF models evaluated with bootstrap sampling method.  
Figure 14: RMSE of raw forecast and post-correction results in each forecast period for: (**a**) temperature and (**b**) relative humidity at station 51359.  
Figure 13: The bar-plot of mean RMSE values by raw forecast for (**a**) temperature and (**b**) relative humidity, AutoML, SLR, LR, and RF models evaluated with bootstrap sampling method. The uncertainty bar was added.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Camelina sativa L
#### 3.2.3 Prevention in Metabolic Diseases and Others  
Researchers see great potential for the health properties of camelina oil. [PERSON] et al. [55] conducted a complex analysis of the quality and nutritional value of 29 cold-pressed camelina oils from seeds grown in Poland. The results confirmed that cold-pressed camelina oils are characterized by high nutritional value, which results from the favorable composition of fatty acids (n-3 to n-6; 1.79-2.17), as well as optimal nutritional quality indicators: low atherogenic index (AI: 0.05-0.07) and thrombogenic index (TI: 0.1-0.2). In addition, high content of tocopherols was noted (55.8-76.1 mg/100 g). In cold-pressed camelina oils, the content of phytosterols (331-442 mg/100 g) as well as carotenoids (103-198 mg \(\beta\)-carotene/kg) was assessed [55]. Camelina oil is a rich source of PUFAs, including ALA, an n-3 essential fatty acid [54; 55; 56; 57]. Some reports confirm that ALA supplementation in the diet is associated with amelioration of the lipid profile, lowering C-reactive protein (CRP) levels, and diminishing the risk of cardiovascular diseases [67; 68]. For this reason, researchers are working to understand camelina oil's role in the improvement of health parameters. Regrettably, there are only a few reports available in these areas. Nevertheless, the access reports indicate several potential research directions regarding camelina. In the studies of [PERSON] et al. [69], it was shown that supplementation of camelina oil and prebiotic (resistant dextrin) in combination with a low-calorie diet showed promising results in improving various health parameters in patients with non-alcoholic fatty liver disease (NAFLD), suggesting the possibility of using such therapy as a complement to treatment. Among others, a significant reduction in insulin levels, homeostatic model assessment for insulin resistance (HOMA-IR), a decrease in body weight and body mass index (BMI), a considerable reduction in high-sensitivity C-reactive protein (hs-CRP) level, and an improvement in scores on the depression, anxiety, and stress scale were noted [69]. In a study aiming to determine the metabolic effects of consuming lean fish, oily fish, and amelina oil in people with glucose metabolism disorders, the results showed that a diet enriched with amelina oil improved the serum lipid profile compared to a diet enriched with oily or lean fish in the study participants. However, no differences were noted in glucose metabolism or concentrations of inflammatory markers [70]. Studies using an animal model have shown that alcoholic extracts from amelina seeds can improve cognitive function and mood, as well as exhibit antioxidant properties in the brain and gut in animal models of stress-induced irritable bowel syndrome (IBS) [71].
## 4 Result and Discussion
### Communication Performance
#### 4.2.2 Uplink Performance
Accordingly, it can be concluded that, even if the wavelength of operation is changed in an UL connection and the transmit power is low, the DL study can still be extended to determine the UL \(SNR\) uniformity using POR.
# Metrology for Measuring Bumps in a Protection Layer Based on Phase Shifting Fringe Projection
## 2 Fringe Projection System and Sample Details
### System Design  
Figure 1 shows the telecentric 3D measurement system. It has three parts: the optical imaging system, projection system, and object space. The optical imaging system consists of a telecentric lens (Net-Gmbh L-VS-LTc series) with a selectable magnification (0.75\(\times\), 2\(\times\), or 4\(\times\)) and a high-speed area scan camera (Mikrotron EoSens 12 CXP+) with a resolution of 4096 (H) \(\times\) 3072 (V) pixels and image field having a diagonal of 32.6 mm. The camera pixel size is 4.5 \(\upmu\)m \(\times\) 4.5 \(\upmu\)m, which corresponding to the lateral sampling for the selectable magnification 0.75\(\times\), 2\(\times\), and 4\(\times\) are 6 \(\upmu\)m, 2.25 \(\upmu\)m, and 1.13 \(\upmu\)m respectively. The camera can be set to a smaller ROI so that only the pixel information within the ROI is stored in the memory, and the frame rate can be up to 10,100 fps with an active sensing area of 4096 (H) \(\times\) 50 (V) pixel. The measuring speed is determined by the frame rate multiplied by the lateral sampling, which can be up to 60 mm/s (0.75\(\times\)), 22.5 mm/s (2\(\times\)), and 11.25 mm/s (4\(\times\)) respectively. The projection system comprises a telecentric lens (ES Telecentric Lens ESCM35F-42 AL/1.048\(\times\)) and 250 W metal halide light source with lightline lenses (A08806 aperture lenses dimension 3\({}^{\prime\prime}\)). The projection system can project a sinusoidal fringe, and each bright stripe is parallel. Sinusoidal gratings with 4, 5, or 8 lines/mm density can be selected to flexibly and optimally introduce the phase-shifting algorithm into the bump measurement for wide ranges of heights and pitches. The object is placed on a high-precision stage with both \(x\) and \(y\) axial movement range of 300 mm.  
Figure 1: Telecentric 3D measurement system: (**a**) schematic diagram and (**b**) photo.  
A normal incidence reflectometer sensor was implemented on top of the sample to measure the PL thickness. The spectrum is measured in the broadband wavelength range of 375-780 nm. The normal incidence measurement largely relies on the interference between light scattered from different locations on the target structure. The reflectance spectrum typically has regular oscillations over a large wavelength range. The limiting factor in this case is the pixel resolution of the charge-coupled device (CCD) detector, which is about 0.4 nm at the upper wavelength limit of 780 nm.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 3 FDD of Railway S&C Systems
### Model-Based FDD Methods for Railway S&C Systems
#### 3.1.3 FP Methods  
With promising results for RUL prediction of 10 real S&C systems, [PERSON] et al. [82] proposed a model-based prognostic technique based on an autoregressive moving-average (ARMA) model. Tension, compression forces, DC current, voltage in electric circuits, distance between the stock rail and switch rail of railway turnout systems, and the linear position of the switch rails were measured for both normal to reverse and reverse to normal movements of S&C systems. The authors considered only prediction of the contaminated slide chair failure mode. They artificially induced the fault by drying the three farmost slide chairs (initial failure state), and the healthy (fault-free) state was when all 12 traverses were oily.  
Figure 12: AAMSET-based FDD scheme [80].  
From the above reviewed literature, the lack of papers applying a model-based FDD method for S&C systems is obvious. Further, very few among these published papers [12; 74; 82] actually applied and used the residuals principle for detecting and then diagnosing S&C system faults. Thus, there is an opportunity for researchers to consider applying model-based methods to deal with FD, FDD, and/or FP of the different possible fault modes (previously shown in Figure 9) in railway S&C systems.  
Perhaps the major difficulties that researchers can face when applying a model-based FDD technique for S&C systems can explain the lack of research work in this field contrary to expectation. These difficulties can arise from the amount of potential failure modes and their predictability in railway S&C systems (as previously reviewed in Section 2.4), or from the different drawbacks of model-based techniques. These include the fact that the accuracy of the developed model directly affects FDD process performance, and that the construction of high-fidelity mathematical models from physical principles of such a complex system (i.e., a railway S&C system) can become very complicated, time-consuming, and even sometimes unfeasible [83], in addition to the need for much prior knowledge about real systems before model development. Compared with model-based FDD techniques, data-driven FDD methods are generally more practical, in that there is no need to build a reference model (i.e., less prior knowledge required, no need for model accuracy validation to be applied for real-world applications, etc.), such that more research works have been based on data-driven FDD techniques for detecting and diagnosing (and sometimes predicting) the possible faults in S&C systems; more details are provided in the following subsection.
## 2 Materials and Methods  
### Materials  
The basic engineering properties of the marine clay are given in Table 1. The marine clay from Jeram, Selangor, Malaysia, was collected from trial pits at a depth of 3 m. Per the Unified Soil Classification System (USCS), the clay is classified as clay of high plasticity (CH). The plasticity characteristics, grading, standard compaction, and particle density tests were conducted as per [41] standard specification. Silica (SiO\({}_{2}\)), alumina (Al\({}_{2}\)O\({}_{3}\)), and ferric oxide (Fe\({}_{2}\)O\({}_{3}\)) were the dominant oxides in the clay, constituting about 75.84% of the total concentration. Quartz and montmorillonite were the dominant clay minerals, with kaolinite, illite, and feldspar occurring in minor phases [35]. The geotechnical characteristics of the clay have been investigated in the past. The results suggest that the clay possesses suitable geotechnical characteristics in terms of grading, plasticity characteristics, and specific gravity, to be used in the construction of a landfill liner material [3].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Property** & **Value** & **Standard Method** \\ \hline Sand (\%) & 18.1 & [41] \\ Silt (\%) & 43.5 & [41] \\ Clay (\%) & 38.4 & [41] \\ D\({}_{50}\) (mm) & 0.006 & - \\ Fines content (\%) & 81.9 & - \\ Liquid limit (\%) & 77.8 & [41] \\ Plastic limit (\%) & 34.4 & [41] \\ Plasticity index (\%) & 43.4 & [41] \\ Activity & 1.1 & - \\ MDD (g/cm\({}^{3}\)) & 1.50 & [41] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic engineering characteristics of the marine clay.  
MAHA Chemicals Sdn Bhd Selangor, Malaysia, supplied the olivine used in the investigation. The olivine had a coarse sand particle size in its received state, which required more grinding to increase the olivine and clay reactivity before adding to the soft marine clay. The significant oxides of the olivine obtained from X-ray fluorescence (XRF) analysis are presented in Table 2. Alpha Gas Solution Sdn. Bhd. Selangor, Malaysia, supplied the CO\({}_{2}\) gas (concentration of 99.9%) to carry out the experiments on carbonation.
## 2 Material and Methodology
### Test Set-Up and Loading Procedure  
A hydraulic horizontal testing frame of 1000 kN capacity, as shown in Figure 9, at Deakin University's structural lab was used to carry out the push test. The equipment consists of a horizontal hydraulic jack fastened to a rigid frame. The horizontal hydraulic jack is connected to the MTS machine to control the displacement and speed of the applied load over the specimen. It was noticed that the centreline of the vertical loading plate attached to the loading jack was above the machine top frame base by 260 mm, which may cause some inclination if the specimen is not positioned close to the centre level of the vertical press plate. As shown in Figure 10, a structural frame was designed to lift the samples and keep the vertical loading plate's centreline and the profiled sheet's centroid aligned. Figure 11 presents a side view of the push test set-up. As shown in Figure 11, a horizontal loading steel plate to apply horizontal force, marked no. 3, was fabricated and directly fixed to the centre of the vertical loading plate, marked no 1. The horizontal loading steel plate's dimensions, as presented in Figure 12, were designed to evenly distribute force on the concrete side in the longitudinal direction. According to the manufacturer's design guidelines, the horizontal push plate was pointed at the centroid of the profiled sheet, which was 15.5 mm above the profiled sheet. The horizontal loading steel plate was applied directly on the vertical push plate, marked no 4 in Figure 11, and was positioned ahead of the concrete block. A vertical end steel support, marked no 8 in Figure 11, backed the specimen, and heavy-duty clamps, as shown in Figure 13, were used to attach the sample to the structural lift frame and to ensure no movement occurred during the test.  
The mechanism of the push test was to apply a horizontal load on the concrete block to shear it off from the profiled steel sheet. During the testing procedure, a longitudinal shear resistance was developed at the concrete-profiled sheet interface, due to the chemical adhesion property of concrete and mechanical interlock and frictional resistance. A low loading rate of 0.5 mm/min was applied in the displacement-controlled mode to precisely measure the load-slip behaviour of the composite slab.  
Figure 9: Hydraulic horizontal testing frame 1000 KN.  
During the push test, the concrete end slip was measured using a high-precision laser sensor, as shown in Figure 14, to confirm the results obtained from the machine. The displacement results obtained from the MTS machine and the laser sensor were agreed upon.  
Figure 10: Structural lift frame (dimensions are in mm). (**a**) Plan view. (**b**) Side view. (**c**) 3D view.  
Figure 11: The layout of the push test set-up (dimensions are in mm).  
Figure 12: Horizontal loading steel plate for pushing (dimensions are in mm).  
Figure 13: Heavy duty clamps.
## 4 Simulation and Analysis
### System Performance of CPCE-UACE under Different Weight Factor  
The performance of the entire CPCE-UACE algorithm is evaluated in the simulation and compared with \(N\)-best, No-CoMP, and Threshold user association schemes. No-CoMP scheme means each UE, no matter where it is located, can be associated only to the BS with the best RSRP. Threshold scheme allows a specific UE to be associated with multiple BSs whose RSRP is better than a given threshold [16]. We also assess backhaul load and network throughput of CPCE-UACE algorithm under a different weight factor \(\mu\) (increases from 0 to 1 by step of 0.5). Generally speaking, the more BSs each UE in the network can be associated with, the better throughput can be achieved, while heavier the backhaul load will be. Fortunately, the proposed CPCE-UACE algorithm can balance backhaul load and network throughput of CPCE-UACE.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Time Delay** & **Backhaul Load** \\ \hline Random & high & high \\ MPC & low to high & low to high \\ CPCE & low & low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of algorithms in terms of delay and backhaul load.  
Figure 6: Backhaul load under different storage capacity of BSs (\(\gamma=1\)).  
Figure 7: Normalized time delay under different storage capacity of BSs (\(\gamma=1\)).  
throughput by carefully selecting a weight factor \(\mu\). As shown in Figures 8 and 9, network throughput and backhaul load of CPCE-UACE decreases as the weight factor \(\mu\) grows. When \(\mu\) is very small (less than 0.05), throughput of CPCE-UACE is significantly better than the others, but the load of it is also outstandingly heavy. On the other hand, when \(\mu\) is as large as 0.5, both throughput and the backhaul load of CPCE-UACE is lowest in the four schemes considered in the simulation. As a result, we can narrow the range of an optimal \(\mu\) that perfectly balances network performance in terms of the two aspects into \([0.05,0.5]\). We consider \(\mu=0.1\) as the almost optimal weight factor due to relatively high throughput, as well as the low backhaul load, of CPCE-UACE as shown in Figures 8 and 9.
## 3 Simulation-Based System Architecture
### Requests Queue Scheduler
#### 3.4.2. Relocation of RP Vehicles for Serving LL Requests  
As explained previously in Section 3.4.1, _LL_ requests are placed ahead in the list of events to be processed later. Ideally, an RP vehicle should arrive at the very same moment the client is leaving the PT system to pick him up and take him to his final drop-off point (its original destination). If this does not happen, the client may have to wait for a vehicle, raising the possibility that this LL request cannot be served in time due to the non-availability of an RP vehicle close enough to the final PT-AN where the client must be picked up. In order to prevent as much as possible such undesired situations, a vehicle relocation strategy has been adopted, and within a certain amount of time in advance, an event for triggering the relocation of RP vehicles is placed in the simulator events list. This event will make that an RP vehicle will start moving to the LL pickup location, keeping its capability of performing new PUDOs in between, as long as it arrives at the stop in the desired instant to pick up the client. To determine which vehicle should be relocated, a heuristic is developed taking into account (a) the client's arrival time at the destination, (b) the travel time to reach the PT-AN (nearest vehicle) and (c) the possible waiting time of the relocated vehicle.