esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of 3D Printed Dielectric Resonator Antenna Arrays for Millimeter-Wave 5G Applications
###### Abstract  
This paper explores the potential use of fused deposition modeling (FDM) technology for manufacturing microwave and millimeter-wave dielectric resonator antennas (DRAs) for 5G and beyond communication systems. DRAs operating at microwave and millimeter-wave (mmWave) frequency bands were simulated, fabricated, and analyzed in terms of manufacturing quality and radio frequency (RF) performance. Samples were manufactured using a 3D printer and PREPERM(r) ABS1000 filament, which offers a stable dielectric constant (\(\epsilon_{r}=10\pm 0.35\)) and low losses (tan \(\delta=0.003\)) over wide frequency and temperature ranges. Surface profile tests and microscope measurements revealed discrepancies in the dimensions in the xy-plane and along the z-axis, consistent with the observed shift in resonant frequency. Despite these variations, reasonably good agreement between RF-simulated and measured results was achieved, and the DRA array successfully covered the intended mmWave band. However, challenges in achieving high precision may restrict applications at higher mmWave bands. Nevertheless, compared with conventional methods, FDM techniques offer a highly accessible and flexible solution with a wide range of materials for home and micro-manufacturing of mmWave DRAs for modern 5G systems.  
f +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Analysis of 3D Printed Dielectric Resonator Antenna Arrays for Millimeter-Wave 5G Applications. _Appl. Sci._**2024**, 14, 9886. [[https://doi.org/10.3300/app14219886](https://doi.org/10.3300/app14219886)]([https://doi.org/10.3300/app14219886](https://doi.org/10.3300/app14219886))  
Academic Editor: [PERSON] Received: 22 September 2024  
Received: 21 October 2024  
Accepted: 23 October 2024  
Published: 29 October 2024  
**Copyright**\(\copy\) 2024 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Discussion  
We previously revealed that the foundation of the automation process and the elimination of human factors in agriculture are typically built upon optical technologies. These technologies enable the automatic positioning of manipulators in milking robots using LiDAR or TOF cameras, measurements of milking intensity in cows using LED sensors, and the assessment of milk quality through the interpretation of color characteristics [20; 21; 22; 23].  
Feeding processes are equally important factors that shape the profitability of the industry. For farmers, it is crucial to make quick managerial decisions regarding adjustments to feeding rations or crop harvesting. The proposed solution for the optical express analysis of feed crops, exemplified by corn silage, allows farmers to determine the nutritional value of the feed used in-field conditions without the involvement of specialized laboratories.  
In the explored literature and commercial device brochures, the use of NIR analysis methods for the nutritional value of feeds in agriculture is noted. Our presented research contributes to testing the hypothesis of using photoluminescence to determine nutritional value, providing corresponding correlation dependencies of photoluminescence concerning total protein content, moisture, ADF, and other indicators [24; 25; 26].  
A similar spectral method, but based on reflectivity data, was used to detect Fusarium pepper disease [27]. Unfortunately, the creation of a device implementing the method was not reported.  
The device proposed in this study, unlike analogs, does not need to analyze the structure of volatile organic compounds [28] and does not require the construction of an image [29; 30].  
We found that among the existing studies, the most common are manuscripts that reveal the essence of spectroscopy in the near-infrared range (700-1400 nm) as a tool for determining the nutritional value of agricultural feed. At the same time, the essence of the method is to register the reflecting signals associated with the vibrational movements of the molecules of the surface layer of the biomaterial that is being analyzed. Also, the NIR method requires the use of an expensive spectrometer for recording the reflecting signal and hardware with software for processing spectral data.  
We proposed a method related to the effect of photoluminescence in the visible range and confirmed our hypothesis with the results of the experimental studies. At the same time, the practical value of photoluminescence as a tool for determining the nutritional value of feed is the low cost of electronic components (diode and photodiode) compared to a spectrometer and a light source for implementing this task in the near-infrared range.
###### Abstract  
The presented paper focuses on testing the performance of a SLAM scanner Zeb Horizon by GeoSLAM for the creation of a digital model of a bridge construction. A cloud acquired using a static scanner Leica ScanStation P40 served as a reference. Clouds from both scanners were registered into the same coordinate system using a Trimble S9 HP total station. SLAM scanner acquisition was performed independently in two passes. The data acquired using the SLAM scanner suffered from relatively high noise. Denoising using the MLS (Moving Least Squares) method was performed to reduce noise. An overall comparison of the point clouds was performed on both the original and MLS-smoothed data. In addition, the ICP (Iterative Closest Point) algorithm was also used to evaluate local accuracy. The RMSDs of MLS-denoised data were approximately 0.02 m for both GeoSLAM passes. Subsequently, a more detailed analysis was performed, calculating RMSDs for several profiles of the construction. This analysis revealed that the deviations of SLAM data from the reference data did not exceed 0.03 m in any direction (longitudinal, transverse, elevation) which is, considering the length of the bridge of 133 m, a very good result. These results demonstrate a high applicability of the tested scanner for many applications, such as the creation of digital twins.  
GeoSLAM; accuracy; bridge +
Footnote †: journal: Medical Imaging
## 3 AI for the Study of Orbital Tumors
### AI as a Useful Tool for Differential Diagnosis  
When studying a possible orbital lesion, fundamental to the diagnostic process is the differential diagnosis. Indeed, it is important to be able to understand what the possible diagnoses of a clinically and radiologically visible lesion may be in order to understand the correct diagnostic-therapeutic course for the patient. Many cancer lesions are confused for other diseases, often inflammatory or systemic. In fact, differential diagnosis is a major challenge in clinical practice, as many lesions have overlapping clinical and radiological manifestations but require radically different therapeutic approaches. AI could offer an innovative and less invasive solution than biopsies, which are often difficult to perform, especially in frail patients.  
The study conducted by [PERSON] et al. (2024) [PERSON] et al. (2024) developed a radiomics system with the aim of distinguishing solitary fibrous tumors (SFTs) from orbital schwannomas, two mesenchymal tumors with similar appearance but different clinical management, achieving AUC results of 0.90 in the multicenter testing set. In addition, the study conducted by [PERSON] et al. (2023) [PERSON] et al. (2023) developed a system based on LASSO + PCA + SVM, which could distinguish IgG4-related ophthalmic disease (IgG4-ROD) and orbital MALT lymphoma by obtaining an AUC of 0.82. Another study that focused on the differential diagnosis of orbital lymphoma was conducted by [PERSON] et al. (2024) [PERSON] et al. (2024), who tested a deep learning algorithm, based on XGBoost and SHAP, to distinguish it from orbital inflammatory pseudotomors, analyzing MRI scans and obtaining an AUC of 0.89. Finally, the study conducted by [PERSON] et al. (2025) [PERSON] et al. (2025) analyzed a ResNet-based convolutional neural network (CNN) model to distinguish between orbital metastases and primary orbital tumors, which is useful for rapidly guiding oncolytic staging and therapeutic treatment.  
The following table (Table 4) shows studies found in the literature regarding the application of artificial intelligence models in the differential diagnosis of orbital neoplastic lesions. Information such as the models used, the input data processed by the algorithms, the method of evaluation, and the results obtained from the individual studies are also given.
# Evaluation of Emotional Satisfaction Using Questionnaires in Voice-Based Human-AI Interaction
## 3 Results
### Analysis of Variance  
As shown in Figure 4, the emotional scores of users were analyzed based on the four design parameters and two derived axes (pleasurability and reliability). The ANOVA results obtained for the four design parameters are presented in Appendix A.  
Of the four design parameters, \"response time to get to the next interaction\" and \"pace of the answer\" exhibited no difference between levels in terms of both pleasurability and reliability. Among them, \"pace of the answer\" exhibited relatively high emotional satisfaction with four syllables/s; however, statistically, there was no significant difference. The \"number of trials to get the right answer for a question\" shows that both pleasurability and reliability decrease as the number of trials increases. On the pleasurability axis, when the question needs to be asked more than once, the satisfaction decreases significantly, and there is a statistical difference. For reliability, it was observed that up to two trials, the statistically significant difference did not decrease the satisfaction sufficiently, but when three trials were conducted, satisfaction decreased significantly. The \"sentence structure of the answer\" showed that the satisfaction in the pleasurability axis increased significantly when detailed information was presented rather than a simple answer, and it was confirmed that there exists a statistically significant difference. In the case of reliability, because the emotions of \"repeat the question and then answer\" and \"repeat the question and then answer with a clear reference source\" are similar, it is thought that the action that informs the system that the user understands the intention of the user's question is the key to securing trust.
## 3 Optimization Techniques
### Arithmetic Optimization Algorithm (AOA)  
When used for mathematical calculations, the arithmetic operators [54] imply that the multiplication and divisibility operators are the ones that are most often utilized, yielding diverse values or assessments across different domains, which is important for the investigative search process. However, unlike subtractive and additive operators, the divisive and multiplicative prerequisite aid by swiftly accomplishing the objective owing to their significant dispersion. With the help of these operations, we demonstrate a new function to show how the distributions of different operators are related to one another. Because of this, it is feasible that the optimal solution may be originated by an experimental search, which can be performed through repeated experimentation. Additionally, search controllers based on divisive and multiplicative operators are used to enhance the exploitation phase of the search process by means of improved communication during the optimization phase. The mathematical model for updating the location in AOA is provided below as Equation (17):  
\[\begin{array}{l}if\;r_{1}>MOA\\ x_{i,j}(P_{\_}{iter}+1)=\left\{\begin{array}{l}best(x_{j})\div(MOP+\epsilon) \times\left(\left(UL_{j}-LL_{j}\right)\times\lambda+LL_{j}\right),r_{2}<0.5\\ best(x_{j})\times MOP\times\left(\left(UL_{j}-LL_{j}\right)\times\lambda+LL_{j} \right),\;\;\;\;\;Otherwise\\ \end{array}\right.\\ \par x_{i,j}(P_{\_}{iter}+1)=\left\{\begin{array}{l}best(x_{j})-MOP\times \left(\left(UL_{j}-LL_{j}\right)\times\lambda+LL_{j}\right),r_{3}<0.5\\ best(x_{j})+MOP\times\left(\left(UL_{j}-LL_{j}\right)\times\lambda+LL_{j} \right),\;\;\;Otherwise\end{array}\right.\\ \par\end{array} \tag{17}\]  
The control variables' upper and lower limits are denoted by \(UL\) and \(LL\), respectively. As shown in Equations (18) and (19), the \(MOA\) and \(MOP\) are adjusted with each iteration of the mathematics optimizer (_MO_). \(P\_iter\) represents present iteration. \(x_{ij}\) denotes the \(j\)th position of the \(i\)th solution at the current iteration, and best (\(x_{j}\)) is the \(j\)th position in the best obtained solution so far. The tuning parameters \(z\) and \(\lambda\) are assigned the specific values of 5 and 0.5, correspondingly.  
\[MOA(P\_iter)=Min+P\_iter\times\left(\frac{Max-Min}{M\_Iter}\right) \tag{18}\]  
\[MOP(P\_iter)=1-\frac{P\_iter^{1/z}}{M\_Iter^{1/z}} \tag{19}\]  
_Max_ and \(Min\) are the upper and lower limits of the allowable values for MOA. \"\(M\_iter\)\" represents the maximum number of iterations. Figure 1 shows the flowchart of AOA.
## 4 Results
### Petrology and Geochemistry
The average values of EOM and HI in sand-silt intervals are 0.6% and 181 mg/g TOC, respectively, which are lower than those in mudstones (0.89% and 197 mg/g TOC, respectively). Meanwhile, the OSI of sand-silt intervals ranges from 21 to 212 mg/g TOC and averages 105 mg/g TOC, which is greater than that of mudstones (ranging from 10 mg/g TOC to 247 mg/g TOC and averaging 82 mg/g TOC; see Table 1). The geochemical parameters of mudstones with interlayers are intermediate between those of sand-silt intervals and mudstones (Table 1, Figure 5). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Sand-Silt Intervals**} & \multicolumn{4}{c}{**Mudstones with Interlayers**} & \multicolumn{4}{c}{**Mudstones**} \\ \cline{2-10}  & **Min** & **Max** & **Average** & **Min** & **Max** & **Average** & **Min** & **Max** & **Average** \\ \hline Quartz/\% & 13.7 & 67.4 & 32.9 & 9.6 & 44.3 & 27.0 & 7.3 & 45.0 & 22.4 \\ Feldspar/\% & 9.0 & 51.0 & 31.4 & 8.0 & 45.3 & 26.8 & 7.0 & 43.9 & 20.5 \\ Clay mineral/\% & 10.9 & 55.8 & 26.5 & 11.9 & 71.0 & 39.1 & 13.9 & 74.4 & 48.3 \\ TOC/\% & 0.39 & 7.36 & 2.32 & 0.45 & 8.58 & 4.36 & 1.37 & 20.27 & 5.86 \\ S\({}_{1}\)/mg/g & 0.17 & 6.86 & 2.29 & 0.34 & 8.36 & 4.0 & 1.24 & 13.09 & 4.37 \\ S\({}_{2}\)/mg/g & 0.25 & 15.22 & 4.44 & 0.31 & 15.71 & 9.21 & 0.39 & 48.29 & 11.52 \\ EOM/\% & 0.08 & 1.73 & 0.60 & 0.33 & 1.43 & 0.92 & 0.16 & 2.73 & 0.89 \\ HI/mg/g TOC & 64 & 474 & 181 & 54 & 333 & 190 & 51 & 839 & 197 \\ OSI/mg/g TOC & 21 & 212 & 105 & 33 & 185 & 101 & 10 & 247 & 82 \\ Porosity/\% & 0.55 & 8.37 & 4.28 & 0.54 & 7.70 & 3.89 & 0.54 & 7.34 & 3.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mineralogy and geochemistry of mudstones, sand-silt intervals, and mudstones with interlayers. Figure 4: Histograms showing detrital grain size of sand-silt intervals (**A**) and mudstones (**B**).
## 3 Description of Numerical Algorithms
### Krylov Subspace Methods
#### Arnoldi Iteration  
The Arnoldi iteration finds the \(p\) LM eigenvalues of \(s\mathbf{I}_{r}-\mathbf{A}\). In particular, it employs the Gram-Schmidt process in order to find an orthonormal basis of the Krylov subspace. Subsequently, the eigenvalues are extracted by applying the Rayleigh-Ritz procedure. Starting from an initial normalized vector \(\boldsymbol{q}_{1}\), the vector \(\boldsymbol{q}_{k+1}\) at the \(k\)-th step is calculated, as follows:  
\[\begin{split}\hat{\boldsymbol{q}}_{k}&=\mathbf{A} \boldsymbol{q}_{k}-\sum_{i=1}^{k}h_{i,k}\boldsymbol{q}_{i}\,,\\ \boldsymbol{q}_{k+1}&=\frac{\hat{\boldsymbol{q}}_{k }}{h_{k+1,k}}\,,\end{split} \tag{27}\]  
where \(h_{i,k}=\boldsymbol{q}_{i}^{\text{H}}\mathbf{A}\boldsymbol{q}_{k}\), \(h_{k+1,k}=||\hat{\boldsymbol{q}}_{k}||\).  
At the \(k\)-th step, the Arnoldi relation is formulated, as follows:  
\[\mathbf{A}\mathbf{Q}_{k}=\mathbf{Q}_{k}\mathbf{H}_{k}+\hat{\boldsymbol{q}}_{k }\mathbf{e}_{k}^{\text{H}}\,, \tag{28}\]  
where \(\mathbf{Q}_{k}\), \(\mathbf{Q}_{k}\in\mathbb{C}^{r\times k}\), is the matrix with columns \(\boldsymbol{q}_{k}\); \(\mathbf{H}_{k}\), \(\mathbf{H}_{k}\in\mathbb{R}^{k\times k}\), is in Hessenberg form and it has elements \(h_{i,j}\); \(\mathbf{e}_{k}\) denotes the \(k\)-th column of the identity matrix \(\mathbf{I}_{k}\). The algorithm stops when \(h_{k+1,k}=0\), suppose when \(k=p\). Then, relation (28) becomes:  
\[\mathbf{A}\mathbf{Q}_{p}=\mathbf{Q}_{p}\mathbf{H}_{p}\,. \tag{29}\]  
where \(\mathbf{Q}_{p}=[\boldsymbol{q}_{1},\boldsymbol{q}_{2},\dots,\boldsymbol{q}_{p}]\) is an orthonormal basis of the Krylov subspace. Following the Rayleigh-Ritz procedure, the eigenvalue \(\lambda_{i}\), \(i=1,2,\dots,p\), of \(\mathbf{H}_{p}\) is also eigenvalue of \(s\mathbf{I}_{r}-\mathbf{A}\). Moreover, if \(\mathbf{v}_{i}\) is eigenvector of \(\mathbf{H}_{p}\), the Ritz vector \(\mathbf{u}_{i}=\mathbf{Q}_{p}\mathbf{v}_{i}\) is a eigenvector that corresponds to \(\lambda_{i}\).  
The Arnoldi iteration converges quickly if the initial vector \(\boldsymbol{q}_{1}\) has larger entries at the direction of desired eigenvalues. This is usually not the case and, thus, it is common that many iterations are required. On the other hand, the ability to compute the columns of \(\mathbf{Q}_{p}\) is constrained, because of its high memory requirements. For this reason, in practice, the Arnoldi iteration is typically restarted after a number of steps, while using a new, improved initial vector. The restarts can be explicit or implicit. The idea of explicit restarts is to utilize the information of the most recent factorization in order to produce a better initial vector. This is usually combined with a locking technique, i.e., a technique to ensure that converged vectors do not change in successive runs of the algorithm. On the other hand, the idea of the Implicitly Restarted (IR) Arnoldi iteration is to combine the Arnoldi iteration with the implicitly shifted QR algorithm.
## 3 Dataset and Calculation of Objective Values
### Comparison of Skyline Query and Top-\(k\) Query for Irrigation Decision Support  
In this section, we compare the results of the proposed algorithm to those of a top-\(k\) query. We consider the irrigation system depicted in Figure 2, and the six feasible solutionsunder consideration by the government are presented in Table 1. In a skyline query, each solution is regarded as a data point. Thus, we selected only three objective values (rice yield, cane yield, and vegetable yield, which were calculated as discussed in the previous section) so that we could plot the results in a three-dimensional (3D) format. A skyline query checks each objective value for the solutions in pairs, seeking solutions with at least one objective value that is not poorer than that in the other solutions (i.e., solutions that are not dominated by other solutions). For example, in Table 1, all of the objective values (i.e., rice yield, cane yield, and vegetable yield) in Solution 2 are poorer than those in Solution 1, so the system considers Solution 2 to be dominated by Solution 1 and, thus, removes it. Visually, in Figure 3a, Solution 2 is below and to the left of Solution 1. Solution 1 has a smaller cane yield than Solution 3 but performs better than Solution 3 in the other two objectives. Thus, Solutions 1 and 3 do not dominate each other, meaning that both are feasible. Overall, Solutions 1, 3, and 5 are not dominated by any of the other solutions: Solution 1 is the best solution for rice yield, and Solutions 3 and 5 are the best solutions for cane and vegetable yields, respectively. Clearly, all three of these solutions meet government needs.  
To apply a top-\(k\) query, we must first integrate multiple objective values into a single value, as shown in the summary column of Table 1. The top three solutions are Solutions 1, 2, and 4. However, these solutions only rank the highest for rice yield; all objective values in Solutions 2 and 4 are poorer than those in Solution 1, as shown in Figure 3b. The best solutions for cane yield (i.e., Plan 3) and vegetable yield (i.e., Plan 5) are not selected. These results, thus, lack the validity of those presented in Table 1. This example represents a classic shortcoming of the top-\(k\) query: only one among conflicting solutions is selected, as the weights of different objective values are not adjusted well. In contrast, the skyline  
Figure 3: 3D plot representing results of (**a**) skyline query and (**b**) top-\(k\) query.  
Figure 2: Diagram of irrigation example.  
query can find a set of solutions in which all objective values are optimized by at least one solution.
## 3 Hybrid Topology Architecture
### Implementation
#### 3.2.3 LoRaWAN Server  
LoRaWAN specifications from the LoRa Alliance consider the implementation of a Join Server, a Network Server, and an Application Server. These three servers can be deployed in the same physical device or in different locations with the option of using a Join Server from one service provider, a Network Server from another provider, and an Application Server from a third service provider. Installing an MQTT broker is also necessary as MQTT is the protocol used for communication among these servers and communication with the gateway.  
Figure 5: End devices used for the hybrid LoRaWAN topology procedure.  
The LoRaWAN server has been implemented using the open-source software from Chirpstack [38]. This software is widely used for LoRaWAN network deployments. However, it does not completely follow the specifications provided by the LoRa Alliance. In Chirpstack version 3, the network and application servers need to be installed to implement the backend of the LoRaWAN network. Therefore, the join server from the specification is not separate software and the join process is managed by the other servers. Chirpstack version 4 agglutinates the network and application servers into a single server for user convenience. This may lead to confusion due to discrepancies between the software implementation and the explanation provided in the specifications, but Chirpstack supports the functionalities of both LoRaWAN v1.0 and LoRaWAN v1.1 specifications.  
To implement the proposed solution, Chirpstack v4 was employed. Thus, the network and application servers are combined into one software module that performs all the tasks stated in the specifications. We refer to this server as the LoRaWAN server for simplification. Chirpstack includes configuration files that have been modified according to the documentation to determine if the data are forwarded using plain JSON or protobuf or to determine the frequency band on which the LoRaWAN network is operating. These configurations are necessary for any LoRaWAN network using Chirpstack. In this implementation, protobuf and the US915 band are employed, with the ADR turned off and a SF of 7. The server is physically located at the Technical University of Cartagena (Spain), and it has a public IP address to enable access from Santiago de Cali University (Colombia). Our solution for adaptable hybrid network topologies is agnostic to the LoRaWAN server, and thus, no modifications are performed to the Chirpstack software [38].