esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dynamic Contrast Enhancement (DCE)  
Dynamic contrast enhancement in MRI is based on a series of images taken by MRI before, during and after the injection of the contrast [15]. The most common contrast agent used in MRI is gadolinium. The contrast agent will shorten the T1 relaxation time, which will enhance the contrast in the organ; therefore, DCE is also known as permeability MRI. The acquisition time for collecting the image needs to be shortened by up to 5 s to analyse the early enhancement following injection of the contrast. The pharmacokinetic model was used to obtain a quantification about microvasculature permeability and there were some differences between the quantification results for different parameters due to the different models used in the different protocols [4]. The microvessels will form a network between the blood and tissue and, in most diseases that occur, certain changes occur in the microvessels; therefore, these changes could be used as a biomarker to detect a particular disease [4]. In this study, DCE was reported using 4D tissue software for the 12 portions of the prostate The Syngo Tissue 4D image post-processing platform (Siemens, Germany) as shown in Figure S3. The histopathology of the TRUS-guided biopsies was taken as a reference standard when evaluating the sensitivity and specificity of combining DCE from tissue 4D software, T2 weighted and DWI.
## 1 Introduction  
The performance and maintenance of airport pavements are crucial for modern aviation operations, ensuring safety, efficiency, and cost effectiveness. As critical infrastructure, airport pavements must endure growing traffic volumes, diverse environmental conditions, and operational demands. An airport pavement management system (APMS) addresses these challenges by providing a structured framework by which to evaluate pavement conditions, predict future performance, and prioritize maintenance. Focusing on structural integrity, surface friction, and irregularities, APMS has become a vital tool for airport infrastructure management [1, 2].  
However, APMS primarily emphasizes engineering and operational concerns, lacking integration with broader safety management practices. The International Civil Aviation Organization (ICAO) Annex 14 mandates that international airports implement a safety management system (SMS) aimed at identifying and mitigating aviation safety risks [3]. Integrating APMS with SMS introduces a comprehensive approach that balances operational efficiency with safety by incorporating risk management principles into pavement performance evaluations. This study bridges this gap through the development of the Airport Pavement Integrated Risk Management (APIRM) model, which combines APMS criteria with SMS principles for holistic airport pavement evaluation.  
A case study at Istanbul Airport validates the APIRM model. Using input from six experts in construction, operations, and safety management, the study identifies potential hazards (\"distress\" in APMS literature) and evaluates risks through the Fine-Kinney method, which calculates scores based on likelihood, frequency, and consequences. The findings highlight APIRM's ability to enhance resource allocation, prioritize maintenance,and improve safety outcomes. This adaptable and practical framework demonstrates significant potential for airports with diverse infrastructure, advancing both pavement performance and aviation safety.
## 5 Security Analysis and Comparison
### Histogram Analysis  
A histogram is a measure that is used to evaluate the efficacy of an encryption scheme. A good histogram for a secure cryptosystem is a homogenous distribution that makes the process of encryption immune to statistical attacks [29]. The chi-square analysis is used to measure the uniform distribution of the histogram for more evaluation. Figure 12 shows the histograms of the original, compressed-encrypted, and decrypted images; Table 2 illustrates the \(\chi^{2}\) test results for the proposed method.  
The results in Table 2 and Figure 12 indicate that the histograms of the encrypted images are evenly distributed. The results of the above analysis show that the suggested method can effectively conceal the information on the image's pixel value distribution.
## 3 Preliminaries
### FasterNet
#### 3.2.2 PConv  
In FasterNet, PConv only needs to apply conventional convolution to a part of the input channel to extract spatial features. The remaining channels remain unchanged. If the feature map is stored continuously or regularly in memory, the first or last continuous channel is considered to be representative of the entire feature map. When comparing the performance of Conv with PConv on GPU, CPU, and ARM devices, PConv shows lower Latency. A comparison between conventional convolution and PConv is shown in Figure 3.  
Figure 2: U-Net network architecture.  
The computational complexity and memory access of Conv are shown in Equations (5) and (6), and the computational complexity and memory access of PConv are shown in Equations (7) and (8):  
\[h\times w\times k^{2}\times c^{2}, \tag{5}\]  
\[h\times w\times 2c+k^{2}\times c^{2}\approx h\times w\times 2c, \tag{6}\]  
\[h\times w\times k^{2}\times c_{p}^{2}, \tag{7}\]  
\[h\times w\times 2c_{p}+k^{2}\times c_{p}^{2}\approx h\times w\times 2c_{p}, \tag{8}\]  
where \(c_{p}\) is the channel where the convolution operation has is performed (\(c>c_{p}\)).  
According to the experimental results, compared to conventional convolution, PConv achieves a significant reduction in FLOPs, requiring only \(1/16\) of the computational operations. Similarly, memory access in PConv is reduced to \(1/4\) of that in conventional convolution.  
The FasterNet network structure is shown in Figure 4.
## 2 Materials and Methods
### Studied Nuclear Radiation Shielding Parameters  
Several additional gamma radiation attenuation parameters were calculated after finding the linear attenuation coefficients (\(\mu\)). To begin, we used Equation (1) to calculate the mass attenuation coefficients (\(\mu_{\text{m}}\)) of the SM glasses [12].  
\[\mu_{\text{m}}=\frac{\mu}{\rho} \tag{1}\]  
where \(\mu\) is the linear attenuation coefficient, and \(\rho\) is the glass density. Following that, the SM glasses' half value layer (T\({}_{1/2}\)) values were calculated. T\({}_{1/2}\) is a key factor in determining the need for very stringent radiation protective measures [13]. This quantity provides remarkable information about the thickness of a shielding material at which the intensity of a gamma ray received on it is effectively halved. Additionally, key gamma ray shielding characteristics, such as mean free path (\(\lambda\)) [14], effective atomic numbers (Z\({}_{\text{eff}}\)) [15] against gamma-ray attenuation, exposure, and energy absorption build-up factors (EBF and EABF) [16; 17; 18; 19; 20], were measured throughout the gamma-radiation energy range of 0.015-15 MeV. On the other side, we aimed to evaluate the SM glasses' attenuation properties against fast neutrons, which can be beneficial for special utilizations in neutron used/expected (i.e., photodisintegration) radiation facilities. As a result, we calculated the effective removal cross-sections of SM glasses against fast neutrons (\(\Sigma\)R) [21; 22]. Detailed information on the parameters under investigation may be found in the literature and elsewhere [23; 24; 25].
# Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection
## 4 Experiment
### Ablation Experiment
#### 4.3.3 Effect of Mask Embeddings Queries  
To verify the effect of mask classification in cloud-detection tasks, we refer to the method of [PERSON] et al., and compare the structure of PerPixelBaseline with Cloudformer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** & **Reasoning Time (s)** \\ \hline baseline & 85.76 & 86.11 & 95.23 & **0.3360** \\ +SupA (1/8) & 86.32 & 87.26 & 95.77 & 0.3523 \\ +SupA (1/8 + 1/2) & **87.81** & 89.13 & 96.55 & 0.5107 \\ +SupA (1/8) + 1/2 & 87.79 & **89.41** & **96.58** & 0.3610 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pixel-level decoder ablation experiment results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** \\ \hline Baseline & 95.89 & 96.79 & 98.23 \\ +Synchronization & 96.18 & 97.19 & 98.77 \\ +Asynchronous & **96.47** & **97.78** & **98.89** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Asynchronous position coding ablation experiment results.  
In this experiment, the structure of PerPixelBaseline is shown in Figure 6. The figure shows that Cloudformer has better performance for thin cloud detection. The learnable queries in the Transformer module can use features from a more comprehensive perspective to achieve more accurate classification. From the data point of view, Cloudformer also performs better than PerPixelBaseline. The specific data are shown in Table 3.  
At the same time, we can see from Figure 7 that the mask classification method is better than the per-pixel classification method for thin cloud detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** \\ \hline PerPixelBaseline & 92.31 & 97.28 & 98.32 \\ Cloudformer & **96.56** & **98.29** & **99.07** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mask embeddings queries ablation experiment results.  
Figure 6: In PerPixelBaseline, the structure of the pixel-level branch is maintained, and the Transformer module is also retained, but, finally, a traditional cross entropy loss is used for per-pixel classification.  
Figure 7: Comparison of mask classification method and per-pixel classification method on AIR-CD dataset. (**a**) Per-pixel classification. (**b**) Mask classification.
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 2 Mathematical Modeling for the CMfg Scheduling Problem
### Formal Expression of the Main Components
In this way, the mathematical model of real-time scheduling in the CMfg environment can be expressed as Equations (8)-(16), and it is a multi-objectives optimization problem. \[\textit{Minimize}\quad\mathbf{Z}_{t_{b}:t_{e}}\triangleq\frac{1}{\left| \mathcal{C}_{t_{b}:t_{e}}\right|}\sum_{c\in\mathcal{C}_{t_{b}:t_{e}}}\mathbf{Z }^{(c)} \tag{8}\]  
\[\textit{s.t. }\left\{ \begin{array}{ll}\mathbf{Z}^{(c)}\triangleq\left[Z_{m}^{ (c)}\quad Z_{d}^{(c)}\quad-Z_{u}^{(c)}\right]^{T}&\quad c\in\mathcal{C}\, \mathbf{z}\\ \sum_{i\in\mathcal{I}_{k}}x_{i,j}^{(c)}\mathbb{1}_{j}^{(c)}=k=1&\quad j\in \mathcal{J}_{c};c\in\mathcal{C};k\in\mathcal{K}\\ \sum_{i\in\mathcal{I}_{k}}x_{i,j}^{(c)}\mathbb{1}_{\kappa_{j}^{(c)}\
eq k}=0& \quad j\in\mathcal{J}_{c};c\in\mathcal{C};k\in\mathcal{K}\\ p_{j}^{(c)}\leq e_{j}^{(c)}-b_{j}^{(c)}&\quad j\in\mathcal{J}_{c};c\in \mathcal{C}\\ x_{i,j}^{(c)}v_{j}^{(c)}\leq V_{i}^{(k)}(t)&\quad t\in\left[b_{j}^{(c)},e_{j}^{ (c)}\right);i\in\mathcal{I}_{k};k\in\mathcal{K};j\in\mathcal{J}_{c};c\in \mathcal{C}\\ \sum_{(j,c)\in\mathcal{M}_{i}^{(k)}(t)}v_{j}^{(c)}\leq V_{i}^{(k)}(t)&\quad t \geq 0;i\in\mathcal{I}_{k};k\in\mathcal{K}\\ g_{j}^{(c)}\geq\max_{f^{\prime}\in\mathcal{P}_{j}^{(c)}}\left\{e_{j^{\prime}}^{ (c)}+l_{j^{\prime}}^{(c)}\right\}&\quad j\in\mathcal{J}_{c};c\in\mathcal{C}\\ g_{j}^{(c)}+l_{j}^{(c)}\leq b_{j}^{(c)}&\quad j\in\mathcal{J}_{c};c\in \mathcal{C}\\ \end{array} \right. \tag{9}\]  
where \(\mathcal{C}_{t_{b}:t_{e}}\) denotes the set of arrived MPs during time interval \([t_{b},t_{e})\), and the objective function of Equation (8) is the accumulated mean value of objective vector \(\mathbf{Z}^{(c)}\) defined as Equation (9). The composition of the objectives and the Constraints (10)-(16) will be described in detail in the rest of this section.
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Hybrid  
Hybrid systems are combinations of HTOL and VTOL configurations. They usually have the benefits of both groups at the same time, mostly vertical take-off from VTOLs and high cruise speeds from HTOLs. The most popular hybrid configurations are dual systems and tilting aircraft including tilt-body, tilt-wing, and tilt-rotors. The majority of hybrid aerial vehicles face challenges in the transition phase from VTOL to HTOL [15]. The following sections review hybrid designs in AUVs.
## 4 Experiments and Results
### UMAP Feature Distribution  
For a comprehensive visual assessment of the feature extraction capability of the DL models, a uniform manifold approximation and projection (UMAP) technique was considered [60]. UMAP is a dimensionality reduction technique that projects high-dimensional data into a lower-dimensional space, typically 2D or 3D while preserving the global structure and local relationships of the data. This technique is particularly useful for visualizing complex patterns and distributions in the data, allowing for an intuitive understanding of how well different models separate various land cover classes. The resulting 2-dimensional feature distributions, obtained through UMAP, are illustrated in Figure 7.  
The UMAP visualizations revealed the remarkable potential of all models in distinguishing water and urban from other land covers, verifying the observations of the visual interpretation of classification maps. However, for the remaining land cover categories, particularly bog, fen, and swamp wetland land covers, the 2D feature distributions exhibited substantial overlap, making their differentiation challenging. The hybrid models demonstrated superior performance in separating these wetland classes when compared to CNNs and transformers. The overlap between sub-wetland categories is still high for CvT and WMF, with CoAtNet and the proposed model showing better performance distinguishing wetland subcategories. The UMAP representation of the proposed model demonstrated the highest level of separability for distinguishing these wetland subclasses. For example, marsh and water land covers exhibited closely overlapping distributions for most DL models, as shown by the classification maps, yet our proposed model demonstrates the highest degree of separability for these classes.  
In addition to wetland classes, the separation of pasture and grassland categories presented its own set of challenges. These classes often share similar spectral characteristics, leading to overlapping feature distributions. Nevertheless, the hybrid models, particularly the proposed Wet-ConViT, excelled in effectively separating these classes. The enhanced separability observed in the UMAP visualizations for the proposed model can be attributed to its ability to capture both local and global features, thanks to the combined use of convolutional and transformer blocks.  
The detailed interpretation of the UMAP feature distribution results, alongside the classification maps and statistical results (in Section 4.5), provides a comprehensive understanding of how the proposed model achieves superior performance. The separability of different land cover classes in UMAP visualizations demonstrates the effectiveness of the proposed Wet-ConViT model in accurately classifying complex wetland and non-wetland categories.
## 2 Results
### Phylogenetic Relationship of diTPS within Lamiaceae  
Due to the abundant diterpenoids in _S. barbata_, diTPS family genes in TPS-c and TPS-e/f were studied in detail. A maximum-likelihood tree was constructed with diTPS enzymes with known functions (Table S7). _Sb_TPS8 was in the Lamiaceae-specialized CPS clade (Figure 5), together with diTPS that produce diterpenoids with \"normal\" stereochemical configurations [20; 22; 23; 24; 25; 26; 27; 28; 32; 34] with the exception of _S4 CPS1_ from _Salvia divinorum_, which was identified to be an _ent_-CPP synthase [33]. _Sb_TPS9 and _Sb_TPS11 clustered in the _ent_-CPP synthase clade, and TPS9 was closely related to _Ie_CPS1 and _Ir_CPS1, which are involved in the gibberellin biosynthetic pathway in _Isodon erioclary_[21] and _I. rubescens_[32]. _Sb_TPS11 grouped into a Lamiaceae-specific clade which is responsible for specialized _ent_-kaurene, labdane, or clerodane diterpenoid formation in Lamiaceae plants [21; 28; 31; 32; 33; 34]. _Sb_TPS11 was most closely related to _Vac_TPS5, sharing 64.6% sequence identity at the amino acid level. _Vac_TPS5 was identified as a kolaveny diphosphate synthase in _Vitex agnus-castus_ involved in clerodane-type  
Figure 4: Phylogenetic tree of terpene synthase genes in _S. barbata_ with those identified from _Arabidopsis thaliana_. Bold-marked enzymes show diTPS from _S. barbata_ in this study.  
diterpenoid biosynthesis [34]. Thus, the relationship between _Sb_TPS11 and _Vac_TPS5 further indicated that _Sb_TPS11 is responsible for the abundant clerodane-type diterpenoid biosynthesis in _S. barbata_.  
Figure 5: Phylogeny of _S. barbata_ diterpene synthases. The maximum likelihood tree illustrates the phylogenetic relationship of _S. barbata_ diterpene synthases with 127 representative characterized diTPS (Table S7). _Physcomitrella patens_ CPS/kaurene (PpCPSKS) was used as an outgroup. Branches with >80% bootstrap support are indicated with an asterisk. Blue lines show diTPS genes from Lamiaceae involved in normal-CPP/LDPP-mediated diterpenoid metabolism and red lines show the loss of the N-terminal \(\gamma\) domain found in eudicot and monocot KSLs. Yellow lines show genes from Lamiaceae involved in the specialized _ent_-CPP/LDPP related diterpenoid metabolism. Red-marked enzymes show diTPS from _S. barbata_ in this study.  
_Sb_TPS12 and _Sb_TPS13 grouped into the specialized Lamiaceae KSL clade (Figure 5). _Sb_TPS12 had the characteristic \(\beta\alpha\) bidomain architecture through the loss of the \(\gamma\) domain at the N terminus [38]. It was most closely related to _Mv_ELS from _Marrubium vulgare_[26], sharing 72.0% sequence identity at the amino acid level. _Sb_TPS13 was most closely related to _Ir_KSL3 from _I. rubescens_, which has the common \(\gamma\beta\alpha\) tridomain structure and represents the transitional step in the evolution of Lamiaceae KSL with two domains [32]. Thus, _Sb_TPS12 and _Sb_TPS13 are predicted be involved in specialized diterpenoid biosynthesis in _S. barbata_. _Sb_TPS14 was most closely related with _Mv_EKS from _M. vulgare_[26], which was predicted to be involved in gibberell biosynthesis.
## 3 Extended Kalman Filter Estimated SOC  
The SOC estimation of lithium-ion batteries by the ampere-time integration method will be affected by the initial state of SOC, and the estimation result has a systematic error, and the error accumulates with the operation process of the power system, but the Kalman filtering algorithm can solve the problem of the accumulation of the error of the ampere-time integration method [15]. The Kalman filter algorithm is applicable to linear systems, whereas lithium-ion batteries have nonlinear characteristics, so estimating the SOC using the Kalman filter produces large errors. To solve this problem, the extended Kalman filter expands the nonlinear system equations using the first-order Taylor's formula to linearize the original system. Then, the linear system is filtered using the standard Kalman filtering algorithm. Combined with the second-order RC equivalent circuit model selected in this paper, the state-space equations are obtained as follows [12]:  
\[\left[\begin{array}{c}U_{1,k+1}\\ U_{2,k+1}\\ SOC_{k+1}\end{array}\right]=\left[\begin{array}{ccc}\exp(-\frac{\Delta t}{C _{p1}*R_{p1}})&0&0\\ 0&\exp(-\frac{\Delta t}{C_{p2}*R_{p2}})&0\\ 0&0&1\end{array}\right]*\left[\begin{array}{c}U_{1,k}\\ U_{2,k}\\ SOC_{k}\end{array}\right]+\left[\begin{array}{c}R_{p1}&*[1-\exp(-\frac{ \Delta t}{C_{p1}*R_{p1}})]\\ R_{p2}&*[1-\exp(-\frac{\Delta t}{C_{p2}*R_{p2}})]\\ \eta^{*}\Delta t/c_{0}\end{array}\right]*I_{k}+w_{k} \tag{8}\]  
The observation equation is as follows [12]:  
\[U_{k}=h(x_{k},u_{k})+v_{k}=U_{oc}+\begin{bmatrix}1&1&0\end{bmatrix}*x_{k}+I_{k }*R_{0}+v_{k} \tag{9}\]  
The state variables are as follows [12]:  
\[x_{k}=\left[\begin{array}{c}U_{1,k}\\ U_{2,k}\\ SOC_{k}\end{array}\right] \tag{10}\]  
The coefficient matrices \(Ak\), \(Bk\), and \(Ck\) are obtained by taking partial derivatives of the state equation and the observation equation, as follows [12]:  
\[A_{k}=\frac{\partial f(x_{k},u_{k})}{\partial x_{k}}=\left[\begin{array}{ ccc}\exp(-\frac{\Delta t}{C_{p1}*R_{p1}})&0&0\\ 0&\exp(-\frac{\Delta t}{C_{p2}*R_{p2}})&0\\ 0&0&1\end{array}\right] \tag{11}\]  
\[B_{k}=\left[\begin{array}{c}R_{p1}*[1-\exp(-\frac{\Delta t}{C_{p1}*R_{p1}}) ]\\ R_{p2}*[1-\exp(-\frac{\Delta t}{C_{p2}*R_{p2}})]\\ \eta*\Delta t/c_{0}\end{array}\right] \tag{12}\]  
\[C_{k}=\frac{\partial h(x_{k},u_{k})}{\partial x_{k}}=[1\;1\;\frac{\partial U _{oc}}{\partial SOC}|_{SOC_{k-1}}] \tag{13}\]  
The recurrence formula is as follows [12]:  
\[\left\{\begin{array}{l}x_{k|k}=A(k-1)x_{k|k-1}+B(k-1)u(k-1)\\ P_{k|k}=A(k-1)P_{k|k-1}+Q(k-1)\\ L(k)=C^{T}(R)P_{k|k-1}[C(k)P_{k|k-1}C^{T}(k)+R(k)]^{-1}\\ x_{k+1|k}=x_{k|k}+L(k)[y(k)-C(k)x_{k|k}-D(k)u(k)]\\ P_{k+1|k}=[I-L(k)C(k)]P_{k|k}\end{array}\right. \tag{14}\]
# Ultrasonic Surface Rolling Process: Properties, Characterization, and Applications
## 3 Microstructural Development during USRP
### Microstructural Changes in Non-Ferrous Based Materials Subjected to USRP
#### 3.2.2 Ni Alloys  
Ni-based alloys have been subjected to modifications using USRP. [PERSON] et al. [62] have explored the effect of USRP on the hot compression behavior of Inconel 718 superalloy. The authors observed an ultra-fine microstructure near the surface region of the specimen due to USRP. The ultrasonic strikes generated dislocations. Through striking dislocation walls in succession, dislocation entanglement was created through dislocation propagation, recombination, and annihilation. With time, this led to the creation of randomly oriented, ultra-fine grains. [PERSON] et al. [63] generated nanograins on the top surface of the Ni alloy 690 (Ni 61 wt.%, Cr 30 wt.%, Fe 9 wt.%) using USRP. At a depth of 1 \(\upmu\)m, randomly oriented grains of size less than 100 nm were observed. With the increase in USRP, more dislocations were activated and were blocked by the grain boundary. [PERSON] et al. [64] investigated the effect of USRP on the microstructure change in inconel 690 alloys. They used a vibration frequency of 20 kHz and static force of 350 N. They noted that during USRP, the surface grains were deformed along the radial direction and were refined. They also observed that about 23 % of the top grains were deformed, and in about 51 % of the grains, substructures were observed. [PERSON] et al. [65] explored microstructure changes in Inconel 690 TT alloy after USRP. They used a WC-Co carbide ball of 20 mm to strike at a frequency of 28 kHz. The authors observed a GNS layer near the top surface, consisting of nanoscale lamellar grains of 47 nm and equiaxed nanocrystalline grains of a mean diameter of 40 nm. The boundaries of sub-crystals were fuzzy. They noted that USRP gradually generates dislocations that initially generate small angle grain boundaries and, later, high angle grain boundaries. Annihilation and recrystallization of grain boundaries occur continuously, generating a refined structure at the top and the extent of refinement is reduced along with the depth. [PERSON] et al. [66] explored the effect of rolling reduction during USRP on inconel 690 TT. They noted that as the amount of rolling reduction increases, the extent of deformation increases, and when rolling reduction is high, cracks even appear on the surface.