esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification  
### Construction of Experimental System  
In this paper, the RAM-5000 SNAP nonlinear ultrasound system is used to excite a 0.5 MHz sinusoidal pulse to detect microcracks in a 6061-t6 aluminum alloy sheet. The Output Level is adjusted to 65 and the signal amplitude is approximately 500 V. At this point, the nonlinear effect is already obvious. (When the Output Level is determined, the connected probe is different, and the output voltage is different). In addition, a Hanning window was added, which makes the received signal frequency pure and is good for frequency analysis. The modulation signal's burst width is set to 10 \(\upmu\)s and the corresponding output signal amplitude is about 270 V. The experimental system block diagram is as Figure 13, the oscilloscope is used to observe and extract the time domain waveform, which is convenient for parameter debugging, and the sampling rate is 4 GSa/s:  
Due to the complicated environment, there are many interference factors in the experiment, and there are many noises in the received signal. An important feature of EMD is that the frequency  
Figure 12: Evaluation chart.  
Figure 13: System block diagram of the experiment.  
can be selected. The noise frequency in the received waveform is far from the signal frequency, and the EMD decomposable condition is satisfied. The specific frequency can be extracted by the prior information. The number of IMFs decomposed is affected by the extreme points and the screening termination conditions. This article takes the default parameters and analyzes the frequency of each IMF programmatically. Finally, the noise is removed to achieve the filtering effect, as shown in the following Figure 14:  
As can be seen from the above figure, EMD decomposes the original signal into intrinsic mode functions of different frequencies and removes the frequency higher than the second harmonic for signal reconstruction. After EMD filtering, the signal-to-noise ratio of the signal is obviously increased.  
The center frequency of the 0.5P13*13 adjustable angle probe is 0.5 MHz and it is used to excite the 0.5 MHz lamb wave. Signal reception uses a 1P13*13 adjustable angle probe to better receive the second harmonic because of its 1 MHz center frequency. Excitation and reception probes are matched with RAM-5000 SNAP by BNC. The wedges are made of plexiglass, which is the key to exciting the S0 mode lamb wave. The coupling agent uses glycerin. The microcracks are produced by tapping and are about 3 mm long. The physical map of the microcrack is shown in Figure 15. The microcrack center point is still used as the coordinate origin, and the crack extends along the y-axis. Through simulation comparison, it can be seen that the microcrack positioning map is ideal when the receiving and sending positions are all five and symmetrically distributed, so the experiment adopts the same arrangement.  
Figure 14: Signal processing: (**a**) Original waveform; (**b**) waveform filtered by EMD.  
Figure 15: Physical map of crack.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 3 Channel Access
### FBE Variants  
The basic FBE algorithm is trivial and does not offer any contention resolution mechanisms. From Figure 1 it is apparent that the only airtime available to other contending nodes to initiate their transmissions is during the idle period. Therefore, a single node can easily dominate channel access (as we show in Section 4). Several variants of FBE have been proposed (Figure 3) to overcome the lack of a contention resolution mechanism, and we describe their operation in the following.
###### Abstract  
In this article, we investigate in different scenarios the feasibility of using massive multiple-input-multiple-output (mMIMO) with reconfigurable intelligent surfaces (RISs) to increase the throughput and coverage with high energy efficiency, considering sub-6 GHz, mmWave, and THz bands. With that objective, a centralized radio access network (C-RAN) suitable for beyond fifth-generation (B5G) systems is considered, where we integrate the base stations (BSs) with multiple RISs and IoT devices or user equipment. RISs with a large number of quasi-passive reflecting elements constitute a low-cost approach capable of shaping radio wave propagation and improving wireless connectivity. We consider a scenario where multiple RISs are combined with mMIMO in the uplink in order to provide connectivity to a smart city (with thousands of active low-power IoT devices), wirelessly, in the 3.6 GHz and 28 GHz bands. We also address a scenario where RISs are adopted with mMIMO in the downlink so as to offer connectivity to a stadium with a pitch, (and thousands of active users' equipment) in the 28 GHz band. Finally, we also studied the connectivity at 100 GHz of a factory in which several RIS panels, replacing most of the BSs equipped with mMIMO, assure improved throughput and coverage. We concluded that RISs are capable of improving the performance in any of these analyzed scenarios at the different frequency bands, justifying that they are a key enabling technology for 6G.  
RIS; massive MIMO; system-level simulation; C-RAN; precoding; B5G; IoT 28 November 2023  
19 January 2024  
Accepted: 24 January 2024  
Published: 28 January 2024
## 4 Results
### Simulated Data
#### 4.1.4 Fourth Experiment  
The fourth experiment belongs to the second category of experiments. We have realized this experiment for the evaluation of the potential of the wavelet-based polynomial fitting method. We considered the case of a single target, but this time we generated a third order polynomial trajectory, shown in Figure 6a. We have added AWGN on the component corresponding to \(y\) axis obtaining a signal with a SNR of 0 dB. After the simulation of the tracking based on the cooperation of the Hungarian data association algorithm and the Kalman filter algorithm, we obtained the simulated trajectory in Figure 6b. Next, we applied the wavelet-based polynomial fitting method proposed in this paper, obtaining the result in Figure 6c. We have denoised the sequence \(\{y_{t}\}\) using the Daubechies MW with four vanishing moments (Daubechies eight). The threshold for the hard thresholding filter was selected following the rule of \(3\sigma\). We have estimated the standard deviation of the noise component that remained in the signal in Figure 6b, \(\sigma\), and we have selected the value \(3\sigma\) for the threshold of the hard thresholding filter used for denoising. Next, we have extracted the polynomial part of the result obtained, computing once again the DWT, using  
Figure 5: Partial and final results of the third experiment with simulated data: (**a**) spatial spreading of targets after data association; (**b**) the effect of Kalman filtering; (**c**) the effect of the explicit polynomial fitting method.  
the same MW because it has four vanishing moments, as it is required for a polynomial of third-degree extraction, retaining only the approximation coefficients and computing the IDWT of the result (using the same MW).  
Comparing the waveforms at the top and bottom of Figure 6, it can be said that only the border errors affect the estimation precision. These border errors are a consequence of the DWT initialization problem.  
Therefore, the wavelet-based polynomial fitting procedure is effective even in the case of third order polynomial trajectories, practically removing the entire uncertainty from the output of the Kalman filter.
## 4 Discussion
### Influence of Mechanical and Thickness Parameters  
As humans age, the skin undergoes significant structural and mechanical changes, including the degradation of dermal elastic fibers and the denaturation of collagen bundles. These changes lead to substantial variations in Young's modulus and tissue thickness compared to younger skin [37]. In this section, we explore how the mechanical properties and thickness of different skin layers influence wrinkle formation. Figure 9 presents the \(x\)-direction strain at Point A on the skin surface under varying epidermal thicknesses and Young's moduli. The results indicate that a lower Young's modulus in the epidermis reduces the likelihood of wrinkle formation. However, as shown in Figure 9, the effect in the dermis is reversed, and wrinkles become more pronounced as Young's modulus of the dermis decreases. In terms of thickness, both the epidermis and dermis follow a similar trend: thinner layers are more susceptible to wrinkle formation.  
The x and y coordinates at the inflection points of the curves in Figure 9-e correspond to the critical muscle compression and critical surface strain required for wrinkle formation. These critical parameters, influenced by variations in skin layer thickness and [PERSON]'s modulus, are further analyzed in Figure 9,e. The results show that while dermis and hypodermis thickness significantly impact critical muscle compression, they have minimal influence on critical surface strain. Conversely, an increase in epidermal thickness leads to a decrease in critical surface strain. Regardless of the specific skin layer, a greater overall thickness results in higher critical muscle compression thresholds for wrinkle formation. Young's modulus exerts a more pronounced effect than layer thickness among the examined factors. A stiffer epidermis decreases the critical muscle compression required for wrinkles to form when simultaneously reducing the critical surface strain. In contrast, a softer dermis facilitates wrinkle formation by lowering the required critical muscle compression and surface strain. These findings underscore the intricate biomechanical interplay between skin layers in the wrinkle formation process and highlight the importance of considering both mechanical properties and layer thickness when studying age-related skin deformation.  
Figure 8: FE results of skin wrinkling caused by **(a)** SC for young skin, and **(b)** MC for aged skin.  
With human age, biomechanical alterations in skin structure collectively increase wrinkle susceptibility [37]. Epidermal stiffening and thinning, coupled with hypodermal fat loss, synergistically elevate critical surface strains while reducing the muscle compression required for wrinkle formation, as shown in Figure 9. This mechanistic triad explains why aging skin develops wrinkles more readily under smaller facial movements.
## 5 Conclusions  
This study proposes a novel ship image generation method and a novel ship image annotation method. The proposed method was developed based on a 3D simulated ship and CNN algorithms. Firstly, a large number of simulation ships were generated by 3D simulation software. The experimental image scene used as the background image was selected. Secondly, the proposed generation method of simulated ship images includes three key parts: (1) the posture of the simulation ship; (2) the trajectory of the ship; (3) the size of the ship. The postures of the simulation ships arselected manually according to the actual navigation posture of the ships; the trajectory and the size of the simulation ship can be computed with the model proposed in this paper. There was a high degree of similarity between the SRS image and the ship image of the real scene. The experimental results show that the SRS images achieved a higher detection accuracy than the same number of the ship images. The automatic annotation methods proposed can be used as a bounding box annotation and contour annotation. The ship image data set and automatic annotation program of this study will be published subsequently.  
There are still some problems to be solved in this study. First, we manually selected the posture of the 3D simulation ship according to the posture of the ship in the real scene. In a follow-upwork, the proposed method will automatically identify the ship's posture in the real scene, on this basis, it will automatically set the 3D simulation ship posture. Second, there were about 20 kinds of ships in the 3D simulation ship dataset; thus, the number of ship types and the similarity between SRS images and real ship images need to be increased. Third, we calculated the position and the size of the simulation ship in Euclidean space. In the follow-up work, time-domain and frequency-domain characteristics will be used to calculate the position of the target ship in SRS images.  
Conceptualization, J.Y. and Z.H.; data curation, J.Y. and Z.H.; funding acquisition, J.Y. and Z.H.; project administration, C.P. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) (No. 51679181), Research project of provincial teaching reform in Colleges and universities of Hubei Province (No. 2020809), Teaching Research Project of College of Technology, Hubei Engineering University (No. 2020)Y03), Scientific Research Project of College of Technology, Hubei Engineering University (No. 2021 Hgaky18).  
Data Availability Statement: Data sharing is not applicable.  
The author would like to thank all teachers and colleagues from Wuhan university of technology, and all involved in this research.  
The authors declare no conflict of interest.
# Biosorption of Pb(II) Using Coffee Pulp as a Sustainable Alternative for Wastewater Treatment
###### Abstract  
The present research shows the results obtained from the biosorption process of Pb, using coffee pulp as a biosorbent in synthetic waters. To do this, the lignin and cellulose content and the percentage of removal of Pb\({}^{2+}\) ions was determined; additionally, the sorption's optimal variables, such as the optimum pH, the point of zero charge (pHpzc), the kinetics and the adsorption isotherm, were determined. A comparison was made with other by-products derived from coffee crops. According to the results obtained in this research, the cellulose percentage was 29.12 \(\pm\) 0.22% and the lignin percentage was 19.25 \(\pm\) 0.16% in the coffee pulp, the optimum pH was 2.0 units and the kinetic model, which adjusted to the biosorption's process, was the pseudo-second order of [PERSON] and [PERSON], presenting an isotherm of [PERSON]'s model and pHpzc of 3.95 units. Lastly, the removal of the pollutant was 86.45%, with a capacity of maximum adsorption of 24.10 mg:g\({}^{-1}\) obtained with a particle size of 180 \(\mu\)m, time of contact of 105 min and at 100 RPM. Finally, we express that (a) the coffee pulp can be used as a sustainable alternative for the removal of the pollutant mentioned in synthetic and/or industrial wastewater matrices, to meet goals 3.9 and 6.9 of the Sustainable Development Goals of the 2030 agenda, and (b) the novelty of this research is the use of an agricultural waste of easy acquisition as a sorbent, without chemical modification, since it presented a high percentage of efficiency in the removal of Pb\({}^{2+}\) ions. In turn, the challenge of this research is implementing this green technology on a pilot, semi-industrial and/or industrial scale in wastewater treatment systems.  
biosorption; coffee pulp (CP); lead; sustainability; wastewater Article
# A Stigmergy-Based Differential Evolution
## 3 Stigmergic Differential Evolution  
### Differential Evolution  
Differential Evolution (DE) is an algorithm proposed in 1995 to solve continuous optimization problems [7]. Even though there are several variants of DE [8], one of the more popular and powerful variants is the DE/best/1/bin, which since its development has solved several problems in science and engineering [48; 49; 50]. The notation in the next explanations considers lower bold letters as vectors, lower italic letters with subindexes as vector components, upper bold letters as matrices and upper or lower letters without subindexes as single real or integer numbers.  
The first step in the algorithm is the initialization of a random population:  
\[\begin{array}{l}\mathbf{X}=x_{i,j}^{k}=l_{j}+rand()(u_{j}-l_{j})\\ j=1,\cdots,D;\;i=1,\cdots,N_{p};\;k=0\end{array} \tag{1}\]  
where \(N_{p}\) is the population size, \(D\) represents the problem dimensionality, \(k\) is the actual iteration, \(l\) and \(u\) are the lower and upper limits of the search space and \(rand()\) is a random number drawn from a uniform distribution. In this phase the parameters' scaling mutation and crossover factor \((F,Cr)\) are also set. The second stage creates a mutant vector:  
\[\mathbf{v}^{k}=\mathbf{x}_{best}^{k}+F\cdot\left(\mathbf{x}_{r_{1}}^{k}-\mathbf{x}_{r_{2}}^{k}\right) \tag{2}\]  
considering \(r_{1}\) and \(r_{2}\) as random integers uniformly distributed in \([1,N_{p}]\) with the restriction that \(r_{1}\
eq r_{2}\
eq i\), and \(\mathbf{x}_{best}^{k}\) is the best individual found so-far. The next step obtains the trial vector:  
\[u_{j}^{k}=\begin{cases}v_{j}^{k}&if\;rand()\leqslant C_{r}\;or\;j=j_{rand}\\ x_{i,j}^{k}&otherwise\end{cases} \tag{3}\]  
where \(j_{rand}\in\{1,2,\cdots,D\}\) is a random integer taken from a uniform distribution. In the last phase, the algorithm applies the selection operation by evaluating the trial and the original individual with the objective function \(f\):  
\[\mathbf{x}_{i}^{k+1}=\begin{cases}\mathbf{v}^{k}&if\;f\left(\mathbf{u}^{k}\right)<f\left( \mathbf{x}_{i}^{k}\right)\\ \mathbf{x}_{i}^{k}&otherwise\end{cases} \tag{4}\]  
The previously mentioned steps (mutation, crossover and selection) are repeated until a criterion is met, usually a maximum number of iterations.
## 4 Mobile Head CT Scanner and Rotorcraft Retrofitting
### Design Considerations for Retrofitting Head CT Scanners into Rotorcraft
#### 4.2.1 Major Components of the Mobile Head CT Scanner  
The total weight of a CT scanner is from the accumulation of different components assembled within the structure. It is therefore important to identify the components and determine if they can be, replaced, removed, or modified to reduce the size and/or weight where possible. Unlike the conventional hospital floor-fixed CT scanners, the portable CT scanners are the smaller counterparts, and they are designed to allow hospital staff to transport the scanner to the patient. Therefore, they have lesser components and are drastically lighter in exchange for lower imaging quality and are more suitable for fitting into an aircraft. The typical parts that can be found in a portable head CT scanner are labeled in Figure 4.  
Table 2 describes the role of each component, determining whether to keep, remove, or modify, and justification for each decision. When modifying the CT scanner to be retrofitted, it is important to first identify the major functioning components and their roles, and, correspondingly, determine whether modification will interfere with the primary function of the CT scanner. In order to simplify the design pro  
Figure 4: Components of CereTom by Neurologica.  
Figure 3: Portable head CT scanners: CereTom (left) and xCAT (right). Figure has been reprinted with permission from Springer Nature, Int J CARS, “Evaluation of portable CT scanners for otologic image-guided surgery”, [PERSON] et al, copyright 2012 [29].  
will be kept at default to avoid disrupting the primary function of the CT scanner to avoid complicating the modification process.
## 2 Geographical Setting and Morphometry of the Study Area  
TOPMODEL was applied as a conceptual distributed runoff model to generate runoffs in the target watersheds. TOPMODEL considered the topographic factors estimated by DEM in the watershed. This study investigated two mountainous watersheds, namely, the Dongkok and Ieemokjung watersheds, using TOPMODEL, as shown in Figure 1. The Dongkok watershed is located upstream of the Wicheon river, a tributary of the Nakdong River, and contains one rain- and a number of streamflow-gauge stations. Similarly, the Ieemokjung watershed is a subcatchment of the Pyeongchang River, a tributary of the South Han River, and contains one rain- and one streamflow-gauge station. Figure 1 shows the locations of the rain- and streamflow-gauge stations in the Dongkok and Ieemokjung watersheds.  
Table 1 lists the topographical and morphological characteristics of the Dongkok and Ieemokjung watersheds. The drainage area, river length, and main tributary length in the Ieemokjung watershed are larger than those in the Dongkok watershed. However, the mean width of the basin and shape factor in the Dongkok watershed is larger than that in the Ieemokjung watershed. These conditions indicate that the Dongkok watershed has a more circular shape and a wider channel than the Ieemokjung watershed. In terms of the river density and basin slope, the Dongkok watershed is slightly larger than the Ieemokjung watershed.  
The applied DEM spatial resolution values were 5, 10, 20, 40, 80, 160, 200, and 300 m, using ArcGIS. DEM data scales for Dongkok and Ieemokjung watersheds were 1:5000 and 1:25,000, respectively. The hydrological data were based on rainfall data, and the runoff data were obtained from the International Hydroological Programme (IHP) report [24]. The present study chose four rainfall-runoff events in the Dongkok watershed and seven events in the Ieemokjung watershed. The parameters of the selected rainfall-runoff events are listed in Table 2. Rainfall-runoff events in the 1990s were adopted because the applied DEM data and verified streamflow data for  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Dongkok Watershed** & **Ieemokjung Watershed** \\ \hline Drainage area (km\({}^{2}\)) & 33.63 & 55.76 \\ River length (km) & 8.00 & 16.55 \\ Main tributary length (km) & 39.92 & 53.25 \\ Mean width of basin (km) & 4.203 & 3.370 \\ Shape factor & 0.525 & 0.204 \\ Drainage density (km/km\({}^{2}\)) & 1.188 & 0.955 \\ Basin slope & 0.452 & 0.344 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Topographic characteristics of the Dongkok and Ieemokjung watersheds.  
watersheds were created in the late 1990s by the National Geographic Information Institute in Korea ([[https://www.ngini.go.kr](https://www.ngini.go.kr)]([https://www.ngini.go.kr](https://www.ngini.go.kr))) [24; 25].
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 2 System Description
### Conventional Frequency Offset Estimation Methods  
In the large frequency offset scenario, i.e., the normalized frequency offset \(|\epsilon|>1\), the conventional estimation methods firstly separate \(\epsilon\) by two parts, i.e., \(\epsilon=\epsilon_{I}+\epsilon_{F}\), where  
Figure 1: The baseband transmitter and receiver structure of an OFDM system with pilot-based frequency offset estimation.  
is an integer called IFO, and \(|e_{F}|<1/2\) called FFO. Then, the IFO \(e_{I}\) and the FFO \(\epsilon_{F}\) are estimated by different methods, which are simply introduced as follows.