esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup and Method  
In this study, we attempted to solve the deterioration in the accuracy and reliability of inspection, which is known to be caused by the deformation and misalignment of the bogie part. The misalignment occurs due to the usage environment of the pre-developed multi-joint robotic ultrasonic inspection system. To this end, a software algorithm capable of automatically compensating for the level values of the multi-joint robot was developed and applied to an actual system for verification. The ultrasonic system used for the analysis generated/received signals using a pulser/receiver; amplification and filter devices were installed in consideration of the attenuation of ultrasonic signals. To convert the received analog signal to digital, an analog-digital converter was installed and programmed.  
First, to examine the axis misalignment of the system intuitively, the water columns of the two robots were accurately aligned with no product to be inspected, as shown in Figure 6. Data were acquired while the robots were moved by 100 mm in the \(z\) direction and by 650 mm in the \(y\) direction, in 3 mm intervals; the data were expressed as 2D images. The frequency of the ultrasonic sensor was set to 0.5 MHz. Regarding the ultrasonic sound pressure, the linear scale was set to 50% and the log scale to 80%.  
Figure 6: Shape of the squirter.  
After intuitively examining the axis alignment, the spatial coordinates were measured at the respective control points of the R1 robot (\(x\)-axis origin distance: 2393.058 mm; \(y\)-axis origin distance: 0.000 mm; \(z\)-axis origin distance: \(-\)250.535 mm) and the R2 robot (\(x\)-axis origin distance: 2104.081 mm; \(y\)-axis origin distance: 311,737.000 mm; \(z\)-axis origin distance: \(-\)250.554 mm); this was achieved by using a laser tracker (Absolute Tracker from Leica, Switzerland) to obtain the spatial coordinates of the bogie axis that was misaligned.  
The roll, pitch, and yaw were calculated using the measured coordinate values and Equations (3)-(5). Subsequently, these roll, pitch, and yaw values were applied in the rotation matrices of Equations (6)-(8) with the objective of calculating the misaligned coordinate values on all the \(yy\)-axes. In the developed algorithm, correction values were derived by using these values, with the aim of performing movements while each axis was corrected.  
To verify the performance of the algorithm, a reference specimen that simulated artificial defects was fabricated, as shown in Figure 7.  
The geometry and simulated defects of the specimen were fabricated in accordance with the Airbus standard. The specimen was scanned to acquire an image before compensating for the misalignment of the bogie axis, as shown in Figure 8. Another image was also acquired after performing the pitch and yaw corrections. The images were compared and analyzed. In addition, for a composite material specimen without defects, the images obtained before and after correction were compared and analyzed.  
Figure 8: Scanning for reference specimens with artificial defects.  
Figure 7: Shape and dimension of reference specimen (mm).
# Effects of Valve, Armature, and Armature Pin Guidance on Diesel Injector Performance
## 2 Materials and Methods
### System and Problem Definition
Finite impulse response filters are used to digitally filter the signal. The critical element within the HDA setup is the piezoceramic sensor, which generates a pressure wave in the test liquid in the chamber. The same sensor at the top of the chamber  
Figure 2: (**a**) Injector assembly and testing process flow and (**b**) HDA measurement schematic. detects the reflecting waves. The average chamber pressure is recorded as the initial pressure (\(P_{1}\)) before the measurement begins. The speed of sound is also measured and recorded as the initial sound speed (\(c_{1}\)). Subsequent measurements of the mean pressure in the chamber (\(P_{2}\)) and speed of sound (\(c_{2}\)) are taken during the injection process. These measurements are integral to calculating the total mass, as outlined in Equation (2). [30]. This study's reference pressure (\(P_{1}\)) is set to 50 bar. Commercially available Divinol CAF test oil (Item Nr. : 2928V3), a product of Zeller+Gmelin GmbH & Co. KG (Eislingen/Fils, Germany), was used for HDA measurements. Regular calibration of the HDA machine by the manufacturer ensures the stability of measurements, and repeatability is verified by multiple measurements of previously produced injectors to avoid any deviations. A gravimetric procedure is employed for calibration. If the first set of stability injectors fails, a second set is measured under identical conditions to ascertain whether deviations arise from the injectors or the calibration equipment. A standard calibration procedure is implemented prior to measurements; results are verified, and no abnormalities are detected with the machine. Stable injector behavior is critical for achieving accurate results in HDA measurements. Proper vertical movement of the armature is essential for establishing a hydraulic pressure balance and ensuring injector stability. This stability necessitates precise guidance of the armature pin and valve interior. Excessive clearance in the guidance mechanism leads to off-axis armature movement and vibration, which can cause premature pressure differences and needle activation. Conversely, inadequate clearance may result in surface abrasions, particle formation, delayed pressure difference formation, and delayed needle opening. While the existing literature predominantly focuses on needle movement and fuel injection, research addressing the connections between the valve, armature, and armature pin--crucial determinants of needle movement--remains insufficient. Operational vibrations can lead to eccentric armature movement, which fails to achieve complete sealing and the desired hydraulic pressure balance. Consequently, rapid pressure drops in the control chamber may prematurely open the needle, leading to excessive fuel injection. Thus, the critical role of precise guidance in injector operation becomes evident. The findings of this study have significant potential to positively impact millions of injectors currently in production.
###### Abstract  
In this paper, we investigate the relay selection problem for the unmanned aerial vehicle (UAV)-assisted vehicular ad-hoc networks (VANETs). For the considered network, we first model and analyze the link quality of service (LQoS) from the source node (SN) to the neighbor node and the node forward capacity (NFC) from the neighbor node to the destination node (DN). Then, the relay selection problem is formulated as a multi-objective optimization problem by jointly considering the LQoS and the NFC. Afterward, we decompose the problem into two subproblems and propose a relay selection protocol with the storage-carry-forward (SCF) method. Moreover, we define a utility function with the node encounter frequency (NEF) and the message time-to-live (TTL) taken into account, based on which a redundant copy-deleting approach is devised. Furthermore, we analyze the security of the designed protocol. Finally, the simulation results demonstrate that the proposed relay selection protocol can improve the message delivery ratio, reduce the average end-to-end delay, and limit the overhead.  
relay selection; storage-carry-forward (SCF); unmanned aerial vehicle (UAV); vehicular ad-hoc networks (VANETs) +
Footnote †: journal: Applied
# Wind-Based Parametric Design in the Changing Climate
## 4 Discussion  
In the unique morphological and wind conditions of the Loudden Docks' case study site, three FlowBrane shapes, all designed as lightweight tensile membranes, interact with the wind to adjust the microclimate of the place. Three different design directions are pursued: (i) deflecting the wind to protect swimming baths, (ii) accelerating the flow to improve the efficiency of wind energy harvesting, and (iii) creating a fluid architectural form, aiming at reducing the robustness of building structures.  
The dynamic ambient conditions are a significant factor influencing architectural design. With the progress in digital designing, the effects of the environment can be simulated and predicted already in the conceptual design phase, which enables the creation of environment-fitting sustainable architectural solutions through employing the iterative process of RTD.  
The proposed wind-based design approach intertwines parametric modeling with fast parametric CFD simulations. The wind-based shape-optimization design process consists of wind-driven form-finding in Grasshopper, followed by wind simulations in Swift, and then looping back to the parametric adjustments of the geometry until the desired performance of the design in the wind is achieved.  
The three tensile-membrane architectural interventions transform the environment, characterized by Venturi effect, wind turbulence, and an unpleasant environment for pedestrians to a habitable \"wind park\". The wind is controlled in a specific way, employing each FlowBrane. Only one shape for every FlowBrane was analyzed utilizing CFD with suggestions on how to continue the wind-based shape optimization. The wavy FlowBrane No. 1 can deflect not only the prevailing westerly but also the strong southerly winds. However, the lift angle and the height of the middle part of the tensile membrane could be adjusted to protect the pools even better. The set of three membranes of FlowBrane No. 2 are wrapping the group of three silos and enhancing the Venturi effect between them. The Venturi effect is in this case desirable because, through the membranes equipped with piezoelectric straws, it is used for wind energy harvesting. The unfavorable wind effect was transformed into a desirable phenomenon by employing architecture. In both examined wind directions, FlowBrane No. 2 can accelerate the wind squeezed between membranes to twofold speed. The third membrane, designed to function as an auditorium, was aerodynamically-shaped. As the envelope spans over tens of meters, its lightness is essential. Fluid architecture resists the wind flow much less, resulting in reduced pressure on the envelope and, consequently, lighter structures.
## 1 Introduction
For instance, [16, 17, 18] exploit remote sensing and other ancillary geo-information to optimize the sampling design of cropland areas, the monitoring of land cover or the delineation of watersheds. In the field of Disaster Risk Reduction and Risk Management, while the value of geo-information is being increasingly recognized [19,20] and the use of Geographical Information Systems (GIS) is widespread [21], the application of sound statistical frameworks to optimize the collection of relevant _in situ_ information is limited, and the concept of prioritization mostly refers to the selection of hotspots for natural disasters (see, for instance, [22]). The combination of prioritization approaches and mobile mapping technology proposed in this paper has been tested in a representative site in Istanbul (Turkey). The considered study area is introduced in Section 2. In Section 3 the proposed approach for rapid data collection and the prioritization strategy are described. The custom-made mobile mapping system employed in the field activities is briefly described in Section 4. The results of the preliminary activities are presented in Section 5 and expanded upon in Section 6, with conclusions outlined in Section 7.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 5 Discussion
### Common Operational Pipeline  
The description of the state-of-the-art models for real-time semantic segmentation, which has been presented in Section 3, has shown sound evidence that most models share a common operational pipeline.  
In particular, the first type of semantic segmentation networks which achieved real-time performance is based upon the encoder-decoder architecture. Representative example is ENet [8]. The encoder module uses convolutional and pooling layers to perform feature extraction. On the other hand, the decoder module recovers the spatial details from the sub-resolution features, while predicts the object labels (i.e., the semantic segmentation) [25]. A standard choice for the encoder module is a lightweight CNN backbone, such as GoogLeNet [5] or a revised version of it, namely Inception-v3 [10]. The design of the decoder module usually consists of upsampling layers based on bipolar interpolations or transposed convolutions.  
In the effort to design efficient and at the same time accurate models, two-branch and multi-branch networks have been proposed. Instead of a single branch encoder, a two-branch network uses a deep branch to encode high-level semantic context information and a shallow branch to encode substantial/rich spatial details of higher resolution. Under the same concept, multi-branch architectures integrate branches handling different resolutions of the input image (high, low and medium). However, the features extracted from the different branches must be merged to proceed to the segmentation map. To this end, two-branch networks introduce a fusion module to combine the output of the encoding branches. The fusion module can be a Feature Fusion module in which the output features are joined by concatenation or addition, an Aggregation Layer (BiSeNet V2), a Bilateral Fusion module (DDRNet), or a Cascade Feature Fusion Unit (ICNet).  
In a nutshell, there are three types of modules used in the operational pipeline of real-time semantic segmentation: an encoding, a decoding and a fusion module. There are several configurations where the fusion module is part of the decoder. Figure 26 shows the three modules and the corresponding standard yet dominant, design choices.
## 3 Method
### Timed Colored Petri Net Model  
Colored Petri nets (CPNs) form a graphical language for constructing models and analyzing properties of concurrent systems and distributed systems, which extends PNs with data types, functions, and modules. A CPN is a directed bipartite graph that includes two types of nodes, namely the place and the transition [22]. The nodes are connected via directed arcs. The place is used to describe resources and status in the system, which contains colored tokens. The colored token is used to describe the entity attributes. The transition is used to describe the event in the system, which can be fired based on the preconditions of input arc expressions and guards. In a CPN, places, transitions, arcs, and guards are graphically represented by ellipses, rectangles, arrows, and parentheses respectively. In order to evaluate the performance of the model system, TCPNs are introduced with a global clock to represent the model time [23]. Each token has a time attribute called the time stamp which describes the earliest time at which a token becomes available. Hence, both the temporal behavior and the logical behavior can be described by TCPNs explicitly in a concise manner. The definition of a TCPN can be formalized as follows [24]. A TCPN is defined as a nine-tuple, \(TCPN=\{P,T,A,\)\(\Sigma\)\(,N,C,G,E,I\}\) which satisfies the following conditions:  
1. \(P=\{p_{1},p_{2},...,p_{m}\}\) is a finite set of places. 2. \(T=\{t_{1},t_{2},...,t_{n}\}\) is a finite set of transitions, \(P\cup T\
eq\varnothing\) and \(P\cap T=\varnothing\). 3. \(A=\{a_{1},a_{2},...,a_{l}\}\) is a finite set of directed arcs, \(A\subseteq P\times T\cup T\times P\). 4. \(\Sigma\) is a finite set of non-empty color sets. 5. \(N\) is a node function defined from \(A\) into \(P\times T\cup T\times P\). 6. \(C\): \(P\rightarrow\sum\) is a color set function that assigns a color set to each place. 7. \(G\): \(T\to EXPR\) is a guard function that assigns a guard to each transition \(t\) such that: \[[Type(G(t))=Bool\wedge Type(Var(G(t)))\subseteq\Sigma].\]
8. \(E\): \(A\to EXPR\) is an arc expression function that assigns an arc expression to each arc \(a\) such that: \[\forall a\in A:[Type(E(a))=C(p(a))_{MS}\wedge Type(Var(E(a)))\subseteq\Sigma]\]  
Figure 2: The simplified schedule plan of a job. where \(p(a)\) is the place of \(N(a)\). 9. _I:_\(P\to EXPR\) is an initialization function that assigns an initialization expression to each place \(p\) such that: \[\forall p\in P:[Type(I(p))=C(p)_{MS}\wedge Type(Var(I(p)))\subseteq\varnothing].\] \(Type(v)\) denotes the type of the variable \(v\). \(Var(expr)\) denotes a set of variables in the expression \(expr\). Based on the description of the production logistics problem, TCPN is used to model production and logistics in an FMS. The TCPN model of the self-adaptive collaboration method is established as shown in Figure 3.  
In the TCPN model, the meaning of each transition has been briefly explained in Table 1. Table 2 explains the meaning of each place. The global color set declarations are given in Table 3.
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
## 4 Communication Receiver
Thus, the CBFM waveforms (in particular the Lorenz CBFM waveform) are good candidates for communication systems while simultaneously offering advantages for the monostatic and bistatic radars presented in the subsequent sections.
# Improvement of Porous GaN-Based UV Photodetector with Graphene Cladding
## 2 Materials and Methods  
This work can be divided into two stages, namely device fabrication and sensing evaluation. The device fabrication stage comprised synthesis of porous GaN and graphene materials and assembling the UV photodetector. For sensing evaluation, the photodetector will be tested on its current-voltage (IV), sensitivity-transient, and response/recovery time under both dark and UVA illumination.
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 4 Aircraft Wake Recognition
### Wake Recognition Results  
To more accurately reflect the impact of the YOLOv8n-CBAM-EfficientNetV2 model, four wake images with disparate background conditions were randomly selected for this experiment. The objective was to compare the influences of different models on wake detection. The resulting images are presented in Figure 14 for a visual analysis. Figure 14a-d illustrate the four sample images of the input. Figure 14a-c depict the wake samples generated by an aircraft in upwind, clear, and downwind conditions, respectively. Figure 14d demonstrates the separation of the left and right vortex structures subsequent to the wake spreading over a period of time. The LV and RV in the output image represent the names of the left and right vortex categories of the wake, respectively. The numbers indicate the confidence level associated with the model's prediction of the target's category. A higher confidence level indicates a greater degree of certainty in the model's prediction.  
The results of the detection models demonstrate that both the EfficientNetV2 network and CBAM have enhanced detection capabilities. However, the YOLOv8n-CBAM  
Figure 14: Visual comparison of test results for each model. (**a**) normal vortex with upwind; (**b**) normal vortex with clear sky; (**c**) normal vortex with downwind; (**d**) separate vortex with clear sky.  
EfficientNetV2 model exhibits a more pronounced improvement in detection effectiveness, compared with the YOLOv8n model.
## 2 Materials and Methods
### Material Characterization  
Transmission electron microscopy (TEM) was performed on a Jeol 2100 microscope, operated at 200 kV. The GNPs were dispersed in water under sonication for 5 min. After this procedure, the carbon and copper sample holder (grid) were immersed in this solution and dried at room temperature. Raman scattering spectroscopy was performed on a JASCO spectrometer coupled to a CCD detector. This analysis was performed with an argon-ion laser with a wavelength of 514.5 nm operated at a power of 200 mW. The Zetasizer NanoZS equipment was used to obtain the zeta potential (\(\zeta\)), dynamic light scattering (DLS), and polydispersity index (PdI). For the pH variation (pH = 3, 5, 7, 10, and 12) used in the zeta potential measurements, the pH was adjusted with HNO\({}_{3}\) (Synth, 70%) and NH\({}_{4}\)OH (Synth, 24%). The particle hydrodynamic size of the GNPs was evaluated by DLS in water and in the same DMEM used in this study to determine the biological properties of these nanomaterials.
## 4 Experiments and Results
### Quantitative and Qualitative Results on The Synthetic Images
Figure 9 shows some of the haze-free images obtained by the algorithm in this paper and several other algorithms on the HSTS test set. As shown in Figure 9, the DCP algorithm led to a serious color distortion problem in the haze-free image. The AOD-Net algorithm did  
Figure 8: Visual results on the SOTS dataset: (**a**) hazy images; (**b**) corresponding haze-free images; (c) DCP; (**d**) AOD-Net; (**e**) GCANet; (**f**) EPDN; (**g**) YOLY; (**h**) proposed method. not completely remove the haze of the image, and the overall brightness of the haze-free image obtained was low. GCANet led to a lack of image details. EPDN had the problem of not recovering the image details well. YOLY had the problem of partial color distortion. The results obtained by the proposed algorithm in this paper not only had good visual effects, but also did not show color distortion, while it also retained the detailed features of haze images well.
## 2 Related Works
In addition, all of the above methods require the cooperation between clients (MNs) and networks (APs) to monitor radio signal, measure location and predict motion. In the cell-ID method, the sector of AP/BS will broadcast its sector information, thus an MN can use these information to identify its location. However, the accuracy of motion detection is limited by the size of the cell sector. The pattern matching method [32] uses a location server which contains a signal characteristics database to analyze the location of an MN. When an MN moves in a network, the MN measures the radio pattern (e.g., RSS, signal-to-noise ratio) from the current serving AP and sends the measured pattern to the location server. The location server will compare the radio pattern with signal characteristics database and identify the location of the MN. Then, the communication system can use this location information to predict the motion of the MN and improve the handoff process. However, the signal characteristics database must be constructed in advance, and hence the pattern matching method may not be usable in a highly variable environment. The authors in [27] presented a motion detection algorithm based on Moving Average Convergence / Divergence (MACD) and also introduced an MACD-based handoff algorithm. The MACD technology is a trend-following indicator that is widely used in stock price prediction. The MACD-based handoff algorithm enables the MN's interfaces when the MN is moving away from its current serving AP. The authors claim that the MACD-based motion detection approach can reduce 30 percent of battery power consumption than that of the always-on approach. However, because of the overlapped sample problem, the MACD-based motion detection algorithm is insensitive, especially when the MN is far away from the BS or moves at a slow speed.
## 3 Technical Preliminaries
### Advanced Encryption Standard (AES)  
The AES [32] is a symmetric encryption technique, which provides a high degree of security. AES encryption converts data into an unintelligible form, called ciphertext. Conversely, the decryption converts this ciphertext into its original form, called plaintext. The AES algorithm can generate block ciphertexts of 128 bits, with three different key sizes, namely, 128, 192 and 256 bits. If the AES encryption key is an EC point in the beginning, it can be transformed into an integer (e.g., 256-bit key) through hashing the point's \(x\) and \(y\) coordinates for the subsequent process.