esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 3 Results and Discussion
### External Validation of Three-Gene Signature  
As depicted in Figure 5, the OOB error rate diminishes as the number of trees increases, ultimately settling at 6.02% with 500 trees (Figure 5a). The achieved AUC was 0.9898, underscoring the potent predictive capability of the three-gene signature for heat stress in Arabidopsis.  
AT5G44050, located on chromosome 5, encodes the MATE efflux family protein, also known as MRH10.16. Scholars have previously reported on the active function of the MATE gene in other crops to enhance general stress tolerance, including OsMATE1 and OsMATE2 in rice [34] and DTX/MATE in cotton [35]. Ref. [36] reported 174 A MATE families in four Cucurbitaceae species coping with severe salt stress.  
AT2G47180 encodes a galactinol synthase 1 (GolS1) that has been reported to be induced by drought and high-salinity stresses in Arabidopsis [37]. In a study, [38] reported the GolS1 promotor as a potential biosensor for heat stress and fungal infection in Arabidopsis. Another study has revealed that GolS1 expression is regulated by other stressors, including ionic, osmotic, and heat stresses [39].  
Figure 4: A random forest algorithm to identify the performance of the three-gene signature common among LASSO, IG, and ReliefF selection (**a**). The ROC is plotted by the true positive rate against the false positive rate (**b**). Mean decrease accuracy and mean decrease gini to confirm and rank the importance of the selected genes (**c**).  
AT1G70700 encodes a TIFY domain/divergent CCT motif family protein (TIFY7). TIFY, also known as JAZ9 (Jasmonate-Zim-Domain Protein 9), which plays an important role when plants are subjected to various stresses. The role of the TIFY family in response to various stress has been reported in different species, e.g., in tomato (_Solanum lycopeneum_) [40], wheat (_Triticum aestivum_) [41], and rice (_Oryza sativa_) [42].
## 4 Discussion
### About the Use of ET Metrics for Evaluating Usability Affordances  
The study sought to identify ET metrics that effectively gauge the usability of jar opening ADs. In this sense, significant differences in metrics aligned with differences in on-screen assessment ratings, along with significant correlations between these metrics and affordance ratings. Specifically, fixation and visit metrics, such as TDoF%, NoF%, TDoV%, and NoV, demonstrated significant correlations with on-screen assessment affordance ratings, suggesting their relevance for assessing usability affordances. Although the correlation coefficients were not high--a common feature of [PERSON]'s coefficients--they were significant and consistent with the hypothesis that higher affordance ratings correspond to more frequent and prolonged visual attention. In particular, TDoF%, NoF%, and TDoV% showed the highest correlations within the AoI Model. This result aligns with prior research on product selection suggesting that products receiving higher attention are more likely to be selected [39; 40]. In contrast, metrics related to the first fixation--such as TtfF% and DoFF%--provided limited information, with low and non-significant correlations with on-screen assessments. This limitation may be due to other factors unrelated to affordance perception. ET records revealed that participants' initial fixations often centred on the screen due to the gaze restriction blue dot used before affordance assessment, potentially distorting these metrics for centrally located models. Future studies should ensure that models are not placed in the central region of visual stimuli.  
The differences observed in fixation and visit metrics across Aols align with the findings of [PERSON] and [PERSON] [29], who argued that affordances can be perceived through visual inspection of critical product elements. For affordances such as _comfort_, _easiness to grip_, _effort level_, and _easiness to use_, the Aols Grip and Placement on Lid were the most observed, as these regions convey essential information for the evaluation of these specific affordances. The results also corroborate findings by [PERSON] and [PERSON] [31], who emphasised that fixation times are prolonged in areas associated with product manipulation, a pattern they observed in usability affordances of tools, where the manipulation zones attracted significantly more attention compared to other parts of the tools.  
In summary, this study has identified TDoF%, NoF%, and TDoV% as key metrics for assessing usability affordances in ADs, while first fixation metrics offer limited utility due to contextual bias in this study. The addition of rubber to the grip area significantly improved on-screen assessment affordance ratings, as reflected in higher fixation and visit metrics. These findings underscore the importance of targeted AoI analysis in uncovering the visual strategies underlying affordance perception. Future research should refine experimental designs to minimise biases and further explore the interplay between ET metrics, product characteristics, and perceived affordances.
## 3 Proposed Methods
### Highway Crack Detection Methods
#### 3.1.4 Loss  
The dataset used in this study is a UAV remote sensing highway crack dataset, which presents challenges due to unclear cracks, with a low percentage of crack pixels in the dataset samples caused by the high flying height of the UAV, environmental noise, and complex crack patterns. To address these issues, a combination of binary cross-entropy loss and focal loss was chosen as the loss function for the CrackNet model. Binary cross-entropy loss is commonly used for binary classification problems, while focal loss is designed to address the issue of class imbalance in the dataset. The combination of these two loss functions helps to improve the model's ability to distinguish between positive and negative samples and to focus on hard-to-classify samples.  
The dataset contains \(N\) images, denoted as \(S=\{(X^{n},Y^{n}),n=1,\ldots,N\}\), where \(X^{n}\) denotes the original input image, \(Y^{n}\) denotes the ground truth crack label map corresponding to \(X^{n}\), and \(I\) denotes the number of pixels in each image. The purpose of our training network is to generate prediction maps that are close to the ground truth. In this encoder-decoder architecture, let \(K\) be the number of convolution levels; then, at level \(k\), the feature map generated by the jump layer can be formulated as \(F^{\mathit{fuse}}=\left\{f_{i}^{(k)},i=1,\ldots,I\right\}\), where \(k=1,\ldots,K\). In addition, the multi-scale feature fusion layer can be defined as \(F^{\mathit{fuse}}=\left\{f_{i}^{(\mathit{fuse})},i=1,\ldots,I\right\}\).  
There are only two categories in our crack detection, one for cracks and one for background, so it can be considered as a binary classification problem. We use cross-entropy loss to measure the prediction error. Usually, in a crack image, ground truth crack pixels belong to a few categories, which leads to detecting cracks as an unbalanced classification or segmentation. Some previous works addressed this problem by giving more weight to a few categories. However, we find that giving cracks a larger weight in crack detection generates more false positive samples, so we define the pixel-level prediction loss as:  
\[l(F_{i};W)=\begin{cases}log(1-P(F_{i};W)),&\text{ if }\quad y_{i}=0,\\ log(P(F_{i};W)),&\text{ otherwise,}\end{cases} \tag{5}\]  
where \(F_{i}\) is the output feature map of pixel \(i\) after passing through the network model, \(W\) is the standard set of parameters in the network layer, and \(P(F)\) is a standard sigmod activation function that converts the feature map into a crack probability map. The final total loss can be formulated as:  
\[\mathcal{L}(W)=\sum_{i=1}^{I}\left(\sum_{k=1}^{K}l\left(F_{i}^{(k)};W\right)+ l\left(F_{i}^{\mathit{fuse}};W\right)\right) \tag{6}\]
###### Abstract  
Background: The fit accuracy of removable partial dentures (RPDs) is essential for the functionality, patient comfort, and durability of RPDs. Traditional fabrication methods, like lost-wax casting, are reliable, but labor intensive, potentially affecting the fit accuracy of RPDs. Advances in digital fabrication techniques offer new avenues to improve RPD precision. This systematic review and meta-analysis will assess the impact of digital fabrication methods on the fit accuracy of RPDs compared to conventional techniques. Objective: To evaluate whether digital fabrication methods, specifically CAD/CAM and additive manufacturing, offer superior fit accuracy for RPD frameworks over conventional methods. Methods: The study protocol was registered with PROSPERO (registration number CRD42024586891). A comprehensive literature search was conducted across PubMed, the Cochrane Library, Scopus, and Ovid MEDLINE databases, covering publications published up to July 2024. The inclusion criteria comprised in vitro studies comparing the fit accuracy of digital versus conventional RPD fabrication techniques, with quantitative outcomes, such as the mean gap size or seating accuracy. The data were extracted and synthesized using a random-effects meta-analysis model. Results: Eleven studies met the inclusion criteria, with seven studies included in the meta-analysis. The mean gap size for digitally fabricated RPDs was 140 \(\upmu\)m, compared to 164 \(\upmu\)m for conventional methods, with a weighted mean difference (WMD) of 26.29 \(\upmu\)m, favoring digital techniques. The subgroup analysis indicated variability in the fit across different digital techniques, with milling showing the best results, although the differences were not statistically significant. Limitations: The analysis included only in vitro studies, limiting the clinical generalizability of the findings. Additionally, heterogeneity in the study design and measurement methods persisted, which could have impacted the overall conclusions. Conclusions: Digital fabrication methods demonstrated a trend toward improved fit accuracy in comparison to conventional techniques, although the differences were modest. Future research should focus on standardizing digital workflows and conducting clinical trials to confirm these findings.  
removable partial denture frameworks; fit accuracy; computer-aided design/manufacturing (CAD/CAM); 3D printing; selective laser melting (SLM); selective laser sintering (SLS)
## 3 Conventional and Proposed Methods
### Conventional Methods
#### 3.1.2 CSI-Based Proportional Allocation Method  
In the CSI-based proportional allocation method, the transmission power of each antenna is allocated in proportion to the CSI gain \(g_{m,n}\). As a result, more power is allocated to antennas with better channel conditions, while power distribution to antennas with poorer channel conditions is suppressed.  
The CSI gain \(g_{m,n}\) represents the squared magnitude of the complex channel coefficient \(h_{m,n}\), which characterizes the channel conditions between the \((m,n)\)-th transmit antenna and the receiver. Mathematically, it is defined as  
\[g_{m,n}=|h_{m,n}|^{2}. \tag{21}\]  
Here, \(h_{m,n}\) is the complex channel coefficient, which is influenced by path loss, shadow fading, and multipath effects. A higher \(g_{m,n}\) value indicates stronger received signal strength, whereas a lower \(g_{m,n}\) suggests higher attenuation due to poor channel conditions.  
The transmission power of each antenna element \(P_{m,n}\) is then expressed as  
\[P_{m,n}=P_{\text{total}}\cdot\frac{g_{m,n}}{\sum\limits_{k=1}^{M}\sum\limits_{ l=1}^{N}g_{k,l}}. \tag{22}\]  
Based on the transmission power allocated to each antenna element, the total received signal power at the receiving end, \(R_{\text{rec,proportion}}\), is expressed as follows:  
\[R_{\text{rec,proportion}}=\left|\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N} \sqrt{\frac{P_{\text{total}}\cdot\frac{g_{m,n}}{\sum\limits_{k=1}^{M}\sum \limits_{l=1}^{N}g_{k,l}}}}\cdot h_{m,n}\cdot e^{i\theta_{m,n}}\right|^{2}. \tag{23}\]
## 2 Materials and Methods
### Network Architectures  
We performed a benchmark with different architectures and based on the results, we selected EfficientNet [36] as the main architecture for our system. This family of models achieves state-of-the-art accuracy on the ImageNet dataset by employing a simple, yet powerful concept where the network models are not only scaled in depth, but also in width and resolution. To achieve such a scheme, the authors propose a compound coefficient (\(\Phi\)) that uniformly scales the network along the three dimensions. The coefficient controls the distribution of resources available for scaling the model under the constraint of a maximum operation growth of 2\({}^{\Phi}\) FLOPS. For our CADx, we employed the variant B4, which has a total of 19 million parameters. The B4 variant was the preferred option for two main reasons; one, it achieved a higher performance against state-of-the-art polyp classification architecture while reducing the number of parameters; and second, it allowed the best memory performance on our CADx setup. Additionally, several other commonly-used architectures were considered for the development of our CADx system, therefore we trained all alternatives and compared their performance with EfficientNet. The architectures were selected based on the most common networks employed in state-of-the-art polyp detection and classification studies. For this reason, we selected the following networks: VGG16, ResNet50, ResNet101, Xception, and InceptionResNet.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 3 UWB Localization System Design
### Anchor Node Selection  
At the start of each superframe, the tag selects a set of anchors which are woken up to range with. This process consists of two phases: (i) bootstrapping and (ii) anchor node selection during operation lifetime.  
* During _bootstrapping_, the UAV has no prior information regarding its position. As such, during initialization, the set of neighbors is populated with random anchor node IDs, selecting anchor nodes at random until the UAV receives at least one ranging message.
* During the _operational lifetime_, the set is continuously updated to use the anchor nodes that are nearest. To this end, at the end of each superframe, the UAV selects the most nearby anchor that the tag is currently ranging with. For subsequent superframes, the UAV each time selects the set of anchor nodes that are closest to the nearest anchor node (prior to deployment, the drone is provided either with all anchor node locations, or with a dictionary where for each anchor node, the most nearby anchor nodes are provided.)
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
## 5 Numerical Simulation of Thermal-Structural Coupling of the Subsea Connector
### Analysis of Coupling Stress Examples under a Steady-State Temperature Field
Along the negative direction of the \(\theta\)-axis, the theoretical maximum value is 91.17 MPa, and the simulation maximum value is 83.78 MPa. The theoretical analysis is 8.82% larger than the simulation analysis. As shown in Figure 16c, the maximum value of the axial stress \(\sigma_{z}\) also occurs at the contact center. Along the negative direction of the \(z\)-axis, the theoretical maximum value is 344.08 MPa, and the simulation maximum value is 326.71 MPa. The theoretical analysis is 5.32% larger than the simulation analysis. At the same time, Figure 16 shows that at \(z_{0}\) = 0.27 mm, the sealing width of theoretical analysis is about 5.52 mm, and the sealing width of simulation analysis is about 6.28 mm. The theoretical analysis is 12.10% lower than the simulation analysis. The absolute values of the stress of \(\sigma_{\theta}\) and \(\sigma_{z}\) on the negative semi-axis of the \(x\)-axis are less than those on the positive semi-axis of the \(x\)-axis. After analysis, it can be seen that the theoretical coupling stress analysis first performs the two-dimensional stress analysis and then performs the three-dimensional expansion, while the simulation directly performs three-dimensional finite element analysis. There is a certain algorithm error between the two. Since the two contact surfaces are assumed to be smooth in the theoretical analysis, the edge position of the sealing width is greatly affected. At the same time, due to the influence of friction, the stress at the bottom end of the contact position of the sealing gasket is greater than that at the top end, and \(\sigma_{\theta}\) and \(\sigma_{z}\) are the most obvious stresses affected by this influence. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parts**} & \multicolumn{4}{c}{**Material Parameter**} & \multicolumn{4}{c}{**Boundary Conditions**} \\ \cline{2-9}  & \multirow{2}{*}{**E(MPa)**} & \multirow{2}{*}{\(\
u\)} & \multirow{2}{*}{\(\rho\) **(g/cm\({}^{3}\))**} & \(\lambda_{k}\) & \multirow{2}{*}{\(\alpha^{*}\) (\(\
u^{c}\)C)**} & \multirow{2}{*}{\(q_{\text{max}}\) **(MPa)**} & \multirow{2}{*}{\(P\) **(MPa)**} & \multirow{2}{*}{\(\tau\) **(\({}^{\circ}\)C)**} \\  & & & & & & & \\ \hline Gasket & \(2.06\times 10^{5}\) & 0.25 & 7.93 & 16.7 & \(1.70\times 10^{-5}\) & \multirow{2}{*}{458.03} & \multirow{2}{*}{34.5} & \multirow{2}{*}{10.17} \\ Hubs & \(2.1\times 10^{5}\) & 0.3 & 7.93 & 16.3 & \(1.68\times 10^{-5}\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Coupling stress calculation parameters.
# Rearrangeable and Repackable S-W-S Elastic Optical Networks for Connections with Limited Bandwidths
## 4 RNB and RPNB SWS1(\(q\), \(p\), \(n\)) Switching Fabrics with \(2\leq m_{\max}\leq n\)
### RPNB SWS1(\(q\), \(p\), \(n\)) Switching Fabrics with \(2\leq m_{\max}\leq n\)  
Given an SWS1(\(q\), \(p\), \(n\)) switching fabric with \(p\geq q^{2}\) for \(m\)-slot connections, \(1\leq m\leq m_{\max}\) and \(2\leq m_{\max}\leq n\), from Property 1, we can see that only under the condition that connections depart will a blocking state arise. Recall that time is less critical for connection rearrangement during departures than during arrivals. Thus, for RPNB, rather than rearranging existing connections once a blocking state has occurred, rearrangement is done immediately after a departure. In other words, while a connection, say \((i,j,m)\), departs, Routing Algorithm 2 (for Rearrangements) is applied immediately. From Property 1 and Theorem 2, Theorem 3, given below, can be derived immediately.  
**Theorem 3**.: _An SWS1(\(q\), \(p\), \(n\)) switching fabric for \(m\)-slot connections with \(1\leq m\leq m_{\max}\) and \(2\leq m_{\max}\leq n\) is RPNB if \(p\geq q^{2}\)._  
**Proof.** The theorem holds because of Property 1 and Theorem 2, while existing connections, that is, \((i,j^{\prime},m)\) and \((i^{\prime},j,m)\) for \(1\leq i^{\prime},j^{\prime}\leq q\), are rearranged by Routing Algorithm 2 (for Rearrangements) after connection \((i,j,m)\) departs.  
Similar to Theorem 2, from Theorem 3, we can see that the proposed RPNB SWS1(\(q\), \(p\), \(n\)) switching fabric with \(2\leq m_{\max}\leq n\) requires fewer middle switches than the SNB SWS1(\(q\), \(p\), \(n\)) switching fabric given in [7] as \(\frac{q^{2}-1}{2(q-1)}\leq m_{\max}\leq\frac{nq}{2q-1}\) (Equation (1)). Thus, the upper bound of the RPNB SWS1(\(q\), \(p\), \(n\)) switching fabric for \(m\)-slot connections, \(1\leq m\leq m_{\max}\) and \(2\leq m_{\max}\leq n\), reduces by around half the number of middle switches relative to the number in the SNB SWS1(\(q\), \(p\), \(n\)) switching fabric as \(\frac{q^{2}-1}{q-1}\leq m_{\max}\leq\frac{nq}{2q-1}\). It is worth noting that even though the RPNB and RNB SWS1(\(q\), \(p\), \(n\)) switching fabrics have the same results, the former does connection rearrangements during departures, which requires less critical time than during arrivals.
## 3 Results
### PWFs for Spherical Waves  
Now Consider Spherical Waves (i.e., L\({}_{\text{S}}\) equal to L) for Which \(\gamma=1-\)u. \(W_{\text{T}}(\text{u})\) for Different d, F\({}_{\text{N}}\), and R is Plotted in Figure 4.  
Figure 3: Normalized path-weighting function \(W_{P}(u)\) for the covariance of piston amplitude for plane waves. (**a**) Point sources with various \(F_{N}\) (\(F_{N}=D^{2}/\lambda L\)) and \(d\) (the distance between the aperture centers) values. (**b**) Distributed sources with various \(R\) (\(R=\)\(D_{\text{S}}\)/\(D_{\text{S}}\)) and \(d\) values when \(F_{N}=1\).  
For a point source, _the_\(W_{T}(u)\) for different \(F_{N}\) and \(d\) values is shown in Figure 3(a). Not surprisingly, for \(d=0\), the \(W_{T}(u)\) for a large \(F_{N}\) value (dot line) has the form \((1-u)^{5/3}\) that can be used to measure \(r_{0,s}\); the \(W_{T}(u)\) for a small \(F_{N}\) value deviates slightly from \((1-u)^{5/3}\) due to the effect of diffraction. The two curves of \(W_{T}(u)\) for \(d=0D\), \(F_{N}=5\) and \(d=2D\), \(F_{N}=1\) almost coincide with each other, as the effect of diffraction on the tilt covariance is smaller than that on the variance. For a distributed source, the \(W_{T}(u)\) for different \(R\) and \(d\) values and a fixed Fresnel number \(F_{N}=1\) is shown in Figure 3(b). It can be seen that the \(W_{T}(u)\) for a finite-size source deviates significantly from \((1-u)^{5/3}\); therefore, the larger the \(R\) value is, the faster the decline in \(W_{T}(u)\) is. When comparing the \(W_{T}(u)\) for \(d=0\) and \(d=2D\), again we can see that the effect of diffraction on the tilt covariance is smaller than that on the tilt variance.  
For spherical waves, \(W_{P}(u)\) for different \(d\), \(F_{N}\) and \(R\) values are plotted in Figure 5.  
Figure 4: Normalized path-weighting function \(W_{T}(u)\) for spherical waves. (**a**) Point sources with various \(F_{N}\) (\(F_{N}=D^{2}/\lambda L\)) and \(d\) (the distance between aperture centers) values. (**b**) Distributed sources with various \(R\) (\(R=D_{S}L/DL_{S}\)) and \(d\) values when \(F_{N}=1\).  
Figure 5: Normalized path-weighting function for the covariance of piston amplitude \(W_{P}(u)\) for spherical waves. (**a**) Point sources with various \(F_{N}\) (\(F_{N}=D^{2}/\lambda L\)) and \(d\) (the distance between aperture centers) values. (**b**) Distributed sources with various \(R\) (\(R=D_{S}L/DL_{S}\)) and \(d\) values when \(F_{N}=1\).  
One can see that the peak position of \(W_{P}(u)\) for fixed \(d\) and \(R\) values moves towards the end of the path with an increasing \(F_{N}\) value, as shown in Figure 5a; that the peak position of \(W_{P}(u)\) for fixed \(F_{N}\) and \(d\) values moves towards the start of the path with an increasing \(R\) value, as shown in Figure 5b; and that the peak position of \(W_{P}(u)\) for fixed \(F_{N}\) and \(R\) values moves towards the end of the path with an increasing \(d\) value, as shown in Figure 5a,b.