esa-sceva/satcom-chunk-collection huggingface dataset:
# DALLoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation
## 4 Methods and Material  
### Mesh-Based Visual Localization  
In this section, we first introduce the hierarchical mesh-based visual localization algorithm, which is mainly divided into four steps:  
Step 1: Image retrieval. Given a query image, a coarse search at the image level is performed to find the most relevant views in the database. This is done by matching the global descriptors of images, and the top-k NN are selected as the prior images. This step is efficient given that there can be far fewer images to match in the database.  
Step 2: Local feature matching. This step generates the 2D-2D local feature correspondences between the query and top-k retrieved database images. These 2D-2D  
Figure 1: Illustration of different localization methods. Dark blue and cyan flow shows the general hierarchical localization pipeline, and dark blue and yellow flow shows the DLALoc pipeline.  
correspondences will be lifted to the 2D-3D matches in the next step. It is common to use state-of-the-art deep-learning local features. Additionally, local features can be matched by exhaustive matched or deep-learning matching strategies.  
Step 3: Lift local feature correspondences to 2D-3D matches. Since we can get the depth map by rendering the mesh, each feature \(\mathbf{f}_{ij}\) (where i indicates the image index, and j indicates the feature index) has a valid depth in the scene, and the 3D points \(\mathbf{P}_{i}\) can be recovered from the depth. There are only some noisy 2D feature measurements which will be propagated to 3D points (i.e., even though \(\mathbf{f}_{1j}\),\(\mathbf{f}_{2j}\)... corresponding to the same 3D point in the physical world, the corresponding 3D points recovered from the depth map will be slightly different). There is no mismatch of 2D-3D matches in the SFM model (i.e., \(\mathbf{f}_{1j}\),\(\mathbf{f}_{2k}\)... corresponding to the different 3D points but was categorized to be the same projection of a 3D point), which greatly influences the efficiency and accuracy of the localization algorithm.  
Step 4: Pose estimation: The last step is to use the resulting 2D-3D matches to calculate the image poses. The P3P method calculates the poses from the minimal sample of the three 2D-3D matches in each iteration, and the RANSAC framework is performed to refine the optimal estimation inside and after RANSAC with a nonlinear refinement for all inliers. Figure 2 shows the mesh-based scene representation.
## 5 Discussions  
In order to verify the effectiveness of the proposed method, two applications are selected to give the benefits of the MMW (Millimeter Wave) integrated cellular network proposed by MiWEBA (Millimetre-Wave Evolution for Backhaul and Access): \"context-aware cache\" and \"small cell on/off\". The \"Context Aware Cache\" consists of dual connectivity user equipment, a macro base station (LTE eNB), and a millimeter wave junior unit. The client side runs a video streaming application while on the move. As a context-aware cache entity of the intermediate layer between the video application and network connection, it adopts the connection status as context information to perform data requests.  
The video file is extracted from the content server and provided to the video player. When in the coverage of a millimeter wave cell, the cache pre-extracts and saves the video file chunks at the local maximum available rate. For this end, the cache creates a database of all the chunks in the currently playing video. Each chunk is associated with a state to indicate whether it is available locally, being downloaded, or not locally available. The recently played chunk is saved as an indicator and used to determine the next chunk to be downloaded. When outside the scope of the junior unit, the cache extracts the content on a timely basis to reduce the load on the LTE link. The content is seamlessly available from the user's perspective.  
Next, f5G and WLAN convergence mechanisms, such as the 5G and WLAN convergence network switching mechanism, 5G and WLAN convergence, sister network wireless resource management strategy, 5G and WLAN convergence network access control strategy, etc. need further research.
## 5 Discussion
### The Impact of Granularity of Predicate Decomposition on KGC Performance  
As seen in Figures 7-9, we found that when the granularity of predicate decomposition is low, there is a roughly positive correlation between the granularity of predicate decomposition and the performance improvement of PDEC. When the clustering quality threshold \(\eta\) is set high enough, the lower the parameter \(\eta\) is set, the more new predicates are generated by decomposition, and the performance of PDEC improves accordingly.  
We can also observe that PDEC performance reaches its optimal level when the number of new predicates generated by decomposition approaches the number of original predicates. However, as the number of new predicates generated by decomposition increases, the computational efficiency of PDEC will decrease, which needs to be balanced.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology  
### Preliminaries  
In this study, a classifier was provided with a list of suspected nodules and was tasked with determining whether each suspect was a nodule or a non-nodule. For this purpose, the model was trained using a supervised method, where it received a large number of samples that guided it on making the final decision. The primary objective of this work was to train the model to become an efficient and accurate classifier, capable of correctly categorizing each input. The suspects were represented by 3D arrays \(X\), and the classifier's job was to transform these arrays into logits \(z\) through a function \(f\), where \(z=f(X)\), and \(j\) was the number of classes. These logits were then converted into probabilities for each class through a softmax operation defined as  
\[P(y=k|X)=\frac{\exp(z_{k})}{\sum_{j}\exp(z_{j})} \tag{1}\]  
for each class \(k\).  
To enhance the model's ability to generalize to new, unseen data, this work implemented a series of augmentation techniques, both individually and in combination. Thesetechniques were categorized into geometric and photometric augmentations. Geometric augmentations included rotations, translations, scaling, and flipping of images. These manipulations simulated the variations that might occur due to differences in patient positioning and the orientation of organs in diverse clinical settings, thus preparing the model to handle such discrepancies effectively. On the other hand, photometric augmentations altered the visual attributes of the images, such as adjusting brightness, contrast, and adding noise. These adjustments were crucial for training the model to process images that might vary in visual quality due to differences in imaging equipment or settings across different healthcare facilities. The effectiveness of the augmentation techniques and the overall model performance were quantitatively assessed, using a suite of evaluation metrics. These included Accuracy, \(F_{1}\) Score, \(F_{2}\) Score, \(F_{5}\) Score, and the MCC.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 3 Optimization Process and Performance Analysis
### Downlink from Signal Source to Relay Node  
The signal source transmits the signal \(x_{S}(t)\) to the relay node \(u_{r}\) on the downlink sub-band \(f_{d}\). The received signal at the relay node is:  
\[y_{1}^{(d)}(t)=\sum_{j=1}^{L_{d}}h_{S,r}^{(d)}\sqrt{a_{j}p_{s}}x_{S}(t)A_{S}^{ (d)}(d_{S},f)+n_{1}^{(d)}(t) \tag{15}\]  
where \(h_{BS,r}^{(d)}\) is the channel gain from the signal source to the relay node \(u_{r}\) on the downlink sub-band \(f_{d}\), \(n_{1}^{(d)}\) is the noise on sub-band \(f_{d}\), and \(a_{j}\) is the power allocation factor, satisfying \(\sum_{j=1}^{L_{d}}a_{j}=1\). The received signal at the relay node is \(y_{1}^{(d)}(t)\), and the decoded signal is \(\hat{x}_{BS}(t)\).
###### Abstract
At a constant melt-wetting temperature, the obtained nanostructures showed the higher mass ratios of the lower \(T_{g}\) segment due to the thermal-induced polymer chain mobility in the AAO templates. [PERSON] et al. [19] studied the coarsening mechanism of poly(methyl methacrylate) (PMMA) and tetrahydrofuran (THF) confined in AAO templates. In that case, the phase separation of PMMA and THF was induced by the difference in hydrogen bond affinity between PMMA-AAO and THF-AAO. With the stronger hydrogen bond of THF-AAO, the formation of THF droplets on the AAO pore wall was promoted by surface-induced phase separation, resulting in porous PMMA nanostructures after the removal of THF. The formation of porous nanotubes of polystyrene (PS) and fluorescent pyrene-ended PMMA (Py-PMMA) using the solvent-wetting method was also investigated by [PERSON] et al. [20]. Moreover, [PERSON] et al. [21] have reported on the Rayleigh instability of PMMA/PS blends confined in AAOs using the solvent-wetting method assisted by thermal annealing. That method can produce PMMA/PS bilayer nanotubes. The thermally-induced Rayleigh instability stimulated the transformation of nanostructures with polymer nanotubes into nanorods and nanospheres. Various transformation and phase separation behaviors of polymer nanomaterials in AAO templates and other atmospheres using chemical solvent and thermal annealing methods have been investigated [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Furthermore, similar polymer phase separations and transformation behaviors at the microscale have also been observed [34; 35; 36; 37; 38]. Because of the success of the AAO templating method, many publications have explored various AAO applications such as thermal and physical properties of polymer nanostructures under nanoconfinement conditions, fabrication of higher-order nanostructures, polymerization and crystallization in AAO templates, and the double infiltration nanoencapsulation process [39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. In this study, we mainly focus on the formation of VAPNs fabricated using thermal nanoimprint with AAO templates. We investigate the phase separation and the transformation behaviors of polymer blends confined in AAOs. The material models used are polystyrene/poly(vinyl alcohol) (PS/PVA) blends. PS/PVA blends are prepared by the advanced melt-mixing process, using a high-shear machine which effectively produces the ultra-fine dispersion of PVA segments. Several surface propertiesincluding surface morphology, hydrophobicity, frictional properties, and the surface mechanical properties of the fabricated VAPNs have been experimentally evaluated. This novel technique has the advantages of being a greener (non-chemical use), rapid process, of high-precision, high-profit, and great reproducibility.
## 5 Results and Discussion
### Fuel Comparison  
The hybrid system performance with other gaseous fuels, such as propane and butane, are also analyzed in this study as their reforming temperatures are close to the fuel cell operating temperature. Also, methane, propane, and butane reserves are abundantly available.  
Figure 10a shows that replacing methane with propane and butane reduces PCFC performance, because the fuel cell temperature is largely the same when fueled with propane or butane, as shown in Figure 10b. The drops in fuel cell power output are mainly due to less hydrogen produced, as shown in Figure 11a. The higher H\({}_{2}\) production when the system is fueled with methane helps the fuel cell to produce more power. This is the reason for the lower total exergy destruction of methane fueled case as compared with propane and butane fueled cases (Figure 10a). On the other hand, propane- and butane-fueled cases produce more CO, as shown in Figure 11a. Although CO cannot be utilized in PCFC, they can be used in CC. This explains why the turbine in this system produces slightly more power when the hybrid system is fueled with propane or butane. This can also be observed from TIT (Figure 10b). Overall, replacing methane with propane or butane does not significantly affect the system efficiency. The system still has high energy efficiency of 68%, or 3% lower than when fueled with methane, as shown in Figure 11b. Therefore, for areas where they are more readily available, propane and butane can be two viable alternative fuels.  
Figure 10: Comparison of methane-, propane-, and butane-fueled PCFC/GT hybrid systems: (**a**) PCFC power output, GT power output and total exergy destruction of system, and (**b**) PCFC operating temperature and turbine inlet temperature.  
The system efficiency of 68% with butane and propane is higher compared to the discussed high temperature SOFC/GT hybrid systems in the introduction. The exergy efficiencies of methane-, propane-, and butane-fueled PCFC/GT systems are 77%, 75%, and 75%, respectively. The higher exergy efficiency compared to the reported works is achieved by conceptual thermal management and fuel cell operation proposed in this work. The higher turbine power for the propane- and butane-fueled systems compared to the methane-fueled system can partially recover the power loss in the fuel cell. A PCFC/GT hybrid system can achieve similar system efficiency with the same operating conditions when fueled with butane, propane, and methane fuels. This study provides a strategical approach to achieve higher system energy conversion efficiency with thermal management and fuel utilization.
# A Novel Liquid-Solid Fluidized Bed of Large-Scale Phase-Changing Sphere for Thermal Energy Storage
## 1 Introduction
In addition, the internal flow inside the particle would not be apparent because of the small confined scale and discrete distribution of energy-storage material across the particle. In contrast, when the particles are large, the internal flow conditions would inevitably impact the general energy-storage features. With hydrated sodium sulfate encapsulated as the PCM in a spherical shell of 25 mm, [PERSON] et al. [28] made a comparison between the performance of a fixed bed and a fluidized bed. It was found that the capsules in the fluidized bed still have 60% of the initial energy-storage capacity after more than 100 cycles of energy absorbing and releasing, while the capsules in fixed bed decrease to 38% of the initial capacity only after 7 cycles. This research manifests the great superiority of the fluidized bed in preventing the hydrated sodium sulfate inside the capsules from stratification. [PERSON] et al. [29] visually measured the solidifying and melting process of the 1-octadecanols encapsuled inside a long elliptic sphere with a length of 5 cm. It was found that the solidifying and melting duration of an unfixed capsule can shrink by 35% and 50% compared to a fixed capsule, respectively. With the techniques particle image velocimetry (PIV) as well as planar laser-induced fluorescence (PLIF), they confirmed that the accelerated phase changing was due to the oscillating movement of the PCM solid-liquid interface and that the interface movement was caused by the minor amount of gas that was encapsuled. The authors of the present study [30] experimentally investigated the influence of oscillating movement on the latent heat-absorbing and -releasing features of a PCM sphere with internal diameter of 56 mm. The oscillating stroke amplitude and frequency were the main studied factors. It was found that the melting and solidifying can be accelerated by 50% and 94% to a maximum, respectively, and the internally relative motion between the solid-liquid interface and the capsule wall that resulted from the exterior oscillation was deemed to play a great role in the enhancement of heat transfer. It can be seen from the literature review above that the fluidization movement can obviously improve the energy-storage rate. However, to the authors' knowledge, few research efforts have been put towards the internal flow of the PCM capsules in fluidization, which could have great a influence on the phase-changing heat transfer. Therefore, a novel type of fluidized bed is designed in this paper with water being the heat-transfer fluid as well as the liquid phase to fluidize the large-scale PCM spheres in the liquid-solid two-phase system. The fluidization status as well as the heat-storage features of the fluidized bed are obtained via visual measurement techniques. The underlying reasons for the improved heat-transfer performance are then shown in close relation with the internal flow status of the PCM sphere.
## 3 Results and Discussion
### Detection of Schiff Bases of Alanine with Sugar-Derived Aldehydes through Isotope Labeling
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & \\ Glu [1\({}^{3}\)C-U]/Ala/FeCl\({}_{2}\) & C\({}_{3}\)[1\({}^{3}\)C]H\({}_{8}\)NO\({}_{2}\) & C\({}_{3}\)[1\({}^{3}\)C]\({}_{2}\)H\({}_{10}\)NO\({}_{2}\) & C\({}_{3}\)[1\({}^{3}\)C]\({}_{2}\)H\({}_{10}\)NO\({}_{3}\) \\  & (\(-\)3.01 ppm) & (\(-\)0.97 ppm) & (0.96 ppm) \\ \hline Ala [1\({}^{3}\)C-3]/Glu/FeCl\({}_{2}\) & C\({}_{3}\)[1\({}^{3}\)C]H\({}_{8}\)NO\({}_{2}\) & C\({}_{4}\)[1\({}^{3}\)C]H\({}_{10}\)NO\({}_{2}\) & C\({}_{4}\)[1\({}^{3}\)C]H\({}_{9}\)NaNO\({}_{3}\) \\  & (\(-\)2.04 ppm) & (\(-\)3.07 ppm) & (\(-\)3.35 ppm) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Label incorporation and observed intensities of the ions corresponding to \(\alpha\)-alanine-derived Schiff bases from formaldehyde, acetaldehyde and glycolaldehyde shown in their oxazolidin-5-one isomeric forms, in glucose/\(\alpha\)-alanine reaction mixtures. Figure 3: Observed iminium ions formed between 4-methyl-1,3-oxazolidin-5-one and various aldehydes (see also Table 3). Figure 2: Reaction of formaldehyde with alanine and the rationale behind divalent metal ion catalysis of oxazolidin-5-one formation (see also Figure 3 and Table 3). It can be concluded that the Schiff bases were detected mainly due their existence in the more stable oxazolidin-5-one form, otherwise, they would have rapidly undergone hydrolysis before being detected. The ions listed in Table 2 incorporated one \({}^{13}\)C-3 alanine atom, indicating the presence of alanine and up to four carbon atoms from glucose-derived aldehydes. The incorporation of one carbon atom from glucose can indicate that a formaldehyde Schiff base adduct was formed; incorporation of two carbon atoms can indicate acetaldehyde or glycolaldehyde Schiff base formation, and incorporation of one, three or four carbon atoms can indicate multiple addition adducts, considering their HRMS data. and glycolaldehyde to the reaction mixtures. There were statistically significant increases in the relative intensities of the corresponding ions upon spiking with the appropriate aldehyde; these increases are shown in Table 2. In addition, spiking the model system (Ala/Glu/FeCl\({}_{2}\)) with excess formaldehyde (see Table 2) lead to an increase in the formation of adducts incorporating not only formaldehyde (R = CH\({}_{2}\) in Figure 3), but also acetaldehyde (R = CHCH\({}_{3}\)), and glycolaldehyde (R = CHCH\({}_{2}\)OH). These aldehydes have been previously reported to be thermally generated from formaldehyde in the presence of metal ions through the formace reaction (Butlerov reaction) [21; 22].
## 1 Introduction  
Accurate determination of renal dimensions is important in that it can inform presence and progression of chronic kidney disease (CKD) [1; 2; 3] and polycystic kidney disease (PKD) [4; 5; 6], inform inclusion criteria in PKD trials, and assessment of response to drugs in those trials [7]. Measurement of renal dimensions is critical to inform suitability of the donor kidney for implantation in the recipient especially within the pediatric population and patency of the transplanted kidney [8; 9; 10]. Determination of total kidney volume (TKV) is critical for debulking of renal tumors as remnant nephron mass will govern renal function. Due to the significant role that renal dimensions play in monitoring renal disease, accurate determination of renal dimensions is critical.  
Renal imaging followed by use of an ellipse-based formula is most frequently used to estimate renal dimensions [11; 12; 13; 14]. Nevertheless, data from a number of studies [11; 12; 13; 15] indicate that use of this formula underestimates actual renal dimensions regardless of the typical imaging modality used i.e., ultrasonography or magnetic resonance imaging (MRI). In fact, the ellipse-based formula underestimates total kidney volume (TKV) by up to 29% [13; 15] compared to the kidney volume calculated using the more sophisticated but labor-intensive magnetic resonance imaging-based voxel count method or measured at autopsy using the gold standard fluid displacement method. Using a murine model of kidney disease, this laboratory proposed [16] a modification to the current ellipse-based formula for estimating renal dimensions which demonstrated enhanced fidelity to the measured dimensions. In the present study, we sourced kidneys from multiple models of renal disease and PKD to determine whether the modified ellipse-based formula will produce a more accurate representation of actual renal dimensions.
## 2 Data Acquisition and Processing
### AIS Data Acquisition and Processing  
AIS data consist of dynamic and static information. Due to various factors, such as weather, location, and equipment, the update frequency of AIS data varies, leading to significant data gaps in some areas. To address this, linear interpolation is employed to fill in missing data, interpolating longitude, latitude, and speed every five minutes. The ship's sailing distance is calculated by altering the longitude and latitude of adjacent AIS points in the interpolated data. Given the hourly collection frequency of meteorological and sea condition data, the AIS data are aggregated hourly to maintain consistent time resolution.
## 4 Theoretical Rationale
### The Multi-Theoretical Perspective  
In order to understand emerging technologies adoption, researchers have advocated integrating more than one theoretical perspective [88]. The multi-theoretical perspective overcomes the inherent limitations in each theory and enhances the understanding of technology adoption with high explanatory power [7]. Moreover, it can help to understand better the organizational decision-making about accepting the new technology, where variables are customized based on the technology under consideration through a comprehensive understanding of the research [88]. However, IoT adoption from a multi-theoretical perspective is still relatively rare [89].  
Previous studies that examine IoT adoption in the MS at the organizational level concentrated on the TOE framework [12; 16; 18; 27], or the integration between TOE and DOI [7; 8]. The literature found that integrating the TOE framework and the DOI theory explained the technology adoption more effectively [88]. However, this study suggested integrating the VAM model with TOE and DOI, as it offers beneficial characteristics that are important in assessing technology adoption, such as perceived value. Furthermore, VAM can aid organizations in identifying the areas of their business that can benefit from new technology and any potential barriers to understanding the adoption process better [88]. The previous studies showed that the VAM had a high explanatory power when combined with other models to examine the newly emerging technology, such as the information systems success model [90], and the expectation-confirmation model [91].  
Thus, the conceptual model was developed to explain IoT adoption in the MS by integrating VAM, DOI, and TOE framework to identify the antecedents that affect IoT adoption. This multi-theoretical perspective will provide an exhaustive understanding of IoT adoption by assessing different aspects of technology adoption, as shown in Figure 2. Table 1 illustrates the mapping matrix of constructs from VAM, DOI, and TOE based on the previous literature.  
Figure 2: The integration of VAM [78], DOI [83], and TOE [87] (the integration method adapted from [92]).