esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
In this paper's investigation of the effects of varying noise levels and varying lighting levels on speech and gesture control command interfaces for aerobots, a custom multimodal speech and visual gesture interface were developed using CMU sphinx and OpenCV libraries, respectively. An experiment was then conducted with 37 participants, who were asked to repeat a series of 12 UAV applicable commands at different UAV propulsion/ambient noise levels, varied in step-sized increase of 5 dB from 55 dB to 85 dB, for the first part of the experiments. For the second part of the experiment, the participants were asked to form finger counting gestures from one to five, under different lighting level conditions from 10 Lux to 1400 Lux, lighting colour temperatures of white (5500 K), yellow (3500 K), and mixed, and on different backgrounds of green, blue, and white, and the quality of the gesture formed was rated on a scale of 1-10. The results of the experiment were presented, from which it was observed that speech recognition accuracy/success rate falls as noise levels rise. A regression model was developed from the empirical observation of nearly 3108 speech command utterances by 37 participants. Multi-word speech commands with primary and secondary modifier parameters were thought to be more reliable and effective than single-word commands due to the possibility of syntax error checking. In addition, it was observed that some speech command words were more noise resistant than others, even at higher noise levels, and that a careful selection of these as part of the speech command phrase could improve the robustness and accuracy of speech recognition at such high noise levels. Speech, based on the custom CMU Sphinx ASR system developed, is not an effective means of control interaction with an aerobot beyond 75 dB noise level, as the speech control interaction becomes very unreliable due to poor recognition. There is a need to push the upper limit beyond 75 dB to at least 80 dB or more, based on current UAV noise ratings (Table 1). Suggestions were made on how to push this limit. From the results of the gesture-lighting experiment, the effects of both lighting conditions and the environment background on the quality of gesture recognition were almost insignificant, less than 0.5%, which means that other factors such as the gesture capture system design and technology (camera and computer hardware), type of gesture being captured (upper body, whole body, hand, fingers, or facial gestures), and the image processing technique (gesture classification algorithms), are more important in developing a successful gesture recognition system.
## 3 Optimization Methods
### Differential Evolution Algorithm
#### 3.2.1 Mutation  
After initialization, a mutant vector corresponding to each target vector (parent) \(\textbf{X}_{i}^{t}\) in the current generation (\(t\)) is obtained according to the mutation operation. Numerous scholars have proposed different mutation operation methods. The six most widely used mutation strategies are described below:  
DE/rand/1:  
\[V_{i}^{t}=X_{r_{1}}^{t}+F(X_{r_{2}}^{t}-X_{r_{3}}^{t}) \tag{19}\]  
DE/best/1:  
\[V_{i}^{t}=X_{best}^{t}+F(X_{r_{1}}^{t}-X_{r_{2}}^{t}) \tag{20}\]  
DE/rand/2:  
\[V_{i}^{t}=X_{r_{1}}^{t}+F(X_{r_{2}}^{t}-X_{r_{3}}^{t})+F(X_{r_{4}}^{t}-X_{r_{5 }}^{t}) \tag{21}\]  
DE/best/2:  
\[V_{i}^{t}=X_{best}^{t}+F(X_{r_{1}}^{t}-X_{r_{2}}^{t})+F(X_{r_{3}}^{t}-X_{r_{4 }}^{t}) \tag{22}\]  
DE/rand-to-best/1:  
\[V_{i}^{t}=X_{i}^{t}+K(X_{best}^{t}-X_{i}^{t})+F(X_{r_{1}}^{t}-X_{r_{2}}^{t}) \tag{23}\]  
DE/current-to-rand/1:  
\[V_{i}^{t}=X_{i}^{t}+K(X_{r_{1}}^{t}-X_{i}^{t})+F(X_{r_{2}}^{t}-X_{r_{3}}^{t}) \tag{24}\]  
where \(i\) represents an individual in the population, the indices \(r_{1},\ r_{2},\ \ r_{3},\ r_{4}\) and \(\ r_{5}\) are mutually exclusive integers randomly generated within the range \([1,\,N{\rm p}]\) and \(r_{1}\
eq r_{2}\
eq r_{3}\
eq r_{4}\
eq r_{5}\
eq r_{i}\). \(\textbf{X}_{best}^{t}\) is the best individual vector with the best fitness function in the population at generation \(t\). \(\textbf{X}_{r1}-\textbf{X}_{r2}\) is the difference vector, \(K\) is randomly selected within the range \([0,\,1]\), and the mutation factor (scale factor) \(F\) is a positive control parameter for scaling the difference vector and is generally selected within \([0.4,\,0.99]\).
# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 2 Materials and Methods  
### Literature Search  
The purpose of this review was to identify challenges and issues that were found by previous research regarding AR-based tools applied to orthopedic surgery procedures.  
To find the published research articles, a comprehensive search on the following electronic databases was performed: PubMed, Google Scholar, Scopus, IEEE Xplore, Science Direct, and Wiley Online Library. Two reviewers conducted the search in online digital libraries in January 2022.  
For our work, only English-written articles published between January 2018 and December 2021 inclusive were considered. The choice of limiting the time span to the last four years stems from the desire to identify challenges and open questions regarding the state of the art of AR-based technologies in orthopedics, thus excluding solutions that use tools that are now up-to-date, or whose technicalities have already been solved.  
Article searching was performed through the use of the following keywords: Orthopedic; Orthopedics; Orthopaedics; Orthopaedic; Orthopedic Surgery; Augmented Reality; Mixed Reality; Extended Reality; Extended Medicine. All the research terms were analyzed isolated or combined between them.  
A PICOS approach [22]--Patient (P); Intervention (I); Comparison (C); Outcome (O), and Study design (S)--was used to formulate our research questions.  
According to the aim of our review, only articles describing orthopedic surgical procedures (P) assisted by AR (I) were selected and subsequently analyzed in terms of AR utilization, in order to find which are the most relevant challenges to the scientific community (O). To this end, randomized and non-randomized controlled studies, such as prospective, retrospective, cross-sectional, observational studies, case-series, pilot, and case-control studies, were included (S).  
Our research questions were focused on identifying the most relevant aspects for our purpose:  
RQ1: In which orthopedic surgical procedures has the use of AR technology been most studied?  
RQ2: Which hardware equipment and visual tools have been most used in AR-supported orthopedic procedures?  
RQ3: Which are the recording and tracking methods that have been most used in the development of AR applications for AR-supported orthopedic procedures?
## 3 Results and Discussions
### Stereo-Microscopic Investigation
#### 3.3.2 Laser Cutting Samples  
The microscopic investigation was performed for edges cut at cutting angles of 15\({}^{\circ}\) and 60\({}^{\circ}\), for which high roughness parameters were recorded. The processing at 0\({}^{\circ}\) was used for the comparison. The phenomena of smoothing the surface in the late wood area, as a result of melting the wood material, were also observed by stereo-microscopy analysis.  
Figure 18: Microscopic images of the CNC routed surfaces for the most disadvantage processing angles and feed speeds as compared with processing angle of 0\({}^{\circ}\): (**a**) processing angle of 0\({}^{\circ}\) and 2 m/min feed speed (reference); (**b**) processing angle of 15\({}^{\circ}\) and 2.5 m/min feed speed; (**c**) processing angle of 30\({}^{\circ}\) and 3 m/min feed speed; (**d**) processing angle of 60\({}^{\circ}\) and 4 m/min feed speed.  
The cut edges were investigated with 60\(\times\), 120\(\times\), 180\(\times\) and 240\(\times\) total magnifications. In Figure 19, only images with 60\(\times\) and 180\(\times\) magnifications are shown.  
At the microscopic level, in Figure 19, the difference between the early wood surface and the late wood surface is more visible for the cutting angle of 60\({}^{\circ}\), given the more cross-cut orientation of the processing. The latewood zone, where the melted material such as honey topping appears, is smoothed compared to the earlywood zone. This explains the smoothed irregularities in the latewood areas of the roughness profiles. On the other hand, at the border between growth areas (earlywood in the new ring and latewood from the previous), a dark line is visible, indicating a totally carbonized layer showing as gap in the  
Figure 19: Microscopic images of laser cutting surfaces: (**a**) cutting angle of 0\({}^{\circ}\) and 60\(\times\) magnification; (**b**) cutting angle of 0\({}^{\circ}\) and 180\(\times\) magnification; (**c**) cutting angle of 15\({}^{\circ}\) and 60\(\times\) magnification; (**d**) cutting angle of 15\({}^{\circ}\) and 180\(\times\) magnification; (**e**) cutting angle of 60\({}^{\circ}\) and 60\(\times\) magnification; (**f**) cutting angle of 60\({}^{\circ}\) and 180\(\times\) magnification.  
roughness profile (Figure 13). The wood structure corresponding to this carbonized area is clearly recognizable and belongs to the first cells in the earlywood, where the laser seems to have removed more material in comparison with the remaining area of the growth ring. This explains the waviness observed in the measured profiles (Figure 13).  
In conclusion, processing with CNC at 2 m/min gives similar processing roughness as the laser, with the advantage of a surface unchanged in color and not affected by carbonization. However, if processing under angles different than 0 is required, the laser offers a better quality due to the smoothing effect produced by material melting.
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
###### Abstract  
We present a comprehensive database of near-shore wind observations that were carried out during the experimental campaign of the RUNE project. RUNE aims at reducing the uncertainty of the near-shore wind resource estimates from model outputs by using lidar, ocean, and satellite observations. Here, we concentrate on describing the lidar measurements. The campaign was conducted from November 2015 to February 2016 on the west coast of Denmark and comprises measurements from eight lidars, an ocean buoy and three types of satellites. The wind speed was estimated based on measurements from a scanning lidar performing PPIs, two scanning lidars performing dual synchronized scans, and five vertical profiling lidars, of which one was operating offshore on a floating platform. The availability of measurements is highest for the profiling lidars, followed by the lidar performing PPIs, those performing the dual setup, and the lidar buoy. Analysis of the lidar measurements reveals good agreement between the estimated 10-min wind speeds, although the instruments used different scanning strategies and measured different volumes in the atmosphere. The campaign is characterized by strong westerlies with occasional storms.  
coastal; experiment; lidar; near-shore; offshore; wind resources +
Footnote †: journal: Remote Sensing
# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
## 3 Junction-Aware Vehicle Selection Scheme  
In this section, the proposed Junction-Aware Multipath Video Streaming (JA-MVS) scheme is presented considering the various vehicle positions at the junction area. The multipath transmission considers the junction-aware concept in hop-by-hop transmission. The consideration of road junctions during transmission enhances routing decisions to achieve quality video streaming delivery. A number of multipath transmission schemes have been suggested in some research studies, but few studies have considered road features such as junctions in hop-by-hop transmission. Some of these studies use the junction point as a forwarding area without considering the freshness of the direction of the vehicle. In a junction area, the location and direction of the vehicle are essential when the vehicles' signal coverage extends to a junction area, because the selected NFV might change its direction of navigation, which could lead to a video packet drop, thereby affecting the quality of the video streaming. Therefore, there is a need to explore the characteristics of the junction area including the vehicles exiting the junction, vehicles at the junction and vehicles before the junction. The vehicles before the junction are considered to be the vehicles at the end of the road, which is at the traffic light. The vehicles at the junction do not have a road-ID but might be in the direction of the Destination Vehicle Node (DVN), while the vehicles after the junction have recently changed their road-ID, navigating towards the direction of the DVN. Considering the vehicles at a road junction, three cases have been considered, as stated in the aforementioned discussion.  
In the first case, the vehicles which have exited the junction are preferred and considered to be vehicles that have already chosen their direction of navigation. Hence, their direction is known and probably closer to the DVN; hence they are employed as the NFVs. In the second case, a vehicle at the end of the road, which is close to the junction, is the second preferred selection area for NFVs, because a vehicle before the junction might need to wait for a traffic light; thus, its direction does not change instantly and it can be used to forward video streaming to the direction of the DVN. The third case is employed if there is no suitable vehicle that has exited the junction and no vehicle at the end of the road; then, the vehicle inside the junction area is selected. However, the third case is only employed if the first and second cases do not occur. Therefore, in the proposed JA-MVS, the vehicles that have already exited the junction or intersection area are considered based on the freshness of their location, direction and speed information. Consequently, the JA-MVS scheme is in two stages: information exchange and a video streaming data forwarding stage in the junction area. The detailed discussion of the JA-MVS scheme is given in the next subsections.
## 4 Arms and End Effectors for Aerial Manipulation
### Design of End Effectors for Holding/Grabbing and Manipulation  
The devices shown in Figure 1 are commercially available and intended for installation on the power lines by human workers. To overcome this, new types of end effectors need to be designed to enable the autonomous installation of these devices. Typically, in this scenario, there are two main functions of end effectors: holding/carrying the devices to be installed, and fixing them to the power line. The installation procedure depends on each particular device, as mentioned in Section 2.2. This can be done by applying sufficient force (clip-type), twisting the device around the power line (helical), or tightening the screw on the device (electrical spacer). After the device is fixed to the power line, the end effector must be released and detached from the installed device.  
The two main functions of end effectors can be achieved by two individual end effectors, e.g., one end effector holds and carries the device to be installed, and the other end effector takes care of fixing it to the power line. This approach requires two different manipulators: one for each end effector, and a certain coordination between them. This increases the weight of the whole system and requires more precise manipulation. The advantage of this approach is that the individual end effectors are mechanically simpler and can be used for different purposes, e.g., one gripper type can be used to hold a clip-type bird diverter as well as a helical-type bird diverter.  
The other approach integrates both functions on the same end effector, designed specifically for the device in question. These types of end effectors are more complex and are suited only for operation with one device type. The advantage of this approach is that only one end effector is needed to install (remove) the device to (from) the power line, using only one manipulator, which results in the need for less manipulation precision. The example of such an end effector for the installation of electrical spacers is shown in Figure 4, whereas Figure 5 shows the installation of the clip-type bird diverter.  
Figure 4: End effector for electrical spacers installation.
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 3 Preservation Technologies and Optimization
### Chemical and Biological Preservation
#### 3.2.1 Nanoemulsions  
Nanoemulsions are kinetically stable oil-in-water colloidal systems with droplet sizes of 20-200 nm, typically composed of bioactive oils (e.g., cumin, thyme), water, surfactants, and emulsifiers, such as chitosan or whey protein [22; 23]. Their nanoscale dimensions create a large interfacial area that enhances the solubility, stability, and bioavailability of hydrophobic, antimicrobial, and antioxidant compounds, while the moisture-proof and gas-barrier properties of the colloidal matrix protect these actives from environmental degradation [89].  
Nanoemulsions inhibit fish spoilage through three complementary mechanisms. First, antimicrobial constituents--cuminaldehyde and \(\gamma\)-terpinene from cumin, thymol and carvacrol from oregano and thyme, and eugenol from clove--insert themselves into bac  
Figure 4: Flowcharts depicting coating application, ozonation exposure, and antioxidant diffusion processes [22; 23; 87; 88].  
terial lipid bilayers, increasing membrane permeability, collapsing proton gradients, and causing leakage of intracellular contents, ultimately leading to cell death [90; 91]. Second, the released bioactives bind to and inhibit endogenous spoilage enzymes (e.g., AT-Pases, proteases, dehydrogenases), delaying the autolytic breakdown of myofibrillar and connective-tissue proteins and preserving muscle texture [92; 93]. Third, the encapsulated antioxidants scavenge free radicals and interrupt radical-propagation chains, reducing thiobarbituric-acid-reactive substances (TBARS) and peroxide values (PVs), thereby delaying lipid oxidation and maintaining flavor, color, and nutritional quality [24; 87; 94].  
In refrigerated trials, nanoemulsion-loaded coatings have demonstrated significant preservation effects. Chitosan-based nanoemulsions containing cumin oil reduced _Pseudomonas_ spp. and _Shewanella putrefaciens_ counts by over 2 log CFU/g and lowered TBARS by 60% in sardine fillets over 10 days at 4 \({}^{\circ}\)C [90; 95]. Similarly, thyme-oil nanoemulsions achieved a 1.5 log CFU/g reduction in aerobic bacteria and extended the shelf life of rainbow trout by 8 days under the same conditions [92; 93]. Curcumin- and rosemary-extract nanoemulsions also decreased PV in trout, further demonstrating their antioxidant efficacy [87; 94].  
Nanoemulsions are applied through dipping, spraying, or integration into edible films and coatings. For example, 0.1% nanoemulsion sprays in chitosan or whey-protein films provide a dual barrier against oxygen and moisture while delivering actives in a controlled manner [89; 95]. Optimization of droplet concentration, emulsifier type, and active-loading levels is essential to maximize antimicrobial and antioxidant effects without introducing off-flavors or off-odors [24]. This balance makes nanoemulsions a versatile tool for preserving both high-fat species (e.g., salmon, mackerel) and lean fish (e.g., cod, trout) in refrigerated storage.
## 1 Introduction
The authors in [25; 26] survey current ML techniques used for RIS-aided communications, quoting those ML techniques applied for enhancement. Their research framework compares the ML applications on RISs by the type of algorithm and model architecture used, the type of system model according to the type of inputs and outputs provided, single- and multiple-user scenarios, and the goal of each paper examined. The study in [27] presents a signal processing-based review of research frameworks that merged AI and RIS. The authors divide the study of AI-RIS into different scenarios, such as environmental sensing, channel acquisition, beamforming design, and resource scheduling. The literature review comprises the type of AI algorithm employed and the application scenario. The paper by [PERSON] et al. [28] focuses on the optimization approaches for RIS-aided wireless networks. Among the described techniques, the research enlists several ML algorithms for RIS wireless communications, introducing the novelty concepts of graph, transfer learning, and hierarchical learning. This research summarizes the application of ML on RIS communications, dividing it into different solutions such as the maximization of sum rate/capacity, energy efficiency, phase-shift resolutions, scenarios, channel settings, and the channel state information (CSI) dissemination method. Some constraints about the signal-to-interference noise ratio (SINR), the RIS phase, and computational resources are also stated. The authors also provide each revised framework's drawbacks, difficulties, and application scenarios [31]. In contrast to these previous works, this paper is focused on novel road maps for implementing AI and ML applications in RIS-assisted wireless communication systems and the potential of RIS to enhance the capacity and quality of communication links, showing the convergence of ML and RIS to unleashes the potential of both technologies towards an optimal design of future wireless communication systems. The main contributions of this paper are summarized as follows:  
* We provide an updated list of the research frameworks that apply ML methods, DL architectures, and the underlying learning schemes for RIS-assisted communications. * We present a list of data sources for training the ML algorithms that enhance the performance of the RIS-assisted communication system. We highlight research works that provide the source code for testing and replicability of the revised approaches. * We present an overview of future challenges and opportunities in ML applications for RIS-assisted wireless communication systems, and we provide new directions on ML for designing RIS-assisted communication systems for engineers and researchers. The rest of the paper is organized as follows. Section 2 states the basis of the RIS technology. Section 3 describes the ML frameworks existing in the literature. Section 4 describes the reviewed papers related to the applications of ML in RIS-assisted wireless communication systems. Based on the reviewed papers, challenges and future trends are discussed in Section 5. Finally, the conclusions are shown in Section 6.
## 2 Computational Simulation Details
### Simulation Process  
The simulation process was implemented using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) software. In this study, the simulation process was separated into equilibrium state simulation and uniaxial stretching simulation phases. Firstly, the dynamic equilibrium of 500 ps was performed using the NPT ensemble. The simulation utilized a time step of 1 fs, and the Nose-Hoover thermostat was adopted to control a temperature of 300 K, along with pressure at 0 KPa. In order to guarantee the reliability of the equilibrium analysis, relevant data were used from the last 100 ps of the equilibrium state simulation. Secondly, the uniaxial (Z direction) tensile tests were simulated, to analyze the mechanical performance and stretching behavior of different C-S-H/SiO\({}_{2}\) systems. The uniaxial tensile load strain rate was 0.001/ps during the tensile tests, and the maximum strain was 0.25. The system's temperature and time steps were consistent with the equilibrium state simulation process.