esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 3 Optically Interconnected Multi-Chip Machine-Learning System
### Connection to Each Machine-Learning Accelerator  
Figure 3 shows a cost-effective and technically feasible design of optical connection to machine-learning accelerators. Obviously, directly interconnecting small DaDianNao chips (about 8 \(\times\) 8 mm\({}^{2}\)) with optical cables is infeasible and very costly. Therefore, we propose putting several chips into a package by 2.5D integration with a silicon optical interposer and interconnecting multiple packages with off-chip (off-package) interconnects. As shown on the left hand side of Figure 3, we reduced the size of link drivers in an electrical PHY (2.5 mm\({}^{2}\) per PHY, an estimation with reference to [17]) by half to match reduced output loading (reaching only to the silicon interposer) and added a trans-impedance amplifier (a purely electrical component in the optical receiver, Rx) on PHY. We changed I/O pads in PHY to bump metal pads to attach the chip on the silicon interposer through \(\mu\)-bumps by flip-chip bonding [13; 14] and moved PHYs from the edges toward the center of the chip for stable bonding. The right side of Figure 3 shows the inside of a package including four DaDianNao chips optically connected in 2D-mesh topology. Because the laser diode (in the optical transceiver, Tx), modulator driver (Tx), modulator (Tx), photodetector (Rx), and waveguide can be directly implemented on the silicon interposer [12], we reasonably sized and placed them on a 25 \(\times\) 25 mm\({}^{2}\) silicon optical interposer with reference to [12]. For example, both we and [12] use a single laser diode for four waveguides and its size is estimated from the photograph of their silicon optical interposer. To mitigate thermal issues, we place the laser diodes outside the area below the accelerator chips. Consequently, two adjacent chips are bidirectionally interconnected. In each direction, a bandwidth of 100 Gbps is provided through four 25 Gbps waveguides or two 50 Gbps waveguides (waveguide pitch can be under 100 um [14]). As we discussed in Section 2.3, a bandwidth of 400 Gbps through four 100 Gbps waveguides (or wavelengths) can also be provided. Outgoing (or incoming) waveguides across the interposer boundary are switched to off-chip (off-package) optical fibers by waveguide-to-fiber connectors [14].
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Bose Multi-Chaotic Public Key Algorithm (BMPKC)  
A public key encryption algorithm based on the multi-chaos system (\(\widetilde{m}\)-CS) [27] is divided into the following basic steps. For ease of reading, let us assume \(\widetilde{m}\) = 2, so \(\widetilde{m}\)-CS is C-CS (a couple of chaotic systems) in this case. Assume there are two different one-dimensional chaotic maps \(F_{1}(x_{1},p_{1})\) and \(F_{2}(x_{2},p_{2})\), such that \(x_{1}(i+1)=F_{1}(x_{1}(i),p_{1})\), \(x_{2}(i+1)=F_{2}(x_{2}(i),p_{2})\), where \(p_{1},p_{2}\) are control parameters, \(x_{1}(0),x_{2}(0)\) are initial conditions, and \(\{x_{1}(i)\}\),\(\{x_{2}(i)\}\) denote the two chaotic orbits.  
Step 1. [PERSON] and [PERSON] determine the linear transformations \(f_{1}(x)\) and \(f_{2}(x)\), chaotic mappings \(F_{1}(x_{1},\ p_{1})\) and \(F_{2}(x_{2},\ p_{2})\), and initial vector \(x_{0}\) through public negotiation \(x_{1}(i+1)=F_{1}(x_{1}(i),p_{1}),x_{2}(i+1)=F_{2}(x_{2}(i),p_{2})\), where \(p_{1},p_{2}\) are control parameters, \(x_{1}(0),x_{2}(0)\) are initial conditions, and \(\{x_{1}(i),x_{2}(i)\}\) denote the two chaotic orbits.  
Step 2. [PERSON] randomly selects key seeds \(s_{A1}\) and \(s_{A2}\) and a large number \(n_{A}\in[0,\ N]\), inputs these parameters into a pseudo-random sequence generator, outputs a \(\{0,\ 1\}\) sequence \(k_{iA}\) of length \(n_{A}\), iterates \(x_{0}\) for \(n_{A}\) times based on the content of \(k_{iA}\) to obtain \(x_{nA}\), and passes \(x_{nA}\) as the public key to [PERSON],  
\[g(x_{1}(i),x_{2}(i),\ldots,x_{\widetilde{m}}(i))=\left\{\begin{array}{c}1, \text{ if }x_{1}>x_{2},\ x_{1}>x_{3},\ \cdots,x_{1}>x_{\widetilde{m}},\\ 2,\text{ if }x_{2}>x_{1},\ x_{2}>x_{3},\ \cdots,x_{2}>x_{\widetilde{m}},\\ \vdots\\ r,\text{ if }x_{r}>x_{1},\ x_{r}>x_{2},\ \cdots,x_{r}>x_{\widetilde{m}},\\ \vdots\\ \widetilde{m},\ \ \text{ if }x_{\widetilde{m}}>x_{1},\ x_{\widetilde{m}}>x_{2},\ \cdots,x_{\widetilde{m}}>x_{\widetilde{m}-1}.\end{array}\right. \tag{13}\]
# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 4 The Utility-Based Ontology Recommendation System  
The utility-based recommender system is a type of knowledge-based recommendation system. It computes the utility of each item using a utility function and applies this function to generate a ranked list of recommended items [25]. Its advantage is that it avoids many of the problems of other recommender systems such as new users, new items, and data sparsity problems. However, it needs a significant number of user interactions to obtain a complete user preference function, which allow is to give accurate recommendations [26]. The Multi-Attribute Utility Theory (MAUT) is used to apply objective measures to help the decision-maker choose the alternative (item) that yields the greatest utility [27]. Attribute values and their weights are combined and aggregated into a multi-attribute utility function to assign a utility to each alternative, and the importance of each attribute is determined by a weight value. Finally, the alternative with the highest score is expected to be preferred by the user.  
Our utility-based ontology recommender system needs to calculate the utility of each candidate ontology and the utilities of their candidate classes to generate recommendations. It consists of four main components: (1) the ontology evaluation component, which receives information about the user's preferences and evaluates each candidate ontology from the ontology repository according to four evaluation criteria, i.e., ontology domain, popularity, coverage, and type; (2) the concept evaluation component, which checks each ontology for a class(es) that has a corresponding match with the user's input class and evaluates this class(es) based on its context semantic matching and the concept semantic richness; (3) the feedback management component which receives user feedback (i.e., selected ontology to reuse), interprets it as the implicit user preferences underlying his selections and interactions, and accordingly updates the user model; and (4) the ontology recommendation and ranking component, which receives the scores from the former components and determines the total ontology utility (\(TOU(O,c)\)) to calculate the final score for each ontology \(O\) along with its matching concept \(c\) and generate a ranked list of ontology recommendations. Next, we provide a detailed description of each component in our recommendation system.
## 2 Materials and Methods
### Activation Function  
The ReLU activation function is widely used due to its simplicity, efficient gradient computation, and ability to mitigate the vanishing gradient problem. However, a drawback is that neurons producing consistently negative outputs can become inactive during training. To address this limitation, the PReLU activation function introduces a parameter \(\alpha\), which allows negative outputs to take on small negative values rather than being zero [47]. \(\alpha\) adapts during training and can be unique to each weight or shared across multiple weights. In this case, \(\alpha\) is shared across each feature channel of every layer, efficiently increasing model complexity with a modest addition of parameters (around 3000), enhancing its ability to learn and represent complex patterns.
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 2 Principles of Mueller Matrix Analysis
Error analysis of a MM polarimeter carried out in [49] provided a basis for the calibration procedure in order to correct angular misalignments and compensate for retardance errors and, thus, obtain accurate values of the Mueller matrix elements. It is worth noting that the sample type--biological tissue, solid surface or aqueous suspension, etc.--determines the scheme for receiving light after interaction with the sample (S). Radiation scattered or reflected from the sample can be detected either directly or using a collecting and directing optical system. For example, weakly scattering samples can be placed in immersion cuvettes. In many cases, it is convenient to use standard spectroscopic plane-parallel cells, which require operation together with lenses. Lenses can be moved to focus light from different areas of the sample. The photodetector (PD) is used in conjunction with a signal acquisition system to interface with a computer to determine the MM based on digital signal processing algorithms. The block diagram (Figure 1) shows that the measurement of the Mueller matrix of an object requires the introduction of polarimetric blocks PSG and PSA into the optical system before and after the object. The next section shows general schemes of the most common optical instruments (scatterometers, OCT systems, transmission or fluorescence spectrometers, ellipsometers) used to study various types of bioorganic systems, which, when supplemented with polarimetric elements, enable the measurement of the complete Mueller matrix. Basic Schemes for Using Mueller Matrix Polarimetry in Optical Diagnostics of Biological and Bioorganic Systems  
Optical methods of non-destructive diagnostics can be divided into four groups. The first group includes methods based on measuring the angular distribution of light scattered by a sample (scatterometers) and the polarization features of the scattered light. The second group is imaging systems for densely structured objects, such as biological tissues, in particular those which detect backscattered light from a narrow layer in depth through low coherence interferometry; this is called optical coherence tomography (OCT). The third group includes spectrophotometers that measure the dependence of the transmission/absorption of light by a sample of the light wavelength, that is, spectral methods. The fourth group is devoted to fluorescence spectrometers separately. Considering the literary sources, the schematic features of all these groups and the conditions for their use are analyzed below. To these techniques, the general principle of modifying the optical design by surrounding the studied sample with polarimetric blocks (PSG and PSA) can be applied in order to expand measurement capabilities from light intensity alone to the complete Mueller matrix.
## 6 Conclusions  
The results are indicative of the quality of the maps drawn by the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF). In general, the following conclusions can be drawn:  
(1) Geomorphological factors, geological factors, geo-environmental factors, and anthropogenic factors were used for the development of the landslide model. The preliminary selection of these 16 conditioning factors was based on the multicollinearity diagnosis. The TOL and VIF values of all the conditioning factors indicated no multicollinearity.  
(2) According to the results of the WoE model, most occurred at slopes of 10-20\({}^{\circ}\) with the south aspect, elevations of 600-800 m, distance to rivers of <200 m, distance to roads of <300 m, and a farmland land cover category.  
(3) WoE-RF possessed relatively good accuracy when compared to the WoE-LR and WoE models. By using the ROC curve, the AUC values of the training dataset produced by these three methods were 0.802, 0.773, and 0.720, respectively. For the validation dataset, the AUC values were 0.782, 0.763, and 0.695, respectively. It can be concluded that the proposed hybrid models are promising approaches for the spatial prediction of landslides and can also be applied in other landslide-prone areas.  
[PERSON] and [PERSON] collected the field data and conducted the landslide mapping and analysis. [PERSON] and [PERSON] wrote the manuscript. [PERSON] provided critical comments in planning this paper and edited the manuscript. All authors discussed the results and edited the manuscript.  
This study was supported by the Opening Fund of Key Laboratory of Degraded and Unused Land Consolidation Engineering, the Ministry of Land and Resources (Grant No. SXDJ2018-04), the National Natural Science Foundation of China (Grant No. 41807192), the China Postdoctoral Science Foundation (Grant No. 2018T111084, 2017M613168), and the Project funded by the Shaanxi Province Postdoctoral Science Foundation (Grant No. 2017 BSHYDZZ07).  
No potential conflict of interest was reported by the authors.
# An Improved Self-Born Weighted Least Square Method for Cylindicity Error Evaluation
## 5 Experimental Verification and Literature Verification
### Analysis of Results  
The ISWLS was compared with the standard PSO, standard GA, and LS methods through experiments and literature validation. It was proved that ISWLS satisfies the definition. A more accurate cylindricity error evaluation result and a shorter running time were obtained. By inserting gross errors, it was verified that the ISWLS method is more resistant to gross errors than other algorithms, with more stable results and better robustness. The uncertainty of the cylindricity error was evaluated by the GUM method and the MCM method. The result of the GUM method was 0.002222 mm and the result of the MCM method was 0.002349 mm. The absolute deviations of \(d_{low}\) and \(d_{high}\) for each endpoint of the two included intervals were 0.0002 and 0.0001, which was less than the uncertainty value tolerance of 0.0005. The uncertainty results of the MCM method can verify the results of the GUM method, which proved the validity of the results and the robustness of the ISWLS method.
## 4 Results and Discussion
### Synthetic Workflows  
We firstly evaluated our approach against two complex synthetic data analysis workflows, where the full-parallel approach showed its limitations due to a high degree of parallelism. The dependencies in these workflows should be understood as execution constraints. For instance, clustering has to be performed before classification for adding labels to an unlabeled dataset, or a classification task is performed after the discovery of association rules for user classification purposes. The first test was carried out on a synthetic workflow with 42 nodes. Table 6 provides a detailed description of each task in the workflow, while their dependencies are shown in Figure 4.  
The first step is to predict the memory occupancy and execution time of each task of the workflow: the regression model was able to accurately estimate the peaks on storage and execution memory and the duration, as shown in Table 7.  
Figure 4: Task dependencies (Workflow 1). We firstly considered a configuration characterized by 14 GB available for running the workflow, which was used up to 60% due to the Spark unified memory model. Table 8 shows an execution example with the IIWM, focusing on its main steps: (i) the scheduling of tasks based on their decreasing memory weight; (ii) the allocation of a new stage; (iii) the exploitation of the estimated execution time while computing the marginal increase. This last aspect can be clearly observed in Iteration 17, where task \(t_{17}\) is assigned to stage \(s_{7}\)
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
## 3 Results
### Characteristics of Coastline Changes
#### 3.1.1 Variation of Coastline Length
The other artificial boundary lines in Zhifu, Mouping, and Penglai decreased the most, by 1.32, 0.74, and 0.58 km, respectively. The percentage of reduction was 75.00%, 31.62%, and 9.98%. The length of each boundary line in Zhaoyuan and Laishan changed slightly, among which the other artificial boundary lines in Zhaoyuan changed the most with a decrease of 0.07 km, accounting for 0.57% of the total change of the boundary line. The boundary line of the estuary in Laishan changed the most, and it also decreased by 0.07 km, accounting for 7.53% (Tables 4 and 5)
## 3 Simulation Model and Simulation Methods  
### Finite Element Model  
Based on the actual dimensions of the aircraft brake disc, a full-scale 1:1 three-dim-ensional model was created in SolidWorks v2021. The model consists of five rotating discs and four stationary discs stacked alternately, ensuring high detail fidelity and dimensional accuracy. During the braking process, the rotational structure of the brake discs disturbs the airflow within the limited volume. Therefore, a simplified model using a rectangular fluid domain as a virtual wind tunnel was adopted. The length of the rectangular fluid domain is approximately 2.6 times the outer diameter of the brake disc, measuring 900 mm; the height is approximately 1.8 times the outer diameter, measuring 600 mm; and the width is 540 mm. The right side of the rectangular domain serves as the air inlet, and the left side as the air outlet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(t\) (s) & \(v\) (m/s) & \(\omega\) (rad/s) & \(q\) (W/m\({}^{2}\)) \\ \hline
0 & 36.7 & 104.86 & 80,090,208.97 \\
2.5 & 33.03 & 94.37 & 72,081,188.07 \\
5 & 29.36 & 83.89 & 64,072,167.18 \\
7.5 & 25.69 & 73.40 & 56,063,146.28 \\
10 & 22.02 & 62.91 & 48,054,125.38 \\
12.5 & 18.35 & 52.43 & 40,045,104.48 \\
15 & 14.68 & 41.94 & 32,036,083.59 \\
17.5 & 11.01 & 31.46 & 24,027,062.69 \\
20 & 7.34 & 20.97 & 16,018,041.79 \\
22.5 & 3.67 & 10.49 & 8,009,020.90 \\
25 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The heat flux density changing with time.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{Parameters}\) & \(\mathbf{Value}\) \\ \hline Initial speed \(v_{0}\) (m/s) & 36.7 \\ Braking distance \(s\) (m) & 458.75 \\ Acceleration \(\alpha\) (m/s\({}^{2}\)) & \(-1.468\) \\ Gravitational acceleration \(g\) (m/s\({}^{2}\)) & 9.8 \\ Braking time \(t\) (s) & 25 \\ Number of brake discs \(n\) & 5 \\ Aircraft wheel radius \(R_{w}\) (mm) & 350 \\ Friction coefficient \(f\) & 0.3 \\ Contact area between discs \(A_{P}\) (m\({}^{2}\)) & 0.2013 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some parameters of aircrafts under braking conditions.  
To visually observe the airflow disturbance caused by the brake disc, a cylindrical fluid domain with a diameter of 400 mm was created around the brake disc. To analyze the flow field behind the brake disc, the disc was positioned at one-quarter of the length of the fluid domain. The centre height of the brake disc was set to 300 mm, matching the sum of the aircraft wheel radius and the rail surface height, and the disc was placed in the centre of the width direction. Different fluid and solid domains were then defined, completing the construction of the brake disc computational model. The full-scale finite element model of the brake disc and the cylindrical fluid domain are shown in Figure 3.
## 3 Technology Modules
### Fabrication of the Silicon Nano-Ridge
For our final device, an \(n\)-type \(<\)100\(>\)-oriented silicon-on-insulator wafer will be used to define the silicon nano-ridge as illustrated in Figure 1. The LOCOS oxide needs to be relatively thick in order to enable a proper adjustment of the tip region of the nano-ridge. Therefore, if a thin oxide is desired as the insulator for the side-gates (for instance, to provide improved screening of electrically-active defects), the LOCOS oxide can be removed, and a thin, high-quality gate oxide can be deposited via remote plasma-enhanced chemical vapor deposition (CVD). In contrast to a second thermal oxidation, depositing an oxide preserves the geometry of the tip of the nano-ridge. As will be shown in the next section, our CVD-deposited oxide exhibits a quality level comparable to a thermally-grown oxide [45]. Subsequently, titanium nitride (TiN) is deposited via reactive sputtering with a thickness substantially larger than the depth of the TMAH-etched grooves. Chemical-mechanical polishing is then used to planarize the wafer to form self-aligned TiN side-gates, as depicted in Figure 3e. The columnar growthbehavior of reactively sputtered TiN leads to void formation where the (100) and (111) facets meet. This might be circumvented by changing sputter conditions or depositing the TiN layer conformally via atomic layer deposition (ALD). Note that in Figure 3e, slight over-polishing removed the tip-region of the nano-ridge; adding extra support structures in close vicinity to the silicon nano-ridge would be effective to avoid over-polishing. Next, a thin Al\({}_{2}\)O\({}_{3}\) layer will be deposited onto the planarized sample surface via ALD that serves as the gate dielectric for the array of top-gate electrodes. High-density top-gates aligned perpendicular with respect to the nano-ridge will then be fabricated, as discussed in more detail in Section 3.3.
## 3 Results and Discussion
### Sensitivity of the Mechanochromic Sensor and Prediction of Displacement
The high estimation performance of the deep learning algorithm highlights its ability to predict displacement in an end-to-end manner without requiring post-processing or manual judgement, while also achieving robust generalization through the comprehensive interpretation of six channel representations. As shown in Figure 9, ViT-s achieved an R\({}^{2}\) value of 0.997, successfully regressing the displacement values from the cropped sensor images, contrary to using the average intensity of each channel to regress the sensor displacement value. Thus, the ViT-s model demonstrated a high performance for displacement regression, allowing for a comprehensive interpretation. The ability of the ViT-s model to learn patch-wise importance within images and perform multidimensional feature extraction enables it to assign weights to the extracted features of critical patches across the six channels, including R, G, B, H, S, and V values. In this study, both RGB and HSV color spaces were simultaneously input into the model. While the RGB color space maintains the original image characteristics, the HSV color space provides a hue-based separation, making it less sensitive to lighting variations. By combining these two spaces, we not only eliminate the need for a separate three-stage separation process but also reinforce the overall robustness of our displacement prediction under different environmental conditions. Consequently, our method demonstrated high applicability as an end-to-end approach compared to previous studies.