esa-sceva/satcom-chunk-collection huggingface dataset:
# New Hybrid Techniques for Business Recommender Systems
## 4 Interview Findings: Case Structure and Similarity Measure  
As mentioned in Section 3.1, we performed two rounds of interviews with consultants to understand their current work and knowledge processing procedures. Here, we summarise the findings from both rounds of interviews (see also [3] for more details on the first round):  
* Customers often come to the meetings with some important KPIs and dimensions (i.e., solution elements) already in mind. However, the degree to which customers have initial ideas can vary greatly. We have reflected this variance by creating queries at different verbosity levels.
* In terms of company demographics, consultants consider the industry of a customer as the main criterion for finding similar past cases. Further relevant variables that we elicited were the target group of the solution (e.g., only management or all employees) and the goal of the BI project (expressed in natural language). Finally, consultants use all known customer preferences from initial meetings (see above), i.e., any already known solution elements to remember past cases with similar elements.  
The business process was also mentioned by consultants as an important variable. Because of its importance, we chose not to use it simply as a ranking criterion for the retrieval of similar cases, but as a filter. For a given company, we created separate cases for each business process the company wanted to analyse and retrieved only cases with the same business process. (Analogously, we built separate case base graphs for the graph recommender, see below.)
* In the second round of interviews, we asked the consultants to quantify the relative importance of these types of attributes in terms of percentage weight. This task can be difficult as the weights identified by different consultants can vary. However, since the relative importance of the attributes is based on domain-knowledge and not on personal preferences of the consultants, we were quickly able to arrive at a consensus about the weights. Although quantifying something as abstract as a variable's contribution to a similarity score is a hard task, we were able to verify in some preliminary experiments that the chosen weights gave quite good results compared to other potential weight configurations. The resulting weights are shown in Table 1.
* When talking to a customer from an unknown industry, consultants tried to remember cases of customers from _similar_ industries. Since our attempts to use an industry taxonomy for improved similarity assessment in a graph-based recommender were not particularly successful, we did not consider this kind of reasoning in this work. However, we did use the industry taxonomy to define a local similarity measure for industries within a CBR recommender (see Section 5.3).
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 2 Proposed Greedy Randomized Adaptive Search Procedure for TSP-D
### Representative Solution and Supportive Data Structures  
The candidate solution, \(tspd\), is represented using an array that contains both truck and drone deliveries. The first and last indices refer to the depot. The elements in the array are ordered by the visit sequence. Each element in the array consists of a node \(id\) and node \(label\) (\(t\): truck node, \(d\): drone node, and \(c\): combined node, where the location is visited by both the truck and drone). An example of a solution is shown in Figure 2, where Figure 2a illustrates the representative solution of the actual graph presented in Figure 2b. As part of the algorithm's initialization, the node IDs are initialized with the solution of the TSP tour. The node labels are initialized to \(t\).  
The locations of the customer and the depot are stored in a location matrix, \(L\). The column index represents the node \(id\). The first row contains the x-coordinates, and the second row contains the y-coordinates. The values of the x and y-coordinates are read from the input data set.  
In addition, two cost (adjacency) matrices are used to specify the cost over time (seconds): a drone cost matrix (\(C_{D}\)) and a truck cost matrix (\(C_{T}\)). Two cost matrices are used, as the drone and truck differ in terms of speed (\(s_{D}\) and \(s_{T}\), respectively). The drone and truck speeds are read as inputs from the dataset. First, the Euclidean distance \(d_{ij}\) between nodes \(i\) and \(j\) for each \(i\); \(j\) in \(L\) is computed using the x and y-coordinates. Following that, \(C_{D_{ij}}\) and \(C_{T_{ij}}\) are respectively computed using the following equations: \(C_{D_{ij}}=\frac{d_{ij}}{s_{D}}\) and \(C_{T_{ij}}=\frac{d_{ij}}{s_{T}}\).
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 3 Method
### Overall Architecture  
Figure 1 illustrates the architecture of MCAG. Unlike BiSeNetV2 [23] and CCNet [21] which adopt single-path architectures, we propose a three-path architecture to explore a robust semantic segmentation network with multi-path convolutional attention. Specifically, we devise a four-stage, three-path structure in which the feature maps are generated with different scales and channels starting from the second stage. Our proposed structure better utilizes global and boundary information while maintaining a lower number of parameters compared to the Transformer architecture, allowing for improved learning of high-level semantic information. The central main path (MP) serves as the primary route and incorporates the MSCAN structure. The image resolution is successively reduced to \(1/4\), \(1/8\), \(1/16\), and \(1/32\) of the original resolution across the four stages, with an increasing number of channels. Each stage is composed of a convolutional attention structure and introduces a spatial and channel reconfiguration module to enhance representative features and suppress redundant spatial features. The MP is responsible for parsing long-range dependencies, as detailed in Section 3.2.  
The results of the first stage of the MP are fed into two subsidiary paths: the detail path (DP) and the boundary path (BP). The DP maintains the same high resolution across all stages, emphasizing the extraction of detailed features. Starting from the second stage, guided by the MP, the DP selectively learns high-level semantic information. The BP, on the other hand, fuses with the lower-resolution MP at each stage, further integrating global information and focusing on boundary details while maintaining the same high resolution across all stages. Both paths utilize convolution-based feature extraction, with the resolution  
Figure 1: The encoder network architecture of MCAG. AGFM stands for attention-guided fusion module; PFM stands for pyramid pooling module; and MP, DP and BP stand for main path, detail path and boundary path respectively.  
and number of channels remaining constant, but differing in how they leverage guidance from the MP, as elaborated in Section 3.3.  
After completing the fourth stage, a fusion module called the attention-guided fusion module (AGFM) is employed to merge the results from the three paths, producing the final result of the encoder section. For the decoder part, we adopt the lightweight Hamburger model [9] to generate improved segmentation results, as detailed in Section 3.4.
# Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia
## 3 Results
### Spatial and Temporal Variation in Vegetation's CUE and WUE  
Analyzing the trajectory of NPP and GPP can help improve one's understanding of the factors that contribute to changes in CUE. This is because both NPP and GPP have an influence on CUE. The combined effects of these two factors caused CUE to decrease at a rate of 0.1% per year between the years 2001 and 2020 in Inner Mongolia, even though both NPP and GPP showed statistically significant upward trends of growth (\(p<0.05\)) throughout this same time period (Figure 5a). Only a small portion of the area (38.5%) passed the significance test (\(p<0.05\)) (Figure 6b); only 10.58% of the areas showed a decreasing trend, primarily near Mawususu Sands and Hulun Lake. The rate of spatial variation of CUE in Inner Mongolia ranged from \(-0.139\) to 0.074 (Figure 6a), with the area showing an increasing trend accounting for 89.42% of the vegetation cover in the study area. The WUE in Inner Mongolia decreased at a rate of 0.008 g C\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) from 2001 to 2020, but the overall change was not significant (\(p>0.05\)). This indicates that the interannual trend of WUE was relatively stable during the study period, which was jointly determined by the significant growth in ET and GPP (Figure 5b). The geographical variability of WUE in Inner Mongolia ranged from \(-0.789\) to 0.235 g C\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) and varied slightly (Figure 6c). Among the areas, 43.21% and 56.79% of the vegetation cover in the study area had increases and decreases in WUE, but only 15.2% of the area passed the significance test (\(p<0.05\)), of which 7.63% of the area had significant decreases, mainly in the typical grassland in the northeastern part of Xilinguole League; 7.57% of the area showed significant increases, distributed in the Erdos region and in the Maowususu Sands of Inner Mongolia (Figure 6d).  
Figure 4: Spatial distribution characteristics of CUE (**a**) and WUE (**b**), and the mean values of CUE (**c**) and WUE (**d**) in different vegetation types in Inner Mongolia from 2001 to 2020.
# Integrated Biomimetics: Natural Innovations for Urban Design, Smart Technologies, and Human Health
## 3 Biomimetics and Artificial Intelligence
### Combined Applications of Biomimetics and AI
#### 3.2.2 Computer Vision Systems in Smart Cities  
Computer vision systems play a key role in the development of smart cities, optimising autonomous navigation and urban monitoring by implementing biologically inspired algorithms and advanced neural networks. These technologies improve robotic orientation in complex environments and enable the real-time analysis of environmental factors, strengthening urban management and sustainability.  
Autonomous navigation systems in smart cities draw directly on natural orientation strategies to enhance robotic efficiency and adaptability: for instance, algorithms modelled on desert-ant navigation allow mobile robots to orient themselves without GPS by integrating visual and motion cues [54], while hippocampus-inspired global localisation techniques employ LiDAR sensors to map and navigate unknown spaces with precision [55]. Concurrently, neural networks underpin urban monitoring by processing vast sensor data streams to identify pollutants in real time, thus aiding pollution mitigation [37], and by forecasting particulate-matter concentrations to support air-quality management and protect public health [56].
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 2 Optical Packaging
### Laser-to-PIC Integration  
For many sensing applications in photonics, it is desirable to locally generate either a continuous wave or modulated light signal on the Si-PIC. Since there is no monolithically integrable laser-diode available for CMOS, this necessitates either (i) the heterogeneous integration of III-V material on the Si-PIC; or (ii) the hybrid integration of a III-V device on the Si-PIC.  
Heterogeneous integration refers to the bonding of III-V material with optical gain onto the Si-PIC (either directly or with an intermediate polymer adhesive layer), followed by the etching of material to create a cavity condition for lasing. The cavity can be formed between two etched facets, two Bragg reflectors, or with a micro-ring, depending on the application [37; 38]. The resulting laser emission is then evanescently coupled into the on-PIC waveguide. Heterogeneous integration can be regarded as a post-processing step, rather than photonic packaging, and is often used to create semiconductor amplifiers (SOAs), which provide optical gain to offset the insertion-loses and waveguide losses on the Si-PIC [39]--see Figure 8. The optical gain from these integrated SOAs has a strong (Arrhenius) thermal dependence, and so requires temperature stabilization for stable performance.  
Figure 7: Evanescent coupling from an optical-chip to a Si-PIC; (**a**) Schematic of the evanescent coupling scheme, showing the inverted-taper on the Si-PIC and the wave-guiding region of the optical-chip (high-index polymer core surrounded by a low-index polymer cladding); (**b**) Mode propagation and 3D-FDTD (finite difference time domain) simulations illustrate the transfer of light between the optical-chip and Si-PIC.  
Hybrid integration involves the coupling of light from a discrete III-V laser device onto the Si-PIC, either directly or by using a \(\upmu\)Opto-Electro-Mechanical (\(\upmu\)OEM) stage. Hybrid integration schemes tend to have lower integration densities than heterogeneous integration schemes, but have the advantage of deploying only 'known good devices', which leads to higher yields and tighter performance profiles. Two promising hybrid laser integration schemes are the micro-optical bench, and direct vertical cavity surface emitting laser (VCSEL) integration.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Five-Step Interactive Quality Control Based on Knowledge  
#### 3.7.1 Step One: Interactive Quality Control Based on Geographical Rules  
The geographical rules, including the single-temporal rules and the multi-temporal geographical rules (list in Table 3), were used for inspecting the obvious errors in preliminary classification results. If the assigned land cover class of an object broke these rules, it would be revised by an automatic or manual method. The single-temporal rules were mainly concerned with the relationships between the land cover classes and the relevant geographical environment. For example, open waters usually reside in relatively flat or low relief areas. The multi-temporal rules paid more attention to those land cover classes which changed frequently with seasons. For instance, if an object was assigned as deciduous vegetation, but its spectral signatures did not demonstrate an obvious change within a year, this object was obviously misclassified and needed further revision.
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
###### Abstract  
In this paper, we describe how researchers and weather forecasters work together to make satellite sounding data sets more useful in severe weather forecasting applications through participation in National Oceanic and Atmospheric Administration (NOAA)'s Hazardous Weather Testbed (HWT) and JPSS Proving Ground and Risk Reduction (PGRR) program. The HWT provides a forum for collaboration to improve products ahead of widespread operational deployment. We found that the utilization of the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) soundings was improved when the product developer and forecaster directly communicated to overcome misunderstandings and to refine user requirements. Here we share our adaptive strategy for (1) assessing when and where NUCAPS soundings improved operational forecasts by using real, convective case studies and (2) working to increase NUCAPS utilization by improving existing products through direct, face-to-face interaction. Our goal is to discuss the lessons we learned and to share both our successes and challenges working with the weather forecasting community in designing, refining, and promoting novel products. We foresee that our experience in the NUCAPS product development life cycle may be relevant to other communities who can then build on these strategies to transition their products from research to operations (and operations back to research) within the satellite meteorological community.  
s 2020 12 886 10.3390/rs12050886 10 February 2020; Accepted: 5 March 2020; Published: 10 March 2020
###### Abstract
However, these efforts largely ignored yielding mechanisms, which introduce additional complexities, such as strain-dependent stiffness degradation, frictional energy dissipation, and post-yield load redistribution [10; 30]. Recent advancements in constitutive modeling--including the integration of nonlinear coupling springs, strain-softening criteria, and anisotropic damage laws--have enhanced the fidelity of bolt simulations [31; 32]. For example, [PERSON] et al. [9] simulated yielding bolt performance under cyclic loading, highlighting the influence of bolt geometry on energy absorption. Despite these strides, comprehensive numerical investigations integrating pull-out, shear, and bending analyses for yielding bolts remain scarce [33; 34]. This gap hinders the optimization of critical design parameters, such as bolt length, spacing, and yielding element configuration, which directly govern support effectiveness and economic viability [35; 36; 37]. To address these issues, the pull-out tests of conventional bolts and previously developed yielding bolts were simulated using FLAC3D. The detailed axial force distribution and its dynamic evolution under pull-out conditions for both bolt types were investigated. Additionally, a comprehensive analysis of the shear resistance of the bolts was conducted. Furthermore, a comparative study was undertaken to evaluate the support efficacy of conventional bolts against yielding bolts within the context of soft rock tunnel environments. The findings elucidate the mechanical behavior of yielding bolts under combined loading regimes, and this work also bridges critical gaps in the understanding of energy-absorbing support systems. A comprehensive numerical simulation of the pull-out, shear, and bending analysis of yielding bolts was performed. A distinctive spindle-shaped axial force distribution in yielding bolts during plastic deformation was revealed, contrasting with the hourglass-shaped pattern in conventional bolts. This mechanistic insight explains the superior elongation capacity of yielding bolts.
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 1 Introduction
### Microgrids  
A microgrid is a small electrical network that includes loads, sources, and possibly storage, and can trade power with the larger electrical grid or, at times, operate in islanded mode. For example, a microgrid installation at the University of California San Diego can generate more than 90% of the electricity used on campus annually with wind and solar power, saving more than $800,000 per month [3]. There are several areas of microgrid research that support the overall goal of maximizingthe usage of locally produced (often renewable) energy. A microgrid with significant renewable components requires careful frequency and voltage control, AC/DC conversion, and other electrical considerations [4]. Optimal power point tracking can be applied to get the most energy transfer possible from a turbine or PV system [5]. Correctly sizing the renewable components for a given load is another microgrid research topic. For example, researchers have studied the problem of sizing a hybrid solar/wind system with battery storage as a multiobjective optimization, using a genetic algorithm (GA) to minimize both annual system cost as well as the probability of losing power [6]. Researchers have also considered optimizing the control algorithms for island microgrid operations, with management of battery storage and diesel generation using forecasting [7]. Previous work by the authors reported on studies with a control scheme for a microgrid with battery and thermal storage, and a methodology for sizing these components [8; 9].  
If microgrids can trade energy with one another, this provides another opportunity for improving the overall renewable penetration as well as the value of each microgrid. In this case, when one microgrid has excess energy it has the option to transfer this energy to microgrids with an energy deficit. Blockchain technology for automatically tracking such transactions has recently been developed [10]. A microgrid network in Brooklyn, New York applies this technology, allowing members to efficiently manage and record energy transactions within the local network [11]. A summary of case studies involving peer-to-peer energy sharing is provided by [12]. The concept of optimizing the overall performance of a network of microgrids is relatively new in the literature. A framework for such a network can be studied as a convex optimization problem combining transactions and distributed energy production costs, using algorithms such as alternating direction method of multipliers (ADMM) to maximize renewable penetration while minimizing costs [13]. Other researchers have created an energy sharing structure to integrate prosumers into an energy sharing zone with an internal pricing model based on supply and demand ratio for renewable energy [14]. A model predictive control (MPC) framework is applied to a microgrid network to optimize the scheduling of power exchange between members as well as control local energy storage systems using forecasts of load, supply, and prices [15].
# Study of Copper/Aluminum Bimetallic Tube Rotary Ring Spinning Composite Forming Characteristics
## 4 Results and Discussion  
### Analysis of the Force in the Process of Rotary Ring Spinning  
Because of the wide discrepancy in the mechanical characteristics of the basic and cover tube materials, the deformation of the metal in the spinning area during the rotary ring spinning process is complex, which results in different forces on the basic and cover tubes. In addition, the presence of friction between the two tubes causes a different stress distribution in their spinning area directly below the downward pressure of the rotary ring. Figure 3 shows the distribution of the equivalent stress in the rotary ring spinning process of a bimetallic tube.  
From Figure 3, it can be seen that, in terms of magnitude, high stresses are generated in the downward pressure area of the rotating ring, and the difference in the largest equivalent stress of the basic tube and the cover tube is large; in terms of area, the largest equivalent stress between the basic tube is greater in the stress area. In the rotary ring spinning process, the cover tube only contacts the rolled part of the rotary ring and the contact area is limited, while the outside surface of the basic tube contacts the inside surface of the cover tube and the contact area is larger; thus, the area of maximum equivalent stress generated by the basic tube is also larger.  
Figure 4 shows the distribution of radial equivalent stress during the rotary ring spinning of a bimetallic tube. It can be seen that in terms of stress magnitude and stress area, both show a similar pattern to the distribution of equivalent stress, with the basic tube being subjected to dispersed stress and the cover tube being subjected to concentrated stress; there is less variation in the distribution in the radial direction, as the size of the tubes is only \(1\) mm in the radial direction.  
Figure 3: Equivalent stress distribution during rotary ring spinning of bimetallic tube.  
Figure 2: Finite element model of bimetallic tube rotary ring spinning.  
During the forming process, the boundary between the two tubes has a material discontinuity, and a pair of forces is applied to the outer surface of the basic tube and the inner surface of the cover tube, which results in different stresses on the tubes due to the different areas of forces on them. Therefore, the cover tube is subjected to much greater stress than the basic tube, as shown in Figures 3 and 4. In order to make the distortion of the two tubes more coordinated during the rotary ring spinning, the yield strength of the basic tube should be lower than that of the cover tube, otherwise the basic tube may not enter the plastic deformation stage during the plastic deformation of the cover tube, which would lead to uncoordinated deformation of both tubes, and then to the inability to form an effective interface strength.