esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, we analyzed the achievable data rate of an RIS-assisted SatCom system asymptotically, considering limitations such as practical CSI acquisition. In particular, we derived the asymptotic upper bound of the achievable rate and designed a passive beamformer that can asymptotically achieve the upper bound using numerous reflecting elements on the RIS. Moreover, we asymptotically derived closed-form expressions for the ergodic achievable rate and outage probability of the proposed scheme. We proved that the derived asymptotic analyses can accurately determine the performance of the RIS-assisted SatCom system concerning the ergodic achievable rate and outage probability without requiring extensive simulations. Leveraging the derived asymptotic results in the unicast system, we extended our results to cover the multicast system, such as enabling a UAV to serve multiple UEs simultaneously. From the simulation results, we can observe that  
Figure 5: Performance comparison of the achievable rate ratios for a multicast system.  
Figure 6: Performance comparison of the outage probabilities as a function of \(N\) when \(\delta=0.8\).  
the estimated ergodic rate and outage probability were in agreement with the simulation results. Moreover, the proposed scheme asymptotically achieved the optimal performance, verifying the asymptotic optimality of the proposed scheme. We expect that the proposed scheme can significantly reduce signal overheads and the amount of feedback, rendering it a promising technique in practical SatCom systems.  
Conceptualization, M.J.; methodology, H.S.; software, M.J.; validation, M.J. and H.S.; formal analysis, M.J.; investigation, M.J.; resources, M.J.; data curation, M.J.; writing--original draft preparation, M.J.; writing--review and editing, H.S.; visualization, M.J.; supervision, H.S.; project administration, M.J. and H.S.; funding acquisition, M.J. and H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the metaverse support program to nurture the best talents (IITP-2023-RS-2023-00254529) grant funded by the Korea government (MSIT) and by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2021R1C1C1012950 and No. 2021R1F1A1051075).  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
###### Abstract  
In our previous study, we proposed a vision-based ranging algorithm (LRA) that utilized a monocular camera with four lasers (MC4L) for indoor positioning in dark environments. The LRA achieved a positioning error within 2.4 cm using a logarithmic regression algorithm to establish a linear relationship between the illuminated area and real distance. However, it cannot distinguish between obstacles and walls. Hence, it results in severe errors in complex environments. To address this limitation, we developed an LBP-CNNs model that combines local binary patterns (LBPs) and self-attention mechanisms. The model effectively identifies obstacles based on the laser reflectivity of different material surfaces. It reduces positioning errors to 1.27 cm and achieves an obstacle recognition accuracy of 92.3%. In this paper, we further enhance LBP-CNNs by combining it with fast Fourier transform (FFT) to create an LBP-FFT-CNNs model that significantly improves the recognition accuracy of obstacles with similar textures to 96.3% and reduces positioning errors to 0.91 cm. In addition, an inertial measurement unit (IMU) is integrated into the MC4L device (MC4L-IMU) to design an inertial-based indoor positioning algorithm. Experimental results show that the LBP-FFT-CNNs model achieves the highest determination coefficient (R\({}^{2}\) = 0.9949), outperforming LRA (R\({}^{2}\) = 0.9867) and LBP-CNN (R\({}^{2}\) = 0.9934). In addition, all models show strong stability, and the prediction standard index (PSI) values are always below 0.02. To evaluate model robustness and MC4L-IMU work reliably under different conditions, the experiments were conducted in a controlled indoor environment with different obstacle materials and lighting conditions.  
local binary pattern; fast Fourier transform; self-attention mechanism; CNNs; inertia-based indoor positioning algorithm; visual localization; obstacle recognition; dark environment +
Footnote †: journal: Applied sciences
Alginate is used as a polymer in hydrogels because of its highly adsorptive capacity, high cost-effectiveness, biodegradability, and antibacterial characteristics [13]. Alginate hydrogels are also highly porous, contributing to the functionality of this matrix in wound dressings by keeping wounds moist without damaging newly formed tissue [14]. However, hydrogels based only on alginate exhibit poor structural stability that degrades further on contact with biological fluids due to loss of the crosslinker ion calcium (Ca\({}^{2+}\)) [15]. To increase the handling and stability of alginate hydrogels, covalent or ionic crosslinking can be employed, or alginate can be combined with other biopolymers such as gelatin or chitosan [16; 17; 18]. Plasticizers are another excellent option to increase hydrogel stability because they reduce intermolecular forces between polymer chains, decrease brittleness, and increase flexibility, toughness, traction, and resistance; however, plasticizers also compromise the porosity of the resultant gel [19]. Recently, our group reported the impact of poly(ethylene glycol) methyl ether methacrylate (PEGMA) as a plasticizer in calcium alginate hydrogels. The addition of PEGMA created a spongious material composed of semispherical and interconnected 50-100 \(\upmu\)m pores with increased stability in aqueous media and structural resistance to manipulation and bending [20]. To determine if Ca-alginate-PEGMA hydrogels (CAPH) could confer site-specific delivery of antibodies, a murine IgG1 pan-specific TGF-\(\beta\) neutralizing antibody, 1D11, was incorporated into CAPH [21]. The ID11 antibody was selected based on the capacity to neutralize the three isoforms of TGF-\(\beta\) and the implication of this cytokine in wound healing, and on previous studies reporting visible improvements in scarring, reduced infiltration of immune cells, less extracellular matrix (ECM) deposition of collagen fibers, and reduced vascularity observed following neutralization of TGF-\(\beta\)[22; 23; 24]. Therefore, the capacity of CAPH to release TGF-\(\beta\) neutralizing antibodies at the site of a wound and reduce the fibrotic effects of TGF-\(\beta\) was investigated in this study using a mouse model of dorsal bilateral excisional wound healing. Wound contraction ratio, collagen content, and wound healing assessment were determined to evaluate the activity of 1D11 and effectiveness of CAPH as a delivery vehicle.
## 3 Saddle Point Approximation for Mutual Information
### Average Mutual Information over Doubly Correlated MIMO Rayleigh fading Channels  
The average mutual information over doubly correlated MIMO Rayleigh fading channels is computed as below,  
\[\mathcal{I}(\mathbf{x};\mathbf{y})\approx\log_{2}\mathbf{N}-\frac{1}{N}\sum\limits_{m=1}^{N }\mathbb{E}_{\mathbf{H}_{\text{WC}}}\Bigg{\{}\log_{2}\Bigg{[}\sum\limits_{k=1}^{N }\exp\Bigg{(}-\frac{||\mathbf{c}_{m,k}||^{2}}{\sigma^{2}}\alpha_{m,k}\Bigg{)}\Bigg{]} \Bigg{\}} \tag{13}\]  
Since \(\mathbf{c}_{m,k}=\mathbb{E}_{\mathbb{R}}{}^{1/2}\mathbf{H}_{\text{WC}}\mathbb{E}_{ \mathbb{T}}{}^{1/2}\mathbf{U}_{\mathbb{T}}{}^{\text{H}}\mathbf{d}_{m,k}\), it is still quite hard to compute the expectation of \(\mathbf{H}_{\text{WC}}\). Consequently, (13) remains unsuitable for theoretical applications. Obviously, when SNR varies from \(-\infty\) to \(+\infty\), \(\alpha_{m,k}\) satisfies \(1/3<\alpha_{m,k}<1/2\) by (11), so average mutual information is approximated by  
\[\begin{array}{c}\mathcal{I}(\mathbf{x};\mathbf{y})\approx\log_{2}\mathbf{N}-\frac{1}{2N }\sum\limits_{m=1}^{N}\mathbb{E}_{\mathbf{H}_{\text{WC}}}\Bigg{\{}\log_{2}\Bigg{[} \sum\limits_{k=1}^{N}\exp\Bigg{(}-\frac{||\mathbf{c}_{m,k}||^{2}}{2\sigma^{2}} \Bigg{)}\Bigg{]}\Bigg{\}}\\ -\frac{1}{2N}\sum\limits_{m=1}^{N}\mathbb{E}_{\mathbf{H}_{\text{WC}}}\Bigg{\{}\log _{2}\Bigg{[}\sum\limits_{k=1}^{N}\exp\Bigg{(}-\frac{||\mathbf{c}_{m,k}||^{2}}{3 \sigma^{2}}\Bigg{)}\Bigg{]}\Bigg{\}}\end{array} \tag{14}\]  
For the simplified calculation, the following proposition provides approximate solution:  
**Proposition 2**.: _Average mutual information integral over \(\mathbf{H}_{\text{WC}}\) is lower bounded by_  
\[\begin{array}{c}\mathcal{I}(\mathbf{x};\mathbf{y})\geq\log_{2}\mathbf{N}-\frac{1}{2N} \sum\limits_{m=1}^{N}\log_{2}\Bigg{[}\sum\limits_{k=1}^{N}\prod\limits_{l=1}^{ N_{\text{R}}}\Big{(}1+\frac{|\mathbb{E}_{\mathbb{R}}|_{l}}{2\sigma^{2}}d_{m,k}{}^{ \text{H}}\mathbf{\sigma}{}^{\text{H}}\mathbf{\Sigma}_{\text{T}}\mathbf{\sigma}d_{m,k}\Big{)} ^{-1}\Bigg{]}\\ -\frac{1}{2N}\sum\limits_{m=1}^{N}\log_{2}\Bigg{[}\sum\limits_{k=1}^{N}\prod \limits_{l=1}^{N_{\text{R}}}\Big{(}1+\frac{|\mathbb{E}_{\mathbb{R}}|_{l}}{3 \sigma^{2}}\mathbf{d}_{m,k}{}^{\text{H}}\mathbf{\sigma}{}^{\text{H}}\mathbf{\Sigma}_{ \text{T}}\mathbf{\sigma}d_{m,k}\Big{)}^{-1}\Bigg{]}\end{array} \tag{15}\]  
_where \(\mathbf{P}=\mathbf{U}_{\mathbb{T}}{}^{\text{H}}\)._  
The proof of Proposition 2 is shown in Appendix C.
## 3 Results and Discussion  
### In Situ XRF Results  
Table 2 reports the elemental composition, as obtained by XRF measurements, for all the investigated areas. In addition, Figure 3 shows some XRF spectra collected on representative areas of the painting, corresponding to the red from [PERSON]'s cloak (S1, Figure 3a), the green from [PERSON]'s cloak (S4, Figure 3b), the flesh-toned from [PERSON]'s cheek (S10, Figure 3c), the grayish-white from the cloud under [PERSON]'s sandal (S12, Figure 3d), the yellow-orange from [PERSON]'s cloak (S13, Figure 3e), the light blue from the cerulean shades under the angel's feet (S15, Figure 3f), the blackish-brown from [PERSON]'s hair (S20, Figure 3g), and the left side of the canvas, at the bottom (S28, Figure 3h). Results are discussed by color type, highlighting how similar-looking color tones may arise from the use of different pigmenting compounds, thereby characterizing the artist's palette. As far as red areas are concerned (S1, S2) the observation of Hg and Fe can suggest, in agreement with previous literature [6; 7; 8; 9], the use of vermilion (HgS) mixed with red ochre (Fe\({}_{2}\)O\({}_{3}\)). The detection of Pb in both cases could be ascribed, other than to the preparatory layer, to the use of Pb-based compounds, reasonably white lead (2 PbCO\({}_{3}\):Pb(OH)\({}_{2}\)) or minium (Pb\({}_{3}\)O\({}_{4}\)), in order to obtain the desired reddish nuance. In particular, in the case of point S2, Ti is also observed, indicating the employment of a few amounts of a Ti-based compound, such as the white pigment TiO\({}_{2}\), in order to give whiteness and opacity. Orange, yellow-orange, and yellow colors (S3, S7, S13, S24, S26, S27) seem to be obtained by a Pb-based or Pb/Sb-based compounds, i.e., massict (PbO) or \"Naples yellow\" (Pb\({}_{2}\)Sb\({}_{2}\)O\({}_{7}\)), mixed with a Fe-based compound, i.e., yellow ochre (\(\alpha\)-FeO(OH)), according to what already reported in [7; 8; 9; 10]. A mixture of a Fe-based pigmenting agent, reasonably green earth (K[(Al,Fe\({}^{3+}\)), (Fe\({}^{2+}\),Mg](AlSi\({}_{3}\),Si\({}_{4}\))O\({}_{10}\)(OH)\({}_{2}\)), with a Cu-based compound, that could be malachite (Cu\({}_{2}\)(OH)\({}_{2}\)CO\({}_{3}\)), lightened by white lead, was used for the green tonalities (S4, S5, S8, S9). The elemental composition of flesh-toned areas (S6, S10, S19, S22, S23, S25) is characterized by the presence of Pb in quite large amount (according to the relative intensity of the corresponding transition lines with respect to those of the other elements). For all fragments, with the exception of S22, Hg and Fe are also observed. Starting from that, the application of a similar receipt, composed by red Pb-, Hg- and Fe-based compounds, such as minium, vermilion and red ochre, respectively, lightened by a white Pb-based pigmenting agent, such as white lead, can be hypothesized.
## 3 Access Class Barring Configuration (ACBC) Scheme
### Recursive Least-Squares (RLS) Algorithm
```
0: the number of filter coefficients \(M\)
0: regularization parameter \(\delta>0\)
0: forgetting factor \(\lambda\)
1: Initialize the vector of filter coefficients \(\boldsymbol{w}\left(0\right)\) and the input vector \(\boldsymbol{u}\left(0\right)\) as \[w_{m}\left(0\right)=u\left(-m\right)=0,\qquad m\in\left\{0,1,\ldots,M-1\right\}\] (10)
2: Initialize the inverse correlation matrix \(\boldsymbol{P}\left(0\right)=\delta^{-1}\;\boldsymbol{I}\)
3:for all\(j=1,2,\ldots\)do
4: Filtering process: \[y(j)=\boldsymbol{w}^{\mathsf{T}}(j-1)\boldsymbol{u}(j)\] (11)
5: Adaptive process: \[\boldsymbol{k}(j) =\frac{\lambda^{-1}\,\boldsymbol{P}\left(j-1\right)\,\boldsymbol {u}(j)}{1+\lambda^{-1}\,\boldsymbol{u}^{\mathsf{T}}(j)\,\boldsymbol{P}\left(j- 1\right)\,\boldsymbol{u}(j)}\] (12a) \[\varepsilon(j) =d(j)-y(j)\] (12b) \[\boldsymbol{w}\left(j\right) =\boldsymbol{w}(j-1)+\boldsymbol{k}(j)\varepsilon(j)\] (12c) \[\boldsymbol{P}\left(j\right) =\lambda^{-1}\,\boldsymbol{P}\left(j-1\right)-\lambda^{-1}\, \boldsymbol{k}(j)\,\boldsymbol{u}^{\mathsf{T}}(j)\,\boldsymbol{P}\left(j-1\right)\] (12d)
6:endfor
```  
**Algorithm 2** RLS adaptive algorithm.
## 2 Case Study of Inverter-Fed Electrical Motor  
The vast majority of modern electrical machines use inverters for variable frequency drive and speed control. A common example of an inverter-fed machine is a surface-mounted PM motor. Figure 1 shows the machine configuration along with the winding layout. The windings have a concentrated layout without any overlap. As a result, the machine has nearly the same output power with a much lower stack length compared to the machine with distributed windings. In the rotor design, a 22-pole PM array is used. The machine's dimensional specifications and electromagnetic parameters are listed in Table 2. The operation of this machine involves excitation through a three-phase inverter, leading to the generation of non-sinusoidal currents in the stator windings. In Figure 2, we present the measured three-phase current waveforms at base speed. The primary emphasis of this paper revolves around the additional winding losses induced by these highly distorted current waveforms.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Number of slots & 24 & Number Of Rotor Poles & 22 \\ Stator outer diameter & 190 Mm & Number Of Turns Per Phase & 360 \\ Stator inner diameter & 100 Mm & Rated Power & 5.85 Kw \\ Tooth width & 7.75 Mm & Rated Mmf Per Phase & 3900 At \\ Rotor yoke height & 10 Mm & Rated Phase Current & 10.8 A (Rms) \\ PM thickness & 3.5 Mm & Stator Material & M270-50a \\ Stack length & 110 Mm & Rotor Material & M330-50a \\ Air gap length & 0.8 Mm & Pm Material & Ndfeb 42 sh \\ Rotor outer diameter & 98.4 Mm & Base Speed & 6000 Rpm \\ Rotor inner diameter & 70.6 Mm & Switching Frequency & 6.6 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometrical and electromagnetic parameters of the surface-mounted PM motor.  
Figure 1: A three-phase surface-mounted PM motor. (**a**) Machine configuration. (**b**) Cross section view. (**c**) Winding layout.
## 4 Discussion  
From the analyses previously conducted, the following considerations can be drawn on the efficacy of the involved diagnostic techniques for the study of IMS dataset 1:  
* The initial statistical approach allowed the fault detection phase to be executed, bringing to light the occurrence of different faults on bearings 3 and 4, and suggesting the time excerpts of the signal to focus on in the following analyses: in particular, RMS value, Kurtosis and Detectivity were proven to be the most informative statistical parameters.
* A rather late inner race fault on bearing 3, occurring since day 32, which does not show directly but through the excitation of the spectral harmonics of the shaft rotation frequency; the latter also has modulation effects on the BPFI (even though the interpretation of this fault as located at multiple frequencies of 301 Hz instead of the nominal 297 Hz is still potentially acceptable);
- An outer race fault on bearing 4, detectable for certain from day 28, due to the emergence of various harmonics of the BPFO, without modulating phenomena;
- A rolling element fault on bearing 4, detectable from day 26, with the appearance of the harmonics of the BSF and modulated by those of the cage rotation (FTF), raised themselves by the fault.  
On the other hand, since these techniques have been applied directly on the raw (i.e., not pre-processed) signal, they then present high noise and masking components within the amplitude-frequency response pattern, and therefore they must be preceded by suitable signal processing methods to obtain accurate results.  
Figure 49: Average of Correlation, Monotonicity and Robustness computed on the statistical parameters applied to the signal from sensor 1 on bearing 1 (**a**), sensor 3 on bearing 2 (**b**), sensor 5 on bearing 3 (**c**) and sensor 7 on bearing 4 (**d**).  
* TSA, ALP and Daubechies' Wavelets did not prove to be sufficiently useful as signal pre-treatment techniques in combination with SES.
* FIR and adaptive filters based on the Kurtogram were not able to select the frequency band dominated by fault signs.
* The CPW technique turned out to be a very powerful and efficient tool to pre-whiten the signal in combination with SES, by removing all the masking components and noise and therefore highlighting the fault signatures.
* The second-order cyclostationary techniques, especially IES, which are directly applied on the raw signal, were able to correctly detect the harmonics of the fault frequencies for the defective bearings presenting relatively low noise levels.
* It should be underlined that CPW and IES, which were found to be the most useful techniques, were used for the first time in the present work for the diagnostics of the IMS rolling bearing dataset.
* The brief insight made into the prognosis domain, to quantify the suitability of statistical parameters to make predictions on the signal future trends, represents an interesting starting point that may be worthy of being further developed.
## 2 Epitaxial Growth on InP-on-Insulator Substrate
### Growth of Multi-Quantum-Well Double-Hetero Structure
), whereas the latter is an as-grown sample. Figure 7d,e show the moving average of the contrast profiles extracted from the HAADF-STEM images, respectively. The contrast profiles are normalized because the signal intensity depends on the thickness of the sample lamella. The size of each pixel is 0.12 nm, and each well/barrier level (red line) is determined by averaging 21 pixels. We evaluated the transition width between the 10 and 90% contrast level of each well/barrier to compare the steepness. The average transition width was 2.91 nm for the MQW grown on the InP-on-insulator substrate and 2.40 nm for the MQW grown on the InP substrate. In terms of the steepness of the well/barrier interfaces, we observed no significant degradation. A possible reason for the relatively larger transition width is the thicker MQW lamella for the one grown on the InP-on-insulator substrate. A thicker lamella results in a larger number of atoms along the electron-transmission direction. Therefore, the TEM contrast profile becomes more sensitive to both the depth-directional crystal distribution and misalignment of the crystal orientation to the electron transmission angle. Figure 6: (**a**) PL spectra of indium-gallium-aluminum arsenide (InGaALAs) MQW grown on InP-on-insulator substrate and same MQW grown on standard InP substrate; (**b**) PL-intensity map for MQW grown on InP-on-insulator substrate measured with PL microscope; (**c**) PL intensity-map for MQW-BH after regrowth of InP layer. We also investigated the distribution of AI, P, Ga, As, and In by energy dispersive X-ray spectrometry (EDX). Figure 8 shows the cross-sectional EDX map of the MQW-BH on the InP-on-insulator substrate, together with the extracted line profile along the depth direction. The crystal composition of each MQW stack was almost the same. Together with the contrast profile using HAADF-STEM, we conclude that there is no significant structural degradation in InGaAs MQWs grown on an InP-on-insulator substrate. Figure 8: (**a**) Cross-sectional energy dispersive X-ray spectrometry (EDX) map of MQW-BH on InP-on-insulator substrate; (**b**) extracted line profile along depth direction. Figure 7: (**a**) Cross-sectional bright-field scanning transmission electron microscope (BF-STEM) image of BH on Si substrate; (**b**,**c**) high-angle annular dark-field (HAADF)-STEM image of MQW-BH grown on InP-on-insulator substrate and MQW grown on InP substrate; (**d**,**e**) moving average of contrast profile extracted from HAADF-STEM images.
# Nanotechnology, Nanomedicine, and the Kidney
## 3 Renal Clearance of Nanoparticles
### Surface Treatment  
Recently, [PERSON] et al., demonstrated the possibility to increase NPs' renal clearance by surface treatment [25]. They developed glycosaminoglycan (GAG)-coated NPs to mimic the glycan surface of viruses that show renal clearance despite their large size (about 200 nm diameter). The GAG-coated NPs showed rapid renal clearance (74% of the injected dose at 2 h) while reducing liver accumulation. However, the authors highlighted that the excretion of intact NPs occurs via the proximal tubules but not via glomerular filtration.
## 3 A Beam-Hopping-Based Global Beam Scheduling and User Resource Allocation Algorithm
### Algorithm A: Beam-Footprint Partitioning Algorithm
#### 3.1.2 Beam-User Matching  
After obtaining the initial beam center coordinates, an optimization model is constructed to maximize the number of users served by each beam. A binary decision matrix of size \(U\times MN\) is defined, where \(U\) denotes the number of users and \(MN\) denotes the number of candidate beams. Each element in this matrix is a binary decision variable denoted as \(x_{ij}\), where \(i\in\{1,2,\ldots,U\}\) and \(j\in\{1,2,\ldots,MN\}\). If \(x_{ij}=1\), it indicates that the \(i\)-th user is scheduled by the \(j\)-th beam ; conversely, if \(x_{ij}=0\), the \(i\)-th user is not served by the \(j\)-th beam.The total number of successfully scheduled users can be computed by summing all elements across both user and beam dimensions of the decision matrix. Accordingly, the optimization objective can be expressed as follows:  
\[\max\sum_{i=1}^{U}\sum_{j=1}^{MN}x_{ij}\] (9a) s.t. \[x_{ij}\cdot d_{ij}\leq R,\forall i,j \tag{9b}\] \[\sum_{i=1}^{U}x_{ij}\leq F,\forall j\] (9c) \[\sum_{j=1}^{MN}x_{ij}\leq 1,\forall i. \tag{9d}\]  
where \(d_{ij}\) denotes the distance between the \(i\)-th user and the center of the \(j\)-th beam. The constraints are interpreted as follows: constraint (9b) represents the coverage constraint, where a user is eligible for scheduling by a beam only if it is located within the beam's coverage radius \(R\). If \(d_{ij}>R\), then \(x_{ij}\) must be 0. Constraint (9c) is a capacity constraint, which states that each beam can serve no more than \(F\) users. Constraint (9d) is also a capacity constraint, ensuring that each user can be scheduled by at most, one beam.