esa-sceva/satcom-chunk-collection huggingface dataset:
# Relative Pose Estimation between Image Object and ShapeNet CAD Model for Automatic 4-DoF Annotation
## 3 Proposed Pose Estimation Pipeline
### Retrieval of a CAD Model and Similarity Measurement
Suppose the \(3\times 3\) affine matrix \(A_{ij}^{k}\) between the RGB query image and \(k\)-th rendered CAD image is determined by matching the DELF features between them. And suppose \(I_{ij}\) is the same size identity matrix. Here, \(i\) and \(j\) are indices of the matrix elements. In addition, let \(N_{inl}^{k}\) be the number of feature inliers between them. We use following three criteria for image similarity measurement \(\rho_{k}\) for the \(k\)-th rendered CAD image. The final candidate is selected which has the maximum \(\rho_{k}\). We demonstrated through the experiment that the affine transformation model is sufficient to perform the geometric verification between the query and rendered images. * Similarity measure 1 (SM1): \[\epsilon_{k}=\sum\
olimits_{i,j}\left|\frac{A_{ij}^{k}}{\max\left(A_{ij}^{k} \right)}-I_{ij}\right|\] (10) \[\rho_{k}=N_{inl}^{k}\;of\;k\text{-th {\emph{rendered image among five smallest} }}\epsilon_{k}\]
* Similarity measure 2 (SM2): \[\rho_{k}=N_{inl}^{k}\] (11)
* Similarity measure 3 (SM3): \[\epsilon_{k}=\sum\
olimits_{i,j}\left|\frac{A_{ij}^{k}}{\max\left(A_{ij}^{k} \right)}-I_{ij}\right|\] (12) \[\rho_{k}=(1-\alpha(\text{sigmoid}(\epsilon_{k}))\times N_{inl}^{k}\;(\alpha =0.35)\]
## 3 Results
### Prediction of VMD Components
#### 3.2.2 Prediction of Low-Frequency Components Based on ARMA Model  
Thus, the low-frequency components are predicted via the ARMA model. First, the sample's autocorrelation function (ACF) and partial autocorrelation function (PACF) are obtained to determine the initial order of the model. This work uses IMF4 as the example to illustrate, and other low-frequency components are not described again. The ACF and PACF of IMF4, respectively, are shown in Figure 8.  
According to the AIC order code determined in Reference [41] and the above figure, both the autocorrelation and the partial autocorrelation plots of the IMF4 component have tailing characteristics. Also, the autocorrelation coefficient is not zero when the lag order is 3, and the trailing characteristic is apparent when the lag order is greater than 3. The partial autocorrelation coefficient is not zero when the lag order is 6, but the trailing characteristic is obvious after the lag order is greater than 6. Thus, we can initially determine that \(p=6\) and \(q=3\). To make the model more accurate, the values of \(p\) and \(q\) can be relaxed. Using the AIC criterion, the minimum value is taken as the optimal model. The AIC values under the various model orders are shown in Table 4.  
From the above table, the optimal model of IMF4 is ARMA(7, 3). Then, using the above method, the components IMF5 and IMF6 are ordered. The specific situation is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Order** & **ARMA(6, 3)** & **ARMA(6, 4)** & **ARMA(7, 3)** & **ARMA(7, 4)** & **ARMA(8, 3)** & **ARMA(8, 4)** \\ \hline The value of AIC & \(-2.902\) & \(-2.869\) & \(-2.609\) & \(-2.823\) & \(-2.807\) & \(-2.817\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Akaike Information Criterion (AIC) values of different order models.  
Figure 7: Prediction of IMF1-IMF3 component.  
Through the above analysis, the components IMF4 to IMF6 were predicted using the trained model, which is shown in Figure 9.  
From Figure 9, we can see that using the ARMA model to predict low-frequency components, with relatively gentle fluctuations, results in a small error. ARMA has strong nonlinear fluctuation data learning ability, which is suitable for low-frequency component prediction.
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 5 Experiment and Analysis
### Analysis of Network Energy Consumption  
This paper first verifies the energy performance of five opportunistic routing algorithms based on the number of dead nodes. With the same network topology composed of 90 nodes, the number of nodes that died as time increases is shown in Figure 2. Algorithm 1 mainly calculates the forwarding priority based on the residual energy, so it effectively guarantees the energy consumption balance in the early stage. However, as the number of simulation rounds increases, selecting candidate nodes only depending on the residual energy will lead to some important nodes running out of energy, and the network transmission performance will decline sharply. Algorithm 2 and Algorithm 3 select forwarding nodes by combining residual energy and distance between nodes and sink. No adaptive weights are designed to balance different influencing factors. When the energy gap is not obvious, the nodes with better transmission performance will be frequently used to transmit data. At the same time, because the transmission cost of the candidate set is not considered, the network performance will decline rapidly. Our algorithm and LCOR algorithm construct the candidate set by calculating the path cost, which can effectively avoid invalid transmission to a certain extent. Our algorithm considers both the impact of important nodes on the network connectivity and the impact of residual energy, so the network energy consumption can be evenly distributed to the neighbor nodes, reducing the number of dead nodes.  
In WSN with different numbers of nodes, it can be seen from Figure 3 that the standard energy consumption per package of the studied algorithm is significantly lower than that of the other four algorithms. This means that our opportunistic routing algorithm can still transmit data with low energy consumption under different network topologies.  
Figure 2: The number of failed nodes.
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 4 Closing the Technological Gap: The ConTAD Approach
### Obtaining Information on Land  
In case of lost containers overboard, a quick reaction by salvage companies is important to recover most of the cargo while it is still floating, therefore reducing pollution and the cost of the salvage operation. Immediately informing the recipient about the lost  
Figure 2: Main Elements of the ConTAD tracking unit.  
cargo is also important, since they can take quick action to replace necessary deliveries. To obtain the information about the loss of containers ashore, methods like GSM or satellite communication are available. It is also possible to analyze AIS signals received by land stations or satellites to detect containers in case the tracking unit is equipped with an AIS transceiver, as suggested in Section 4.2.
## 4 Results
### Characteristics of Trace Elements
#### 4.2.3 Well D1  
The average contents of LILE such as Rb, Sr, Pb and Ba are 54.0 \(\times\) 10\({}^{-6}\), 96.9 \(\times\) 10\({}^{-6}\), 10.9 \(\times\) 10\({}^{-6}\) and 576.3 \(\times\) 10\({}^{-6}\), respectively, lower than the average value of UCC. The average contents of HFSE such as Nb, Hf, Zr, Th, U and Y are 13.6 \(\times\) 10\({}^{-6}\), 4.7 \(\times\) 10\({}^{-6}\), 229.5 \(\times\) 10\({}^{-6}\), 3.4 \(\times\) 10\({}^{-6}\), 1.6 \(\times\) 10\({}^{-6}\) and 11.5 \(\times\) 10\({}^{-6}\), respectively, lower than the average value of UCC, except Nb. The average contents of ferrophilic magnesium mineral elements such as Cr, Co, Sc, Ni and V are 15.1 \(\times\) 10\({}^{-6}\), 7.4 \(\times\) 10\({}^{-6}\), 4.6 \(\times\) 10\({}^{-6}\), 5.9 \(\times\) 10\({}^{-6}\) and 26.1 \(\times\) 10\({}^{-6}\), respectively, much lower than the average value of UCC.  
Therefore, the trace element contents of Well XC1 and Well D2 show the characteristics of the enrichment of large ion lithophile elements and high field strength elements and the relative loss of iron and magnesium elements, while the trace element content of Well D1 shows the characteristics of loss of large ion lithophile elements and high field strength elements, as well as the serious loss of iron and magnesium elements.  
In the UCC\(-\)normalized trace element spidergrams, the trend of the trace element/UCC ratio from Well D2 and Well XC1 is relatively consistent, and there is a certain difference with Well D1, which is consistent with the analysis results of the major elements, indicating that the provenance of Well D1 is different from that of Well D2 and Well XC1 (Figure 5).
## 5 Industrial Considerations
The application of complex modelling online at line speed has been challenging, but with the advent of the internet of things and high-powered edge computational capacity, this is no longer a major barrier to application. Furthermore, while sensory quality is one group of variables that is of predictive interest, other technological quality and processability variables could also be predicted from a single scan coupled with a suite of different computational models, making this approach applicable to simultaneously address many diverse industry challenges.
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 3 Proposed Method
### Feature Set
#### 3.3.2 Bhattacharyya Distance  
The database was divided into two classes, positive and negative, to identify the difference between COVID-19-positive data and negative data. Features were extracted from the cough segment, and the separation between the two classes was calculated for the same feature vector. Equation (1) is the formula for calculating the Bhattacharyya distance, where \(\mu_{1}\) and \(\mu_{2}\) represent the averages of each class and \(\sum\limits_{1}\) and \(\sum\limits_{2}\) represent the covariances of each class. The larger the difference between the two classes, the larger the distance.  
\[D_{B}=\frac{1}{8}(\mu_{2}-\mu_{1})^{T}\bigg{[}\frac{\sum_{1}+\sum_{2}}{2} \bigg{]}^{-1}(\mu_{2}-\mu_{1})+\frac{1}{2}\ln\frac{\bigg{|}\frac{\sum_{1}+ \sum_{2}}{2}\bigg{|}}{\big{|}\sum_{1}\big{|}^{1/2}\big{|}\sum_{2}\big{|}^{1/2}} \tag{1}\]  
Table 4 presents the results for the Bhattacharyya distance sorted in descending order. The feature with the largest difference between the two classes was MFCC, with a value of 0.207171, and the feature with the smallest difference was onset with a value of 0.002387. The final feature set to be used in this study consisted of the top four features: MFCC, \(\Delta^{2}\)-MFCC, \(\Delta\)-MFCC, and spectral contrast.
# Analysis of Sushi Rice: Preparation Techniques, Physicochemical Properties and Quality Attributes
## 3 Results and Discussion
### Shelf-Life Study of Cooked Sushi Rice
#### 3.3.1 TPA
R1 demonstrated higher cohesiveness on day 4 (0.30 \(\pm\) 0.02), suggesting that lower water-to-rice ratios may enhance structural integrity over time. On the other hand, the decrease in cohesiveness for R5 (0.16 \(\pm\) 0.01 on day 10) resulted in weaker internal bonding, particularly after storage. Cheviness is influenced by a combination of hardness, cohesiveness, and springiness. Lower water ratios (R1) create firmer grains, contributing to higher chewiness. Over time, the retrogradation process further enhances chewiness due to the increased firmness of rice. R1 showed the highest chewiness values throughout the study, with a peak of 155.4 \(\pm\) 14.19 on day four and a slight decrease (\(p\) < 0.05) by day 10 (152.9 \(\pm\) 13.52). In contrast, R5 exhibited the lowest chewiness values across the storage period (25.28 \(\pm\) 1.28 on day 10), reflecting its softer and less cohesive texture. These results suggest that higher water content reduces chewiness, potentially making the rice more appealing for specific uses. Resilience showed a gradual decline in most groups over the storage period, with R1 exhibiting the highest values (\(p\) < 0.05) on day 4 (0.16 \(\pm\) 0.01), indicating its ability to recover its structure after deformation. R5 had the lowest resilience (0.05 \(\pm\) 0.00), which correlates with its lower cohesiveness and chewiness, indicating structural degradation over time in rice with higher water content. In summary, the results of the TPA indicate that the water-to-rice ratio significantly influences the textural attributes of cooked rice during storage. Lower water-to-rice ratios (e.g., R1) result in firmer, less adhesive rice with higher cohesiveness and resilience, maintaining its structural integrity over time. Conversely, higher water-to-rice ratios (e.g., R5) produce softer, stickier rice with reduced chewiness and resilience, potentially limiting its shelf stability. These findings underscore the importance of optimizing the water-to-rice ratio to achieve the desired textural properties for specific applications, while minimizing the impact of storage on rice quality. For sushi preparation, moderate water-to-rice ratios (e.g., R3 or R4) may provide balance between softness, stickiness, and cohesiveness, particularly in the case of rice intended for immediate use. For storage, adjustments to minimize retrogradation, such as controlling temperature and humidity, are essential to maintain optimal texture.
###### Abstract  
This study compares dosimetric approaches for lung dosimetry in 166 Ho radioembolization (Ho-TARE) with direct Monte Carlo (MC) simulations on a voxelized anthropomorphic phantom derived from a real patient's CT scan, preserving the patient's lung density distribution. Lung dosimetry was assessed for five lung shunt (LS) scenarios with conventional methods: the mono-compartmental organ-level approach (MIRD), voxel S-value convolution for soft tissue (kST, ICRU soft tissue with 1.04 g/cm\({}^{3}\)) and lung tissue (kLT, ICRU lung tissue with 0.296 g/cm\({}^{3}\)), local density rescaling (kST\({}_{L}\) and kLT\({}_{L}\), respectively, for soft tissue and lung tissue), or global rescaling for a lung mean density of 0.221 g/cm\({}^{3}\) (kLT\({}_{221}\)). Significant underestimations in the mean absorbed dose (AD) were observed, with relative differences with respect to the reference (MC) of \(-64\%\) for MIRD, \(-93\%\) for kST, \(-56\%\) for kST\({}_{L}\), \(-76\%\) for kLT, \(-68\%\) for kLT\({}_{221}\), and \(-60\%\) for kLT\({}_{L}\). Given the high heterogeneity of lung tissue, standard dosimetric approaches cannot accurately estimate the AD. Additionally, MC results for 166 Ho showed notable spatial absorbed dose inhomogeneity, highlighting the need for tailored lung dosimetry in Ho-TARE accounting for the patient-specific lung density distribution. MC-based dosimetry thus proves to be essential for safe and effective radioembolization treatment planning in the presence of LS.  
l pacs: 15, 958 +
Footnote †: journal: Applied sciences  
_Article_  
Academic Editor: [PERSON] Received: 14 January 2025  
Accepted: 16 January 2025  
Published: 19 January 2025  
Microspheres
# Improving the Quality of Laser-Welded Butt Joints of Metal-Polymer Sandwich Composites
## 1 Introduction
It was noted in [22] that the reduction in the peak power in the pulse with increasing spot diameter reduces the peak power density, thus, reducing the penetration depth. There are maximum and minimum values for both the spot diameter and peak power. It was demonstrated that the small spot diameter and, therefore, high power densities not only contribute to increased depth of the welding zone, but also to the formation of spatter on the surfaces to be welded. In [23], along with such technological factors as laser beam power, welding speed, overlap factor, pulse duration and shape, the effect of laser spot diameter on the quality of pulsed butt welding of dual-phase steel was investigated. It was determined that during constant energy and pulse duration, an effect of reduction in penetration takes place when the spot diameter increases over the critical value, at which the spattering of metallic material stops. In was also established that the remelted zone of the butt joint was harder than both the base metal and the metal within the heat-affected zone, which was not subjected to melting. In [24], the results of pulsed laser butt welding of nickel-based superalloy samples are presented. The possibility of improving the quality of the welded joint by redistributing the energy density of laser irradiation with increasing the size of the laser spot was demonstrated. The assessment of strength properties of joints proved this statement. In [25], it was determined that laser exposure with its well-defined and accurately localized energy input offers the possibility to reduce the growth of intermetallic layers in the butt joint. The necessity of using special optics for a more precise dosage and the ability to perform a special redistribution of the laser beam energy locally in the laser treatment area was determined. As a rule, hardening structures with low plasticity and developed interdendritic microheterogeneity, which are characterized by increased hardness, are formed in the cast zone. When the size of the impacting laser spot is increased, the cooling rate decreasesslightly. This can eliminate the possibility of the formation of a coarse-grained dendritic structure, which is prone to brittle fracture. The purpose of this study is to improve the quality of the butt joint of metal-polymer sandwich composites during laser welding by redistributing the energy density of laser irradiation while increasing the diameter of the laser spot. It should be noted that increasing the diameter of the laser spot requires an increase in the total energy, which is supplied to the material. During the welding of metal-polymer composites, this can have a negative impact on the polymer core. To avoid this problem, partial penetration of the steel sheets was adopted as a prerequisite to avoid degradation of the polymer core layer.
# Storing Set Families More Compactly with Top ZDDs
## 4 Top ZDD
### Details of the Data Structure
#### 4.1.2 Information on Vertices  
We explain how to store information on vertices of \(T^{\prime}\) except for dummy vertices. Each vertex corresponding to a leaf in the original top tree is a cluster for a single edge in the spanning tree, and it is a non-dummy leaf in \(T^{\prime}\). We sort these vertices in preorder in \(T^{\prime}\) and store information on edges towards them in the following two arrays. One is an array _label_span_ to store differences of levels between endpoints of edges. Let \(u\) and \(v\) be the starting and the ending points of the single edge of the cluster corresponding to the \(i\)-th leaf, respectively. Then we set \(\textit{label\_span}[i]=\ell(v)-\ell(u)\). The other is an array _type_span_ to store if an edge is 0-edge or 1-edge. We set \(\textit{type\_span}[i]=0\) if the edge corresponding to the \(i\)-th vertex is a 0-edge, and \(\textit{type\_span}[i]=1\) otherwise.  
Each vertex of \(T^{\prime}\) corresponding to vertical merge or horizontal merge is an internal vertex. We sort internal vertices of \(T^{\prime}\) in their preorder. Then we make a bit vector \(B_{H}\) so that \(B_{H}[i]=0\) if the \(i\)-th vertex stands for vertical merge, and \(B_{H}[i]=1\) if it stands for horizontal merge. For vertices corresponding to horizontal merge, we do not store additional information. For vertices corresponding to vertical merge, we use arrays _preorder_diff_ and _label_diff_ to store the differences of preorder ids and levels between the top and the bottom boundary nodes of the merged cluster. Let \(x_{i}\) be the \(i\)-th vertex in preorder corresponding to vertical merge, \(cl_{i}\) be the cluster corresponding to \(x_{i}\), \(t_{i}\) be the top boundary node of \(cl_{i}\), and \(b_{i}\) be the bottom boundary node of \(cl_{i}\). Note that \(t_{i}\) and \(b_{i}\) are nodes of the ZDD. Then we set \(\textit{preorder\_diff}[i]=(\text{the local preorder id of $b_{i}$ inside cluster $cl_{i}$})\) and \(\textit{label\_diff}[i]=\ell(b_{i})-\ell(t_{i})\).
# Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games
## 3 Experiment 2
### Results
#### 3.2.4 Fun  
The analysis on fun revealed a significant main effect of character sounds (\(F\)(1,31) = 5.32, \(p\) = 0.03, and \({\eta_{p}}^{2}\) = 0.15), with more reported fun when character sounds were present compared to when they were absent (\(M\) = 3.92 vs. 3.66 on a scale from 1 to 5). The main effect of ambient sounds showed a marginally significant trend (\(F\)(1,31) = 3.44, \(p\) = 0.07, and \({\eta_{p}}^{2}\) = 0.10), with more reported fun when ambient sounds were present compared to when they were absent (\(M\) = 3.84 vs. 3.73 on a scale from 1 to 5). The interaction between ambient and character sounds was also significant (\(F\)(1,31) = 4.36, \(p\) = 0.045, and \({\eta_{p}}^{2}\) = 0.12). Post hoc t-tests revealed that the presence of ambient sounds led to more fun compared to their absence when character sounds were turned off--\(t\)(31) = \(-\)2.72, \(p\) = 0.01, and [PERSON]'s \(d\) = 0.48 (\(M\) = 3.78 vs. \(M\) = 3.54 on a scale from 1 to 5). No such difference was found when character sounds were turned on--\(t\)(31) = 0.38, \(p\) = 0.71, and [PERSON]'s \(d\) = 0.07.
## 1 Introduction
Moreover, ongoing efforts are focused on enhancing the performance of microstrip patch antennas [20, 21, 22, 23, 24, 25]. In this paper, we propose a dual-circularly polarized antenna design that can be utilized with a non-linear tag based on IM3 responses to enhance the accuracy and efficiency of bio-signal sensing. Figure 1 illustrates the conceptual system of bio-signal sensing using a two-tone signal as an input and IM3 responses as an output. Here, the two-tone signal consists of two closely spaced frequencies, namely\(f_{1}\) (23.4 GHz) and \(f_{2}\) (23.7 GHz), and is transmitted through a Tx system as a right-hand circularly polarized (RHCP) wave. The transmitted signal will then be received by the non-linear tag, which includes the proposed antenna and a third-harmonic generator. Once the two-tone signal is received by the tag antenna, it goes through the third-harmonic generator circuitry to produce IM3 responses, which in this case, is 24 GHz (Z\(f_{2}-f_{1}\)). The non-linear tag would then re-radiate the output IM3 responses with the vital sign information modulated as a left-hand circularly polarized (LHCP) wave, which will then be received and processed by the Rx system. Therefore, the proposed antenna is designed to operate from 23.4 to 24 GHz with a selective direction of circular polarization depending on which port is chosen while ensuring sufficient inter-port isolation with a transmission coefficient below \(-15\) dB. To simplify the antenna structure and enable the implementation of dual-CP with a two-port configuration, the proposed design employs the traveling wave series-fed design [26, 27, 28, 29, 30]. Here, we use only four patch elements, unlike previous works, making it compact and compatible for use with a non-linear tag to detect bio-signals in dynamic and complex environments.