esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Studies have been conducted on the production of prosthetic restorations using CBCT data, such as crown restorations and implant-supported acrylic resin interim crowns, showing clinically acceptable results. However, no studies have been found regarding the use of CBCT data for more complex restorations that require more precise scanning, such as inlay restorations. The limited number of studies using CBCT for inlay restorations may be attributed to the inherent complexity of inlay cavity designs, which require higher scanning precision than that typically achieved by CBCT. As a result, previous research has predominantly focused on simpler restorations, such as crowns, which are more compatible with the current resolution capabilities of CBCT. Class II inlay cavities with a proximal box have a notably more complex configuration than Class I and supragingival crowns [9; 18]. These designs, which complicate clinical steps such as cavity preparation and optical scanning, can become even more challenging when obtained using CBCT scanning data. Inlay restorations, particularly those with Class II preparations, pose greater challenges in terms of marginal and internal adaptation compared to full-coverage crowns [18]. Their internal geometries often include deep boxesand undercuts, which complicate the scanning and milling processes, potentially leading to reduced accuracy in adaptation. Although CBCT is commonly used for diagnostic and surgical planning purposes, its potential integration into the prosthetic workflow could be advantageous, particularly in cases where patients have already undergone CBCT imaging for other clinical reasons. In such scenarios, utilizing existing CBCT data for inlay restoration fabrication could eliminate the need for additional impression procedures, thereby reducing patient discomfort, clinical time, and associated costs. Therefore, investigating the clinical relevance and applicability of CBCT for inlay restorations may expand its utility beyond diagnostics into restorative dentistry. The purpose of this study is to evaluate whether CBCT, commonly used for diagnostic purposes, can serve as a viable alternative to intraoral and model scanners in the fabrication of ceramic inlay restorations. To this end, the marginal and internal fit of CAD/CAM ceramic inlays produced from CBCT, intraoral, and model scanner data were compared using micro-CT. The null hypothesis was that there would be no significant difference among the three methods in terms of marginal and internal fit.
# Effects of Manure Waste Biochars in Mining Soils
## Results  
### Material Characterization  
#### 3.1.1 Raw Materials and Biochars  
Table 1 shows the main characteristics of manure wastes, biochars, and soils. Two manure wastes and their corresponding biochars showed alkaline pH (9.01-10.85). EC values of biochars (0.40-0.50 dS\(\cdot\)m\({}^{-1}\)) were slightly higher than those of manure wastes (0.36 dS\(\cdot\)m\({}^{-1}\) and 0.44 dS\(\cdot\)m\({}^{-1}\) for RM and PL, respectively). The Coxi content of manure wastes decreased after pyrolysis, especially at the highest temperature (600 \({}^{\circ}\)C). The evolution of CEC during after pyrolysis greatly depends on feedstock characteristics. Biochar obtained after pyrolysis of PL showed the highest CEC values, whereas BRM600 exhibited lower CEC than RM waste. Pyrolysis decreased the P-Olsen content, particularly in biochars obtained after pyrolysis of RM (BRM450 and BRM600).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **PL** & **BPL450** & **BPL600** & **RM** & **BRM450** & **BRM600** \\ \hline pH & 9.01a1  & 10.07b & 10.73c & 9.14a & 10.59c & 10.88c \\ EC (dS\(\cdot\)m\({}^{-1}\)) & 0.44 ab & 0.47 bc & 0.50c & 0.36a & 0.40a & 0.47 bc \\ C-oxi (\%) & 12.9c & 11.4b & 4.30a & 25.8d & 11.6 bc & 4.99a \\ SOC (\%) & 1.14d & 0.36b & 0.10a & 0.57c & 0.15a & 0.04a \\ CEC (mmol\(\cdot\)kg\({}^{-1}\)) & 89a & 123b & 131c & 139d & 151e & 132c \\ P-Olsen (mg\(\cdot\)kg\({}^{-1}\)) & 3214c & 2427b & 2123b & 3296c & 959a & 751a \\ K (kg\(\cdot\)kg\({}^{-1}\)) & 4.87a & 19.26c & 20.73d & 7.01b & 19.31c & 18.72c \\ Ash (\%) & 48.26 & 61.02 & 60.23 & 41.69 & 54.44 & 62.74 \\ C (\%) & 33.79 & 32.71 & 25.27 & 45.67 & 29.13 & 24.99 \\ H (\%) & 4.55 & 2.70 & 0.84 & 6.17 & 1.76 & 1.22 \\ N (\%) & 2.06 & 2.19 & 1.20 & 4.01 & 1.50 & 0.38 \\ O (\%) & 10.87 & 0.91 & 12.10 & 1.69 & 12.79 & 10.31 \\ S (\%) & 0.47 & 0.47 & 0.36 & 0.77 & 0.38 & 0.36 \\ H/C ratio (\%) & 1.62 & 0.99 & 0.40 & 1.62 & 0.73 & 0.64 \\ O/C ratio (\%) & 0.24 & 0.02 & 0.36 & 0.03 & 0.33 & 0.31 \\ BET surf. & - & 4.28 & 7.03 & - & 5.68 & 35.97 \\ area (m\({}^{-2}\)g\({}^{-1}\)) & - & 245.56 & 199 & - & 166.23 & 104.81 \\ Aslope. average pore width (Å) & - & 1.14 & 2.37 & - & 1.79 & 4.20 \\ Amicro (cm\({}^{3}\)g\({}^{-1}\)) & - & 0.03 & 0.03 & - & 0.02 & 0.09 \\ Vinex (cm\({}^{3}\)g\({}^{-1}\)) & - & 0.05 & 0.07 & - & 0.04 & 0.07 \\ Vmaxco (cm\({}^{3}\)g\({}^{-1}\)) & - & 1.25 & 1.03 & - & 1.65 & 2.12 \\ Porosity (\%) & - & 66.19 & 63.95 & - & 75.25 & 72.72 \\ Cd (mg\(\cdot\)kg\({}^{-1}\)) & 0.03a & 0.24c & 0.33d & 0.06b & 0.36e & 0.35 de \\ Cr (mg\(\cdot\)kg\({}^{-1}\)) & 2.01b & 4.48c & 4.72c & 0.97a & 8.22d & 8.33d \\ Cu (mg\(\cdot\)kg\({}^{-1}\)) & 33.9a & 53.79c & 65.7d & 44.3b & 73.7e & 61.1d \\ Fe (mg\(\cdot\)kg\({}^{-1}\)) & 2833a & 6763c & 8715e & 5657b & 7991d & 14.916f \\ Ni (mg\(\cdot\)kg\({}^{-1}\)) & 2.85a & 4.63c & 5.58d & 4.34b & 6.66e & 7.93f \\ Pt (mg\(\cdot\)kg\({}^{-1}\)) & 7.43a & 15.4d & 13.6c & 7.95a & 9.99b & 11.0b \\ Zn (mg\(\cdot\)kg\({}^{-1}\)) & 468a & 546b & 701d & 623c & 870f & 753e \\ \hline \end{tabular}
\end{table}
Table 1: Main properties of manure wastes and biochars.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Modeling of a Channel Using the FDTD Method between UAVs and Sea Surface Vehicles  
[PERSON] et al. used wideband technology, in [21], to develop a channel model for communication between UAVs and surface vessels using the FDTD method [28]. They developed a random sea surface based on a spectral model. Implementation of the channel model was done in a two-dimensional cross section, where sea surface was assumed to be a perfectly conducting boundary. Using FDTD simulation, the transient impulse response for a random sea surface was numerically evaluated. On the basis of the resulting transient pulse, parameters were calculated. This process was repeatedly executed for multiple sea surface heights to generate a good amount of population data.  
In [21], authors used the JONSWAP (Joint North Sea Wave Project) spectral model, which was first proposed in [49]. The spectral content of the sea surface proposed in this channel model was formulated as:  
\[S(\omega)=\alpha\frac{g^{2}}{\omega^{5}}\exp[-1.25(\frac{\omega_{0}}{\omega}) ^{4}\gamma^{\exp[-(\omega-\omega_{0})^{2}/2\sigma^{2}\omega_{0}^{2}]}\ ] \tag{41}\]  
where \(g\) is the acceleration due to gravity, \(\omega\) is the frequency, \(\omega_{0}\) is the spectral peak frequency, \(\gamma\) and \(\sigma\) are peak enhancement coefficients, and \(\alpha\) is the scale coefficient. The FDTD method was used to solve [PERSON]'s equations [33] in this channel model. Readers can see Section 4.2 for the details. After getting the received signal wave from the FDTD simulation, it was found that the obtained results were not accurate for the channel properties. The channel impulse response was used to find the total information of the channel.  
This wireless channel model is incomplete because important channel parameters like mean excess delay, RMS delay spread, etc. were ignored. Apart from this, it was necessary that authors of [21] mention what kind of model was used to calculate the path losses. Besides, due to refraction in the duct layers, it was also necessary to explore the atmospheric duct effects in the channel model.
## 2 Materials and Methods
### Field Data Collection and Laboratory Measurements
#### 2.2.1 In Situ  
Sampling was carried out from a boat equipped with an outboard electric motor. GPS was used to arrive at the sampling points, always situated at a certain distance off the coastline (100 m) ensuring that the measurements are taken in a pure water S2-MSI pixel and surrounded by pixels of water. Once anchored, the coordinates of the sampling point were geo-referenced with GPS. First of all, SDD was measured using a white disk (20 cm in diameter), which was submerged vertically until it was no longer visible. Using SDD depth as the sample depth, a PVC tube or hydrographic bottle was used to take samples from the surface down to the sample depth because integrated samples are more representative and avoid missing possible phytoplankton peaks [22]. The samples were stored under dark and cold conditions for transfer to the laboratory to analyze the CDOM, TSS, and Chl_a.  
Additionally, a C3 Submersible Fluorometer (Turner Design Instruments; San Jose, CA, USA) was used to measure the PC and Chl_a. For this reason, Chl_a is the variable with more samples. For PC, the calibration of fluorescence intensity was performed with a calibration curve using a standard phycocyanin extract from _Spirulina_ sp. (Sigma-Aldrich Chemicals). The Chl_a values were calibrated using the Chl_a concentration data calculated in the laboratory using spectrophotometric analysis.  
During a three-hour interval before and after the satellite pass, the above-surface water radiometry was measured using an ASD FieldSpec(r) HandHeld2 spectroradiometer and an Ocean Optics (HR 4000) spectrometer, of which the characteristics are detailed in [PERSON] et al. [6]. The water-leaving radiance was obtained by measuring above-surface water spectroradiometry according to the methodology described by [PERSON], 1999 [23]. Once the in situ remote sensing reflectance (Rrs) spectra were calculated, they were convoluted to the S2-MSI spectral bands using the Sentinel-2 Spectral Response Functions (S2-SRF) v3.0 [24].
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 2 The Proposed Methods
### Proposed CNN-CBAM Algorithm and Flowcharts  
The overall CNN-CBAM algorithm is shown as Algorithm 1. The algorithm contains the two most important parts. (1) Training the CNN-CBAM model to obtain the partition labels of CU in the image. (2) We embed the trained model into HEVC to predict the CU partition in advance.  
The framework of the CNN-CBAM is displayed in Figure 6. The proposed method firstly avoids unnecessary redundant computation of violent RDO compared with the traditional CU classification method; secondly, the proposed CNN, which is compatible with different levels of CU, can predict all the CU partition results at one time, significantly reducing the coding complexity. In addition, the convolution block is designed to enhance the information exchange of the same path of the CU to obtain richer texture features. Finally, CBAM is introduced into CNN to build CNN-CBAM. The CNN-CBAM is embedded in the HEVC coding framework to predict the partition of CU in advance. The CBAM can effectively improve the intermediate features by dynamically suppressing or emphasizing the feature map through the attention mechanism, thus improving the representational ability of the CNN-CBAM network without increasing the computational complexity.  
```
1:Input: current image information
2:Output: return a set of split CU with RDO costs
3: Processed processing: I-pro
4:{// train CNN-CBAM model to obtain current CU split_flag
5:If\(W=64\) and \(H=64\)then
6:Ifsplit_flag = 0 then
7: skip CU partition
8:Else\(W=32\) and \(H=32\)then
9:Ifsplit_flag = 0 then
10: skip CU partition
11:Else\(W=16\) and \(H=16\)then
12:Ifsplit_flag = 0 then
13: skip CU partition
14:Else\(W=8\) and H = 8 then
15:split_flag \(\leftarrow\) 0
16:endif
17:End if
18:End if
19:End if
20: }
21:frontier \(\leftarrow\) stack (LCU)
22:decision results \(\leftarrow\)\(\emptyset\)
23:While is empty (frontier) is false do
24:CU block \(\leftarrow\) POP (frontier)
25:If execute Model (CNN-CBAM, CU block) is not split do
26:decision results \(\leftarrow\) decision results \(\cup\) CU block
27:Else
28:Sub CU \(\leftarrow\) GET (Sub CU, CU block)
29:Sub CU RDO \(\leftarrow\)\(\emptyset\)
30:For sub-CU in Sub CU do
31:If is leaf (sub-CU) is true then
32:Sub CU RDO \(\leftarrow\) Sub CU RDO \(\cup\) calculate (RDO, sub-CU)
33:Else
34:PUSH (frontier, sub-CU)
35:End if
36:End for
37:Parent RDO \(\leftarrow\) calculate (RDO, CU block)
38:If parent RDO \(<\) sum of 4 sub-CU RDO then
39:decision results \(\leftarrow\) decision results \(\cup\) CU block
40:Else
41:decision results \(\leftarrow\) decision results \(\cup\) Sub CU
42:End if
43:End if
44:End while
45:Return decision results
```  
**Algorithm 1** (CNN-CBAM)Figure 6: Comparison of the two flowcharts.
## 6 Security Analysis and Performance Evaluation  
We conducted security analysis and experimental evaluation on the key components of the HBCE-FL framework, particularly in terms of privacy preservation, computation time, and model accuracy. Section 6.1 shows the security analysis of the access control rules implemented using ACE and the blockchain deployed on the edge servers. The performance evaluation of the multi-level ACE-based IoT knowledge sharing algorithm is presented in Section 6.2. The experimental evaluation of privacy-preserving PFL is provided in Section 6.3.
## 3 Results & Discussion
### Statistical Analysis & Process Optimization
#### 3.2.2 Optimization of MAE's Operational Conditions & Model's Verification  
Regarding the optimization process followed, the examined responses of yield and pigment content were set to maximize, and IC\({}_{50}\) was set to minimize, as the independent variables ranged in their domain. Among the proposed solutions, the final choice was based on the maximization of the objective function of desirability, the possibility of low microwave power application and, thus, the moderate cooling needs of the extraction vessel to maintain a temperature-controlled system. Therefore, MAE's optimal conditions chosen were 60 \({}^{\circ}\)C, 300 watts, 14 min, and 22 mL\({}_{\text{solv}}\)/g\({}_{\text{biom}}\).  
Finally, a confirmation experiment was carried out under the proposed set of operational conditions, the results of which are presented in Table 3. None of the experimental  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Value** & \\ \hline T (\({}^{\circ}\)C) & 60 & \\ P (watts) & 300 & \\ t (min) & 14 & \\ R (mL\({}_{\text{solv}}\)/g\({}_{\text{biom}}\)) & 22 & \\ \hline
**Response** & **Predicted** & **Experimental** \\ \hline Yield (\(\%\)\(w\)/\(w\)) & 12.00 & 11.14 \\ \hline Total Chlorophylls (mg/g\({}_{\text{extr}}\)) & 67.73 & 63.36 \\ Selected Carotenoids (mg/g\({}_{\text{extr}}\)) & n/a * & 7.06 \\ Total Carotenoids (mg/g\({}_{\text{extr}}\)) & 22.83 & 24.88 \\ Total Phenolics (mg/g\({}_{\text{extr}}\)) & n/a * & 9.34 \\ IC\({}_{50}\) (mg\({}_{\text{extr}}\)/mg\({}_{\text{GDPPH}}\)) & 43.00 & 40.49 \\ \hline \hline \end{tabular}  
* Not applicable.  
\end{table}
Table 3: Optimal MAE conditions of bioactive compounds from _Chorella vulgaris._  
Figure 6: Experimental versus predicted values of (**a**) extraction yield, (**b**) total chlorophyll content, (**c**) total carotenoid content, and (**d**) antioxidant activity. The error bars refer to the experimental coefficient of deviation.  
responses exceeded 10%, indicating the sufficient description and adequate precision of the models [66, 67].
## 4 Problem Formulation
### Mathematical Proof of the Optimal Solution  
**Theorem 1**: _The optimized algorithm achieves an optimal configuration of the target array \(\mathbf{T}_{\mathbf{best}}\) for the given physical matrix \(\mathbf{H}\)._  
**Definition 1**: \(\mathbf{F}_{\textit{current}}\)_: The configuration of the sliding window before any replacements. \(\mathbf{F}_{\textit{next}}\): The configuration of the sliding window after the algorithm has performed replacements on defective PEs. \(\mathbf{T}_{\mathbf{best}}\): The optimal configuration of the target array that minimizes the utility function \(\mathbf{u}(\mathbf{F})\).  
**Proof.** Let \(\mathbf{H}\in\mathbf{R}^{\textit{m}\times\textit{n}}\) represent the matrix containing defects in the set \(\mathbf{D}\). The algorithm utilizes a sliding window \(\mathbf{F}\in\mathbf{R}^{\textit{p}\times\textit{q}}\) to focus on specific areas of \(\mathbf{H}\). In each iteration, the algorithm identifies and replaces faulty processing elements (PEs). We denote the configuration of the sliding window before replacements as \(\mathbf{F}_{\textit{current}}\) and the updated configuration after replacements as \(\mathbf{F}_{\textit{next}}\).  
Firstly, the Replacement Metric \(\mathbf{R}(\mathbf{F})\) guarantees the following:  
\[\mathbf{R}(\mathbf{F}_{\textit{next}})\leq\mathbf{R}(\mathbf{F}_{\textit{current}}) \tag{12}\]  
Since the algorithm is designed to replace at least some defective PEs in \(\mathbf{F}_{\textit{current}}\), thereby reducing the count of faulty PEs or leaving it unchanged if no further replacements can be made.  
Secondly, the Quality Metric \(\mathbf{T}(\mathbf{F})\) guarantees the following:  
\[\mathbf{T}(\mathbf{F}_{\textit{next}})\leq\mathbf{T}(\mathbf{F}_{\textit{current}}) \tag{13}\]Since the algorithm seeks to minimize the clustering of defective PEs, resulting in a more favorable distribution in \(\mathbf{F}_{next}\).  
Thirdly, the utility function \(\mathbf{u}(F)\) combines both the Replacement Metric and Quality Metric.  
\[\mathbf{u}(\mathbf{F}_{next})\leq\mathbf{u}(\mathbf{F}_{current}) \tag{14}\]  
The relationship follows from the previous inequalities, as both \(\mathbf{R}(F)\) and \(\mathbf{Q}(\mathbf{F})\) improve in \(\mathbf{F}_{next}\). Thus, the utility function reflects a non-increasing trend through iterations.  
Finally, the iterative process continues until no further improvements can be achieved, leading to a configuration \(\mathbf{T}_{best}\) where  
\[\mathbf{u}(\mathbf{T}_{best})=\mathbf{min}_{\mathbf{F}\mathbf{u}}(F) \tag{15}\]  
This indicates that \(\mathbf{T}_{best}\) represents an optimal arrangement of PEs, as further iterations do not yield lower utility values. The minimization of \(\mathbf{u}(F)\) confirms that \(\mathbf{T}_{best}\) optimizes both the number of faulty PEs and their spatial distribution. Therefore, we conclude that \(\mathbf{T}_{best}\) minimizes the utility function \(\mathbf{u}(F)\).  
In conclusion, the optimization algorithm conclusively achieves optimality in configuring the matrix \(\mathbf{H}\) supported by rigorous evaluation of relevant metrics. \(\square\)
## 1 Introduction  
Heart failure (HF) is the most common cardiovascular disease worldwide, with more than 25 million people being affected in industrialized countries [1].  
Despite advances in pharmacologic therapies for treating patients with HF with reduced ejection fraction (HFFEF), the prognosis of such patients remains poor [2, 3]; therefore, device-based therapy has become increasingly important in recent years for the treatment of HFreF.  
The most widely used device-based therapy for the treatment of HFrEF is cardiac resynchronization therapy, which can lead to improved cardiac performance and prognosis in patients with HFrEF and wide QRS (duration \(>\) 150 ms) [4].  
Unfortunately, only 30% of HFrEF have a QRS duration \(>\) 150 ms [5]. For patients with persistent symptoms or frequent HF-related hospitalizations but with narrow QRS,a new device (Optimizer Smart(r)) capable of delivering cardiac contractility modulation (CCM) therapy has been available for several years [6].  
Optimizer Smart delivers non-excitatory electrical impulses on the interventricular septum during the absolute refractory period of cardiomyocytes; therefore, it does not alter the cellular action potential and does not affect heart rhythm [7].  
CCM affects Ca\({}^{2+}\) handling in cardiomyocytes, the expression of genes encoding factors that play a crucial role in Ca\({}^{2+}\) metabolism, and molecular pathways involved in interstitial fibrosis [8; 9].  
These actions result in reverse left ventricular remodeling and increased cardiac performance, improving symptoms, functional capacity, quality of life, and reduced HF-related hospitalizations [10; 11].  
However, to date, there is little evidence available on the effect of CCM on right ventricular performance; thus, in this study, we analyzed the effects of CCM on right ventricular systolic function and right ventricle-pulmonary artery (RV-PA) coupling in patients with HFrEF.
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 3 Numerical Results
### Rotman Lens Results
#### 3.2.3 Comparison of the Two Rotman Lens Topologies  
The return loss (input reflection coefficient) of the two designed Rotman lenses are depicted in Figures 23 and 26. An improvement by at least 4 dB is clearly observed in the topology with the absorbers, while an acceptable matching over the entire band is offered at the same time. The corresponding coefficients are presented in Figures 21a-f and 24a-f when input ports \(1,2,3\) are activated. A reduction in the amplitude variation is first observed. However, the direct path transmission coefficient \(S_{41}\) is higher. Although a theoretically uniform distribution is sought, higher signal levels at the middle output ports are observed (Figures 21a-f and 24a-f), which yield shaped antenna array aperture excitation and thus lower sidelobes. Furthermore, the observed large amplitude variations in transmission coefficients are degrading the antenna array excitation, as shown in Figure 26. This may be due to the small number of output ports and we expect an improvement in a design with more output ports.  
Comparing Figures 21b,d,f and 24b,d,f, the output phase versus frequency linearity is impressively improved when the proposed design of the absorbers is used. Thus, the Rotman design with absorbers provides group delays with smaller variation versus frequency and thus more accurate beamsteering.