esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Path-Tracking Control Strategy
### CG Generalized Force Redistribution  
Due to the unique structure of the SRT, the second and third modules only have one independent steering running gear and cannot provide driving torque. Therefore, after calculating the required CG generalized forces of the second and third modules, if the module itself provides the CG generalized force, then when one component is satisfied, significant errors will occur in the other two. Especially for the third module, the CG generalized force error can significantly increase tracking error and even non-convergence. Therefore, this paper proposes a CG generalized force redistribution method: the path-tracking-required CG generalized forces for the third module are provided by thesecond and fourth modules through hinge plates, as shown in Figure 6. For the second module, only the yaw moment requirement is met, ignoring the resulting longitudinal and lateral force errors.  
Firstly, the required front and rear hinge forces of the third module are calculated based on the required CG generalized forces:  
\[F_{hy,r}^{3}+F_{hy,f}^{3}=F_{y}^{3}, \tag{42}\]  
\[F_{hy,f}^{3}\times l_{r,3}-F_{hy,r}^{3}\times l_{f,3}=M_{z}^{3}, \tag{43}\]  
\[F_{hx,r}^{3}=F_{hx,f}^{3}=0. \tag{44}\]  
For the fourth module, due to the hinge rotation angles, the front hinge force after redistribution is as follows:  
\[F_{hx,f}^{4}=-F_{hx,r}^{3}\cos\lambda^{3,4}+F_{hy,r}^{3}\sin\lambda^{3,4}, \tag{45}\]  
\[F_{hy,f}^{4}=-F_{hx,r}^{3}\sin\lambda^{3,4}-F_{hy,r}^{3}\cos\lambda^{3,4}. \tag{46}\]  
Similarly, the rear hinge force of the second module is as follows:  
\[F_{hx,f}^{3}=-F_{hx,r}^{2}\cos\lambda^{2,3}+F_{hy,r}^{2}\sin\lambda^{2,3}, \tag{47}\]  
\[F_{hy,f}^{3}=-F_{hx,r}^{2}\sin\lambda^{2,3}-F_{hy,r}^{2}\cos\lambda^{2,3}. \tag{48}\]  
Therefore, for the fourth module  
\[F_{x}^{4*}=F_{x}^{4}-F_{hx,f}^{4}=-F_{hx,f}^{4}, \tag{49}\]  
\[F_{y}^{4*}=F_{y}^{4}-F_{hy,f}^{4}, \tag{50}\]  
\[M_{z}^{4*}=M_{z}^{4}-F_{hy,f}^{4}\times l_{f,A}. \tag{51}\]  
where \(F_{x}^{4*}\), \(F_{y}^{4*}\), and \(M_{z}^{4*}\) are the required CG generalized forces for the fourth module after redistribution.  
Figure 6: Schematic diagram of the CG generalized force redistribution in the second, third, and fourth modules.  
For the second module  
\[F_{x}^{2*}=F_{x}^{2}-F_{hx,f}^{2}=-F_{hx,f}^{2}, \tag{52}\]  
\[F_{y}^{2*}=F_{y}^{2}-F_{hy,f}^{2}, \tag{53}\]  
\[M_{z}^{2*}=M_{z}^{2}+F_{hy,f}^{2}\times l_{r,2}. \tag{54}\]  
where \(F_{x}^{2*}\), \(F_{y}^{2*}\), and \(M_{z}^{2*}\) are the required CG generalized forces for the second module after redistribution.
# Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology
## 3 Proposed UAV Topology and Simulation  
The planning link between a system and other systems and/or users is termed \"Network Topology\", where a Radio Frequency (RF) or an optical links can be utilized. The proposed work aims to develop an intelligent cellular structure design for a UAV wireless communication topology using an AI framework. The proposed AI framework includes Self Organizing Maps (SOMs) and an NN fitting tool that can be simulated using the Graphical User Interface (GUI) toolbox in MATLAB. The proposed intelligent wireless communication system also aims to tune various parameters of link budget, as well as cellular structure design for automatic optimization. Researchers in [36; 37; 38] emphasize the power of the GUI toolkit to complete a visual analysis. Moreover, the developed GUI software is useful to carry out the needed calculations of the proposed model in an efficient and simple manner. This section presents the proposed UAV topology and simulation.  
Figure 3 shows the proposed intelligent heterogenous 5G topology, where different UAV-based systems integrate with IoE and AI frameworks. The figure consists of two main  
segments, where each has its own software and different software capabilities [39]. Segment one has two parts: ground and space. The former includes Ground Control Centre (GCC) to control the aerial platform's functionality and flight tasks via a backhaul link called \"Control signal\" [40]. In addition, there are numerous wireless communication links within the footprint that can serve fixed and/or mobile users, ranging from indoors to outdoors, that support IoE-wide smart applications, such as smart buildings, smart cities, smart farming, and support supply chains [41]. The latter includes at least one UAV platform that carries communication payloads, an integrated energy system (e.g., solar arrays, batteries), and a station-keeping system [42].  
Figure 3: Proposed intelligent heterogenous 5G topology that integrates a UAV-based system with IoE and AL.  
Segment two comprises the proposed AI framework that encompasses SOMs and an NN fitting tool for automatic optimization that helps plan a ubiquitous and seamless 5G cellular structure design from a UAV perspective. This would help in making smart decisions, which can save time and effort [43]. The purpose of considering the proposed AI framework here is twofold: First, to optimize cellular cell design, which affects the link budget parameters using a SOMs unsupervised learning algorithm. Second, to validate the SOMs using the NN fitting tool. Noticeably, using a 5G MIMO antenna is not only seen as an advanced mobile communications system, but also lays a solid foundation for intelligence of everything [44; 45].
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 2 Related Work
### Visual Attention Mechanism  
Inspired by the human cognitive system, researchers design various attention modules to help visual models focus on the essential features, thus model performance can be improved [45]. In [46], the Squeeze-and-Excitation Block designs a method to learn the weight for each channel of the convolution block's output, then applies the weight to choose the best representation from features of different channels. Convolutional BlockAttention Module [47] applies sequential channel attention and spatial attention to refine the intermediate feature map at every convolutional block. DANet [48] weights and selectively aggregates the features of all positions through the position attention module, and selectively emphasizes interdependent channel maps by integrating associated features among all channel maps by the channel attention module. RANet [49] introduces two blocks, i.e., the Region Construction Block and the Region Interaction Block, to embed local features of each layer into an elongated representation. ACFNet [50] extracts global context from a classification perspective by proposing an Attentional Class Feature module for improving semantic segmentation performance.  
In this study, we introduce attention mechanism in the U2 Net network, aiming to simultaneously enhance the network's feature extraction and full-scale feature fusion capabilities. To the best of our knowledge, this is the first attempt to use the attention mechanism simultaneously in feature extraction and full-scale feature fusion stages.
## 4 Poster Presentations  
_P-01. Upcoming Nationwide CF Implementation in Germany- Chances and Potential Pitfall_  
[PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)  
\({}^{1}\) Institute for Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany  
\({}^{2}\) Clinic for Pediatrics, University Medicine Greifswald, Greifswald, GermanyObjectives: In August 2015, the G-BA voted for the nationwide implementation of the Cystic fibrosis newborn screening (CF-NBS) in Germany. Until its final realization in 2016, Mecklenburg-Vorpommern (MV) is the only German state, with an CF-NBS for all newborn in due course of an EU founded Interreg IVa project. The experience, gained since 2012 is a valuable pool of information concerning the optimization of processes in accordance to the proposed CF-NBS screening protocol and for analytical drawbacks.  
Methods: The nationwide proposed CF-NBS protocol is three staged. The first two steps are conventional laboratory analysis of (1) immunoreactive trypsinogen (IRT) and of (2) pancreatitis associated protein (PAP). As a third step, a molecular genetic analysis follows. CF-NBS will be positive in case of an elevated IRT > 99.9 th percentile or with at least one mutated CFTR-gene. In contrast, a two-step strategy was used since 2012 in MV--including the analysis of IRT and PAP.  
Results: Despite the missing molecular analysis, a profound data base with more than 55,000 screened newborns was established. The acceptance of the CF-NBS reached soon after its introduction more the 95%. The mean time between positive CF-NBS was significantly reduced to 33 days in 2015--ranging now in European cystic fibrosis society standards of care recommendations. Furthermore, the applied cut-offs for IRT and PAP detected all seven positive CF-NBS newborns.  
Conclusions: The challenges for nationwide implementation are, nevertheless, diverse. The inclusion of the third analytical step, the resulting tight time schedule until the sweat test and the safe use of the altered IRT cut-off to reduce the false positives will be challenging. Furthermore, reimbursement concerning the sweat tests, the additional laboratory analytics, and the tracking, have not yet been solved.
## 2 Principles and Methods
### Signal Preprocessing and Feature Extraction
#### 2.1.2 MFCC Feature Extraction  
MFCC is a commonly used feature in the field of radio recognition. They are primarily extracted based on Mel filters, which are designed to mimic the characteristics of the human auditory system. Due to the human ear's sensitivity to low-frequency sounds and relative insensitivity to high-frequency ones, Mel filters are characterized by being dense in the low-frequency region and sparse in the high-frequency region [17; 18]. The approximate expression for the conversion between Mel frequency and linear frequency is as follows:  
\[mel(f)=2595.lg\bigg{(}1+\frac{f}{700}\bigg{)} \tag{3}\]  
In the equation, \(f\) represents the linear frequency, measured in Hertz (Hz).  
The process of MFCC feature extraction includes preprocessing, Fast Fourier Transform (FFT), Mel filtering, logarithm operation, Discrete Cosine Transform (DCT), and dynamic feature extraction, as illustrated in Figure 1.  
Firstly, the sound signal is segmented into frames and windowed. Then, FFT is applied to each frame of the sound signal to obtain its spectrum. Next, the spectrum is passed through a series of Mel filters to filter out energy within specific frequency ranges. The outputs of the filters are then subjected to logarithmic operation to enhance the features of low-energy frequency bands. Finally, the logarithmic energy vectors obtained from the Mel filterbanks are subjected to Discrete Cosine Transform (DCT). By setting the number of outputs of the DCT, different dimensional MFCC features can be obtained.  
\[C(n)=\sum_{m=0}^{N-1}s(m)cos\bigg{(}\frac{\pi n(m-0.5)}{M}\bigg{)},n=0,1,2...,L \tag{4}\]  
In the equations, \(M\) represents the number of filters; \(N\) denotes the number of points in the FFT; \(L\) represents the order of the MFCC features; \(s(m)\) represents the logarithmic energy output of each filterbank.  
Figure 1: The extraction process of MFCC.
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 3 Component Modeling  
Here, two variants of the same approach to model SI-POF systems are reported. The core of both is the power-flow differential equation that describes light propagation through the fiber, but while one solves this equation in the temporal domain, the other works in the frequency domain. Although the fiber itself is the defining component in any POF-based system, there are a number of other important components that make the system work. A transmitter and receiver are obviously needed, but so are the physical connectors to connect them to the fiber and to possibly connect multiple fiber segments. Moreover, it is often the case that a software model needs to capture a phenomenon or a behavior that is observed experimentally, but does not necessarily fit into its own physical component of the system. Strong initial diffusion, fiber bends and the coupling of light from the end of a fiber to the active area of a detector are examples that fall into this category. All these components and effects introduce localized perturbations of the intensity distribution, which do not modify the temporal properties of the signal. Thus, their models only have a spatial dependency and therefore, can be equally utilized in the temporal and frequency domains. In this section, we describe the methods that we use to model various building blocks and behavior that must be included in the system-level simulation framework. In particular, we describe our modeling approach for the SI-POF, Transmitter, Injection, Connector, Fiber Bend, and Detector Coupling models.
# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 1 Introduction
For air target, experimental results illustrated that it could detect an airplane crossing the FSR baseline by using DVB-T signals [3]. Despite the research done so far, the literature is lacking in the results for ground target detection and classification in passive FSR, especially the experimental investigation. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. In addition, the target's distance from the radar sensor can also be determined. The analysis was carried out by exploiting the actual LTE signal transmitted through the air from the base station. Three different vehicles that represent three different categories were used during the test campaign. The three vehicle categories are named as: Compact, Saloon and Small Sport Utility Vehicle (SUV). The passive FSR experimental results highlight two important findings. First, the vehicles were successfully detected, even by raw received signals without any complicated signal processing techniques. Second, the proposed classification system provides results of excellent classification performance. The results from this paper can enhance the passive radar system capability by integrating the FSR mode onto the conventional PBR system. The LTE has remarkable characteristics that make it attractive to be used as an illuminator of opportunity for passive radar applications. Among the characteristics, the LTE signal has a broad bandwidth differs from 1.4 MHz to 20 MHz which provides a high range resolution in comparison to other illuminators of opportunity (i.e., GSM, DVB and DAB). The variety of the LTE allocated frequency bands (800 MHz-3.5 GHz) allowing the LTE to be deployed in many countries, consequences a future broad coverage of the LTE in the worldwide [18]. Additionally, the number of commercial LTE networks is seeing a massive increase year by year. Therefore, increasing the LTE signal availability will expand LTE-based passive radars' deployment opportunities. Analysis on the LTE signal Ambiguity Function (AF) shows that low side-lobes and high range resolution can be achieved [19]. The above-mentioned LTE signal characteristics and others motivated the authors to use the LTE signal as an illuminator of opportunity for passive FSR system. The layout of the paper is organized as follows: Section 2 focuses on the target detection technique in passive FSR by exploiting the enhancement of the target RCS. Moreover, the fundamental received signal equation is derived and the proposed passive FSR system is also explained. Section 3 elaborates on the approach for target classification by using the PCA in the first part, followed by target range discrimination and classification performance analysis. The conclusion and direction for future work are outlined in the last section.
## 6 Properties of Two-Dimensional Tin(II) Sulfide  
Two-dimensional SnS properties have been determined by numerous groups via experimental and computational methods, showing a range of intriguing properties compared to the bulk. Due to the anisotropic properties of SnS 2D crystals, different values for mobility are obtained, with the zigzag direction (20 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\)) having a mobility over 1.7-fold greater than that of the armchair direction as determined by [PERSON] et al. [94]. Tin(II) sulfide monolayers have been found to have a high carrier mobility (10,000 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) to 38,000 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) for the monolayer along the \(a\) direction) [93] compared to 300 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) to 1000 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) for monolayer black phosphorous along the \(x\) direction [95].  
Two-dimensional SnS has been determined to have nonlinear optical properties with a saturable absorption response. The strong coulomb interactions give rise to exciton enhanced annihilation (EEA) (rate 4.34 \(\times\) 10\({}^{-4}\) cm\({}^{2}\) s\({}^{-1}\)). The quasiparticle interaction was determined by femtosecond transient absorption by [PERSON] et al. [89]. Using a computational approach, [PERSON] et al. have successfully determined the thermoelectrical properties of SnS monolayers. It was found that monolayer SnS has high levels of thermal anisotropy as well as a high thermoelectric figure of merit in both directions (ZT\({}_{\rm ac}\) = 4.5 and ZT\({}_{\rm ZZ}\) = 2.9 at 800 K) [96]. This was found to be higher than materials such as phosphorene (0.22) [97] and arsenene (0.8) [98], as well as bulk SnS (0.4) [75], (0.16) [29], (0.52) [74]. Thicker (100 nm) SnS nanobelts have already been successfully used as thermoelectric materials by [PERSON] et al. [99]. Tailoring of nanoscale SnS has also been performed by [PERSON] et al., utilising doping with Na and Ag [100].  
For applications in devices, a high level of stability is required to ensure a long lifetime. Two-dimensional SnS has been found to remain stable under harsh conditions such as in acids and electrolytes, as well as exhibiting long term cycling stability [94]. This makes it particularly useful compared to similarly structured semiconductors such as black phosphorous [101] and germanium(II) sulfide, which require anhydrous/anaerobic environments [102]. Calculations on SnS performed by [PERSON] et al. have established the thermal and dynamic stabilities for both monolayer and bilayer via the use of VASP and PBEsol DFT [93].
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 2 Mathematical Models
### Model of CBA  
The equations of the 6 DOF nonlinear model studied herein are presented below:\[\left\{\begin{aligned} \dot{x}=V\cos\gamma\cos\chi\\ \dot{y}=V\cos\gamma\sin\chi\\ \dot{z}=-V\sin\gamma\end{aligned}\right. \tag{1}\]  
\[\left\{\begin{aligned} \dot{u}=v\tau-wq-g\sin\theta+\left(F_{x}+T_{x} \right)/m\\ \dot{v}=-ur+wp+g\cos\theta\sin\phi+\left(F_{x}+T_{x}\right)/m\\ \dot{w}=uq-vp+g\cos\theta\cos\phi+\left(F_{z}+T_{z}\right)/m \end{aligned}\right. \tag{2}\]  
\[\left\{\begin{aligned} \dot{\phi}=p+\left(r\cos\phi+q\sin\phi \right)\tan\theta\\ \dot{\theta}=q\cos\phi-r\sin\phi\\ \dot{\beta}=p\sin\alpha-r\cos\alpha+\left(F_{y}+T_{y}\right)/mv \end{aligned}\right. \tag{3}\]  
\[\left\{\begin{aligned} \dot{p}=\left(c_{1}r+c_{2}p\right)q+c_{3} \overline{L}+c_{4}N\\ \dot{q}=c_{5}pr-c_{6}(p^{2}-r^{2})+c_{7}M\\ \dot{r}=\left(c_{8}p-c_{2}r\right)q+c_{4}\overline{L}+c_{9}N \end{aligned}\right. \tag{4}\]  
Here, \(x_{a}\), \(y_{a}\) and \(z_{a}\) are the positions of the CBA in the Earth-surface inertial reference frame; \(V\), \(\chi\) and \(\gamma\) are the ground velocity, flight-path azimuth angle and flight-path angle, respectively; \(m\) is the mass of the CBA; \(g\) is the acceleration due to gravity; \(u\), \(v\) and \(w\) are the velocity components in the aircraft-body reference frame; \(\alpha\), \(\beta\), \(\phi\) and \(\theta\) are the angle of attack, side-slip angle, roll angle and pitch angle, respectively; \(p\), \(q\) and \(r\) are the rate of roll, rate of pitch and rate of yaw, respectively, in the aircraft-body reference frame; \(F_{x}\), \(F_{y}\) and \(F_{z}\) are the aerodynamic components in the aircraft-body reference frame; \(T_{x}\), \(T_{y}\) and \(T_{z}\) are the engine thrust components in the aircraft-body reference frame; and \(\overline{L}\), \(M\) and \(N\) are the rolling moment, pitching moment and yawing moment, respectively, of the CBA in the aircraft-body reference frame.  
Writing \(x_{1}=\left[\begin{matrix}\phi&\theta&\beta\end{matrix}\right]^{T}\) and \(x_{2}=\left[\begin{matrix}p&q&r\end{matrix}\right]^{T}\), Equations (3) and (4) become  
\[\left\{\begin{aligned} \dot{x}_{1}(t)=A+Bx_{2}(t)\\ \dot{x}_{2}(t)=F+Gu(t)\end{aligned}\right. \tag{5}\]  
where \(u=\left[\begin{matrix}\delta_{a}&\delta_{e}&\delta_{r}\end{matrix} \right]^{T}\) with \(\delta_{a}\), \(\delta_{e}\) and \(\delta_{r}\) being the aileron, elevator and rudder deflection angles, respectively, and \(A\), \(B\), \(F\) and \(G\) are parameter matrices of the CBA system. For specific details, which can be found in the reference [38].
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication
### WDM OWC Receiver with a Single Surface Grating Coupler
#### 3.4.2 Experiment  
In Figure 11 the diagram of the experimental setup used in the experiment is shown. Four tunable lasers emitted light in the C-band with wavelength spacing of 200 GHz (approx. 1.6 nm) on ITU grid. All wavelengths were modulated with 25 Gbps NRZ PRBS15, amplified with EDFA, and combined with three stages of 3-dB combiners. Light is coupled into the free space with a collimator. Then, a one-inch diameter lens and 50 mm focal length focused light on the surface grating coupler. The distance between the collimator and the lens is 5 cm, and between the lens and the PIC is another 5 cm. Therefore, the total length of the wireless path is 10 cm. The collimator and lens were tilted 9\({}^{\circ}\) with respect to the surface of the PIC. The light was then coupled out of the PIC and amplified with the EDFA to compensate for the losses. Then, four wavelengths were demultiplexed with a commercial AWGR with 200 GHz channel spacing. Data transmission performance was analyzed using an Optical Spectrum Analyzer (OSA), a Digital Communication Analyzer (DCA), and a Bit Error Rate Tester (BERT).
## 2 Materials and Methods
### Industrial Tests, Sampling, and Laboratory Analysis  
The olive oil extraction experimental tests for the NIR device calibration were carried out from November 15 th up to December 20 th, 2023. During this period, the extraction plant worked regularly for 32 working days, processing olive fruits (_Olea europaea_ L.) of the Coratina (60%), Leccino (20%), and Cima di Bitonto (20%) varieties. The olives were harvested mechanically and transported to the mill from 2 pm to 5 pm every day; then, they were processed on a first-come, first-served basis, usually within the next 10 h. The malaxation time and temperature were, respectively, 60\({}^{\prime}\) and 27 \(\pm\) 1 \({}^{\circ}\)C. The decanter worked without adding process water.  
Figure 1: Layout of extraction plant: 1 defoliator, 2 hammer crushers, 3 piston pumps, 4 malaxing machines, 5 cavity pump stator, 6 horizontal centrifugal decanter, 7 vertical separator, 8 NIR probe.  
Figure 2: (**a**) Transmittance interface probe of the NIR tool scheme: 1 horizontal centrifugal decanter, 2 piston pumps, 3 NIR probe, 4 PC, 5 wi-fi transmitter. (**b**) Transmittance interface probe of the NIR tool photo.  
For each of the 32 test days, at regular time intervals, 3 samples of pomace, weighing 0.5 kg, were collected immediately after the NIR probe, by the sample collection point equipped with a valve (Figure 2a). A total of 96 pomace samples were collected. For each sample, the exact time of sampling was recorded.  
Moisture and oil content in the samples taken were then determined according to the official method [32].