esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis
### JCAS STPA--A Design Example
#### 3.1.2 Practical STPA Design  
An example of an STPA is simulated in CST Studio. The radiating elements are patch antennas, as in Figure 10a, designed on the Rogers RO3003 substrate with a thickness of \(0.25\) mm, a relative permittivity of \(\epsilon_{r}=3\), a copper cladding thickness of \(35\)\(\mu\)m, and excited by discrete ports. At \(f_{c}=28\) GHz, the \(10\) dB-bandwidth is \(863\) MHz and the \(3\) dB-beamwidth is \(\theta_{3 dB}<29.1^{\circ}\) for various \(\phi\)s. Upon the knowledge of the positions of phase centers for tiles, as shown in Figure 7a and the orientation of dominoes (horizontal or vertical), the STPA has obtained and shown in Figure 10b. The radiation from the STPA can be simulated by simultaneous excitation of the two horizontally or vertically juxtaposed patch antennas (corresponding to a tile) with the same phase. The number of patch antennas is \(250\), whereas the number of phase shifters and tiles is \(125\). As an example, the S11 parameters of \(18\) antennas (\(9\) dominoes) at subarray \(1\) are shown in Figure 10c when all antennas are excited simultaneously for scanning \((\theta,\phi)=(0^{\circ},0^{\circ})\) and Figure 10d for scanning \((\theta,\phi)=(30^{\circ},45^{\circ})\). The changes in the resonance frequency and magnitude of S11 are noticeable due to the mutual coupling effects. The radiation from the STPA at the frequency of \(28\) GHz and beamformed to boresight \((\theta,\phi)=(0^{\circ},0^{\circ})\) is shown in Figure 10e where the maximum directivity is \(29.4\) dB, the \(3\) dB-beamwidth is \(8^{\circ}\), and the maximum SLL is \(-13.6\) dB, whereas in case of beamformed towards \((\theta,\phi)=(30^{\circ},45^{\circ})\), the maximum directivity is \(26.5\) dB, the maximum SLL reaches \(-11.9\) dB and \(3\) dB-beamwidth to \(9.2^{\circ}\), as shown in Figure 10f. The decrease of directivity and SLL suppression in the full-wave simulations is due to the non-isotropic radiation of patch antennas, the mutual coupling effects, and the asymmetric radiations of horizontal and vertical tiles.  
Figure 9: The level of maximum SLL in EBP with \(\zeta=1.5\) for CUPA, SPA and STPA.
## 3 Proposed Design of Preamble for Synchronization  
As depicted in Figure 2, the proposed preamble structure consists of an OFDM symbol of length \(N\) as well as the two cyclic extensions of this symbol. Two groups of TSs are alternatively allocated in sub-carriers with indices of \(\{4k+1\}\) and \(\{4k+3\}\), respectively, in the frequency domain. The first group of TSs allocated in the \(\{4k+1\}\) sub-carriers starts from the initial position, while the other group of TSs allocated in the \(\{4k+3\}\) sub-carriers are cyclically right shifted by \(\Delta L\), thus formulating the frequency-domain sequence \(\{Y_{k}\}_{k=0}^{N-1}\) as given by  
\[\left\{\begin{array}{ll}Y_{4k+1}=c_{k},&k=0,1,\cdots,L-1\\ Y_{4k+3}=c_{(k-\Delta L)\ {\rm mod}\ L},&k=0,1,\cdots,L-1\\ Y_{k}\ \ \ \ =0,&\text{others}\end{array},\right. \tag{4}\]  
where \(\{c_{k}\}_{k=0}^{L-1}\) is the length-\(L\) (\(L<N/4\)) pseudo-random TS with a good property of auto-correlation, and mod represents the modular operation. Different signaling information can be conveyed through the variation of the cyclically shifted length \(\Delta L\). To convey log\({}_{2}L\) bits of signaling, there can be totally \(L\) choices for the shifted length. Note that all the preamble designs and the corresponding synchronization methods, including the proposed and the conventional ones, are considered to have the same preamble length for fair comparison.  
Afterwards, the active sub-carriers in (4) are differentially encoded and then processed by the \(N\)-point IFFT operation defined in (1) to obtain the time-domain preamble. As shown in Figure 2, the derived time-domain OFDM symbol can be regarded as two opposite parts, as represented by '\(-\)A' and 'A', since only odd sub-carriers are occupied. Then, the latter half part 'A' is multiplied by a scrambling sequence \((-1)^{n}\) and appended to the rear to form the '**S(A)**' part. Meanwhile, the latter half part 'A' itself is copied to the front to play the role of CP as well. The whole time-domain transmitted preamble signal is hence denoted by \(\{p_{n}\}_{n=0}^{2N-1}\) given by  
\[p_{n}=\left\{\begin{array}{ll}x_{n+N/2},&0\leq n<N/2\\ x_{n-N/2},&N/2\leq n<3N/2\\ (-1)^{n}\cdot x_{n-N},&3N/2\leq n<2N\end{array}\right., \tag{5}\]  
where \(\{x_{n}\}_{n=0}^{N-1}\) denotes the time-domain length \(N\) OFDM symbol generated by the \(N\)-point IFFT operation, as illustrated by the yellow part in Figure 2. The operation of scrambling could effectively relieve the contamination of the NBI, as explained in detail in the following section.
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 3 Results
### Rough Samples  
The etched sample has no _form_ or _waviness_, just fine-scale _roughness_ from the etching process. Figure 8 shows a velocity map captured from the etched Ti-6246 specimen overlaid on to a photograph of the surface, with the grain structure visible thanks to etching. The etching process produces roughness on a fine scale with different materials and processes producing different feature sizes scattering the light to different degrees. For this sample, despite the strong contrast in reflectance and the roughness imparted on the surface from the etching process, the SRAS measurement is still possible, with minimal data dropout of 4% and clear contrast between the grains in the SAW velocity map.  
Figure 7: Optical photographs (**left column**) and SRAS velocity maps (**right column**) from sidewall of as-deposited Ti-6 Al-4V WAAM specimens in (**top row**) sm undeformed state and post- (**middle row**) 50 kN and (**bottom row**) 75 kN interpass rolling. Scale bar in velocity maps indicates 2 mm.  
L-PBF samples were designed to have no _form_ or _wasiness_ so that the small-scale _roughness_ could be examined in isolation. L-PBF currently produces very challenging surfaces for laser ultrasound inspection, as the surface roughness is related to the particle size used for the powder layer. For the samples used in this study, this is around 30 \(\upmu\)m and leads to an \(R_{a}\) in the range of 10 \(\upmu\)m-15 \(\upmu\)m, with a similar correlation length. Given that the acoustic wavelength is around 80 \(\upmu\)m and the focal spot for the probe is around 30 \(\upmu\)m, this has a large impact on both the generation and detection of ultrasound.  
Figure 9a shows two samples made via L-PBF. The left shows a top surface scan on a Ti-6 Al-4V cube scanned using the SKED (see Figure 2b). Here, there are a considerable number of dropouts due to the extremely rough surface. The velocity measured shows little contrast due to the small grain size and strong texturing, in line with previous results in polished specimens [44]. By changing the detection spot distance from the generation patch, it is possible to combine scans to reduce the drop-out rate. The combination of six scans reduces the dropout from around 50% to below 10% at the cost of total scan time.  
Figure 9b shows results using the Quartet detector on an AlSi10 Mg sample made with internal defects [42], the velocity map of the top, and a small section of the side wall around the side hole feature. Again, there are some drop outs, but the texture information is visible in the velocity maps. The two velocity maps are plotted on the same velocity scale, indicating a difference in the dominant crystallographic plane between the two faces.  
Figure 8: Optical micrograph of an etched Ti-6246 sample with overlay of velocity map, taken with 16 averages per point. The scale bar shown in the lower left-hand corner indicates 5 mm. The transparency of the velocity map is used to allow the etched grain structure to be shown for comparison.
## 1 Introduction
### Continued Fraction Regression  
In 2019, a new approach for multivariate regression using continued fractions was introduced in [8] and compared with a state-of-the-art genetic programming method for regression. We named this approach 'Continued Fraction Regression', or CFR. The best existing algorithm currently utilizes a memetic algorithm for optimizing the coefficients of a model that approximates a target function as the convergent of a continued fraction [8; 9]. Memetic Algorithms are well-established research areas in the field of Evolutionary Computation, and the IEEE had established a Task Force in Computational Intelligence for their study. Therefore, it is important to refer readers to a number of the latest references and reviews on the field [10; 11]. Very recently, continued fraction regression has been used to obtain analytical approximations of the minimum electrostatic energy configuration of an electron \(n\) when the charge is constrained to be on the surface sphere, i.e., the celebrated Thomson Problem. For other applications of continued fraction regression, please see [12] and references therein.  
A basic introduction on analytic continued fraction approximation is necessary here. A continued fraction for a real value \(\alpha\) has the following form (1) and may be finite or infinite [13] according to whether or not \(\alpha\) is a rational number:  
\[\alpha=a_{0}+\frac{b_{1}}{a_{1}+\frac{b_{2}}{a_{2}+\ldots}} \tag{1}\]  
[PERSON] proved a mathematical formula that allows us to write a sum of products as a continued fraction (2):\[\beta=a_{0}+a_{0}a_{1}+a_{0}a_{1}a_{2}+\ldots+a_{0}a_{1}a_{2}\ldots a_{n}\] \[=\frac{a_{0}}{1-\frac{a_{1}}{1+a_{1}-\frac{a_{2}}{1+a_{2}-\frac{ \ddots}{1+a_{2}-\frac{a_{n-1}}{1+a_{n}}}}}}. \tag{2}\]  
This simple yet powerful equation reveals how infinite series can be written as infinite continued fractions, meaning that continued fractions can be a good general technique to approximate analytic functions thanks to the improved optimization methods such as those provided by memetic algorithms [9]. Indeed, CFR has already demonstrated its effectiveness as a regression technique on the real-world datasets provided by the _Penn Machine Learning Benchmarks_[9].  
In this paper, we use [PERSON] mathematical notation for generalized continued fractions [14] (i.e., a compact notation in which \"K\" stands for the German word \"Kettenbruch\", which means 'Continued Fraction'). Using this notation, we may write the continued fraction in (1) as  
\[\alpha=a_{0}+\mathop{\mathrm{K}}\limits_{i=1}^{\infty}\frac{b_{i}}{a_{i}}; \tag{3}\]  
thus, the problem of finding an approximation of an unknown target function of \(n\) variables \(\mathbf{x}\) given a training dataset of \(m\) samples \(S=\{(\mathbf{x}^{(i)},y^{(i)})\}\) is that of finding the set of functions \(F=\{a_{0}(x)...,b_{1}(x),...\}\) such that a certain objective function is minimized, i.e., we aim to find  
\[f(\mathbf{x})=a_{0}(\mathbf{x})+\mathop{\mathrm{K}}\limits_{i=1}^{\infty} \frac{b_{i}(\mathbf{x})}{a_{i}(\mathbf{x})}. \tag{4}\]
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 5 Overview of WUR Architectures
### ULP OOK WIR  
Recent publications have shown several design trends in three popular ULP receiver architectures for OOK-modulated RF signals: (1) passive energy detector (ED) first, (2) passive mixer first, and (3) LNA first. It should be noted that all three architectures are suitable candidates for NR LP-WUR but not for NB-IoT due to NB-IoT WUS complexity.  
* ED first (Figure 13): ED can be either passive or active. Utilizing an all-passive RF front end, passive ED-first receivers have the lowest power and can achieve nW [47; 48; 49]. On the other hand, active EDs [50; 51] consume more power and exhibit flicker noise but can achieve higher conversion gain compared to passive EDs. However, since the ED has a wide input bandwidth, these receivers rely on (usually bulky off-chip) filters for selectivity and do not provide multi-band support. The large BW of ED also limits the sensitivity of the architecture to \(-\)\(-\)60 dBm.  
* Passive mixer first (Figure 14): Many designs omit the LNA to save power, and instead use a passive mixer, which consumes zero DC power, as the first stage [52; 53]. An ED is often placed before the ADC to limit signal bandwidth and thereby relax the sampling requirement. Thanks to their frequency translation property [54; 55], passive mixer-first receivers offer good selectivity by simply adjusting the LO frequency. However, due to the lack of RF gain, passive mixer-first receivers have high NF when optimized for low power which degrades the sensitivity performance. The architecture can achieve a descent sensitivity level (\(-\)\(-\)80 to \(-\)90 dBm) with a sub-mW budget.  
Figure 13: Architecture of an ED-first ULP receiver.  
* LNA first (Figure 15): Using an LNA as the first stage significantly improves noise performance and is usually used for good sensitivity performance (<\(-\)100 dBm) for long range. However, the LNA can often consume several hundred uW or more. Several circuit design techniques have been demonstrated to reduce LNA power consumption, including bit-level duty cycling [56], the use of low supply voltages [57], subthreshold operation [58], and current-reuse topology [59].
###### Abstract  
The secondary lining of a tunnel is a critical load-bearing component, whose stability and structural integrity are essential for ensuring the overall safety of the tunnel. However, identifying lining structures and estimating their thickness using ground-penetrating radar (GPR) remain challenging due to several inherent limitations. First, the limited electromagnetic contrast between the primary and secondary linings results in weak interface reflections in GPR imagery, thereby hindering accurate delineation. Second, construction errors such as over-excavation or under-excavation often lead to complex interface geometries, further complicating the interpretation of GPR signals. To address these challenges, we propose an enhanced YOLOv8-seg network capable of performing pixel-level segmentation on GPR images to accurately delineate secondary lining regions and estimate their thickness. The model integrates a convolutional block attention module (CBAM) to refine feature extraction by emphasizing critical characteristics of the two interface layers through channel-wise and spatial attention mechanisms. The model is first pretrained on the COCO dataset and subsequently fine-tuned via transfer learning using a hybrid GPR dataset comprising real-world measurements and numerically simulated data based on forward modeling. Finally, the model is validated on real-world GPR measurements acquired from the Longhai tunnel. Experimental results demonstrate that the proposed method reliably identifies secondary tunnel linings and accurately estimates their average thickness.  
radar data; images; model-based transfer learning; tunnel lining layers; lining thickness estimation Article
###### Abstract  
Background: Total ankle arthroplasty (TAA) has grown in popularity and indications, with encouraging results over time. Today, preoperative and postoperative evaluations are mainly performed using clinical test and diagnostic imaging, but there is a deficiency in objectively evaluating the biomechanics of the foot and ankle, which serve as the functional markers for monitoring the effectiveness and outcomes of surgery. Inertial measurement units associated with plantar pressure measurements may provide an accurate and reliable method of evaluating function through the analysis of gait and ankle joint mobility. The aim of this study was to introduce an innovative technology, to assess its accuracy and feasibility compared to standard clinical assessment methods and to objectivity kinematic outcomes in patients with end-stage ankle OA before and after TAA surgery. Methods: A consecutive series of eight patients with symptomatic end-stage osteoarthritis and treated with TAA was prospectively evaluated using clinical scores (AOFAS, MO&PQ, VAS, SF-36, 17-IFFI), physical tests (FPI, ALT), plantar pressure measurements with FLEX EPS/R2 Letsense(r) Radipolometric platform, gait analysis and wearable sensors-based ankle motion and kinematic outcomes using Wiva Science inertial sensors by Letsense(r). Data were collected preoperatively and 4 months after surgery. Results: All PROMs exhibited statistical significance in improvement from pre- to postoperative periods, except for one. Physical examinations showed no significant changes of the foot shape and alignment. Plantar pressure analyses revealed no significant changes in static and dynamic evaluations, but a more uniform distribution of plantar pressure was observed between the two periods. Inertial sensor parameters demonstrated no significant differences, except for a significant reduction in stride length and step length for the operated foot after surgery. Conclusions: Gait analysis using inertial sensors and plantar pressure measurements offer ease of handling, cost effectiveness, portability and swift data reading, making them highly appealing for widespread clinical use. Integrating these tools into the routine assessments of patients with TAA holds promise for advancing precision of treating this condition and our depth of its understanding, contributing to more comprehensive and insightful patient care.  
gait analysis; inertial sensors; total ankle arthroplasty
# Cellular Automata Approach to Topology Optimization of Graded Multi-Material Structures
## 3 Topology Optimization of Graded Multi-Material Structures: The Basic Introduction  
The main concept of present investigations, which needs to be emphasized, is the idea of topology optimization of multi-material structures with a predefined gradation of material properties. This can be treated as the extension of the discussion included in [44]. The two tasks are under consideration, namely topology optimization of the FGM structures and design of sub-domain oriented multi-material structures, including FGM interfaces. Functionally graded materials are composites consisting of two or more constituent phases with continuous variation of properties along one direction, which is directly related to changes in the material properties throughout the volume.  
The model of properties gradation has a significant influence on the behavior of the FGM structure. Following the considerations of [21; 22; 23; 24; 25; 27], in the present paper, the exponential model has been chosen for one-dimensional material gradation, see Equations (7)-(9):  
\[E(x)=E_{0}\epsilon^{xx}, \tag{7}\]  
\[E_{0}=E_{1}\left(\frac{E_{1}}{E_{2}}\right)^{\frac{x_{1}}{x_{2}-x_{1}}} \tag{8}\]  
\[\alpha=-ln\left(\frac{E_{1}}{E_{2}}\right)\frac{1}{x_{2}-x_{1}}, \tag{9}\]  
where \(x\) is a coordinate measured along the material variation direction, \(E_{1}\) is the Young modulus of the material at position \(x_{1}\), and \(E_{2}\) is the Young modulus of the material at position \(x_{2}\). Function \(E(x)\) describes the FGM material. It is assumed that the Poisson ratio varies only slightly between two materials, so its gradation can be omitted.  
In the topology optimization of FGM structure material interpolation schemes, Equations (5) and (6) can be rewritten as FGM-SIMP, see Equation (10), [25] and FGM-RAMP, Equation (11):  
\[E(x)=d_{n}^{p}E_{0}\epsilon^{xx}, \tag{10}\]  
\[E(x)=d_{n}/(1+q(1-d_{n}))E_{0}\epsilon^{xx}, \tag{11}\]  
respectively.  
The possibility of application of FGMs in engineering practice has induced the research on the numerical representation of FGM materials, particularly their implementation into the finite element method, for details see [23]. In this paper, the numerical model of the structure has been built as the set of layers, treated as the isotropic ones. The material properties have been evaluated in the middle of each layer by using power law grading, see Equation (7). For each layer, the individual material properties were defined, so as to approximate the functional variability of material properties for the whole structure [45; 46]. The structure assembled from single-material layers resembles the traditional piecewise material distribution applied in standard topology optimization formulation. It is worth mentioning that in [21; 22; 23; 24; 25] the continuous approximation of material distribution CAMD [47; 48] was adopted. The layer-based formulation is the suitable approach for the numerical modeling of FGM structures from the manufacturing process point of view because step-wise gradation is easier to produce than the continuous one.
# Physicochemical Characterization of Hyaluronic Acid and Chitosan Liposome Coatings
## 3 Results
### Physicochemical Properties of the Coated Liposomes
#### 3.1.2 Nuclear Magnetic Resonance (NMR)  
To study the conformational properties of coated liposomes, proton NMR spectra were recorded at 600 MHz and 298 K. In Figure 2, the spectrum of DOPC/DOPE in D\({}_{2}\)O is reported. The assignment was carried out as previously reported [31; 32] and the typical behavior of the vesicles in solution was revealed: a large signal confirmed the presence of macrosystems in solution and the poor resolution of resonances was a typical characteristic of amphiphilic molecules that form aggregates [33].  
The NMR spectrum of HA-coated liposomes showed the presence of large signals typical of vesicles (signals at an upfield shift) and the presence of proton signals assigned to HA in solution. In particular, in the range of 3.3-4.5 ppm, NMR signals of the H1-H5 and H1-H6' protons of glucuronic acid and acetylglucosamine were evident. At 1.98 ppm, the signal of methyl groups of acetylglucosamine were found [34; 35]. The chemical shift and the signal width of the HA proton suggest the presence of HA on the liposome surface, which highlights that the coating process occurred properly and that HA was  
Figure 2: \({}^{1}\)H-NMR spectra of: (**a**) DOPC/DOPE uncoated liposomes; (**b**) DOPC/DOPE HA-coated liposomes. (**c**) Magnification of the \({}^{1}\)H-NMR spectrum of coated liposomes highlighted the large signal of vesicles in solution.  
homogeneously distributed on the liposome surface. The purification process, by means of dialysis, allowed the statement that there was not any free HA in solution.  
Figure 3 reports the NMR spectrum of the CH-coated liposome recorded in D\({}_{2}\)O at 298 K after the purification processes. In this case, all the NMR signals suggested the presence of large systems in solution. The NMR proton signal at 5.5 ppm was assigned to the H1 of the chitosan, the signal at 3.52 ppm was due to the H3 proton and the H2 signal was at 3.25 ppm. The signal in the range of 3.75-4.34 ppm was assigned to H4, H5, H6 and H6\({}^{\prime}\) protons. The signal at 2.00 ppm was due to the methyl group, -NHCOCH3, of chitin, present in the sample due to the non-total deacetylation of the compound. The large signals at an upfield shift (range 0.5-1.5 ppm) were assigned to the DOPC/DOPE liposome [31].  
The NMR spectrum confirmed the different sizes obtained for the two different coated liposomes. In particular, the HA-coated DOPC/DOPE systems showed a lower size compared with the CH-coated DOPC/DOPE derivatives. The NMR proton signals of HA and CH suggest the presence of these polymers on the surface of the vesicles.