esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
(2013) [39] did not observe any correlations between changes in \(A_{\rm sat}\) and POD based on the data from the peer-reviewed literature, part of the literature could be useful for establishment of photosynthetic responses to POD, which could be easily incorporated into models of the carbon cycling. In the present study, O\({}_{3}\)-induced reductions in net photosynthetic rate were greater under relatively high light intensities and tended to be similar above a PPFD of approximately 500 umol m\({}^{-2}\) s\({}^{-1}\) (e.g., Figure S1), which was also observed in a study by [PERSON] et al. (1995) [41]. This result suggests that the O\({}_{3}\)-induced reduction in \(A_{\rm sat}\) considerably accounts for the reduction in the \(\Sigma P_{\rm n\_est}\). Across the species and leaf types, we found a significant linear correlation, with a slope of 0.93, between the relative value of \(\Sigma P_{\rm n\_est}\) and that of \(\Sigma A_{\rm sat}\times\) days, which is parameter similar to seasonal mean percent reduction in \(A_{\rm sat}\) as reported by [PERSON] et al. (2005) [29] (Figure 3). Several data points, which were obtained from the leaves with a short cumulative period, such as the 2 nd flush leaves of _Q. serrata_ and _Q. mongolica_ var. _crispula_ and the late leaves of _B. platyphyllla_ var. _japonica_, were plotted slightly away from the 1:1 line. This could result from the low number of measurements taken during the accumulation period. Therefore, the \(A_{\rm sat}\), if measured at appropriate intervals throughout the growing season, could be used to evaluate the O\({}_{3}\)-induced percent change in \(\Sigma P_{\rm n\_est}\). Several researchers have reported such seasonal measurements of \(A_{\rm sat}\) in the leaves of deciduous broad-leaved tree species [40; 49; 50; 51]. These data sets could be used to estimate the degree of O\({}_{3}\)-induced reduction in cumulative photosynthetic CO\({}_{2}\) uptake and its response to stomatal O\({}_{3}\) uptake, which is useful in the quantification of the negative impact of O\({}_{3}\) on the NPP of deciduous broad-leaved forest trees.
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 2 Related Work
### Multi-Server Federated Learning  
[PERSON] et al. [18] propose FedMes, a federated learning framework specifically tailored for cellular networks with multiple edge servers. In conventional edge-based FL, individual edge servers are often hampered by limited client populations and biased data distributions due to their restricted coverage areas. FedMes overcomes these challenges by exploiting the overlapping regions between adjacent edge servers. In these overlapping areas, clients receive models from multiple servers and average them, effectively serving as conduits that synchronize and disseminate updated models across servers, thereby reducing latency and improving convergence in practical deployments.  
In a similar vein, [PERSON] et al. [19] introduce MS-FedAvg, another multi-server FL framework that leverages overlapping coverage among regional servers to facilitate model sharing without a central aggregator. In MS-FedAvg, clients situated in overlapping regions download several regional models and compute an initial averaged model before performing local updates via standard SGD. Regional servers then update their models based on these client updates, and the final global model is obtained by aggregating the regional models after a fixed number of communication rounds. While both approaches exploit overlapping areas, MS-FedAvg distinguishes itself by providing a thorough convergence analysis under non-convex settings and by incorporating algorithmic refinements--such as biased client sampling--to enhance both theoretical guarantees and empirical performance in heterogeneous network environments. Despite these promising strategies, both FedMes and MS-FedAvg rely on devices in overlapping regions to act as intermediaries. A potential concern is that since the same devices participate in training across multiple edges, their local models are recalculated by different servers. This can lead to biased updates that may adversely affect the overall global model if not carefully managed.  
[PERSON] et al. [20] has investigated multi-server federated learning architectures for healthcare applications, such as COVID-19 detection, using IoT devices. In these architectures, knowledge is shared across multiple servers, providing benefits like quicker data access and mitigating the issue of limited data on individual devices. However, these studies do not account for the reliability of the shared knowledge, which could lead to significant declines in global model accuracy if unsuitable models are shared between servers.
## 2 Ranging Principle and Analyses of the Questions
### Question 2  
After the onboard time-frequency equipment is powered on, it provides the ISL payload with the fixed phase relationship of the 1 PPS and the 10.23 MHz, and the phase relationship always remains at the boot state. However, the phase relationship of the 1 PPS and the 10.23 MHz is not the same each time the onboard time-frequency equipment boots.  
As shown in Figure 3, \(p_{0}\) means that the system's 1 PPS falls at the 10.23 MHz preset inital phase, \(p^{\prime}_{0}\) means that the 1 PPS falls at the 10.23 MHz phase after each boot of the on-board time-frequency equipment. The 1 PPS may appear at any phase point of the 10.23 MHz. This uncertainty is a 10.23 MHz clock period of approximately 97.75 ns, resulting in an uncertainty of approximately 97.75 ns for \(t_{0}\). This implies that the uncertainty of the ranging error caused by the onboard time-frequency equipment's power switch is approximately 97.75 ns.  
Figure 3: Uncertainty of the initial phase between the 1 PPS and the 10.23 MHz.
## 2 Preliminaries
### Performance Metric  
To demonstrate the effectiveness of MMOEAs, various performance metrics have been proposed, such as the Pareto set proximity (_PSP_) [23] and the hypervolume (_HV_) [24].  
(1) The _PSP_ is calculated as follows:\[PSP=\frac{CR}{IGD_{x}},\] \[IGD_{x}(A,PS^{*})=\frac{\sum\limits_{\mathbf{v}\in PS^{*}}d(\mathbf{v},A)}{| PS^{*}|},\] \[CR=(\mathop{\prod}\limits_{j=1}^{D}\delta_{j})\frac{1}{\Delta \mathbf{v}}, \tag{2}\] \[\delta_{j}=\begin{cases}1,&\text{$V_{j}^{\text{max}}=V_{j}^{\text{ min}}$}\\ 0,&\text{$v_{j}^{\text{min}}\geq V_{j}^{\text{max}}$}\Big{|}|v_{j}^{\text{max}} \leq V_{j}^{\text{min}}\,\\ (\frac{\min(v_{j}^{\text{max}},v_{j}^{\text{max}})-\max(v_{j}^{\text{min}},v_{ j}^{\text{min}})}{v_{j}^{\text{max}}-v_{j}^{\text{min}}}),&\text{otherwise}\end{cases},\]  
where \(CR\) is the cover rate and \(IGD_{x}\) represent the inverted generational distance in the decision space; \(PS^{*}\) and \(A\) represent the true PS and the obtained PS, respectively; The Euclidean distance between \(v\) and solutions in \(A\) is expressed as d(\(v\), \(A\)); And the number of solutions in \(PS^{*}\) is denoted as \(\left\lvert PS^{*}\right\rvert\); For the \(j\)-th variable, \(V_{j}^{\text{max}}\) and \(v_{j}^{\text{max}}\) denote the maximum in \(PS^{*}\) and \(A\), \(V_{j}^{\text{min}}\), and \(v_{j}^{\text{min}}\) are the minimum in \(PS^{*}\) and \(A\). A larger \(PSP\) means the algorithm performs better in the decision space.  
(2) The calculation method for \(HV\) is as follows:  
\[HV(PS)=VOL(\cup_{\mathbf{x}\in PS^{*}}[f_{1}(\mathbf{x}),r_{1}^{*}]\times\cdots\times[ f_{m}(\mathbf{x}),r_{m}^{*}]), \tag{3}\]  
where \(VOL\) 0 is the Lebesgue measure; \(\mathbf{r}^{*}=(r_{1}^{*},r_{2}^{*},\cdots r_{m}^{*})\) represents the reference point in the objective space. The \(HV\) is used to evaluate the performance of the algorithm in the objective space.
# Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification
## 6 Discussion
### Evaluation in Text-Independent Speaker Verification

Table 1: EERs (%) of the baseline and proposed methods for each embedding on the validation and evaluation trials of RSR 2015 part 1. We created 37,904 validation (10,492 target and 27,412 impostor) trials using the validation set. We used the training set to build gender-independent models, the validation set for validation, and the test set to evaluate the performance of the proposed system. We used the VoxCeleb1 dataset to build and evaluate the i-vector-based system only, which satisfies the prerequisite of the GLASSO-PLDA. The configuration for extracting i-vectors was the same as that described in Section 5.2. Figure 11 displays the EERs on the validation (Figure 10(a)) and evaluation (Figure 10(b)) trials of the VoxCeleb dataset. Unlike the results in the TD-SV tasks, the performances were degraded with the proposed method in the TI-SV tasks, even though the prerequisite was satisfied and the GLASSO converged. These results mean that the proposed method is suitable for only TD-SV tasks, where \(\mathbf{\Sigma}_{w}=\mathbf{\Sigma}_{ch}+\mathbf{\Sigma}_{\varepsilon}\) does not contain phrase variability \(\mathbf{\Sigma}_{plr}\). In other words, the true within-class precision matrix is sparse only if \(\mathbf{\Sigma}_{w}\) does not contain \(\mathbf{\Sigma}_{plr}\). The optimums of both channel variability \(\mathbf{\Sigma}_{ch}\) and residual variability \(\mathbf{\Sigma}_{\varepsilon}\) have conditional independence structure, but that of \(\mathbf{\Sigma}_{plr}\) does not. Therefore, the sparse assumption of \(\mathbf{\Phi}_{w}^{-1}\) does not hold and the proposed method is not suitable for TI-SV tasks, where \(\mathbf{\Sigma}_{w}=\mathbf{\Sigma}_{plr}+\mathbf{\Sigma}_{ch}+\mathbf{\Sigma}_{\varepsilon}\) contains phrase variability \(\mathbf{\Sigma}_{plr}\).
# Water Basin Effect of Cofferdam Foundation Pit
## 2 Principle of Cofferdam Foundation Pit Water Basin Effect
### Water Basin Effect in Building Foundation Pits  
The water basin effect in building foundation pits is defined as the phenomenon whereby insufficient backfill compaction allows surface, groundwater, and construction runoff to accumulate beneath the basement slab within an impermeable strata-confined zone (Figure 1). When the hydrostatic uplift force exerted by this trapped water exceeds the structural anti-uplift capacity-comprising self-weight and engineered countermeasures, the basement may experience upward displacement [40].  
This effect commonly induces construction defects, including basement slab heave, wall cracks, and beam-column joint fissures, primarily due to inadequate control of backfill permeability and compaction quality. Theoretically, properly compacted backfill should exhibit lower permeability than undisturbed soil; however, substandard construction disrupts this design intent. During heavy rainfall, surface water infiltrates the foundation pit through poorly compacted backfill zones, forming a confined water basin beneath the basement. The structure then acts as a \"sub-basin\" floating within this larger hydrological system: when buoyant forces surpass the sum of structural dead load and anti-uplift resistances, uplift failure occurs.
## 4 Experiment and Result Analysis
### Model Robustness Testing Under Different Conditions  
To verify the robustness of the model under varying lighting, occlusion, and distance conditions, the 917 test images in the dataset were divided into two experimental groups based on the presence of environmental disturbances. The test images in Experiment 1 mainly include blueberries under close distances, no occlusion, and normal lighting conditions. The test images in Experiment 2 mainly include blueberries under conditions with environmental disturbances such as long distances, partial occlusion, strong lighting, and insufficient lighting. Under the same experimental conditions, the performance of the proposed model was compared with that of the YOLOv8n model. The experimental results are shown in Table 8, and the confusion matrix after testing the models is shown in Figure 13.  
As shown in Table 8, the model proposed in this paper exhibits significant advantages under environmental disturbance conditions, particularly outperforming YOLOv8n in terms of precision, recall rate, and [EMAIL_ADDRESS]. In Experiment 1, under normal environmental conditions, the accuracy of the model proposed is slightly higher than YOLOv8n, with only a small difference in detection performance between the two. However, in Experiment 2, when faced with environmental disturbances, the precision, recall rate, and [EMAIL_ADDRESS] of the model proposed improved by 3%, 5.4%, and 4.6%, respectively, compared to YOLOv8n, significantly enhancing its performance in complex environments. These results indicate that the model proposed exhibits stronger robustness in blueberry detection tasks, better adaptability to environmental changes, and higher stability and accuracy.  
As shown in the confusion matrix in Figure 11, the model proposed outperforms YOLOv8n in both experiments. Although the performance of the two models is similar in Experiment 1, the model proposed has a slight advantage in classification accuracy, especially for blueberry-U. However, in Experiment 2, our model significantly outperforms YOLOv8n, providing more precise classification for all categories (blueberry-R, blueberry-U, blueberry-S) and significantly reducing the misclassification rate for all categories. Overall, the model proposed demonstrates higher stability and accuracy than YOLOv8n in both experiments, especially in more complex classification tasks, and shows stronger model robustness.
## 2 Related Work  
Nowadays, computer-vision-based crack detection approaches can be grouped into three categories: traditional, machine-learning-based, and deep-learning-based approaches.  
**Traditional approach:** In recent works, a traditional approach to detecting cracks in various images, such as road images, concrete images, pavement, buildings, welded parts, and others, has been employed, using traditional techniques to extract crack features.  
\begin{table}
\begin{tabular}{c c} \hline
**Abbreviation** & **Detail of Abbreviations** \\ \hline FT & Fine-tuning \\ GAP & Global Average Pooling \\ GS & Grid Search \\ HOG & Histogram of Oriented Gradient \\ LF & Loss Function \\ LR & Learning Rate \\ mAP & mean Average Precision \\ N & Number of Classes \\ NW & Number of Weights \\ PL & Pooling Layer \\ RCNNs & Regional Convolutional Neural Networks \\ TL & Transfer Learning \\ TN & True Negative \\ TP & True Positive Rate \\ TPR & True Positive Rate \\ \hline \end{tabular}
\end{table}
Table 1: Cont.  
However, these techniques encounter problems related to decisions about optimized thresholding. In addition, they are sensitive to noise and imaging conditions, which ultimately results in poor performance and greater subjectivity. Some of the crack detection approaches based on traditional methods that have been employed to images include the Canny edge detector [19], the Gabor filter [20; 21], wavelet transform [22; 23], and histogram-oriented gradient (HOG) method [24; 25], which show significant improvements in detecting simple cracks, but they are not suitable for diverse and complex cracks, and are laborious and time-consuming.  
**Machine-learning-based approach:** With the rapid growth of machine learning and vision-based technologies, several approaches have been applied to crack detection tasks. These include support vector machines (SVMs) [26; 27; 28], AdaBoost [29], and so on. However, these detection approaches are restricted to detecting learned cracks, making it hard to detect new cracks, and making it challenging to design universal features that can be employed on all types of cracks.  
**Deep-learning-based approach:** Recently, researchers have successfully used deep learning-based methods in various crack detection applications, such as road crack detection [30], weld part defect detection [31; 32], concrete crack detection [33], and so on. These techniques have significant merits over the traditional and machine-learning-based approaches in that they can easily detect various cracks, including complex and diverse cracks; minimize subjectivity; and easily design and employ all types of cracks. However, deep-learning-based crack detection approaches require enormous resources and time to train the model. Therefore, the techniques of TL and FT have been employed to solve the problem of resource usage and cost in the training of a ConvNet architecture from scratch, using pre-trained deep architectures trained on massive ImageNet datasets. Researchers have employed the DCNNs in various applications, such as Alexnet [34; 35], InceptionV3 [36], and MobileNet [37].
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Non-Negativity and Constraints of the Extended Entropy \(\overline{S}\)
#### 3.6.3 Bounds on Linear Terms  
The linear terms \(\mathbf{a}\cdot\mathbf{p}^{(a)}+\mathbf{b}\cdot(\mathbf{r}^{(a)}\times\mathbf{ p}^{(a)})+J^{(a)}\) are constrained by the system's conserved quantities. For example:  
**Translational invariance:**: \[\mathbf{a}\cdot\mathbf{p}^{(a)}\leq\|\mathbf{a}\|\|\mathbf{p}^{(a)}\|\leq \frac{\|\mathbf{a}\|^{2}}{2e}+\frac{e\|\mathbf{p}^{(a)}\|^{2}}{2}\quad\text{( Cauchy-Schwarz inequality)},\] (15)
**Angular momentum:**: \[\mathbf{b}\cdot(\mathbf{r}^{(a)}\times\mathbf{p}^{(a)})\leq\frac{\| \mathbf{b}\|^{2}}{2\eta}+\frac{\eta\|\mathbf{r}^{(a)}\times\mathbf{p}^{(a)}\| ^{2}}{2},\] (16)  
where \(e,\eta>0\) ensure these terms are bounded by the kinetic energy \(\frac{(p^{(a)})^{2}}{2m^{(a)}}\) and the angular kinetic term \(\frac{(J^{(a)})^{2}}{2I^{(a)}}\), respectively. Thus, they do not dominate over \(S^{(a)}(X^{(a)})\) and preserve \(\overline{S}\geq 0\).
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 5 Necessity and Methods to Increase the CFRP Conductivity
### Conductivity Improvement With Nanotechnology and Plastic Materials  
Conductive nanocomposites as the matrix could change electrical properties of CFRPs [191; 192; 193]. In the manufacturing process of nanocomposites with polymers and conductive fillers, some drawbacks are generally generated [194]. Sufficient conductive nanofillers should be added to guarantee high conductivity. However, too much conductive nanofiller also results in the increased viscosity of the nanocomposite mixture, thus making the production process of CFRPs difficult [195]. The agglomeration phenomenon of nanofillers often occurs due to the incomplete mixing, thus decreasing mechanical performances [196; 197].  
Although the above methods have their own disadvantages, they could enhance the conductive performance significantly. The electrical conductivity of CFRPs was almost 12 orders of magnitude higher than that of pure epoxy resin after adding 0.5 wt% multi-walled carbon nanotube (MWCNT) [178]. The air-spraying method was investigated in order to enhance thermal and electrical conductivities. Single-walled carbon nanotubes (SWCNTs) with carboxylic acid groups were air-sprayed on the surface of carbon fiber prepreg, which were stacked and processed into carbon fiber laminates [198]. It was discovered that the electrical conductivity was largely improved by SWCNTs, but the thermal conductivity was not ameliorated. The electrical conductivity of CNT/polymer composites was increased to 10-3 S/cm by \(\sim\)1.0 wt% CNTs. Similarly, after CNTs were grown for only 3 min, the interfacial properties and electrical conductivity of the CFs were increased largely. The in-plane electrical conductivity was improved by more than 170%. The interfacial shear strength of carbon fiber/epoxy composites was increased by \(\sim\)70%. The electrical conductivity in the through-thickness direction was improved by 44% [199].  
Novel multi-functional composites can be developed based on the synergetic effect of different ingredients. In summary, a synergistic enhancement has been obtained in the electrical conductivity of the hybrid CFRP laminate system incorporating CNTs, PANI, and CB [200; 64; 93; 185; 201; 202].  
[PERSON] explored the damage behavior caused by the lightning strike of carbon nanotube (CNT) doped CFRPs and found that increasing the content of CNT in the resin significantly decreased the damage region, indicating that the electrical conductivity in the through-thickness direction of the plate determined the lightning strike damage [203].  
Graphene oxide was also introduced into CFRPs using a wet process for impregnating carbon fiber by epoxy added with Graphene oxide, which resulted in an enhancement in the interfacial performance and SE of composite [204]. The results showed that the incorporation of 0.1 wt% Graphene oxide increased SE in the wide range from 5 to 20 GHz by slight enhancement of CFRP conductivity.
## 3 Review of Gaussian Process Regression Models
### GP and Sparse GP Regression
Again, using properties of the multivariate normal distribution, \(q(\mathbf{f})\) is given by  
\[q(\mathbf{f}) =\int p(\mathbf{f}|\mathbf{u})q(\mathbf{u})d\mathbf{u}.\] \[=\mathcal{N}_{n}\Big{(}\mathbf{\mu}_{\mathbf{x}}+\mathbf{K}_{\mathbf{ \alpha}\mathbf{z}}\mathbf{K}_{\mathbf{\mathbf{z}}\mathbf{z}}^{-1}(\mathbf{m}_{ \mathbf{z}}-\mathbf{\mu}_{\mathbf{z}}),\mathbf{K}_{\mathbf{x}\mathbf{x}}+\mathbf{ K}_{\mathbf{\alpha}\mathbf{z}}\mathbf{K}_{\mathbf{\mathbf{z}}\mathbf{z}}^{-1}(\mathbf{\Sigma}_{ \mathbf{z}\mathbf{z}}-\mathbf{K}_{\mathbf{\mathbf{z}}\mathbf{z}})\mathbf{K}_{\mathbf{ \mathbf{z}}\mathbf{z}}^{-1}\mathbf{K}_{\mathbf{\mathbf{x}}\mathbf{x}}\Big{)}. \tag{15}\]  
Furthermore, given a new set of test inputs \(\mathbf{X}_{*}\), the _approximate posterior predictive_ density for \(\mathbf{f}_{*}\) has form  
\[p(\mathbf{f}_{*}|\mathbf{y}) \approx\int p(\mathbf{f}_{*}|\mathbf{f},\mathbf{u})\,p(\mathbf{f},\mathbf{u}|\mathbf{y})q(\mathbf{u})d\mathbf{f}d\mathbf{u}\] \[=\int p(\mathbf{f}_{*}|\mathbf{u})q(\mathbf{u})d\mathbf{u}. \tag{16}\]  
The integral in (16) is tractable and takes a form analogous to that in (15). Given observed data \(\mathbf{y}\in\mathbb{R}^{n}\), the variational inference approach for approximating the exact posterior of \(\mathbf{f}\) in sparse GP regression reduces to minimizing the _evidence lower bound_ (ELBO) [20]  
\[\log p(\mathbf{y}) \geq E_{q(\mathbf{u})}\Big{[}E_{p(\mathbf{f}|\mathbf{u})}\log p( \mathbf{y}|\mathbf{f})\Big{]}-\mathrm{KL}[q(\mathbf{u})\parallel p(\mathbf{u})]\] \[=E_{q(\mathbf{f})}[\log p(\mathbf{y}|\mathbf{f})]-\mathrm{KL}[q( \mathbf{u})\parallel p(\mathbf{u})], \tag{17}\]  
where \(q(\mathbf{f})\) is defined as in (15). For more details on the ELBO optimization procedure, refer to Section 4 of [19].