esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 3 Multiview Temporal Alignment
### Latent-Space Similarity Metrics
#### 3.1.3 Contrastive Loss  
Finally, we also evaluate the contrastive loss (CL) function described in [25; 31], which given a fixed latent variable from the first view \(\mathbf{f}(\mathbf{x}^{+})\), takes an aligned positive example \(\mathbf{g}(\mathbf{y}^{+})\) and an unaligned negative example \(\mathbf{g}(\mathbf{y}^{-})\) from the second view and attempts to _minimize_ the difference between the distances for the positive and negative examples:  
\[\mathcal{L}_{cl}=\frac{1}{N}\sum_{i=1}^{N} \max(0,m+d(\mathbf{f}(\mathbf{x}_{i}^{+}),\mathbf{g}(\mathbf{y}_{i}^{+}))\] \[-d(\mathbf{f}(\mathbf{x}_{i}^{+}),\mathbf{g}(\mathbf{y}_{i}^{-}))), \tag{7}\]  
where \(m\) is a margin hyperparameter (\(m=0.5\) is used in this work) and \(d(\mathbf{z}^{x},\mathbf{z}^{y})=1-\frac{\mathbf{z}^{x}-\mathbf{z}^{y}}{\|\mathbf{z}^{2}\|\|}\) is the cosine similarity. The negative examples \(\mathbf{g}(\mathbf{y}^{-})\) are generated by shuffling the outputs of the DNN for the second view before the loss is computed. Intuitively, the distances \(d(\mathbf{f}(\mathbf{x}^{+}),\mathbf{g}(\mathbf{y}^{+}))\) in (7) should be small if both views are projected to similar (closer) representations in the common latent space, whereas the distances to the negative, unpaired examples \(d(\mathbf{f}(\mathbf{x}^{+}),\mathbf{g}(\mathbf{y}^{-}))\) should be bigger because they are projected to different locations of that space.
# Metal Color, Marbling, and the Evaluation of Defects in Beef and Port at the Point of Purchase
## 3 Results and Discussion
### Beef and Pork Defects  
When consumers were asked questions about the point of purchase, defects easily attracted consumers' attention. Statistical analysis showed that the result of [PERSON]'s test for the homogeneity of variances was 0.488, which means that it was not significant (\(p>0.05\)), and the population variances for each group were approximately equal; thus, the groups were independent. Between-group significance was determined by the F-ratio of degrees of freedom (dfs) (3.96). The F-ratio was 3.802 and the significance was 0.015 (\(p<0.05\)), which means the selections of defects of beef and pork cuts were significantly different. Using [PERSON]'s HSD test, it was shown between which types of defects there was significant difference (Table 7). It can be noticed according to the results of the Tukey test in Table 7 that significantly different mean values exist between the following types of defects: \"dark cutting beef\", \"excess seam fat\", and \"lack of marbling\". The same case is seen between the defects \"blood splash cuts\", \"excess seam fat\", and \"lack of marbling\". Beef consumers recognized \"dark cutting beef\" and \"blood splash cuts\" as major defects, as samples with these defects obtained the smallest number of consumer selections. This means that these two defects, especially \"dark cutting beef\", are the most undesirable among consumers. However, a significant difference between these two types of defects was not seen (\(p>0.05\)). The small number of selections of \"blood splash cuts\" may have emerged from consumers' opinion that these beef cuts have unpleasant flavors, precisely more livery, bloody, metallic, rancid, salty, and bitter flavor notes [55]. Moreover, \"blood splash cuts\" are not visually appealing. On the other side, consumers usually perceive \"dark cutting beef\" as a characteristic related to contaminated meat. Dark, firm, and dry (DFD) beef cuts with high pH can prompt the development of undesirable microorganisms. However, \"excess seam fat\" and \"lack of marbling\" meat defects were chosen significantly more often by consumers. That means these defects are more acceptable to consumers in comparison with \"dark cutting beef\" and \"blood splash cuts\". When it comes to pork defects (Table 8), the defects \"excess seam fat\" and \"lack of marbling\" were more acceptable according to consumers' choices in comparison to two other types of defects, \"pale pork\" and \"blood spotting\". According to our results, \"lack of marbling\" is the most acceptable defect of pork cuts, which is not surprising, as many studies showed that consumers preferred less marbled pork [56; 57]. In study [57], highly marbled pork chops were less likely to be purchased than leaner chops. However, the multiple comparisons and [PERSON]'s HSD test results showed that there is a significant difference between \"pale pork\" and \"excess seam fat\" and \"lack of marbling\" pork cuts (\(p<0.05\)). On the other hand, \"blood spotting\" was significantly different when compared
## 3 Model Description
### Contrast Information Detection Channel
#### 3.2.1 AMCs  
In the contrast information detection path, the AMC can directly obtain information from the ommatidia output and integrate local pixel brightness information. In our system, AMCs are categorized into two types: One extracts the total brightness within a local neighborhood centered at each spatial location, and the other extracts the total brightness within the surrounding local neighborhoods excluding the central one. Let \(A_{T}(x,y,t)\) represent the output information of the AMC cell receiving the brightness of the central local neighborhood, and \(A_{B}(x,y,t)\) represent the output information of the neuron receiving the brightness of surrounding local neighborhoods excluding the central one. They are defined as follows:  
\[A_{T}(x,y,t)=\Sigma_{(x,y)\in\Phi}P(x,y,t), \tag{12}\]  
\[A_{B}(x,y,t)=\Sigma_{(x,y)\in\Pi}P(x,y,t)-\Sigma_{(x,y)\in\Phi}P(x,y,t), \tag{13}\]  
where \(\Phi,\Pi\) represent the object area and the background area, respectively, and \(\Phi=\{(x,y)|x\in[x-k,x+k],y\in[y-k,y+k]\}\), \(\Pi=\{(x,y)|x\in[x-z,x+z],y\in[y-z,y+z]\}\), \(k,z\) are constants, and \(k<z\).
# A Comparative Assessment of JVM Frameworks to Develop Microservices
###### Abstract
The cost-effectiveness of such an approach will depend mainly on the software technologies used to build the computer system and the auto-scaling mechanisms employed. Depending on the variability of the system load, not only the overall performance of software components, but also the time to instantiate and run a single copy of a microservice, or the time to respond to a first-time request (cold start) may be critical here. One of the most mature and versatile ecosystems for software development is Java. With its Spring Boot application framework, it was one of the first to start supporting the development of applications that follow a microservices approach. However, Java as a multipurpose environment carries features that are not always optimal for microservice systems. These features include, for example, interpreting source code, running programs on a virtual execution environment (JVM) intended to ensure the portability of programs between different computer architectures, etc. Recently, these problems have been recognized, resulting in the development of frameworks and solutions for the Java ecosystem strictly dedicated to develop microservices [6]. Thus, the goal of this work is to assess the most popular microservices development frameworks currently for the Java ecosystem, in order to enable the system architects to make design decisions based on rationales drawn from the outcomes of this study. Admittedly, such topics are occasionally covered in industry blogs and publications, but in the scientific discourse, articles on the subject are few, and the research conducted is quite superficial and usually based on peculiar problems and applications. Therefore, the authors, as part of the research described in this paper, developed their own synthetic test framework to evaluate individual solutions in terms of the most common challenges encountered when implementing real microservices-based systems. This paper is organized as follows: Section 2 gives the background of the research being conducted in terms of an introduction to cloud computing and microservice issues in general, as well as technical means supporting the development of the latter. Related studies are described there as well. Section 3 explains the applied methods, i.e., the composition of the testbed, the selection of application frameworks under test, and the criteria of comparison, while Section 4 presents the results being obtained. Section 5 interprets the results obtained and presents the limitations of the research and further possible directions for it. The last section is the conclusion.
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 2 Microstructure
### Dimensionally-Induced Structural Transformations
#### 2.2.1 Phase Structure Transformation
According to Equations (1) and (2), the bi-phase diagram representing phase stability as a function of reciprocal scale (\(1/\lambda\)) and composition (in this case \(f_{\rm Nb}\)) is used to construct the structure transformation in the NMMFs as shown in Figure 5b. Then the microstructure of NMMFs can be effectively controlled by tailoring their layer thickness and composition. Figure 4: Stability diagram plotting \(\Delta g\) as a function of \(\lambda\)[PERSON] (1984). However, with regard to fcc/bcc NMMFs phase structure transition must be achieved under more rigorous conditions [27; 28; 29]. The common way to shrink characteristic dimensions toward to nanoregime also works in Cu/Fe NMMFs. However, fcc Fe can only be observed when \(h\) is as small as 0.75 nm [30]. Large shear stress induced by misfit strain energy in fcc/bcc NMMFs commonly plays an important role. The phase transition of Nb has been found in Cu/Nb NMMFs [27]. Additionally, a continuous and reversible bcc-fcc phase transformation via a rotation of bcc [110] or fcc [111] planes is observed in the Bain orientation in V/Ag NMMFs [29]. The large stress for structure transition to operate is also dependent on the length scale. Molecular dynamics simulations reveal that the deposited Cu layer initially grows as bcc with misfit located at interface [31]. The phase transition is also detected in Al/W NMMFs with a fixed 5 nm W layer experimentally [28], bcc Al layers was observed in 1 nm Al and 5 nm W NMMF despite the large lattice mismatch and significantly different lattice structures between Al and W. However, bcc Al does not appear in Al/W with equal individual layer thickness of 5 nm because of the reduction of high interfacial stresses.
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 2 Materials and Methods
### Search Refinement and Paper Selection  
The flow diagram applied to the paper selection is shown in Figure 6. The first paper selection was performed by running on the WoS search engine the query reported in Section 2.1. The results were filtered to the period between 1 January 2000 and 30 November 2024, obtaining 6060 papers. On a subset of this preliminary selection, all preliminary generic statistical analyses were carried out by applying a 10-year filter from 1 January 2015 to 30 November 2024, obtaining 5504 papers; the results are reported and discussed in Section 2.1. As is shown in Figure 1, the majority of the papers are concentrated in the past 3 years, so a more selective date filter was applied, between 1 January 2022 and 30 November 2024, obtaining 3291 papers. To refine the search, the query was run on two additional search engines, CAB Abstract and IEEE Xplore, and then filtered to the same period. The two searches yielded 433 and 260 papers, respectively.  
Afterwards, to classify the algorithms and their usage, we selected papers present in at least two of the three search outcomes, reducing the total number to 693. Titles and abstracts were then read, analyzed, and classified for all papers belonging to this subset. As a result, 194 papers (174 research articles and 20 comprehensive reviews) were discarded as non-pertinent or not available in English, and 491 papers were advanced to the next phase, during which the whole article text was read and analyzed. During this last analysis, 103 papers were additionally discarded as redundant (98 research articles and 5 comprehensive reviews), obtaining 30 comprehensive reviews and 358 research articles that were classified on the basis of the agronomic aim, the crop, and the algorithm chosen.  
Figure 5: Histogram showing the annual publications of papers indexed in the Web of Science (WoS) by Sustainable Development Goal during the period 2015–2024.
## 4 Numerical Examples  
### The Direct Heat Conduction Problem (DHCP)  
The first numerical example investigated is the modeling of a 1D DHCP. This example is to evaluate the correctness of our approach and to compare the computed results with those obtained from the space-time localized RBFCM [28]. The governing equation is written as Equation (1). The boundary values are described as follows:  
\[u(x=L,t)=e^{-\sqrt{\frac{\omega_{x}}{2\alpha^{2}}}L}\cos(\omega t-\beta L), \tag{19}\]  
\[u(x=0,t)=\cos(\omega t). \tag{20}\]  
The initial data are given by:  
\[u(x,t=0)=e^{-\sqrt{\frac{\omega_{x}}{2\alpha^{2}}x}}\cos(\beta x). \tag{21}\]  
The solution is as:  
\[u(x,t)=e^{-\sqrt{\frac{\omega_{x}}{2\alpha^{2}}x}}\cos(\omega t-\beta x). \tag{22}\]  
In this 1D DHCP, the thermal conductivity is 1, the space domain is 1 in length, final time is 1, and \(\omega=1\). The final time and boundary data are provided on the top and lateral sides of the 2D space-time domain, respectively. The inner points are then provided in the 2D space-time domain. The numbers of the inner, source and boundary points are 900, 100, and 200, respectively. The terms of the SRPBF and the dilation parameter are determined to be 11 and 4, respectively. Figure 9 indicates the results computed with the exact solution. It is found that the temperature from our approach may achieve great agreement with the exact solution.  
[PERSON] et al. (2016) have applied the space-time localized RBFCM to solve this DHCP [28]. The computed results of the proposed approach are then compared with those of the space-time localized RBFCM [28]. Table 2 depicts the MAE and RMSE adopting several boundary points in the time and space domain. From Table 2, the MAE and the RMSE using the proposed approach can reach to the order of \(10^{-6}\) and \(10^{-8}\). However, the MAE with different arrangements of the boundary points using the space-time localized RBFCM is of the order of \(10^{-4}\) to \(10^{-5}\). In addition, the RMSE with different arrangements of the boundary points associated with the space-time localized RBFCM may only reach to the order of \(10^{-5}\) to \(10^{-6}\)[28], as listed in Table 2. It is significant that the temperature with high accuracy can be acquired using the proposed approach.  
Figure 8: Absolute error at observation point through time.
# Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods
## 4 Methodology
### StarGANv2 and StarGANv2-SPADE+AdaIN  
We employed the official implementation of the base StarGANv2 from GitHub at [[https://github.com/clovaai/stargan-v2](https://github.com/clovaai/stargan-v2)]([https://github.com/clovaai/stargan-v2](https://github.com/clovaai/stargan-v2)) (accessed on 9 October 2024). The minor changes were employed to make all but the mapping network architectures suitable for our image size of \(320\times 200\) that differed from the original size of \(256\times 256\). The networks were too deep otherwise and ran out of the smaller spatial dimensions to work with before the output was produced. The mapping network was the only one not suffering from this issue, and even though it was not used in our experiments, it was left unchanged for framework training consistency.  
We proposed one key change to the base StarGANv2 that was inspired by the Seg2 Eye's success in semantic-guided eye image synthesis. Our hypothesis was that the addition of SPADE would improve the content consistency of the generated images compared to the base method. We replaced every AdaIN layer in the decoding part of the base StarGANv2 generator with the combined SPADE+AdaIN layer. The new layer performs SPADE and AdaIN computations in parallel branches with their output combined by computing an average of two. The SPADE used input segmentation masks in a one-hot encoding that were downsized to match the layer's spatial dimensions using the 'nearest' down-sampling. The implementation of the combined layer was adapted from the original Seg2 Eye ([PERSON] et al., 2020) codebase at [[https://github.com/mcbuehler/Seg2](https://github.com/mcbuehler/Seg2) Eye]([https://github.com/mcbuehler/Seg2](https://github.com/mcbuehler/Seg2) Eye) (accessed on 9 October 2024). The resulting StarGANv2-SPADE+AdaIN method is summarized in Figure 3.  
Figure 3: The outline of the proposed eye image generation method. The original StarGANv2 is modified with SPADE+AdaIN normalization layers in the image decoder and is adapted for domain transfer between real and synthetic eye images.
# Monolithic 3D Inverter with Interface Charge: Parameter Extraction and Circuit Simulation
## 3 Parameter Extraction for Circuit Simulation  
For circuit simulation, the LETI-UTSOI model (version 2.1) [16; 17; 18] in HSPICE [19] was used for the top and bottom MOSFETs [12]. Parameters of the LETI-UTSOI model were extracted by comparing them with the reference TCAD data. First, the threshold voltage (\(Vt\)) roll-off, subthreshold swing (SS) degradation, and interface state factor parameters were extracted. Next, the mobility and series resistance parameters, the velocity saturation parameters were extracted. For the short channel effect, the drain-induced barrier lowering (DIBL), and the channel length modulation (CLM) parameters were extracted. The process was repeated until the parameters were completely extracted. When the DC parameter extraction was complete, the output capacitance parameters were extracted. Finally, the back gate effect parameters were extracted. To reflect the effect of interface trap charge, parameters such as interface state, threshold voltage, and mobility were used, as shown in Tables 1 and 2. Figure 3a-c show the \(I_{ds}\)-\(V_{gs}\) characteristics of the bottom P-type MOSFET in M3 DINV and the top N-type MOSFET at \(T_{\text{ILD}}\) = 10 and 100 nm, respectively. The symbols and lines denote the TCAD and HSPICE simulation results, respectively, using the extracted parameters both with and without interface trap charge distributions. The drain voltages are 0.2, 0.6, and 1 V for N-type MOSFETs and \(-\)0.2, \(-\)0.6, and \(-\)1 V for P-type MOSFETs, respectively. In both top and bottom MOSFETs, the on-current levels are reduced, the threshold voltage is changed, and \(SS\) is reduced by the interface trap charge. The HSPICE circuit simulation results are in good agreement with the TCAD simulation results. Figure 4 shows the total gate capacitance-gate voltage (\(C_{g}\)-\(V_{gs}\)) characteristics. Figure 4a-c shows the total gate capacitances of the bottom P-type MOSFET and top N-type MOSFETs at \(T_{\text{ILD}}\) = 10 and 100 nm, respectively. The solid and dashed lines show the total gate capacitances simulated with HSPICE without/with the interface trap charge, respectively, and the squares and circles show the total gate capacitances simulated with TCAD without/with the interface trap charge, respectively. In the bottom P-type MOSFET, the threshold voltage was shifted by approximately \(-\)0.176 V by the interface trap charge, and the maximum value of the gate capacitance was decreased by approximately 0.05 fF. In the top N-type MOSFETs at \(T_{\text{ILD}}\) = 10 and 100 nm, the threshold voltages are shifted approximately 0.13 and 0.083 V by the interface trap charge, respectively. However, changes in the minimum and the maximum values of the gate capacitance are hardly observed. The
## 6 Discussion
Moreover, strong differences are observed between the results, with absolute SAR images and temporal average amplitude images, both in terms of noise value and reliability windows especially (Figures 17 and 18). Such a difference is caused by the despeckling of SAR images achieved by averaging several images acquired at different times as also shown in Figure 5[86; 87; 88; 89; 90]. The availability of an ascending stack of CSK SAR absolute amplitude images allowed us to also investigate the temporal decorrelation that affected certain sectors in the surrounding areas of the Montescaglioso landslide. As shown in Figures 8 and 9, the time span between the processed images increased from 15 days to over 200 days, which caused an increase of the decorrelated area from approximately 32% to 46%. The temporal resolution of remotely sensed data may also affect the precision and accuracy of the measurements and analyses performed [91]). According to the authors of [8; 92], the time span between two image acquisitions can play an important role in increasing the decorrelated signal [93]. However, in most cases, the time resolution is governed by the data collection procedure (e.g., different satellite revisit times), thus ensuring a significant impact on all types of measurements [94]. Therefore, a compromise has to be found between the correlation loss and the minimum signal detection. For example, with slow landslides, the time span between the two acquisitions has to be large enough to detect the signal, but also needs to be short enough to avoid decorrelation [8; 92; 95]. The above findings indicate that landslides characterized by fast movements and/or episodic occurrences might be more successfully investigated by employing amplitude-based techniques (such as the DIC) than interferometric-based analyses, because the movement may induce important changes in the surface topography. In fact, when a large ground displacement between two SAR images collected at different times in the same geometry is too large to be measured by interferometry (InSAR), image-based techniques, such as mainly the sub-pixel image-correlation method, can be particularly suitable, complementary and successful [96; 97; 98; 99; 100; 101; 37].