esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
**Introduction:** We developed a computed tomography (CT)-based tool designed for automated segmentation of deltoid muscles, enabling quantification of radiomic features and muscle fatty infiltration. Prior to use in a clinical setting, this machine learning (ML)-based segmentation algorithm requires rigorous validation. The aim of this study is to conduct shoulder expert validation of a novel deltoid ML auto-segmentation and quantification tool. **Materials and Methods:** A SwinUnetR-based ML model trained on labeled CT scans is validated by three expert shoulder surgeons for 32 unique patients. The validation evaluates the quality of the auto-segmented deltoid images. Specifically, each of the three surgeons reviewed the auto-segmented masks relative to CT images, rated masks for clinical acceptance, and performed a correction on the ML-generated deltoid mask if the ML mask did not completely contain the full deltoid muscle, or if the ML mask included any tissue other than the deltoid. Non-inferiority of the ML model was assessed by comparing ML-generated to surgeon-corrected deltoid masks versus the inter-surgeon variation in metrics, such as volume and fatty infiltration. **Results:** The results of our expert shoulder surgeon validation demonstrates that 97% of ML-generated deltoid masks were clinically acceptable. Only two of the ML-generated deltoid masks required major corrections and only one was deemed clinically unacceptable. These corrections had little impact on the deltoid measurements, as the median error in the volume and fatty infiltration measurements was \(<\)1% between the ML-generated deltoid masks and the surgeon-corrected deltoid masks. The non-inferiority analysis demonstrates no significant difference between the ML-generated to surgeon-corrected masks relative to inter-surgeon variations. **Conclusions:** Shoulder expert validation of this CT image analysis tool demonstrates clinically acceptable performance for deltoid auto-segmentation, with no significant differences observed between deltoid image-based measurements derived from the ML generated masks and those corrected by surgeons. These findings suggest that this CT image analysis tool has potential to reliably quantify deltoid muscle size, shape, and quality. Incorporating these CT image-based measurements into the pre-operative planning process may facilitate more personalized treatment decision making, and help orthopedic surgeons make more evidence-based clinical decisions.  
keywords: shoulder arthroplasty; clinical decision support tools; machine learning; computed tomography; deltoid muscle modeling; radiomics
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G
### SON, mmWave and Massive MIMO Technologies for 5G Networks  
Compared with 3G and 2G technologies, 4G-LTE was revolutionary, but currently LTE networks are running out of bandwidth; thus, the spectrum usage of the millimeter-wave frequency range is a solution for 5G bandwidth requirements. mmWave is a part of the 5G standard and combined with 3D beamforming techniques, where multiple-element base station antennas use multiple antenna elements to form directional beams for transmission, and it is the key solution for providing low latency and high data speeds [8]. Furthermore, the propagation nature of mmWave leads to shorter communication distances, and thus base stations must be positioned closer if compared with existing mobile networks, leading to even more dense network clusters.  
The binding of SON with mmWave and beamforming techniques is a recent very interesting concept and the authors of [13] proposed a use case related to directional cell search based on the rationale that the simultaneous transmission of broadcast signals reduces the amount of the needed cell search resources including latency during the process of recognizing neighboring cells. The tradeoff for this includes Radio Link Failures (RLFs) due to SINR deterioration (related to handover measurements) and smaller coverage. However, a centralized SON coordinator might improve the former.  
Future research related to SON usage for directional cell search is also related energy savings in case beamforming is applied only in required areas so that coverage overlapping is avoided and with concepts such as the self-optimization of beam direction and transmission power.  
Apart from mmWave, massive MIMO is also one of the key components of 5G networks and a key enabler for the provision of high-speed data rates in indoor and outdoor propagation environments operating either in mmWave frequencies or below 6 GHz [8]. ML algorithms can empower massive MIMO, leading to \"Intelligent massive MIMO\" solutions, and typical examples include massive MIMO power control and user positioning [105].  
This means that ML based SON systems can be combined with massive MIMO and might be an interesting research scenario since beamforming can be controlled by SON platforms.
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 3 Materials and Methods
### Bare Bones Particle Swarm Optimisation  
The BBPSO was introduced to simplify PSO, removing the need for parameters or velocity [28]. Each particle \(p\in\{1,\dots,P\}\) in the swarm holds two properties: (a) its current position \(\mathbf{x}_{p}\) and (b) its personal best position \(pbest_{p}\). At each iteration \(t\), the position of the particle is updated based on \(N(\mu,\sigma)\), a multivariate Gaussian distribution with mean \(\mu\), and standard deviation \(\sigma\) as follows:  
\[\begin{split}& x_{p}(t+1)=N(\mu,\sigma)\\ &\mu=\frac{pbest_{p}(t)+gbest(t)}{2}\\ &\sigma=|pbest_{p}(t)-gbest(t)|\end{split} \tag{2}\]  
In addition to parameter reduction, the multivariate Gaussian distribution operator helps maintain diverse candidates in the population for _pbest_ particles to improve from. The BBPSO clustering algorithm to be used in this study is shown in Algorithm 1. The _gbest_ particles are evolved from _pbest_ particles and refined using cluster-based MOEA/D.  
```
1:Set max number of iterations \(I\), max number of iterations \(T\), Population size \(S\), \(t=0\), \(i=0\), generate population \(P\) and initialize \(gBestList\) using MOEA/D and all _pbest pbestList_
2:while\(i<1\)do
3:while\(t<T\)do
4: Calculate any parameters
5:for Each particle do
6: Calculate new \(p\) using (2)
7: Check new \(p\) is within search range \(R\)
8: Calculate fitness of \(p\) and pBest using quantization error function from [23]
9: If new \(p\) dominates pBest, update \(p\) and pBest with new \(p\)
10:endfor
11: gBestList = getGBestList(_pbestList_) using cluster-based MOEA/D (Algorithm 2)
12:endwhile
13:endwhile
14:Evaluate crowding distance of all solutions in gBestList
15: Choose gBest with largest crowding distance
```  
**Algorithm 1** Pseudocode for BBPSO Clustering
## 6 Conclusions  
Here, the 3D analytical solutions to the wave equations of the expanding and shrinking RDGCs were derived. The TMs of the RECs consisted of multiple expanding and shrinking RDGCs, which were then calculated from the 3D solutions. The TLs calculated from the TMs were validated with the measured results.  
In the derivation of the 3D analytical solution and TMs of the RDGC, the ignorance of some infinite and complex terms is risky, but it can simplify the formulas and reduce the computation. These behaviors are proved to be practicable by experiments and the TLs obtained by the theories in this paper are accurate to a certain extent.  
According to the TLs of the RECs with different baffle configurations, the peaks and troughs of the TL curves of all types move to a higher frequency with the increasing angle (\(\theta\)) between the bevel edge and the axial direction and move to a lower frequency with the increasing length (\(l_{b}\)) of the baffle. The REC with double baffles distributed transversely (Type 2t) is better in acoustic performance than the other types at frequencies from 600 Hz to 1100 Hz. On the other hand, although the pressure losses of all types of RECs increase as \(\theta\) or \(l_{b}\) increases, Type 2t always has a lower pressure loss than other types. In summary, Type 2t generally has good performance in both acoustic attenuation and flow efficiency.  
This achievement of research shall provide a certain reference for designing and improving the acoustic characteristics of the duct systems used in high-speed trains.  
This article was prepared through the collective efforts of all the authors. Conceptualization, methodology, and writing--original draft preparation, Y.S.; validation, Y.S. and L.L.; writing--review and editing, Y.Q. and X.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 12: The pressure losses of the RECs with different baffle angles.  
Figure 13: The pressure losses of the RECs with different baffle lengths.  
**Funding:** This research was supported by the National Natural Science Foundation of China (Grant No. 51975515 and No. 51905474).  
**Institutional Review Board Statement:** The study did not involve humans or animals.  
**Informed Consent Statement:** The study did not involve humans.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declared no potential conflict of interest with respect to the research, authorship and/or publication of this article.
# Determining the Best Dressing Parameters for External Cylindrical Grinding Using MABAC Method
## 5 Conclusions  
The results of an MCDM study on identifying the appropriate dressing mode for external grinding SKD11 tool steel using the MABAC method were presented in this study. In the study, the entropy and MEREC methods were used to calculate the weight of the criteria. Besides, six process factors, including the coarse dressing depth, the coarse dressing passes, the fine dressing depth, the fine dressing passes, the non-feeding dressing, and the dressing feed rate were chosen for the exploration. Moreover, for the experimental design, the Taguchi method with L16 orthogonal array (\(4^{4}\times 2^{2}\)) was chosen. The following conclusions can be drawn from the study results:  
1. The results of using the MABAC method to solve the MCDM problem in the dressing process for external grinding SKD11 tool steel have been investigated for the first time.
2. To verify the results of applying the MABAC method, two methods, TOPSIS and MARCOS were used. As a result, all three methods identified that option three is the best for the dressing process.
3. The Spearman's rank correlation coefficient has been found to be extremely useful in assessing the correlation between various ranking methods.
4. The optimal wheel dressing parameters to achieve the minimum surface roughness, the maximum wheel life, and the minimum roundness simultaneously are: \(n_{r}=3\), \(a_{r}=0.04\) (mm), \(a_{f}=0.005\) (mm), \(n_{f}=2\), \(n_{0}=2\), and \(S_{d}=1\) (m/min.).  
All authors have discussed the original idea. [PERSON], [PERSON], [PERSON] and [PERSON] conducted the experiment (designing and analyzing) and the MCDM problem; This manuscript was written by [PERSON] with assistance from [PERSON], [PERSON] and [PERSON]. All of the authors discussed the manuscript. This work was managed and revised by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by Thai Nguyen University of Technology.  
The authors declare no conflict of interest.  
MCDM--Multi-criteria decision making; MABAC--Multi-Attributive Border; Approximation Area Comparison; SR--surface roughness; T--wheel life; R--roundness; MEREC--Method based on the Removal Effects of Criteria; TOPSIS--Technique for Order of Preference by Similarity to Ideal Solution; MARCOS--Measurement of; Alternatives and Ranking according to COmpromise Solution; RQL--restricted quantity lubrication; EDM--electrical discharge machining; MRR--material removal rate; DoE--design of experiments; EAMR--Evaluation by an Area-based Method of Ranking; MAIRCA--Multi-Attributive Ideal--Real Comparative Analysis; \(a_{r}\)--coarse dressing depth; \(n_{r}\)--coarse dressing passes; \(a_{f}\)--fine dressing depth; \(n_{f}\)--fine dressing pages; \(n_{0}\)--non-feeding dressing; \(S_{d}\)--dressing feed rate.  
\begin{table}
\begin{tabular}{c} MABAC and \\ MARCOS \\ \end{tabular} \\ \hline
0.995 & 0.999 & 0.996 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Spearman’s rank correlation coefficient.
# Economies of Scale and Efficiency in the Wastewater Treatment Sector: A Decision Tree Approach
## 4 Results
### Efficiency Analysis of the Global Sample with the EAT Approach
With these results, we can confirm the existence of a direct relationship between the size of the facilities and the efficient performance of the inputs in the wastewater treatment processes. In other words, bigger WWTPs show better efficiency indicators in terms of the use of the inputs. Due to their relevance for the operation costs, energy management and staff are the most representative cases, in line with the literature [60; 61; 62; 63]. In this sense, the results obtained by [PERSON] [64] show a cost inefficiency associated with the use of energy in plants < 20,000 P.E. As reflected in the study conducted by [PERSON] [52], WWTPs with secondary treatments are influenced by scale economies, so that smaller WWTPs are less efficient. Other work [39] shows that only 3% of the small WWTPs analyzed operate efficiently, while the remaining WWTPs are far from efficient in their performance. The overlap of these results with those obtained in our study demonstrates that scale economies have to be considered, and that energy use represents a key factor in the efficient operation of WWTPs. Table 5 shows the correlations between the different costs (energy, staff, maintenance, residues, and others) and efficiency (as measured using the efficiency analysis tree (EAT) model) for each group. It allows us to analyze the behavior of each input in relation to the score obtained. At a general level, significant negative correlations are observed in all cost categories, implying an inverse relationship between efficiency and higher economic costs. A higher result obtained in each input could imply that operators need to make a greater effort to reduce specific costs. In general, the four groups show a consistent negative correlation, although the inputs dedicated to energy, staff, maintenance, and others are the most strongly correlated. However, in economic terms, this last group (others) does not involve as much investment as the energy, staff, and maintenance groups. The correlation with energy costs, staff, and maintenance offers negative results of \(-0.452\), \(-0.634\), and \(-0.680\), respectively. It is important to note that the behavior of maintenance costs in relation to the scores obtained by each group decreases as the group includes larger facilities, ranging from \(-0.636\) for Group 1 to \(-0.429\) for Group 4. The correlation observed for staff follows a similar pattern: Group 1 presents a stronger correlation than Group 4 in this regard.
## 3 Results and Discussion
### Fabric Physical Property
#### 3.2.2 Fabric Weight  
Figure 10 shows the comparison of fabric weight between eight sets of fabric samples. To investigate the effect of different factors on the fabric weight, the examination of different comparison pairs was conducted. Fabric set A and set E are compared to set B and set F, respectively, to evaluate the effect of weft-backed structure, with or without regulating layer, on the fabric weight. The weight of fabric set A and set E is heavier than that of fabric sets B and F, respectively, which indicates that the fabric weight is slightly increased by adding the regulating layer to the weft-backed structure. By comparing fabric sample sets C and G to fabric sample sets D and H, respectively, to examine the effect of weft density on the fabric weight, it can be seen that the fabric sample sets D and H in a higher weft density are heavier than that of the fabric sample sets C and G in a lower weft density, respectively. From Figure 10, fabric sample sets A, E, B, and F have significantly higher fabric weight than that of fabric sample sets C, G, D, and H, with the average percentage increase of 37.40% because a coarser weft yarn of 150 denier was used in the fabric sample sets A, E, B, and F compared to the weft yarn in a count of 75 denier employed in the fabric sample sets C, G, D, and H. To investigate the effect of face structure with or without the mixing component of black yarn, the fabric sample set A to D without the mixing component of black yarn are compared to their corresponding fabric sample set E to H with the black yarn. It is easy to see that the adding of the black yarn into the fabric face layer shows no significant effect on the fabric weight. In addition, by comparing the result shown in Figure 10 to that in Figure 9, it is easy to observe that they have similar trends, which reflects that fabric thickness has a relationship with fabric weight.
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 6 Influence of Solar Radiation on Temperature Distribution on Bridge Grider Cross-Sections
### T-Shaped Girder Bridge  
#### 6.3.1 Finite Element Model  
One 4-span concrete continuous bridge in Yongchun with a cross-section consisting of 4 T-shaped girders was chosen as case study. The thickness of the concrete overlay is 190 mm. The dimensions of girder cross-section and the installation locations of temperature sensors are illustrated in Figure 19. Four temperature sensors were installed in the concrete within the top flanges (T-1), webs (1-W and 2-W) and bottom flanges (4-B) of the T-shaped girders. The meteorological data, including ambient air temperature, hourly global solar radiation, hourly diffuse solar radiation and wind speed, were measured by an automated meteorological station. The monitoring period were from 15 August 2012 to 25 August 2012 and the time increment between measurements was set as one hour.  
The 2D plane strain element in Midas-FEA was chosen to establish the finite element model of the cross-sections of T-shaped girders (Figure 19). The ambient air temperature data were obtained from the automated meteorological station. The solar radiation was calculated using the estimation models described earlier. The effects of solar radiation and convective and radiative heat transfer were considered at the external surfaces of the T-shaped girders. The surface heat transfer coefficient and the temperature of the surrounding fluid medium were input as boundary conditions for the finite element model. The influence of the solar radiation on different parts of the T-shaped girders was considered by dividing the cross-section into four zones, as shown in Figure 20. (a) The external surfaces of the top flanges are considered as Zone 1, which are influenced by the beam solar radiation and diffuse solar radiation. (b) The external surfaces of the webs of the girders #1 and #4 are considered as Zone 2, of which the parts in shadow are influenced by the diffuse solar radiation and ground reflection, and the parts not in shadow are influenced by the beam solar radiation, diffuse solar radiation and ground reflection. (c) The undersides of the top and bottom flanges are set as Zone 3, which are considered as the inner surfaces in the T-shaped girders and are not influenced by the solar radiation or ground reflection. Moreover, the convective heat transfer coefficient of the inner surfaces in the T-shaped girders was set as 5 W/(m\({}^{2}\)\({}^{\circ}\)C), which is larger than that in the box girders (3.5 W/(m\({}^{2}\)\({}^{\circ}\)C)) [62]. The finite element mesh size was set as 0.02 m. There were 16,165 and 15,149 nodes and elements, respectively, as illustrated in Figure 20. Each node had a single temperature degree of freedom.  
Figure 19: Layout of cross-section of T-shaped girder (cm).
## 3 In Situ Measurement and Analysis on Deformation Pattern
### Qualitative Analysis of the Deformation Mechanism
#### 3.3.2 Confined Aquifer Influence  
The groundwater level in limestone rises by 100 m after the reservoir impounds, which makes the explanation of confined aquifer influence tempting. However, the groundwater head fluctuates with reservoir water level (Figure 6) while valley deformation does not (Figure 5). Besides, the stratum is horizontal at the dam site. The confined water pressure shall lead to uplift deformation rather than valley shrinkage. Therefore, the possibility of a confined aquifer influence is relatively low.
## 4 Discussion  
This study utilized the MOD17A2 algorithm and tower-based ground measurements to reconcile MODIS default GPP estimation (GPP_MOD) in four GPP estimation models, GPP_MODR1 to GPP_MODR4, depending on the diverse parameterization schemes (Table 1). We assessed the performance of these models on GPP estimations as compared to in situ flux tower GPP (GPP_EC).
# Advanced First-Principle Modeling of Relativistic Ruddlesden--Popper Strontium Iridates
## 4 Results and Discussions
### Electronic Structures
#### 4.2.1 Interplay between \(U\) and SOC
Compared to the nonrelativistic DFT-calculated \(t_{\rm 2g}\) bands, the bandwidth of the \(J_{\rm eff}=1/2\) manifolds is largely narrowed by the SOC, giving rise to the enhanced Mott instability. In this situation, even a moderate Hubbard \(U\) is able to open the band gap, leading to the Mott MIT (Figure 3(a3,b3)). In the 3D perovskite SrIrO\({}_{3}\), SOC induces to a k-uniform band-splitting and leads to the emergence of characteristic Dirac-like crossing at the high-symmetry point U [148], which is robust against electronic correlation: a moderate \(U=1\) eV is not sufficient to destroy the Dirac crossing, as shown in Figure 3(c3). As the Hubbard \(U\) is increased further, the band gap formed by the upper and lower Hubbard bands (UHB and LHB, respectively) is increased for \(n=1\) and \(2\) as well (Figure 3(a4,b4)) and the bands forming the Dirac crossing are split for \(n=\infty\) (Figure 3(c4)), causing vanishing of the Dirac state. The larger critical \(U_{\rm c}\) necessary to open the gap increases with dimensionality \(n\) and is correlated to the bandwidth, which increases with increasing \(n\). The combined effect of SOC and \(U\) is schematized in the model density of states shown in Figure 3(d1-d4), showing the transition from a non-correlated and nonrelativistic metal (Figure 3(d1)) to a full Dirac-Mott state (Figure 3(d4)) through  
Figure 3: Band structure evolution as a function of Hubbard \(U\) and SOC strength \(\lambda\) for three iridates. The Fermi energy has been aligned to zero. The rightmost panel schematically shows the corresponding density of states, with \(\xi_{soc}\) being the SOC energy. Note that, to achieve a similar electronic state as in Sr\({}_{2}\)IrO\({}_{4}\), a increasingly larger \(U\) is used for Sr\({}_{3}\)Ir\({}_{2}\)O\({}_{7}\) and SrIrO\({}_{3}\) in the third and fourth rows. transient states distinguished by the degree of splitting between the lower and upper \(J_{\text{eff}}=1/2\) bands (Figure 3(d2,d3)). The SOC controls intra-band splitting, causing the formation of \(J_{\text{eff}}=1/2\) and \(J_{\text{eff}}=3/2\) features: \(U\), on the other hand, governs the inter-band splitting between LHB and UHB.