esa-sceva/satcom-chunk-collection huggingface dataset:
# Lean Maturity Assessment in ETO Scenario
## 1 Introduction
### Engineer to Order (ETO)  
Lean manufacturing has been essentially developed in low-mix and high-volume repetitive push-based manufacturing industries (e.g., make to stock (MTS) [26] and assemble to order (ATO) [27]), yielding cost and lead time reduction, along with quality improvements. However, the increase of globalization, along with frequent recessions, has forced many organizations to reduce costs and to be more responsive to customer demands.  
As shown in Figure 2, by moving the customer order decoupling point (CODP) to an ETO scenario, the implementation of lean strategies grows more challenging. Additionally, it extends beyond manufacturing alone, into pull-based (not standardized) systems [28], low-volume/high-mix methods, and one of a kind or highly customized parts, e.g., the building, construction, machinery, shipbuilding, aerospace and railway industries.  
Figure 2: CODP, also known as freeze point or order penetration point. This refers to the point in the value chain at which a customer triggers a company’s activities [28]. All activities before the CODP share a forecast-driven, push-based approach (dotted lines/arrows). Activities after the CODP are characterized by a customer-driven, pull based approach (continuous lines/arrows).  
From mass production to mass customization, the growing degree of personalization in production has focused efforts on selling margins, rather than on volume. This results in efficiency loss, which is unacceptable in highly competitive global markets [29]. This is a contributing factor to the growing interest in lean methods and tools in ETO organizations [29; 30; 31]. However, some barriers in the implementation of lean practices in an ETO scenario remain [32].  
The purpose of this work was to define an assessment framework for the ETO scenario and provide an objective, absolute evaluation of the current lean maturity level. Additionally, the work purposed to monitor the evolution of lean methodologies within individual or multiple organizations.
## 2 Related Work
### Conventional Multi-Speaker Speech Recognition Methods
Speaker embedding vectors from the speaker encoder trained by the single-talker data in (c) were concatenated to the input of the split encoder of (d) to help splitting the mixed speech and inferring the encoder output to be closer to the single-speaker representations. The proposed method is computationally efficient, and permutation invariant training was not required because the speaker embedding vectors prevent permutation ambiguity. The implementation details of the proposed method are described in Section 3. Figure 2: Four types of the conventional multi-speaker automatic speech recognition methods. (**a**) A combination of acoustic source separation and single-input mixed speech and single output text (SISO) ASR; (**b**) a combination of single-input mixed speech and multiple-output text (SIMO) ASR; (**c**) the addition of speaker embedding vectors as an additional input to a SIMO ASR; and (**d**) the addition of an encoder that splits multiple speakers into multiple representations, with encoder outputs as speaker and text embedding vectors that are suited to SISO ASR decoders.
# Topology and Polarization of Optical Vortex Fields from Atomic Phased Arrays
###### Abstract  
We developed a theoretical formalism for the generation of optical vortices by phased arrays of atoms. Using the Jacobi-Anger expansion, we demonstrated the resulting field topology and determined the least number of individual atoms necessary for the generation of vortices with a given topological charge. Vector vortices were considered, taking into account both the spin and orbital angular momenta of electromagnetic fields. It was found for the vortex field that, in the far field limit, the spatial variation in spin-density matrix parameters--orientation and alignment--is independent of the distance to the radiation source.  
twisted light; polarization; optical angular momentum; atomic arrays; phased arrays +
Footnote †: journal: Applied Sciences
# Virtual Design and Construction (VDC) Framework: A Current Review, Update and Discussion
## 3 Virtual Design and Construction (VDC)
### Client/Business and Project Objectives  
The client and project objectives represent an essential part of the construction project since implementation revolves around these at the top of the VDC element hierarchy. The objectives can be linked to controllable factors, production metrics and project objectives [12].  
[PERSON] et al. [7] define client and project objectives, since they specify high-performance buildings as those that meet the client's objectives during use and operation and at hand-over from the design and construction team to the client. \"A high-performing building can only be achieved through a building with integrated building systems, which can only be produced through an integrated process, which depends on an integrated team with the right people, which needs integrated information, i.e., BIM+, to function effectively and efficiently. Simulation and visualization are the primary ways BIM+ informs the integrated team. Collaboration and co-location are the primary ways for the integrated team to inte  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Production Metrics from the Literature** \\ \hline
1 & Detailed schedule conformance \\
2 & Decision latency \\
3 & Meeting effectiveness \\
4 & Response latency \\
5 & Stakeholder involvement \\
6 & Detailed cost conformance \\
7 & Field-generated requests for information \\
8 & Rework volume \\
9 & Field material delivery \\
10 & Meeting efficiency \\
11 & Meeting agenda appropriateness \\
12 & BIM coordination consistency \\
13 & Budget estimate conformance \\
14 & Client satisfaction \\
15 & Safety \\
16 & Visualization \\
17 & Root causes for plan deviation \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of production metrics, adapted from [63].  
grate processes. Production management methods enable the integrated building system's productive design, fabrication and construction. Finally, outcome metrics define the performance of the building and validate the integrated building system. This is supported by the appropriate agreement or framework\" [7] (p. XXIV).  
How the client and project objectives are structured may affect their understanding and accomplishment. Figure 3 proposes a simple objective framework to describe an objective precisely. The simple objective framework starts with an \"auxiliary verb infinitive plus particip\" (whose combination represents a completed action) + \"what\" (which represents the noun of the objective) + \"where\" (where the objective is located) + \"how much or many\" (that represents the quantified goal of the objective). The conceptual stage (\"verb\" + \"what\") may represent the initial client's needs, while the technical stage specifies how the project should address the client's needs technically and precisely.  
Client objectives usually focus on obtaining a usable, operable and sustainable building. In contrast, project objectives are focused on a buildable building with special attention to the safety, quality, schedule, cost, social and environmental aspects.
## 2 Materials and Methods
### VIS/NIR Spectral Data Acquisition  
A portable spectrometer (ASD Feildspec 3, Analytica Spectra Devices Inc., Boulder, CO, USA) was applied in this experiment to acquire a sample's VIS/NIR reflection spectrum information. The wavelength range of this spectrometer is from 350 to 2500 nm. The sampling interval and spectral resolution of 350 to 1000 nm are 1.4 nm and 3 nm, respectively, and the sampling interval and spectral resolution of 1000 to 2500 nm are 2 nm and 10 nm, respectively. The spectrometer's vegetation probe is composed of a light source (with a wavelength coverage from 350 to 2500 nm) and a fibre-optics probe, which was used to acquire the orange sample's reflectance spectrum. When sampling, the vegetation probe was kept straight down and put in contact with the surface of the orange sample. The stem position, calyx position, equator position, and the position between equator and stem were sampled for each orange sample. Each sampling was repeated 10 times, with the average used as the sampling result. White board correction was conducted before each sampling.
# Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
## 4 Hand Effect on the Proposed Eight-Antenna Array  
This section will analyze the outcome of user hand interaction on the Antenna A array. The simulation is divided into single-hand mode (SHM) for data mode, and double-hand mode (DHM) for read mode. To make the simulation results more applicable to real-world scenarios, the relative permittivity and electric conductivity of the hand are referenced from Speag's product SHO-V3 RW-C/LW-C. The permittivity and conductivity values corresponding to different frequencies (between 3 and 6 GHz) are shown in [47].
## 3 Results and Discussion
### Effect of the Perovskite Absorption Layer (PAL) Thickness  
In a photovoltaic solar cell, the absorption layer absorbs light and creates a hole-electron pair. Thickness optimization is important because the amount of absorbed light can vary depending on the thickness of the absorption layer. In this study, such analysis was conducted in the PAL thickness range of 100-1000 nm. Figure 4 presents the current density-voltage (_J_-_V_) curves for various PAL thicknesses. In Figure 5, each output parameter value (i.e., \(\mathrm{V_{oc}}\), \(\mathrm{J_{sc}}\), FF, and PCE) is shown according to the thickness of the PAL.  
Figure 4: Current density–voltage (_J_–_V_) curves for various PAL thicknesses.  
Figure 3: Current density–voltage (_J_–_V_) curves for various ZnO thicknesses.  
As the PAL thickness increased, the V\({}_{\rm{oc}}\) and J\({}_{\rm{sc}}\) exhibited increasing behavior. However, the FF value decreased at PAL thicknesses of above 500 nm. This behavior was somewhat different from those of the V\({}_{\rm{oc}}\) and J\({}_{\rm{sc}}\). This can be determined from the relationship between the thickness of the absorption layer and resistance and is expressed as shown in Equations (7) and (8) [40].  
As the thickness of the absorption layer increased, the resistance of the absorption layer also increased, owing to an increase in the series resistance. This increased series resistance led to a decrease in the FF value, as given by Equation (9) [41; 42]. Therefore, the FF value was considered to decrease above a PAL thickness of 500 nm:  
\[{\rm{R}}_{\rm{absorber}}=\frac{\rho{\rm{t}}}{{\rm{A}}^{2}} \tag{7}\]  
\[{\rm{R}}_{\rm{series}}\cong\rho{\rm{t}}+\frac{{\rm{R}}_{\rm{s}}{\rm{L}}^{2}}{2} \tag{8}\]  
\[{\rm{FF}}_{\rm{series}}={\rm{FF}}_{0}(1-{\rm{r}}_{\rm{series}}) \tag{9}\]  
Here, we use the following:  
* \({\rm{R}}_{\rm{absorber}}\) = the absorber resistance;
* \(\rho\) = the absorber layer resistivity;
* \({\rm{t}}\) = the absorber layer's bulk region thickness;
* \({\rm{A}}\) = the base area;  
Figure 5: Effect of PAL thickness on (**a**) V\({}_{\rm{oc}}\), (**b**) J\({}_{\rm{sc}}\), (**c**) FF, and (**d**) PCE.  
\begin{tabular}{l l} \(\bullet\) & R\({}_{\text{series}}\) = the series resistance; \\ \(\bullet\) & L = length; \\ \(\bullet\) & R\({}_{\text{s}}\) = sheet resistance; \\ \(\bullet\) & FF\({}_{\text{series}}\) = the FF affected by the series resistance; \\ \(\bullet\) & F\({}_{0}\) = the initial FF; \\ \(\bullet\) & \(\tau_{\text{series}}\) = normalized series resistance (R\({}_{\text{series}}\) J\({}_{\text{sc}}\)/V\({}_{\text{oc}}\)). \\ \end{tabular}  
In the case of a PCE above 800 nm, it appeared to be saturated. At a PAL thickness of 1000 nm, the PCE showed the highest value of 19.7%. The more light was absorbed, the more hole-electron pairs which could be created, which increased each output parameter [43]. However, if the thickness was too thick, the carrier movement path and recombination current increased. Thus, it was necessary to select an appropriate thickness [44]. Accordingly, 1000 nm was selected as the optimal PAL thickness.
# Feature-Level Camera Style Transfer for Person Re-Identification
## 4 Using Transferred Feature for Person Re-ID
### Pre-Training Stage  
The pre-training stage is used to train two feature extractors, namely identity feature extractor \(E_{I}\) and camera style feature extractor \(E_{C}\). The training process is illustrated in Figure 2. ResNet50 [30] and ResNet18 pre-trained on ImageNet are used as the backbone network for \(E_{I}\) and \(E_{C}\), respectively. For both extractors, the last fully-connected (FC) layer in ResNet is replaced by a batch normalization (BN) layer with corresponding dimensions.  
Two extractors \(E_{I}\) and \(E_{C}\) are trained independently as two classification tasks, while person identity classifier \(C_{I}\) and camera style classifier \(C_{C}\) are introduced to calculate identity loss \(\mathcal{L}_{id}\) and camera style loss \(\mathcal{L}_{cam}\) as follows:  
\[\mathcal{L}_{id} =\mathbb{E}_{f_{i}^{c}\sim\mathcal{P}_{I}}[-\log C_{I}(E_{I}(x_{ i}|y_{i}))] \tag{9}\] \[\mathcal{L}_{cam} =\mathbb{E}_{f_{i}^{c}\sim\mathcal{P}_{C}}[-\log C_{C}(E_{C}(x_{ j}|c_{j}))] \tag{10}\]  
where \(x_{i}\) is the input source image with identity label \(y_{i}\), and \(x_{j}\) is the input target image with camera label \(c_{j}\). \(f_{i}^{l}=E_{I}(x_{i}|y_{i})\) is the extracted identity feature, and \(f_{j}^{C}=E_{C}(x_{j}|c_{j})\) is the extracted camera style feature, while \(\mathcal{P}_{I}\) and \(\mathcal{P}_{C}\) are the distributions of corresponding features.  
After finishing the pre-training stage, extractors \(E_{I}\) and \(E_{C}\) are trained to extract high-quality person identity features \(f^{l}\) and camera style features \(f^{C}\); these features are used as input in the generative model training stage to train the GAN for camera style transfer.  
Figure 2: In pre-training stage, feature extractors \(E_{I}\) and \(E_{C}\) are trained independently with identity classifier \(C_{I}\) and camera style classifier \(C_{C}\). Cross Entropy (CE) loss is chosen to be the loss function.
# An Improved CNN Model for Within-Project Software Defect Prediction
## 3 Approach
### Parsing Source Code  
Since we used source code as input, a suitable code representation form was deemed to be beneficial for parsing source code. Code representations include character-level, token-level, AST-node-level, tree-level, graph-level, path-level, among others. The AST-node-level code representations have proven to beat character-level, token-level, and representations of higher granularities in program classification tasks ([PERSON] et al., 2018), so we extracted AST nodes from Java source code.  
According to [PERSON] (2016), three types of AST nodes are extracted as tokens: (1) nodes of method invocations and class instance creations, which are represented as method names or class names; (2) declaration nodes such as method declarations, type declarations, and enum declarations, which are represented by their values; and (3) control-flow nodes including while statements, catch clauses, if statements, and throw statements, which are represented by their node types. We excluded certain node types including assignment and package declaration, because (1) information in the node could not be traced, as in package declaration; (2) there was such little information in the node that a huge amount of labeled data would have been needed to train such a model, as in assignment; (3) the frequency of the node was too low, as in EnhancedForControl. A full list of the selected 29 AST node types is listed in Figure 6.  
Figure 5: The overall workflow of a software defect prediction based on our improved CNN model.  
Figure 6: Selected abstract syntax tree (AST) node types.  
To parse source files into ASTs, we utilized a python tool called javalang. It enables parsing of Java 8, and it is based on the official Java language specification. Due to the limited functionality of javalang, several Java source files could not be parsed correctly, which may have hampered data preprocessing. We considered three strategies to solve the problem: (1) correct the source file grammar so that javalang could parse, (2) delete part of the source code that could not parse, (3) delete the file directly. We adopted the third strategy for simplicity.  
A motivating example of Java code for add calculation is shown in Figure 7. After parsing code by javalang and traversing the AST, a list including 39 elements was generated, which starts with CompilationUnit and ends with MemberReference. After selection of the 39 nodes, a refined list including 26 elements was generated, which starts with ClassDeclaration and ends with MemberReference. Some node types, such as CompilationUnit, Assignment and BinaryOperation were excluded according to the criteria listed above. Next, we replaced some of the node types with its names. For example, we replaced the fourth node ReferenceType with String to express detailed information. In the end, a list including 26 elements was generated, the first five elements of which were CommandParameter, main, args, String, and int.
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 2 Related Works  
Most researches of deployment of MMN focus on node deployment of UAN networks, i.e., sensors and/or surface gateways. [PERSON], [PERSON], and [PERSON] formulated the optimal gateway deployment problem as an Integer Linear Programming (ILP) problem in [10]. They propose an algorithm to deploy multiple surface-level gateways in [12], and use a greedy algorithm to select gateway positions among candidate locations. In these works, they mainly study sensor deployment under 2D space. In [18], [PERSON] and [PERSON] et al. proposed a 3D node deployment algorithm for underwater sensor networks. The proposed algorithm can achieve a large coverage area with the minimal number of nodes. In [19], [PERSON], [PERSON], and [PERSON] proposed a depth adjustment algorithm based on connected tree (CTDA), in which the sink node is used as the first root node of a connected tree, and the whole network is organized as a forest that comprises many connected subtrees. To maximize the network coverage, coverage overlaps between the parent node and the child node are reduced within each subtree. In addition, in [9; 20; 21], [PERSON] and [PERSON] et al. presented 2D and 3D communication architectures, and review deployment algorithms and strategies for UANs from different perspectives. It is found that most existing works focus on the node deployment of underwater subnetwork nodes, and few works jointly consider the network deployment of underwater network and network above the water surface.  
With the popularity of Swarm Intelligence (SI), a number of researches propose to use SI- and SI-based algorithms (SIAs) to tackle the optimization problems in node deployment of traditional wireless sensor networks and communication networks. Ant Colonies Optimization (ACO) is one of the well-known representative SIAs, where complex collective behavior emerges from the behavior of ants. ACO is effective for solving Non-deterministic Polynomial (NP) hard discrete optimization problems,  
Figure 1: The HMMN-EC model.  
and has been successfully applied to a number of scientific and engineering problems, including grid-based deployment for wireless sensor networks [22; 23; 24]. ACO is also applied to the topology optimization [25; 26] and routing algorithm [27; 28] for wireless networks. An ACO algorithm coupled with a local search heuristic is proposed in [29] to deploy a WSN under a certain reliability constraint at the minimum deployment cost. However, algorithms for traditional WSN or communication networks cannot be directly applied for MMNs due to the different characteristics of the deployment environment of ocean in three-dimensional space. To this end, we are motivated to apply ACO for the MMN deployment under three-tier architecture, and formulate a multiobjective optimization problem characterizing the 3D marine environment.
###### Abstract  
Tilapia farming is the predominant aquaculture activity, with 4623 aquaculture farms in Mexico alone. It is relevant to apply technological alternatives to mitigate production costs, mainly those associated with supporting energy savings for aeration and water pumping in aquaculture farms. There is limited information confirming the feasibility of implementing photovoltaic systems connected to the grid (On grid-PV) in aquaculture farms. The working hypothesis proposed for the development of the work was that On Grid PV systems in Tilapia aquaculture farms in Mexico are technically feasible, economically viable and environmentally acceptable. Therefore, the objective of this research is to design a grid-connected photovoltaic system for rural Tilapia aquaculture farms in Mexico and analyze it with a feasibility assessment through technical, economic and environmental variables, as part of the link between academia and the productive sector. Methodologically, the On Grid-PV design was carried out in an aquaculture farm in Vercaruz, Mexico, as a case study. It was developed in two stages: the field phase (1), where a non-participant observation guide and a survey with open questions were applied to perform the energy diagnosis, and the cabinet phase (2) where the calculation of the economic and environmental variables was carried out with the clean energy management software RestScreen expert, the engineering design was based on the Mexican Official Standard for electrical installations, and Sunny Design 5.22.5 was used to calculate and analyze the electrical parameters of the On Grid PV. The results revealed an investment cost of USD 30,062.61, the cost per KWp was of 1.36 USD/Watt, and the economic indicators were the net present value (USD 41,517.44), internal rate of return (38.2%) and cost-benefit ratio (5.6). Thus, the capital investment is recovered in 4.7 years thanks to the savings obtained by generating 2429 kW/h per month. As for the environment, it is estimated that 11,221 kg of CO2 equivalent would be released into the atmosphere without the On Grid-PV. In conclusion, the hypothesis is accepted and it is confirmed that On Grid-PV installations for Tilapia farms are technically feasible, economically viable and environmentally acceptable; their implementation would imply the possibility for aquaculture farms to produce Tilapia at a lower production cost and minimized environmental impact in terms of energy. It is recommended that aquaculture farmers in Mexico and the world implement this eco-technology that supports the sustainable development of aquaculture.  
Orechromis miloticus; sustainable aquaculture; eco technologies; rural development; renewable energy; solar energy; solar photovoltaic system +
Footnote †: journal: Applied Sciences