esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this work, we have studied issues related to mobile network congestion affected by the temporal and spatial flow of users. We focused on the quality of service in regions temporarily supported by UAV-BS. The machine-learning-based solution is divided into two main algorithms, LPA and UCPA, which are used to predict the mobile network utilization and calculate the number and location of ideal UAV-BSs to temporarily support the ground network. LPA predicts the utilization based on the cellular network usage history to find congested macrocells. To temporarily solve the congestion problem, a network of air cells must fly to the location. To estimate the number of aerial cells needed and their positioning, the UCPA was developed. In the load prediction (LPA) phases, the random forest and GRNN models have been tested and have shown accuracy up to 86% in testing and validation. To demonstrate the efficiency of the proposed method, a study based on Manhattan data was conducted. The random forest model achieved the best result, compared to the GRNN model, and showed an accuracy of 99% for the best scenario.  
The UAV-BS number estimation and positioning (UCPA) considered the predicted congestion and the definition of the congested macrocells (LPA). The final positioning of the UAV-BSs was based on the K-means algorithm. In this phase, the cost-benefit ratio was determined to balance between the number of UAV BSs and the quality of service offered. The results showed that there was little difference between the strategies in terms of service quality. However, the optimized strategy uses, on average, 50% less UAV-BS than the naive strategy.  
In general, the intelligent system was able to solve the problem of congestion. Congested regions were supported by the UAV-BSs and congestion was minimized, as shown by the numerical results. The system significantly improved the quality of service in the region and reduced the probability of user blockage. The system can be used to intelligently manage the resources available in a congested region (UAV-BS), improving deployment and increasing the lifetime of the service provided.  
Conceptualization, R.D.A., A.V.d.F.S., E.H.S.C., J.P.L.d.A. and C.R.L.F.; methodology, R.D.A.; validation, R.D.A., E.H.S.C., J.P.L.d.A. and C.R.L.F.; formal analysis, R.D.A., J.P.L.d.A. and C.R.L.F.; investigation, R.D.A., A.V.d.F.S., E.H.S.C., J.P.L.d.A. and C.R.L.F.; writing--original draft preparation, R.D.A.; writing--review and editing, R.D.A., E.H.S.C., J.P.L.d.A. and C.R.L.F. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Coordination for the Improvement of Higher Education Personnel--CAPES, the National Council for Scientific and Technological Development--CNPq, the Municipal Fund for Sustainable Development of Canada dos Carajas--FMDS, and the Support Program for Qualified Production--PROPESP/UFPA (PAPQ), for supporting the research carried out by the authors of this paper.  
The authors declare no conflict of interest.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 3 Agricultural Applications of Quadcopters  
Quadcopters are being extensively used in smart agriculture, with an essential role in optimizing resources, monitoring large agricultural areas, and ensuring better development throughout the agricultural process.  
By integrating quadcopters into each phase of the agricultural cycle, farmers can allocate resources more precisely, detect plant diseases earlier, improve crop yield forecasting, enhance sustainable farming practices, reduce waste, and maximize productivity. With all these countless benefits, quadcopters are becoming an indispensable tool in smart agriculture, achieving significant advancements in the field.  
To highlight this significant role, this section examines the various agricultural applications of quadcopters, including chemical spraying, irrigation, crop mapping and monitoring, planting and seeding, and plant health assessment.
## 3 System Model
### Uniqueness Issue  
Due to the loading matrices factors being unique up to nonsingular matrices, the generalized Tucker-2 model is not essentially unique. This consequence can be verified by using the property of the mode-\(n\) product:  
\[\begin{split}\underline{\mathbf{C}}\times_{1}\mathbf{H}\times_{2 }\mathbf{S}&=\underline{\mathbf{C}}\times_{1}\left(\mathbf{H} \mathbf{\Theta}_{\mathbf{H}}\mathbf{\Theta}_{\mathbf{H}}^{-1}\right)\times_{2 }\left(\mathbf{S}\mathbf{\Theta}_{\mathbf{S}}\mathbf{\Theta}_{\mathbf{S}}^{-1 }\right)\\ =&\underline{\mathbf{C}}\times_{1}\mathbf{\Theta}_{ \mathbf{H}}^{-1}\times_{2}\mathbf{\Theta}_{\mathbf{S}}^{-1}\times_{1}\left( \mathbf{H}\mathbf{\Theta}_{\mathbf{H}}\right)\times_{2}\left(\mathbf{S} \mathbf{\Theta}_{\mathbf{S}}\right)\end{split} \tag{18}\]  
where the noise tensor \(\underline{\mathbf{V}}\) has been omitted for convenience of notation, \(\mathbf{\Theta}_{\mathbf{S}}\in\mathbb{C}^{R\times R}\) and \(\mathbf{\Theta}_{\mathbf{H}}\in\mathbb{C}^{M_{S}\times M_{S}}\) are nonsingular matrices.  
It is shown that applying the uniqueness theorem of the Tucker model in [25], if the core tensor \(\underline{\mathbf{C}}\) is known, then \(\mathbf{S}\) and \(\mathbf{H}\) are unique to a scaling ambiguity, i.e.,  
\[\left(\mathbf{S},\ \mathbf{H}\right)=\left(\mathbf{\bar{S}}\mathbf{\Theta}_{ \mathbf{S}}^{-1},\ \mathbf{H}\mathbf{\Theta}_{\mathbf{H}}^{-1}\right) \tag{19}\]  
where \(\mathbf{\bar{S}}\) and \(\mathbf{H}\) are alternative solutions for \(\mathbf{S}\) and \(\mathbf{H}\), respectively, \(\mathbf{\Theta}_{\mathbf{S}}=\beta\mathbf{I}_{R}\) and \(\mathbf{\Theta}_{\mathbf{H}}\mathbf{=}\left(1/\beta\right)\mathbf{I}_{M_{S}}\). Consequently, the priori knowledge of only one symbol is enough to resolve this scaling ambiguity factor \(\beta\). Compared to the PARAFAC model used in existing receivers, the constructed Tucker-2 model only needs a priori knowledge of one symbol to eliminate the scaling ambiguity. Therefore, our scheme has higher spectral efficiency.
## 5 Toxicity of MPs
### Impact of MPs on Aquatic Organisms  
Many studies provide examples of MP presence in the aquatic environment and its negative effects on living organisms, especially on aquatic species that are considered valuable sources of nutrients for human consumption. MPs have been determined in 800 aquatic species at all trophic levels [48; 49]. The shape and density of MP particles determines their distribution in the aquatic environment (surface, water column or sediment) and thus determines the trophic level of organisms exposed to it [47].  
Aquatic animals are exposed to MPs due to entanglement in plastic waste objects [48; 49], the consumption of contaminated prey [49] or the direct intake of MP particles that they swallow unknowingly due to their small size or mistaking them for food due to their similar shape and color (e.g., white, transparent and blue) [41; 45; 49]. The first potential problem after the MP intake is the mechanical blockage of the digestive system, which causes injuries or death [41; 47; 48; 67], followed by the high toxicity of additives and organic pollutants adsorbed into MP particles. Mesopelagic fish that swallow MPs cannot return to deeper water due to their buoyancy, and therefore, they die [41]. Sediment animals can passively infiltrate MPs. Organisms inhabiting marine sediments are more exposed to the negative effects of MPs because they bind hundred times higher concentrations of pollutants than the sediment itself [5].  
The occurrence, quantity and type of MPs in the gastrointestinal tract of sole fish (_Solea solea_) in the northern and central Adriatic was investigated in 2014 [68]. About 95% of the sampled fish contained MPs, and more than one type of MP was found in 80% of the fish. MPs are mostly accumulated and distributed in the digestive tract, gills and muscles of aquatic organisms [9]. The transfer of MPs from the digestive system to the bloodstream can cause damage and function impairment of many organs [41; 47; 61; 63]. The test organisms mostly used as biological models for evaluating the impact of MPs in aquatic environments are fish, snails, bivalves, crustaceans and insect larvae [65; 69].
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Cluster-Based Routing
#### 3.4.2 An Energy Efficiency Routing Protocol (CRPBA)  
[PERSON] et al. [31] proposed a cluster-based routing protocol CRPBA, which referred to sink nodes as a gateway node. The nodes in the network choose direct communication or cluster-based communication, according to the distance between the gateway and node or data type. The two gateway are located at the waist and neck, respectively. The protocol stipulates that some key data communicate directly with the appropriate gateway node, to reduce transmission delay. The common data are transmitted to the cluster head by the cluster members, and then forwarded to the gateway by the cluster head, so as to reduce energy consumption and improve the success rate of data transmission. With regard to the choice of cluster head, the protocol uses cost function to decide that the node with the lowest cost function is the cluster head, and the cost function is as follows:  
\[Function(d,E)=\frac{distance}{Energy} \tag{11}\]where the _distance_ refers to the distance between gateway and source node, the _Energy_ refers to the residual energy of the node. In a sense, the protocol also adopts the method of double sink, which can theoretically achieve a better performance than a single sink, but the method of choosing cluster heads is too simple, considering only distance and residual energy, the cluster heads of the near gateway consumes too much energy and dies in advance, thus, affecting the performance of the whole network.
## 2 Experimental Study
### Time Domain Analysis  
Although the impact forces generated by the hand hammer are controlled within a range, the signals still need to be normalized in order to compare the responses of different conditions. Each impact force and its related response need to multiply by a coefficient \(C_{i}\), which is obtained as:  
\[C_{i}=\frac{400}{F_{i}^{\text{peak}}}, \tag{3}\]  
where \(F_{i}^{\text{peak}}\) is the peak value of the \(i\)-th test. Figure 5 shows the time-domain waveforms of the two conditions, with an intercepted time duration of 0-50 ms. For Condition A, the waveform attenuates smoothly with the amplitude almost decaying to zero in 10 ms. For condition B, an obvious secondary peak occurs at 7 ms, while the minor peaks continuing until 15 ms. This phenomenon of a time-domain waveform with a stronger reflection energy and a prolonged response is suggestive of the existence of a void. It indicates that the stress waves reflect at the interface between the tunnel and the void, and the reflective ratio of concrete-air interface is higher than that of concrete-soil interface.  
The vibration response can be analysed quantitatively using the fractal box-counting dimension method. The concept of fractal dimension was proposed by [PERSON] [15]. The vibration signal is irregular but self-similar. It occupies a larger area than the one-dimensional line, smaller area than the two-dimensional plane. Its fractal dimension is measured by creating multiscale covers around the signal graph. Square boxes with side length (\(\delta\)) are used to cover the signal waves on the time-amplitude plane. Then, the number of covered boxes (\(N\)) can be obtained (Figure 6). The fractal dimension is defined as:  
\[D=-\lim_{\delta\to 0}\frac{\text{lg}N}{\text{lg}\delta}, \tag{4}\]  
Figure 5: Time histories of the two conditions (A and B).  
For the measured response signals in this paper, square boxes of a series of side lengths (\(\delta_{1}\), \(\delta_{2}\), \(\cdots\), \(\delta_{n}\)) were used to cover the signal waves on the time-amplitude plane, and then a series of covered box quantities (\(N_{1}\), \(N_{2}\), \(\cdots\), \(N_{n}\)) would be obtained. The minimum \(\delta\) should be larger than the sampling interval \(\Delta t\), because the signal wave was a straight line on every sampling interval. After calculating the fitting line of (\(-\)lg\(\delta-\)lgN), the fractal dimension would be obtained as the slope of the fitting line (Figure 7).  
The results show that the fractal dimensions of the conditions A and B are 1.221 and 1.288, respectively. This illustrates that when the waveforms of the two conditions are in the coordinate system, with the same time scale and the same amplitude scale, the waveform B is more complicated. Therefore, when the void exists, the fractal dimension is larger, and the waveform in the time domain is more complicated.
## 4 Derived Products and Data Processing  
From the high-resolution model created in 3 DReshaper, the following products were derived using Agisoft PhotoScan: (a) Orthophotomosaic of 15,590 \(\times\) 12,864 pixels with a resolution of 15 microns/pix (Figures 1a and 5a). The result was exported in TIF format; (b) DEM with the same resolution. In this case, it was exported using different configurations: in 32-bit TIF format and as a hillshade in 8-bit TIF format (Figure 6).  
From the DEM elevation data, other secondary DEMs can be generated, which offer information that our eyes do not perceive at first glance (see Section 4.1). In the same way, from the orthophotomosaic or macro images, image-enhancement techniques that reveal hidden information can be applied (see Section 4.2).  
The intention is to create a single work project where you can analyze all the data and generate a new tracing in the most objective way possible. In this case, we work in Adobe Photoshop, and we incorporate the different data through information layers (Figure 5). All data are georeferenced to the control points, which allows us to combine the different layers to improve visualization, complete and validate doubtful information, etc. We use this program because it is more user-friendly, but we could also work in a computer-aided design (CAD)/GIS environment.  
Figure 5: Detail image of the information layers incorporated into the project and the possible resulting combinations: (**a**) orthophotomosaic; (**b**) high pass filter; (**c**) hillshade; (**d**) overlay of layer (**c**) with a transparency of 35% on layer (**a**); (**e**) final tracing 1994; (**f**) overlay of layer (**e**) (masking lines only) on layer (**a**); (**g**) DStretch-LRE; (**h**) DStretch-YRE; (**i**) DStretch-YBG; (**j**) DStretch-LYE; (**k**) pigment masked from layer (**g**); (**l**) overlay of layer (**c**) with 50% transparency on layer (**k**), which in turn is superimposed on layer (**b**).
## 1 Introduction  
Direct restorations using resin-based composites (RBC) are now the first choice for carious molars. This method only removes the caries-infected tooth substrate, allowing the caries-affected dentin to remain [1]. Calcium phosphate crystals, such as brucite and whitlockite, are deposited in the tubules of caries-affected dentin, thus blocking external stimuli and reducing damage to the dentin-pulp complex [2].  
By contrast, indirect restorations, such as inlay and onlay restorations, are selected to restore large defects caused by caries removal. Recent computer-aided design/computer-aided manufacturing (CAD/CAM) technology has made it possible to perform single-appointment restorations of indirect restorations as well as direct RBC restorations [3]. However, because indirect restoration requires cavity preparation with a resistance form, it is accompanied by the removal of caries-affected dentin and healthy dentin [4]. Deep and large defects can often be filled with a base material, thereby reinforcing the thin and fragile tooth structure [5]. In particular, RBC base materials have higher compressive strength than mineral trioxide aggregate (MTA)-based base materials, and thus can be expected to have a higher reinforcing effect [6].  
Resin coating or immediate dentin sealing can protect the dentin-pulp complex on the exposed dentin surface immediately after cavity formation [7; 8; 9]. Furthermore, studies have reported that the bond strength of resin-based luting cement to resin-coated dentin is higher than that to dentin without a resin coating [10; 11; 12; 13]. The adhesion of composite materials to crown restorations has been severally studied, and almost all of these studies have focused on the adhesion between restorative material and luting cement. These results have suggested that silane treatment of the surface of silica-based restorative materials is effective for enhancing bond strength to silica-based indirect restorations [14; 15]. In clinical practice, the surfaces of cavities reinforced with a base material contain a mixture of exposed dentin and base material, and it is not possible to apply a resin coating only to the exposed dentin surfaces. Therefore, the resin coating is applied to the entire surface of the cavity. The effect of resin coating on the bond strength to luting cement has rarely been reported [6]. The effect of pretreatment with a silane coupling agent on the surface of RBC base material is also unknown.  
The purpose of this study was to investigate the effect of silane treatment of a resin-coated RBC base material on its microtensile bond strength (\(\mu\)TBS) to a CAD/CAM ceramic restorative material. The null hypothesis of this study was that there is no effect of silane pre-treatment, regardless of whether the RBC was resin-coated.
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 4 Applications of Machine Learning in XRD Data Analysis
### Microstructural Characterization  
Strain profiles from XRD data in irradiated or ion-implanted materials were determined by [PERSON] et al. [112] using a CNN model built on usual convolutional, max pooling, and batch normalization layers, together with dense neuron layers. While the accuracy of the results was above 90% for individual parameters, the accuracy for the complete strain profile (the key output of the modeling effort) ranged between 50% and 82%. The highest accuracies for the strain profile were achieved when training the CNN for separate strain ranges: 82% when considering only strains above 0.5%, and 76% for the lower strain region (in the 0.5-2% range) using a purposefully trained CNN. The lowest reported accuracy (50%) for the strain profile was applicable to strains below 2% when the CNN was trained using the complete strain range.  
Residual stress in rails was also determined from X-ray measurements by applying a dimensionality reduction technique to X-ray data characteristic of normal rail regions, followed by multivariate statistical analysis based on the Gaussian distribution; finally, the presence of stress was identified using anomaly detection [113]. After performing dimensionality reduction on the original data using either PCA, kernel PCA, or an autoencoder, each datapoint was given an anomaly score corresponding to the local amount of damage. The accuracy of the model was assessed by comparing the location of the cracks in the rail with the anomalous zones, with appropriate thresholds for the anomaly scores, adjusted for each dimensionality reduction method. Out of the three dimensionality reduction algorithms, the autoencoder resulted in the highest accuracy in residual stress measurement. This is not a surprising result, given that autoencoders are based on neural networks that can represent nonlinear relationships using neuron activation functions, while PCA is a linear analysis technique.  
ML methods such as neural networks and autoencoder excel at nonlinear relationships, capturing nuances conventional methods might miss. However, their success in microstructural characterization relies on high-quality training data and model interpretability remains a challenge. Despite good results for determining individual parameters, because of the limited availability of experimental data, the accuracy of the models employed for several parameters has unsatisfactory accuracy to date. Until further improvement of the databases or the models, conventional methods are preferred in complex situations.
## 5 Discussion  
### Performance Comparison of Traditional and Proposed Method  
The RMSE was used as a quantitative analysis metric for change detection in this experiment. RMSE is defined as the square root of the average of the squared differences between predicted values and true values and is commonly used to evaluate the accuracy of prediction models. The formula is as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{12}\]  
In the formula, \(n\) is the number of predicted samples, \(y_{i}\) is the true value, and \(\hat{y}_{i}\) is the predicted value of the model.
## 4 Discussion
A study conducted on 63 children diagnosed with HT and 47 children without thyroid pathology revealed differences between them (1.75 \(\pm\) 1.46 vs. 0.26 \(\pm\) 0.77; \(p\) < 0.001), results similar to our results. Instead, the cut-off value established for the presence of CAT was 0.31 (92.1% Se, 66% Sp, AUROC 0.828), much lower than the cut-off value we obtained (>0.9). They also found no correlation between SR and TSH, but a positive correlation between SR and ATPO [58]. Another study that included 76 adolescents with HT and 46 without thyroid pathology recommends the cut-off value for CAT diagnosis >0.98 (83% Se; 93% Sp; AUROC 0.929), a result in accordance with the cut-off value we obtained (>0.9; AUROC 0.9, Se 84.62%, Sp 95.45%; PPV 97.8%; NPV 72.4%). The mean values for children with CAT were 1.2 \(\pm\) 0.2, significantly higher than the mean SR values for control subjects \(-\)0.77 \(\pm\) 0.18 (\(p\) < 0.01), results that are similar to our results [59]. Regarding correlations, we found a strong positive correlation between SR and ATPO values (r = 0.65, \(p\) < 0.0001) and a weak positive correlation between SR and ATG values (r = 0.29, \(p\) = 0.0135). In accordance with our results, there were also the results obtained by [PERSON] et al. who also obtained positive correlations between SR and ATPO (r = 6.85), but the possible correlation between SR and ATG was not analyzed [58]. [PERSON] et al. also found a correlation between SR and ATPO (r = 0.439, \(p\) < 0.01) but no correlation between SR and ATG [59]. We did not find correlations between SR and TSH, in accordance with the literature [58]. No correlations between SR and ft4 values were found. We also found correlations between SR and thyroid volume as well as body surface area. These parameters have not been evaluated in other studies on the pediatric population so far. The innovative aspect of our study is the comparison of thyroid parenchyma elasticity between children getting hormone replacement medication and those who are not receiving it.No differences were found between children who receive levothyroxine treatment (1.24 \(\pm\) 0.26) and those who do not (1.18 \(\pm\) 0.25; \(p\) = 0.4615). This direction of research should be further investigated because there are studies on the adult population in which differences were identified between patients who receive treatment and those who do not [52; 60]. A study conducted by [PERSON] et al. found significantly higher values of thyroid elasticity using SWE when comparing patients who were on levothyroxine treatment to untreated euthyroid patients (27.3 \(\pm\) 9.0 kPa vs. 20.9 \(\pm\) 10.4 kPa, respectively, \(p\) = 0.02) [60]. Another study using SE found differences between CAT patients who receive treatment and untreated euthyroid patients (3.45 \(\pm\) 2.53 vs. 2.15 \(\pm\) 1.27, \(p\) < 0.0001). A cut-off value >2.94 was found to be predictive for hypothyroidism status in patients with CAT [52]. We aim to follow up on these patients to determine the evolution of stiffness and possible correlation with the need for treatment once the disease progresses.