esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Advantages and Disadvantages  
This section discusses the advantages and disadvantages of the proposed methods.  
The advantages of the proposed method in the article are summarized as follows: Firstly, it achieves spoofing detection and direction-finding using low-cost commercial board components. The implementation of spoofer direction finding is no longer limited to expensive array antennas. Secondly, it solves the question of power-on clock error in low-cost receiver boards. Moreover, the issue of low-cost antennas being susceptible to unstable antenna phase centers and multipath effects. Thirdly, the method proposed in the article simplifies the algorithm for solving carrier phase ambiguity.  
The shortcomings of the proposed method in the article are summarized as follows: firstly, in low-elevation situations, the calculation accuracy of DOA is relatively poor, which may be limited by the planar geometric configuration of the antennas in this article. Secondly, due to the simplification of the calculation method for carrier phase ambiguity in the article, both the power-on clock error and the hardware delay error are relative values. If we want to obtain an absolute value, we need to fix the carrier phase ambiguity. The unique determination of ambiguity can also provide assistance in determining the final DOA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\Delta g_{n}\)**Mean (\({}^{\circ}\))** & \(\Delta g_{n}\)**Std (\({}^{\circ}\))** \\ \hline G01 & 4.61 & 1.22 \\ G14 & 4.65 & 1.17 \\ G21 & 4.60 & 1.25 \\ G17 & 4.67 & 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical value of the difference between spoofing signal DOA and true DOA.
## 5 Results and Discussion
### Effect of Baffle Structure on Turbulent Dynamics  
The turbulent kinetic energy and turbulent dissipation rates affect the mixing time and liquid-solid suspension quality. Figures 15 and 16 depict the distribution of turbulent kinetic energy and the turbulent dissipation rate for different relative tooth heights (\(A\)/\(W\)) and different relative tooth widths (\(\lambda\)/\(h\)), respectively. An observation can be made that under all operating conditions, the turbulent kinetic energy and turbulent dissipation rates had the largest peaks near the blade tip, while the turbulent dissipation rate decreased rapidly outside the impeller region. In the area near the impeller (from r/R = 0.4-r/R = 0.6), the sawtooth baffles all improved the turbulent kinetic energy of the fluid in the whole vessel. As the relative tooth height (\(A\)/\(W\)) increased and the relative tooth width (\(\lambda\)/\(h\)) decreased, the greater the turbulent kinetic energy of the fluid, the stronger the turbulent diffusion ability. In contrast, the tooth width had a greater influence on the turbulence parameters, indicating that the mixing of the medium was more homogeneous.  
Figure 14: The pressure distribution behind the baffle.  
Figure 15: The effect of relative tooth height on local radial turbulence dynamics (\(\lambda\)/\(h\) = 0.104). (**a**) The turbulent kinetic energy; (**b**) the turbulent dissipation rate.
# Sustainability in Bio-Based Edible Films, Coatings, and Packaging for Small Fruits
## 2 Edible Films and Coatings: Definitions and Regulations  
Edible films are thin layers of edible material formed into a film that can be placed on or between food products. They act as a barrier to moisture, gases, and solutes, helping to preserve freshness and quality. These films are made from natural polymers like proteins, polysaccharides (e.g., starch or cellulose derivatives), and lipids. Edible coatings, unlike films, are applied directly to the surface of food products. They are thin layers made from substances similar to those used for films. They aim to create a protective barrier that improves shelf life, reduces moisture loss, or enhances appearance (e.g., glossy coatings for fruits or candies) [4].  
The food industry has grown interest in edible films and coatings. When using PubMed to search for and place the keywords \"edible coatings and film in food,\" we found 1591 publications (Figure 3) from 1972 to 2025.  
The regulation of edible films and coatings varies by region but generally focuses on the safety of ingredients, manufacturing processes, and the intended use of food. The U.S. Food and Drug Administration (FDA) regulates edible films and coatings under the Food Additives Amendment of 1958. Ingredients used in these films or coatings must either be Generally Recognized As Safe (GRAS) [22], meaning they are widely recognized by qualified experts based on scientific evidence, or be approved as food additives, meaning they have undergone safety evaluations and received FDA approval. When used appropriately, everyday materials like starch, pectin, and chitosan often fall under the GRAS category [16]. The European Food Safety Authority (EFSA) regulates food-contact materials, including edible coatings. Regulations fall under Regulation (EC) No 1935/2004 [23], which outlines general safety requirements for materials that come into contact with food. Specific approval may be needed for novel or non-traditional materials, mainly when they include functional additives such as antimicrobials or preservatives. The Codex Alimentarius Codex [24], developed by the Food and Agriculture Organization (FAO) and the World Health Organization (WHO), provides international guidelines and standards for food safety, including edible coatings and films. While Codex standards are voluntary, they serve as a reference for national food safety regulations. In summary, edible films and coatings are carefully regulated to ensure their safety for consumption, and compliance with regulatory standards is required for their use in food products.
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 4 Results  
### Experimental Set-Up  
Experiments were conducted offshore at the Daewoo Shipbuilding and Marine Engineering shipyard, commonly known as DSME. In the experiments, the performance of the RHLS was evaluated using four decks in the residence. Figure 9 depicts the experimental area. Each deck has a narrow corridor structure (52.7, 36.4, 50.7, and 67.8 m) in total length. In the tracking accuracy test, Wi-Fi, Bluetooth, and geomagnetic data were used for absolute positioning, and a gyroscope and accelerometer were used as PDR sensors. For learning data construction, three to six Wi-Fi APs and BLE beacons were installed for each deck at approximately 5-10-m intervals, for a total of 21 APs and beacons. Figure 9 shows the details of the test space and the locations of the installed APs and beacons. A Galaxy Nexus was used as a reference device to collect training and test data. The specifications of the computer used in the experiments were as follows: Intel i3-4170 (3.7 GHz, 2C/4T) central processing unit (CPU), Nvidia GeForce GTX 1060 (6 GB) graphics processing unit (GPU), and DDR3 8 GB random-access memory (RAM). The proposed method using LSTM was performed using a GPU. The remainder of the comparison group computations were performed using only the CPU.  
Geomagnetic learning data are the norm values of the \(x\), \(y\), and \(z\) axes and were assigned to the magnet intensity, while the averages were used along with the inclination values. The average values of Wi-Fi, Bluetooth, and magnetic field data in the target area were stored as training data, along with the location coordinates, during the construction of the training database. These previously collected data received an average of 9.86 AP or beacon signals for each fingerprint. An Adam optimizer was used as the optimization algorithm, and the training rate was set from 0.0005 to 0.001. The proposed structure was trained by grouping eight input datasets and repeating it up to 1,000,000 times. For the accuracy test, the test point coordinates were stored in the test data, and the accuracy was calculated using the difference in distance between the test point and ground truth. The coordinates were recorded manually directly at each test point. At the test point, the difference in distance between the coordinates that the algorithm derived and the handwritten coordinates represents the positioning accuracy. Figure 10 shows the radio map collected for each deck in the form of heatmaps according to the collection and signal density.  
Figure 8: RHLS scaling function.  
The test data used in the positioning accuracy experiment were collected continuously along a path. Test data were collected twice for each route to minimize bias according to the test route. Figure 11 shows the test paths for each deck.
###### Abstract  
Due to their extraordinary quality factor and extreme sensitivity to surface perturbations, Bloch surface waves (BSW) have been widely investigated for sensing applications so far. Over the last few years, on-chip control of optical signals through BSW has experienced a rapidly-expanding interest in the scientific community, attesting to BSW's position at the forefront towards on-chip optical operations. The backbone of on-chip optical devices requires the choice of integrated optical sources with peculiar optic/optoelectronic properties, the efficient in-plane propagation of the optical signal and the possibility to dynamic manipulate the signal through optical or electrical driving. In this paper, we discuss our approach in addressing these requirements. Regarding the optical source integration, we demonstrate the possibility to couple the MoS\({}_{2}\) mono- and bi-layers emission--when integrated on top of a 1D photonic crystal--to a BSW. Afterward, we review our results on BSW-based polariton systems (BSWP). We show that the BSWPs combine long-range propagation with energy tuning of their dispersion through polariton-polariton interactions, paving the way for logic operations.  
transition metal dichalcogenides; Bloch surface wave polaritons; non-linear optics +
Footnote †: journal: Applied Physics Letters
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 5 Simulation Experiments and Analysis  
### Simulation Environment  
The longitudinal motion of CAV platoon is simulated in this section, where the leading vehicle will conduct the accelerate and brake to verify the performance of the proposed method. Four methods will be compared, including spacing policy based on three principles, headways obtained from Shapley value, average lexicographic value, and constant space policy, which are abbreviated as Normal spacing policy, Shapley value, AL value and CD policy in the results of the experiments. To valid the performance of the various policy, we simulate two representative scenes, including initial velocity error situation and leading vehicle deceleration situation. The initial velocity error situation is used to simulate the self-adjusting ability of the platoon when where is an initial following error. In this situation, the error of stable speeds shall be within \(-5/+0\) km/h according to the limitation in European new car assessment programme (Euro NCAP) [38]. The leading vehicle deceleration situation could show the ability to ensure safety of the platoon in an emergency braking situation. In this platoon, serial number 1 denotes the leading vehicle while others indicate the following vehicles. The main parameters are shown in Table 1.
## 3 Results and Discussion
### Characterization of MGONCs
#### 3.1.5 TGA-DSC  
TGA-DSC (differential scanning calorimetry) analysis was utilized to measure the weight loss of MGONCs as the temperature increased. The thermal behavior of the MGONCs composite could be divided into three segments, as depicted in the TGA thermogram shown in Figure 8a. The first weight loss, approximately 9.13%, occurred up to 130 \({}^{\circ}\)C, which attributed to the evaporation of physically adsorbed water moieties. The second degradation step resulted in a weight loss of about 18.29% up to 340 \({}^{\circ}\)C. This weight loss was mainly caused by the breakdown of the biopolymer skeleton structure of GO within the prepared composite. Subsequently, the third degradation step (30.58%) was observed between 340 and 580 \({}^{\circ}\)C, which was attributed to the presence of metal oxides within the biopolymer matrix.  
Afterward, no significant change was observed, indicating the thermal stability of the prepared composite. The DSC thermogram of MGONCs shown in Figure 8b revealed several endothermic peaks. The first endothermic peak, observed at 50-130 \({}^{\circ}\)C, corresponded to the release of surface free water molecules. The second endothermic peak at 200 \({}^{\circ}\)C was associated with the degradation of polymer backbone chains within the composite. The third endothermic peak occurred at 475 \({}^{\circ}\)C, indicating that the MGONCs exhibit good thermal stability [39].
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
## 2 Materials and Methods
### Preparation of the Fiber Optic Platform  
The optical platform was based on a multimode POF with a characteristic D-shaped sensing region, obtained by erasing the cladding and partially the core of the POF, held in a specially designed resin support, which produced a flat surface (see Figure 1) [13; 14; 36]. One half of the fiber was erased and the exposed POF core was 1 cm long. A multilayer structure was built up over the exposed core with a buffer layer (a photoresist of high refractive index with respect to the core, 1.5 \(\upmu\)m thick), a thin metal film (gold, 60 nm thick) and, finally, an MIP layer as a specific chemical receptor for 2-FAL detection. Figure 1b,c show two typical sensing regions obtained by two MIP layers with different thickness.  
In particular, the buffer layer (Microposit photoresist, MicroChemCorp., Westborough, MA, USA) was deposited on the exposed core, taking advantage of the flat shape by dropping and spinning at 6000 rpm. The so obtained layer was 1.5 \(\upmu\)m thick and the gold layer was deposited over it by sputtering (SCD 500, Leica Microsystems, Wetzlar, Germany), forming a nanofilm 60 nm thick.  
Figure 1b,c show a schematic view of SPR-POF probes, covered by MIP layers of different thicknesses, in which the penetration of the plasmonic wave in the liquid above, in the case of thin and thick MIP layers, is schematically displayed. The layers with different thickness could be obtained by spinning the prepolymeric mixture at different rates, or by depositing multiple layers of MIP.
###### Abstract  
A study was conducted to develop a porous aerostatic rectangular thrust bearing model, with the aim of assessing how different operational conditions and geometric factors influence its static capabilities. Initially, the Reynolds equation was analytically solved. Subsequently, simulations were performed on the rectangular air bearing model. Analyzing the impact of throttle hole configurations, air film thickness, orifice size, and supply pressure revealed their significant effect on the bearing's load capacity, air consumption, peak airflow speed in the air film gap, and rigidity. Experimental validations were further conducted on manufactured bearings, corroborating the theoretical findings. It was observed that extending the length of the rectangular throttle hole array progressively increases gas consumption and diminishes stability, while the load capacity and stiffness initially surge then taper off. A thinner air film enhances load capacity and reduces gas flow, contributing to increased stability. Conversely, enlarging the orifice diameter boosts both load capacity and stability but escalates mass flow and diminishes stiffness. Elevating gas supply pressure enhances load capacity, flow rate, and stiffness, albeit at the cost of reduced stability. A comparative analysis among experimental data, finite element analysis, and analytical solutions showed strong congruence, affirming the precision of the latter two methods for predicting the bearing's performance. This investigation aids with refining bearing design for precision devices and offers insights to enhance bearing efficiency and lifespan and to reduce friction and wear. Given its lower computational demands, the analytical approach provides a rapid means to assess static characteristics, underscoring its utility alongside finite element techniques for optimizing aerostatic bearing parameters.  
caputational modeling; gas bearings; load modeling; mathematical model; numerical models +
Footnote †: journal: Applied Sciences
# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
## 2 General Target Surveillance
### Optimization Objectives
The proposed algorithm does not require global information and can converge to a local maximum within a finite number of steps. The authors conducted simulations with a real data set to demonstratethe effectiveness of their method. For high-quality surveillance of groups of moving pedestrians or vehicles on given paths with unknown speeds, the authors of [24] proposed a computationally simple algorithm to determine the deployment of multiple UAVs. The quality of coverage is calculated based on the distances between the UAVs and targets (the closer, the better). The proposed algorithm only requires local information, with minimal involvement of the central station. The authors proved the local optimality of the proposed algorithm. **Optimizing the energy efficiency:** For the joint objectives considering energy consumption and the number of covered targets of surveillance UAVs, the authors of [25] designed a control system containing a movement decision-maker and proposed a decentralized algorithm to reactively determine a fleet of UAVs' positions in 3D space that contributes more to the coverage. Their simulations indicated that the proposed method achieves better network lifetime and target coverage. The authors of [25] only considered targets on the ground and did not consider possible blockage caused by some high-rise buildings. The authors of [26] studied the application of area-monitoring UAVs. The authors considered the problem of scheduling UAVs to perform either monitoring or recharge tasks so that the network lifetime is maximized given a set of UAVs and a charging depot on the ground.The energy-efficient deployment of a UAV for covering ground targets was discussed in Reference [27]. The power received from a UAV at a target is calculated as a function of the target's horizontal distance from the UAV and the UAV's altitude [27]. The optimal position in the horizontal and vertical dimensions was then decoupled in [27]. The horizontal position was determined using the centre of the smallest circle that covers the given set of targets, and the vertical position was determined using the least amount of transmitting power to cover that circle. Another recent paper [28] used a single UAV to cover a wireless sensor network. The main contributions here were scheduling a wake-up time for sensor nodes and planning the UAV's trajectory so that the maximum energy consumption of sensor nodes is minimized rather than minimizing the UAV's energy consumption.
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 5 Machine Learning Adaptability for WSNs
### Unsupervised Learning  
In this, there is no predetermined training model that is used to acquire knowledge; rather, it learns as it goes along. Moreover, no designations are offered. WSN utilizes these algorithms for data clustering and aggregation [34]. \"K-means\" plays a key role in \"clustering and component analysis\".  
_Principal Component Analysis:_ To improve the scalability of the network and reduce the overall power consumption, data aggregation plays a significant role in WSNs. The \"Principal Component Analysis (PCA)\" [35] is an appropriate technique for pre-processing data from sensor networks, i.e., for performing dimensionality decreases. To minimize energy consumption and maximize network lifetime, the PCA-guided routing algorithm explores the principal component analysis (PCA) technique to determine the shortest paths between nodes and cluster centers in a WSN.  
_K-means clustering:_ When it comes to partition-based algorithms for clustering data, \"K-means clustering algorithmic [36]\" is one of the most widely used models. By prioritizing the CHs with the highest energy, the K-means algorithm ensures that the clusters are well-balanced and have a low overall energy footprint [37; 38; 39].  
_Hierarchical clustering:_ Similar objects are grouped together into clusters using the hierarchical clustering technique, which determines the top-down or bottom-up order of each cluster. Divisive clustering, another name for top-down hierarchical clustering, involves splitting a huge single division recursively until there is one cluster for each observation. Agglomerative clustering, also known as bottom-up hierarchical clustering, allocates each observation to its cluster based on density functions. The hierarchical clustering approach is simple to use and does not require any prior knowledge of the number of groups.  
_Fuzzy-c-means clustering:_ [PERSON] created fuzzy-c-means (FCM) clustering, also known as soft clustering, in 1981. It makes use of fuzzy set theory to assign an observation to one or more clusters. Using similarity measurements like intensity, distance, or connectedness, this technique finds clusters. The algorithms may be taken into consideration for one or more similarity measures, depending on the applications or datasets. To determine the ideal cluster centers, the algorithm runs over the clusters iteratively. Regarding the overlapped datasets, FCM yields the best grouping when compared to K-means.
# An Advanced DEM-FEM Method for Herringbone Gear in Shot Peening
## 1 Introduction
[31] examined the effect of processing parameters on surface topography, discovering that surface roughness initially increases sharply with shot peening time and then gradually stabilizes. [PERSON] et al. [29] studied the fatigue performance of components after shot peening, finding that although increased roughness reduces fatigue life, the overall fatigue life of components increases due to the enhancement from residual stress. [PERSON] et al. [13] studied the grains and morphology of AISI 316L after shot peening, finding finer grains at high coverage rates, with roughness initially increasing and then decreasing as coverage increases. Shot peening models include single-particle models [36; 37], array multi-particle models [34], and random multi-particle models [32; 38; 39]. For planar specimens, finite element models for shot peening can be directly established using finite element software, allowing conventional research methods to study the shot peening process. However, the complex geometry of herringbone gears makes it challenging to study the shot peening process using conventional finite element models. Capturing the collision patterns between numerous high-speed shots and the gear surface is crucial. Therefore, this paper employs a stochastic multi-particle model for finite element simulation of herringbone gears, using a combined DEM-FEM approach to study the shot peening process. By establishing a DEM for shot peening of herringbone gears, incorporating the actual geometric design parameters of the gears, a more realistic simulation of the shot peening process is achieved, exploring the interaction patterns between shots and complex surfaces during shot peening. The interaction patterns obtained from the DEM are applied to the shots in the FEM, facilitating the study of surface integrity parameters of herringbone gears. The FEM model focuses on small local element regions of the gear surface, simulating the strengthening effects at different surface positions by applying impact information to the shots. The DEM-FEM coupled method established in this paper aims to simulate the shot peening process of herringbone gears, investigating the correlation between shot peening process parameters and surface integrity parameters, providing important references for optimizing shot peening process parameters, and guiding the shot peening strengthening of herringbone gears.