esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results from Design Phase and Validation Phase
#### 3.1.1 Comparisons among 2D FEA and 3D FEA Mechanical Properties Testing  
In this section, the results from the design phase (Section 2.2.1) and the validation phase (Section 2.2.2) are reported and compared. In particular, the comparison is made through the graphs reaction force-foot rotation, calculated as explained in Section 2.2.2. The results and comparisons are given for both plantarflexion (top) and dorsiflexion test (bottom), as shown in Figure 6.  
Since the results of simulations and mechanical properties testing were similar for all five prostheses, for reasons of clarity and comparison, in this section only the results for the prosthesis optimized for a user's weight of 60 kg are shown and compared. The curves of dorsiflexion are very similar to each other, while the same cannot be said for those of plantarflexion. Considering the plantarflexion load condition, the 2D FEA and 3D FEA curves diverge from the test curve obtained from the mechanical properties testing of the prototype. The divergence could be caused by the following reasons: (i) the imperfect mounting of the foot prosthesis on the test setup; (ii) the imperfect CAD and FE modeling of the foot shell; (iii) manufacturing defects that can cause slight variations in the geometry of the physical prototype; (iv) the joints, such as the spherical ankle joint, hinge joints between the link and tube connector, and between the link and spring holder, modeled without friction and thus approximated; (v) the imperfect orientation of the fibers during the production phase of the elastic composite components; (vi) the heel of the foot shell not fully covered by the platform; (vii) the interference between the spring holder part and the  
Figure 5: Treadmill test: the participant’s movements are measured thanks to the reflective markers placed on their skin. The participant wore a harness for safety.  
foot shell, as also shown in Figure 7. Since all five prostheses had the same configuration of elastic elements and with difference only in the rolling sequence of the carbon fiber prepreg layers used to produce the same elastic elements, the problem of plantarflexion was found in all five prosthetic devices.
# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 4 Experimental Results and Analysis  
The seismic signal denoising experiments utilizing the DnCNN-based model outlined in this paper were bifurcated into two principal phases: model training and denoising application.  
During the training phase, we input seismic signals corrupted by noise, with the noise itself serving as the label for our network's predictions. The loss function quantifies the discrepancy between the predicted noise and the actual noise. Through iterative forward and backward propagation, the network's parameters were adjusted to minimize this loss, thereby learning to map noisy seismic signals to their noise components. This learned mapping constituted our trained model.  
In the denoising phase, once the model was adequately trained, it was fed noisy seismic signals, and it output the estimated noise component. By subtracting this estimated noise from the original noisy signal, we extracted the denoised seismic signal, achieving the desired noise reduction.  
For these experiments, TensorFlow was employed to build the model, which was trained on a GPU (specifically, a GTX1660). The training parameters included an initial learning rate of 0.001, optimization via the Adam algorithm, 100 epochs, and a batch size of 16. After approximately 25 h of training, the model typically converged around 85 epochs, yielding the final trained model.
# Deep-Learning-Incorporated Augmented Reality Application for Engineering Lab Training
## 3 Design and Implementation of the AR App  
This section describes the design and development of the AR app that integrates two independent frameworks for object detection and augmented reality as shown in Figure 2. Unity 3D combines the output of these systems by inferring the object detection model with OpenCV and using an AR dataset target with a Vuforia Engine. Furthermore, Unity 3D enables the development of interactive user interfaces. Users can first use their mobile device to infer the object detection model to detect the lab equipment. The inference will classify and localize lab equipment that has been targeted with the mobile camera. When an object is detected, a user interface (UI) button appears, indicating that an AR-guided tutorial is available for the object. Then, an AR scenario will be loaded, allowing students to use their mobile camera to aim at a specific target. Following that, a 3D object will superimpose on top of the physical object, activating UI panels with instructions on how to use the equipment.  
Figure 2: Design framework of AR-based smartphone app for lab equipment training.  
The app development process consists of integrating a number of different independent systems with their frameworks. In the interactive tutorial development framework, Unity3D was used as the primary development software for generating specific UI instructions and creating immersive interactions between the mobile app and the user. The development framework was integrated with a MobileNet-SSD DL model and a marker-less superimposition AR that activates immersive modules containing 2D/3D objects. The detailed framework integration is discussed below.
## 3 Results and Discussion
### Comparison of MW-HPCO\({}_{2}\) and CE Methods
The absorption peaks between 1000 and 1400 cm\({}^{-1}\) are defined as the fingerprint region where C-H bending (pyranoid ring), C-O-C stretching (glycosidic bond), C-O stretching (alcohol), and CH\({}_{3}\) bending (COOCH\({}_{3}\)) are located [46]. The absorption peaks at 1010 and 1150 cm\({}^{-1}\) showed that the samples contain pyranose. The peaks at around 910 cm\({}^{-1}\) and 816 cm\({}^{-1}\) represent the D-glucopyranosyl and \(\alpha\)-D-mann  
Figure 2: Scanning electron microscopy images of (**A**) untreated GP; (**B**) grapefruit peel after CE method; (**C**) grapefruit peel after MW-HPCO\({}_{2}\) method. The texture properties (firmness, consistency, and cohesiveness) of apricot jam obtained with MW-HTCO\({}_{2}\)-GPP were evaluated and compared with the control sample (Table 4). The jam sample prepared with GPP had improved quality characteristics compared to the control sample. GPP made the apricot jam thicker, firm, and more cohesive. These results show that it is more difficult to break the gel system of MW-HTCO\({}_{2}\)-GPP apricot jam than the control sample and that the MW-HTCO\({}_{2}\)-GPP is suitable for use as an additive (thickener, gelling agent) for foods such as jam and jelly. Similarly, [PERSON] et al. (2018) stated that the consistency of mango jam prepared without pectin was lower than that of the sample prepared using pectin.
## 3 Results and Discussion
### Microbial Analysis
#### 3.3.3 Beta Diversity Analysis  
The Principal Coordinate Analysis (PCoA) of microorganisms in all samples indicated that the Xiaoqu samples were distinctly dispersed in the coordinates (Figure S2), which highlighted the considerable regional variations in the microflora of these Xiaoqus (\(p<0.01\)). Nevertheless, they still shared some common features, particularly Q1 and Q3, which had highly comparable microbial community structures. In terms of the fermentation process of BRW, each wine sample could be categorized into three stages: the initial starter stage (Xiaoqu), the saccharification stage (0-72 h), and the alcohol fermentation stage (72-336 h).  
In order to gain a more comprehensive insight into the microbial community succession during the fermentation of BRW, the LEFSe was employed to analyze the biomarkers of three groups across three different fermentation stages (linear discriminant analysis, LDA \(>4.0\), \(p<0.05\)). Every fermentation stage of different starters was characterized by biomarkers, and the biomarkers in the initial fermentation and saccharification stages were not alike (Table S4). The biomarkers found in Sichuan Dazhu Xiaoqu were classified as _Weissella_ and _Rhizopus_, while its saccharification stage was attributed to the genus _Saccharomycescopsis_; the Anhui Yingtan sample was marked by its biomarkers of _Bacillus_ and _Rhizopus_ in Xiaoqu, but _Enterobacter_ and unclassified_f_Metschnikowiaceae during the saccharification stage. The characteristic genera _Weissella_ and _Saccharomycescopsis_ were indicative of the Hubei Fangxian Xiaoqu, and _Achromobacter_ was present in the saccharification stage. The discrepancy may be due to the dissimilar initial microbial composition of starters causing different biomarkers during the saccharification stage. Both Q1 and Q3 wine samples were identified as belonging to the genus _Saccharomycescopsis_ during the saccharification stage. It is important to recognize that _Pediococcus_ and _Wickerhamomyces_ were biomarkers in the alcohol fermentation stage of all samples.
## 3 Results and Discussion
### Stiffness Modulus  
Table 5 presents two predominant patterns: as test temperatures rise, stiffness values decline, whereas stiffness values increase as loading time decreases. Notably, at a loading time of 10 s, the SECR exhibits the most elevated stiffness modulus figures under intermediate and low temperatures, measuring 0.07 and 0.49 MPa, respectively. Conversely, the CE displays the least stiffness among all unmodified and modified asphalt binders at intermediate temperatures, registering 0.03 MPa. Similar trends in stiffness modulus emerge at loading times of 5, 1, and 0.5 s, mirroring the observations at a 10 s loading time. At the lowest loading times and temperatures explored in this study, modified asphalt binders manifest distinct behavior. Specifically, the RECR demonstrates the highest stiffness values at low temperatures, reaching 11.31 MPa. Among the warm mix asphalt additives without crumb rubber, Sasobit records the highest stiffness values across all temperatures and loading times, except at low temperatures and loading times of 0.5 and 0.1 s, where stiffness values for CE measure 3.23 and 9.93 MPa, respectively. Comparable stiffness values are observed between RECR and SECR. The influence of loading time on stiffness values is evident from the data in Table 5, revealing a notable impact. Reducing the loading time enables a deeper exploration of asphalt binder stiffness behavior. The presence of crumb rubber yields higher stiffness values compared to warm mix asphalt additives, attributed to the combination of low penetration and elevated softening point values. Table 5 shows stiffness modulus values for virgin and modified asphalt binders at different loading times and temperatures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Asphal ID**} & \multicolumn{3}{c}{**Stiffness (MPa) at Loading Time: 10 (s)**} \\ \cline{2-4}  & **High Temperature** & **Intermediate Temperature** & **Low Temperature** \\ \hline V & 0.01 & 0.04 & 0.31 \\ \hline CR & 0.01 & 0.06 & 0.43 \\ \hline SA & 0.01 & 0.05 & 0.36 \\ \hline CE & 0.00 & 0.03 & 0.31 \\ \hline RE & 0.01 & 0.04 & 0.34 \\ \hline SECR & 0.01 & 0.07 & 0.49 \\ \hline CECR & 0.01 & 0.05 & 0.39 \\ \hline RECR & 0.01 & 0.06 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The stiffness modulus for unmodified and modified asphalt binders.  
Figure 9: The loss on heating for the asphalt binders.
# Optimizing Retransmission Threshold in Wireless Sensor Networks
## 7 Experiment Evaluation
For identical transmission failure probability, the link quality in NS2 simulation is much worse, which leads to a lower packet delivery ratio. Secondly, we have to set stationary retransmission thresholds in advance in NS2 simulations, and the retransmission thresholds cannot be adaptively set based on the link quality and remaining time during the packet delivery. As shown in Figure 5a-c, the proposed algorithm has better performance in real-time data delivery, in the case of worse wireless links or a severe delivery delay requirement. The forth group of experiments is to investigate the computing performance of LPAA and LMDAA, and the correctness of the approximation ratio is verified. We prove that the objective function value of Equation (12) is a lower bound of that of Equation (2), then \(\sum_{i=1}^{n}\left(1-f_{i}(z_{i})\right)\) generated by the optimal solution of Equation (12) is an upper bound of Equation (1). In the experiments, the Approximation Ratio (AR) is the ratio of \(\sum_{i=1}^{n}\left(1-f_{i}(z_{i})\right)\) generated by the optimal solutions of linear programming Equation (13) to that of the approximated solutions output by LPAA and LMDAA. Figure 6a-c demonstrates the relationships between the approximation ratio and the input parameter. Experimental results show that the objective function value of the approximation results returned by LPAA and LMDAA is very close to that of the optimal ones. Additionally, the proposed approximation algorithms can achieve high accuracy in terms of optimal retransmission thresholds. Figure 5: Deadline success ratio by NS2 simulation. (**a**) DSR _vs._ deadlines; (**b**) DSR _vs._ the number of hops; (**c**) DSR _vs._ the average number of packets queued. Figure 6: Computing performance of Linear Programming-based \(\left(1+p_{min}\right)\)-Approximation Algorithm (LPAA) and Lagrange Multiplier-based Distributed Approximation Algorithm (LMDAA). (**a**) Approximation Ratio (AR) _vs._ deadlines; (**b**) AR _vs._ the number of hops; (**c**) AR _vs._ the average number of packets queued.
## 2 Methods
### Monitoring Site and Spatially Dense Air Quality Monitoring Network  
An area with a domain size of 800 \(\times\) 800 m around Seoul City Hall was selected as the monitoring site (37.56665\({}^{\circ}\) N/126.97858\({}^{\circ}\) E). The area was located in the center of Seoul, with a dense road network (a major source of air pollutants in cities), and contained various urban micro-environments, including an urban street canyon, mixture of high- and low-rise buildings, and open spaces with limited traffic flows (Figure 1; Table 1). In addition, an air quality monitoring station (AQMS) operated by the government (the Institute of Health and Environment, Seoul) was located within the monitoring domain, and two automatic weather stations (AWS) operated by the Korea Meteorological Administration (KMA) were located \(\sim\)1 km west and east of the domain, respectively (Figure 1).  
To examine the spatial distributions of air pollutants in various urban micro-environments, we placed sensor-nodes along the roadside of four distinct micro-built environments: an urban street canyon (Site-1; Eulji-ro) with a seven-lane road; a dense mixture of medium-sized low-rise buildings on both sides of a relatively narrow five-lane road with some open spaces (parking lots) (Site-2; Sogong-ro); a mixture of tall and medium-sized buildings  
Figure 1: Map of the sensor network monitoring domain. Yellow dashed-lined rectangles represent the micro-environments (sub-sites) where sensor-nodes were installed (4–8 sensor-nodes for each site). Red stars represent the locations of the portable AWS on the ground (Site-1 in summer and Site-2 in winter) and the sky-blue star denotes the location of the AWS operated by the Korea Meteorological Administration (KMA). The yellow circle represents the location of Jung-gu AQMS, operated by the government.  
along both sides of a wide eight-lane road with a large traffic flow (Site-3; Namdaemun-ro); and a road with medium-sized buildings along one side and open space on the other side with limited traffic flow (Site-4; Deogsugung-gil). Further details of the characteristics of these micro-environments are provided in Table 1.  
Sensor-nodes were installed on lampposts at a height of \(\sim\)2.5 m above the ground along the roadsides. The distance between the sensor-nodes was about 20-40 m in each micro-environment. In addition, to examine the differences in air pollutant concentrations between both sides of the road due to the modified in-canopy wind flows (e.g., in-canopy circulation in an urban street canyon; [22]), we installed pairs of sensor-nodes facing each other alongside the roadside (Supplementary Information (SI), Figure S1). We also determined the vertical distributions of air pollutants by placing two sensor-nodes on the roofs of the tallest building (132 m a.g.l.) and an attached fourth-floor building (\(\sim\)15 m a.g.l.) in the street canyon site (Site-1).
## 2 Problem Formulation and Modeling
### Problem Formulation and Presentation  
The general scenario is that several UAVs are flying from different bases to their respective destinations. However, collisions may occur if all of them choose the shortest path. For safety reasons, any pair of UAVs should keep the minimum separation, which is usually greater than a constant (safe distance). To minimize the cost of the total flight distance, the solution only considers the intermediate waypoints, which are added in the initial trajectory because of collision avoidance. Thus, after a potential collision is detected, trajectories (waypoints) of UAVs should be adjusted so that their trajectories satisfy the collision-free requirement.  
A typical scenario is presented in Figure 1. In the shared airspace, there are six cooperative UAVs performing their tasks. The protected area is represented by the sphere outside the UAVs. The radius of the sphere is slightly greater than the safety distance, which means UAVs would collide when their protected spheres interlace. It is worth noting that the volume of the protected area is relevant to the size of the aircraft.  
As a result, the information needed to solve the problem includes the planned trajectories of UAVs with their initial locations and goal destinations.
# Flue Gas Composition and Treatment Potential of a Waste Incineration Plant
## 3 Results
### NO\({}_{x}\) Emission Reduction Using SNCR Technology (Simulated in Epsilon)  
The available elementary analysis from the waste sample data taken in Jordan was inserted in Epsilon to simulate the NOx emissions (and other emissions such as CO\({}_{2}\), Sox, etc. (each is explained in its related part)). All the inserted parameters are shown in Figure 7 and Table 11.  
The concentration of NOx was around 360 mg/Nm\({}^{3}\). The results of the simulation showed that it is possible to meet BAT requirements with SNCR technology where the NO  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Combustion Chamber (Input)** & \\ \hline  & **MSW** & \\  & value & unit \\ Mass flow & 25 & Kg/s \\ Temperature & 50 & \({}^{\circ}\)C \\ LCV & 7 & MJ/kg \\  & **Air** & \\ Mass flow & 150 & Kg/s \\ Temperature & 25 & \({}^{\circ}\)C \\ \hline  & **Combustion chamber (output)** & \\  & **Fine Gas** & \\ Mass flow & 171 & Kg/s \\ Temperature & 931 & \({}^{\circ}\)C \\ Pressure & 0.8 & bar \\  & **Ash** & \\ Mass flow & 3 & Kg/s \\ Temperature & 931 & \({}^{\circ}\)C \\  & **NO\({}_{2}\)** & \\ Concentration & 190 & ppm \\ \hline \hline \end{tabular}
\end{table}
Table 11: Input and output parameters of the combustion chamber.  
Figure 7: Simulated selective non-catalytic reduction (SNCR) technology for NOx emissions in Epsilon (190 ppm = 357.5 mg/Nm\({}^{3}\)).  
concentration was reduced to 62 ppm (which is equal to 116 mg/Nm\({}^{3}\) and is a little bit less than the target BAT value). This depended on the mass flow of injected ammonia NH\({}_{3}\) in the flue gas treatment stream, where 116 mg/Nm\({}^{3}\) was reached by injecting 0.02 kg/s of ammonia into the flue gas, and where approximately 0.8 kg/Ton of MSW was required.
## 5 Prototype Fabrication and Measurement Result
### Practical Implementation  
The practical implications of this design are significant for mass-deployable 5G systems. By avoiding active components, e.g., phase shifters, and leveraging passive beam steering via inter-element spacing and feedline geometry, the proposed antenna is highly suitable for compact user equipment, automotive radar, and point-to-point wireless backhaul links. Measured performance closely matches the simulated results, demonstrating real-world feasibility. Furthermore, the broad impedance bandwidth (27.3-29.6 GHz) covers key 5G NR bands (e.g., n257, n258), making this array immediately deployable in commercial settings.  
Figure 12: Fabricated prototypes of the proposed 28 GHz linear MPA arrays.  
Figure 13: The OTA measurement setup to test the radiation patterns and gain of the 1 \(\times\) 6 linear MPA array with an integrated power divider in the laboratory.
## 2 Related Work and Positioning System Structure  
In order to solve the above problems, many people have begun to do research on a UWB positioning system. [PERSON] et al. selected the CIR data as input of CNN-LSTM model to classify NLOS/LOS [16]. [PERSON] et al. selected 12 features of the UWB signals as data features, and compared the data classification performance of the SVM, RF, and MLP models [17]. [PERSON] et al. proposed to use a GAN and auto-encoder to enhance the training classification model and achieve better recognition performance [18]. [PERSON] et al. proposed a fast imbalanced binary classification method based on moments (MIBC) to identify the NLOS signals [12]. [PERSON] et al. adopted a fingerprint algorithm to do localization [19]. [PERSON] et al. proposed to utilize the prior information to improve the localization accuracy of the system [20]. [PERSON] et al. used a deep long short-term memory network to extract more features from user distance information [21]. [PERSON] et al. designed a hybrid algorithm based on fingerprint algorithm to identify NLOS in different environments [22].  
After referring to the related work of others, we proposed our positioning system. Figure 1 shows the frame diagram of our positioning system.  
As shown in Figure 1, our positioning system consists of three components: UWB communication, UWB data processing, and localization algorithm. When a coal mine worker enters the positioning area with a UWB tag, the tag will actively communicate with three nearby anchors. Each anchor calculates the distance value between the anchor and the tag based on the time-of-flight principle, and the register in the anchor device will record the frame transmission diagnostic data of the last data frame received during each communication. After completing each ranging, the anchor will transmit ranging data and frame transmission diagnostic data to the host computer. We trained a convolutional neural network that can utilize frame-transfer diagnostic data for NLOS/LOS classification. Then the linear model and Kalman filter are used to eliminate the errors in the ranging data. When the ranging data of the three anchors are processed through the above steps, the localization result is obtained by using the trilateral centroid localization algorithm. Finally,we correct our results with map constraints and display the results. We will describe the whole components in detail in the next two sections.