esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.4 RScan Algorithm Steps and Processes  
Distinct from the grid density clustering algorithm and the GScan algorithm, this study employs a Bayesian optimization algorithm to adjust hyperparameters, determining the optimal grid cell size \(k\) and grid density threshold \(\lambda\), and assesses the clustering results using internal evaluation metrics. The aggregated results for hotspot areas reflect the spatial aggregation pattern of secondary housing transaction activity. As depicted in Figure 2, the main steps of the RScan algorithm include the following:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Condition** & **Element** \\ \hline \(1\) & Denseness & For \(\forall\mathrm{U_{i}}\in\mathrm{H}\), where \(\mathrm{U_{i}}\) represents an independently active grid cell. \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Connectivity} & For \(\forall\mathrm{U_{i}}\in\mathrm{H}\) and \(\forall\mathrm{U_{j}}\in\mathrm{H}\), there exists a path defined by grid density \\  & & reachability, ensuring that every intermediate grid cell in the path also belongs \\ \cline{1-1}  & & to the subset \(\mathrm{H}\). This indicates that grid cells within the active region are \\ \cline{1-1}  & & interconnected through grid density reachability. \\ \hline \(3\) & Maximality & For \(\forall\mathrm{U_{i}}\in\mathrm{H}\), if any grid cell \(\mathrm{U_{i}}\) is grid density reachable from \(\mathrm{U_{j}}\), then \(\mathrm{U_{i}}\in\mathrm{H}\). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grid set composition of active region conditions.  
1. Enter the original spatial dataset with estimates for \(k\) and \(\lambda\) as initial values.
2. The area covered by the spatial data objects is divided into a series of non-overlapping grid cells of size \(k\times k\).
3. Iterate over the original dataset and map the spatial data to the corresponding grid cells using a mapping function.
4. Calculate the data density within each grid cell to identify hotspot grid cells and remove any common, non-hotspot grid cells.
5. Analyze the distribution of data points within hotspot grid cells, calculate their locations, and merge them based on proximity to form regions indicative of varying levels of second-hand housing transaction activity.
6. Apply Bayesian optimization algorithms (BOA) to the grid cell edge length \(k\) and density threshold \(\lambda\), iterating until optimal parameters are achieved.
7. Assess the performance of the RScan algorithm using the selected clustering evaluation metrics.
8. Input the improved values for \(k\) and \(\lambda\) and repeat steps 1-7 to visualize and refine the active region mining results.  
Figure 2: Flowchart of the RScan clustering algorithm.
## 2 Related Work
### Traditional Recommendation Algorithm
#### 2.1.1 Recommendation Algorithm Based on Collaborative Filtering  
Collaborative filtering series of recommendation algorithms start their recommendation with the similarity among users or items. This means that similar users may have the same interests, or users may like items similar to the items they have purchased. Series of collaborative filtering algorithms can be divided into several categories, namely collaborative filtering based on user similarity, collaborative filtering based on article similarity and collaborative filtering based on model. Among these, user-based collaborative filtering was first proposed and implemented in the application of mail filtering. However, with the continuous development of website content, collaborative filtering based on user similarity also shows its own shortcomings. It requires a large amount of computation for each user to calculate and determine their similar users. The main feature of the user side in each major platform is constantly changing, and the item data are relatively fixed. At present, collaborative filtering based on user similarity is mainly used in scenes with drastic changes on the item side, such as news and information recommendation scenarios. Due to the shortcomings of user similarity, collaborative filtering algorithms based on item similarity have developed slowly. In contrast to collaborative filtering algorithms based on the user, collaborative filtering algorithms based on the item mainly combine different users' interactive item lists. The interaction here can include a variety of behaviors, such as purchasing, browsing, purchasing and collecting, and the similarity between different items can be measured by calculating the frequency of items appearing in the same user interaction list. Finally, the user's interest in items that have not been purchased or interacted with is calculated according to the user's interaction history items. After ranking the interest value, the item with the highest score can be recommended to the user. In addition to the above two types of collaborative filtering algorithms based on similarity of users or items, researchers have further proposed a model-based collaborative filtering algorithm. The typical representation of this kind of algorithm is the factorization machine (FM) [8], and a series of similar algorithms were derived from it, such as field-aware factorization machines (FFMs) [9] and factorization-machine-based neural network (DeepFM) [10] combined with deep learning technology.
# New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
## 3 Control Architecture
### Fall Detection Capability  
Specific algorithms and techniques used for fall detection were explored, considering factors such as accuracy, false positive rate, real-time processing, sensitivity, and specificity. A fall-detection model was developed using a Raspberry Pi 2 camera and an NVIDIA Jetson Nano computing platform. This study employs a custom dataset of 10K images, compiled and annotated by the author as shown in Figure 8. using the Roboflow platform [22]. To facilitate robust model training and assessment, the dataset was divided into an 85.5%-9.5%-5% split for training, validation, and testing, respectively.  
The fall-detection model was developed using images acquired from various sources on the internet and processed using an NVIDIA Jetson Nano computing platform. These images encompassed a wide range of scenarios and situations in which falls might occur.  
The labelling technique involved manually annotating the collected images. Human annotators reviewed each image and marked the regions where a person was falling or not falling. This process generated ground truth labels for the training dataset. Figure 8 illustrates a subset of these labelled images to provide an overview of the dataset. This approach allowed for a diverse dataset that covered various fall scenarios.  
Next, YOLOv7 was used as the object-detection algorithm. An object detector performs image-recognition tasks by taking an image as input and then predicting bounding boxes and class probabilities for each object in the image. The YOLO algorithm series uses deep convolutional neural networks (CNNs) to extract features from the image to detect objects [23]. A block diagram that explains the main components of the YOLOv7 algorithm is shown in Figure 9. The \"Backbone\" block is responsible for creating image features. The \"Neck\" block is where a group of neural network layers combines and mixes features. The last block named \"Head\" receives the features from the previous block and generates predictions.  
Figure 8: The collected images are uploaded and labeled images on the Roboflow platform.  
In this research, object detection directly pinpoints fallen individuals, bypassing complex pose analysis and offering a more efficient and reliable solution for fall detection, prioritizing precision in fall detection and minimizes false alarms.  
To reduce the model size and improve its inference speed on the Jetson Nano, the model has been quantized. The model size was reduced by 40% through quantization and an additional 20% through structured pruning in hardware implementation. Normally, quantization is a common machine-learning technique to reduce model size [24]. On the other hand, pruning is a machine-learning technique that decreases the size of complex models by eliminating some of their parameters to make them run faster on hardware [25]. Structured pruning is convenient for structured models like CNN models as it preserves their structure. The final model was able to detect falls at a rate of 30 frames per second.
## 4 Optimization Design Process and Results
### Discussions  
Aiming at the change of flow field caused by the shape variation, the optimal design results are discussed. Figure 22 shows the comparison between the initial shape and the optimal shape. In the x-axis direction, the flap leading edge of the optimal shape is close to the leading edge of the main wing, and the flap leading edge of the optimized shape is downward, which widens the channel between the flap and the main wing, and the deflection angle of the optimal shape is almost unchanged compared with that of the initial shape.  
The pressure coefficients on the airfoil surface before and after optimization are shown in Figure 23. For the upper surface of the main wing, the pressure coefficients of the optimal airfoil are almost the same as that of the original airfoil. At the lower trailing edge of the main wing, the pressure coefficients of the optimized airfoil decrease compared with the initial airfoil, and the area of the inverse pressure gradient decreases. Thus, the interference between main wing and flap is reduced. The flap has a limited influence on the wing, and there is no change on the pressure in front of the wing.  
Figure 21: Comparison of far-field noise before and after optimization.  
Figure 20: Converging process of optimization objectives.  
The z-direction vorticity contours and the iso-surfaces of Q-criterion (Q = 5000) of the initial shape and the optimal shape are shown in Figures 24 and 25. The difference between the flow field of the optimal shape and that of the initial shape is mainly reflected in the gap between the main wing and the flap. After optimization, the area of the inverse pressure gradient region on the main wing surface is reduced, and more air flow can pass by the passage between the main wing and the flap. The passage between the main wing and the flap becomes wider and the blocking effect of the flow is reduced and the separation area at the tail of the main wing is significantly reduced. Consequently, the aerodynamic noise is also reduced.  
Figure 24: z-direction vorticity contours: (**a**) initial shape: (**b**) optimal shape.  
Figure 22: Comparison between initial shape and optimal shape.
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Nanostructured Carbon Substrates
#### Nanoparticles Combined with Carbon Nanotubes  
* Enzyme-Based Immunosensors Because CNTs are difficult to functionalize, or poorly conducting after some oxidative treatments, and also to bring more efficient and more specific catalytic properties to the electrode, some works were reported where CNTs were modified with metal NPs. For example, [PERSON] et al. [41] reported an electrochemical immunosensor based on MWCNTs modified with Au nanoparticles (AuNPs) for detection of human chorionic gonadotropin (hCG), widely used as a marker in some pregnancy test. AuNPs were used to increase further the surface area of the electrode to track down a large amount of capture antibodies as well as to lower the electronic transfer resistance of generated by poorly conductive CNTs. HRP-labeled secondary anti-hCG antibodies were used for the sandwich immunoassay. Linearity was obtained from 5 uU mL\({}^{-1}\) to 500 mIU mL\({}^{-1}\) with a LoD of 3 uIU mL\({}^{-1}\). A similar approach was described by [PERSON] et al. [42] for detection of IgA- and IgG-type anti-tissue transglutaminase (anti-fTG) autoantibodies in real samples from patients suffering with celiac disease, using MWCNT/AuNPs as substrate and AlkP-labeled secondary anti-IgG antibodies for amplification. The analytical signal was based on the anodic redissolution of the enzymatically generated silver from Ag\({}^{+}\) added in solution (Figure 6C). No quantification and even no detection limit were claimed, but positive (+) or negative (\(-\)) results, for real diluted serum samples. The fact that silver must be added in solution impede the use of such sensor for real applications, however.
* Enzyme-Free Immunosensors Only one example of enzyme-free immunosensor could be found during this period, very recently published by [PERSON] et al. in 2017. [43] They described a label-free amperometric immunosensor for the direct determination of zeaarlenone (a mycotoxin). GC electrodes were modified with polyethyleneimine (PEI)-functionalized MWCNTs, then AuPtNPs were electro-deposited. They demonstrated that AuPtNPs increased the surface concentration of antibodies and enhanced sensitivity. Capture monoclonal antibodies were immobilized on these NPs. Using CV with Fe(CN)\({}_{6}\)\({}^{3-/4-}\) as redox probe (diffusing in solution), they probed the target capture over a wide linear range from 5 pg mL\({}^{-1}\) to 50 ng mL\({}^{-1}\), with a LoD of 1.5 pg mL\({}^{-1}\).
# 3D Stress Analysis of Multilayered Functionally Graded Plates and Shells under Moisture Conditions
###### Abstract  
This paper presents the steady-state stress analysis of single-layered and multilayered plates and shells embedding Functionally Graded Material (FGM) layers under moisture conditions. This solution relies on an exact layer-wise approach; the formulation is unique despite the geometry. It studies spherical and cylindrical shells, cylinders, and plates in an orthogonal mixed curvilinear coordinate system (\(\alpha\), \(\beta\), \(z\)). The moisture conditions are defined at the external surfaces and evaluated in the thickness direction under steady-state conditions following three procedures. This solution handles the 3D Fick diffusion equation, the 1D Fick diffusion equation, and the a priori assumed linear profile. The paper discusses their assumptions and the different results they deliver. Once defined, the moisture content acts as an external load; this leads to a system of three non-homogeneous second-order differential equilibrium equations. The 3D problem is reduced to a system of partial differential equations in the thickness coordinate, solved via the exponential matrix method. It returns the displacements and their z-derivatives as a direct result. The paper validates the model by comparing the results with 3D analytical models proposed in the literature and numerical models. Then, new results are presented for one-layered and multilayered FGM plates, cylinders, and cylindrical and spherical shells, considering different moisture contents, thickness ratios, and material laws.  
Keywords:functionally graded materials; 3D shell model; steady-state hygro-elastic analysis; Fick moisture diffusion equation; moisture content profile; layer-wise approach Article
###### Abstract  
The machine learning-based approaches for analysing the mobility needs of users are currently the most prevalent approach in the mobility-on-demand (MoD) analysis. Their efficiency relies on the comprehensiveness and consistency of training datasets. However, this is also the biggest challenge, as high-quality training data are often difficult to obtain. Thus, the Variational Autoencoders (VAE) are investigated as potential generators of synthetic samples for the augmentation of MoD-based datasets. This MoD-based dataset is created using real-world taxi trip data recorded in the _Manhattan_ district of _New York City_, USA. This augmentation by synthetic samples can potentially enable larger, balanced, and more consistent datasets for machine learning analysis of MoD-based data. The proposed VAE approaches are compared with common dimensionality reduction techniques and standard autoencoders concerning their efficiency in 2-dimensional clustering based on collected MoD-based data. The proposed 2-dimensional convolution VAE framework has achieved clustering results comparable with the other analysed approaches. Thus, it generates synthetic samples, known as _\"deepfakes\"_. They are added in different percentages to the initial dataset based on real-world MoD-based data. Thus, this creates augmented datasets of the initial one. The models for predicting the cluster of each sample are used to evaluate the impact of those augmented datasets on their accuracy and learning convergence compared to the initial dataset. Results have shown that the accuracy and learning convergence are improved if those predictive models are trained on an augmented dataset which includes up to 10% of synthetic samples for each cluster.  
variational autoencoders; mobility on demand; training datasets augmentation; dimensional reduction; intelligent transportation systems Article
# Adaptability of Buildings: A Critical Review on the Concept Evolution
## Content Analysis
### Enablers and Promoting Models  
The general understanding of adaptability stems from shifting the conventional perception of a building as a finished, static object towards contemplating it as a dynamic system that consists of the object and the process of construction, change, deconstruction, and reconstruction. Design for adaptability comes about the issue of building obsolescence that is evidently associated with environmental and economic impacts resulting from resource consumption and material loss [49]. The ideology of adaptability of buildings provides a multifaceted theory and set of principles for sustainable and resilient built environment where adaptability can refer to function, structure, space, components, systems, services, and size, manifesting in active and passive patterns of response [25]. This is revealed by literature studies where adaptability is discussed with links to \"Open Building\" [51], flexibility [25; 40; 48], resilience (as a key theme in the Sustainable Development Goals (SDGs) [52]), \"Circular Economy\" principles [53], and design for change [37]. These ground concepts and their basic principles gradually shaped the evolution course of the concept of \"adaptability\" and enabled the development of its strategies. Some of these enablers are highlighted in the following paragraphs.
# Simultaneous Lightwave and Power Transfer for Internet of Things Devices
## 4 Discussion
### Novel SC Materials for Higher Efficiency  
Progress in material science can lead to higher efficiency, including those matching the FSO wavelength windows for long-range transmissions, particularly around the 1550 nm frequency. III-V material-based SCs are showing rapid improvement due to their potential in providing high power conversion efficiency compared to other technologies and could find potential uses in SLIPT IoTs that can be connected using FSO links. Perovskite materials can provide highly-efficient SCs that can co-exist with VLC technology. Making perovskite SCs more stable is the next step for their potential use in outdoor conditions. In order to be used for IoT devices in a large-scale manner, the cost of III-V and perovskite should be comparable to SC ones. Another opportunity for higher energy harvesting while using SLIPT is through shape optimization of solar cells during the manufacturing process as demonstrated in [21], where the authors used neural networks to optimize the shape of organic solar cells to improve the conversion efficiency.
# Effective RNA Regulation by Combination of Multiple Programmable RNA-Binding Proteins
## 3 Results and Discussion
### Specific RNA Binding of TTP-PUF within Cells  
The RNA binding specificities of PUF (A)-(D) for their corresponding target RNAs were also confirmed within living cells using luciferase reporter assays (Figure 3, Figure S1a,b). PUF (A)-(D) fused with tristetraprolin (TTP), an mRNA decay factor, were designed as described before [23,31]. As a control, a TTP expression vector that expresses only TTP without fusing with PUFs was used to estimate the effect of PUF [23,31]. Previous reports showed that a TTP-PUF fusion protein, which can bind to the 3' UTR of the luciferase reporter gene transcript, represses luciferase activity [23,31]. The degree of luciferase repression reflects the RNA binding of TTP-PUF fusion proteins. Here, we prepared reporter vectors that express both firefly luciferase and _Renilla_ luciferase genes driven by a PGK and an SV40 promoter, respectively (Figure S1a,b). A four-times repeat sequence of the PUF target sequences, 4 \(\times\) (a), (b), (c) or (d), was inserted at the 3' UTR of the firefly luciferase gene (Figure 3a). Each TTP-PUF expression or control vectors and a reporter vector were transfected into NIH3T3 cells. In this reporter system, we expected the firefly luciferase activity to be affected by TTP-PUFs, and _Renilla_ luciferase activity to reflect the transfection efficiencies. Thus, the firefly luciferase activity, under the expression of each TTP-PUF, was normalized to the _Renilla_ luciferase activity of the same sample. The normalized luciferase activity was compared to that of the control sample to evaluate the degree of repression. The expression of the TTP-PUF proteins was confirmed via Western blotting assay (Figure S1c). As shown in Figure 3b, TTP-PUF (A), (B), (C), and (D) showed a 4-6-fold repression in luciferase gene expression with corresponding RNA-binding sequences at the 3' UTR. By contrast, they had little effect on luciferase gene expression without corresponding binding sites at the 3' UTR. None of the TTP-PUFs repressed the luciferase activity in the no binding site (nbs) reporter. These results indicated that the PUF proteins specifically bound to the corresponding RNA sequences at the 3' UTR of the luciferase gene even within cells.  
Figure 3: Specific binding of engineered PUF proteins to target RNAs in living cells. (**a**) Schematic representation of TTP-PUF proteins and reporter RNAs. TTP-PUF expression plasmids and luciferase reporter plasmids were co-transfected into NIH3T3 cells. The luciferase reporter genes were transcribed, and reporter RNAs with PUF-binding sequences at the 3′ untranslated region (UTR) region should have been produced in the cells. (**b**) Fold repression of luciferase reporter activities by TTP-PUF proteins. Data are presented as mean \(\pm\) SD.
## 2 Applications and Background of Uwsn
### Applications of Uwsn
#### 2.1.2 Underwater Mapping and Locating Mineral Mines  
Underwater communication is also being engaged in applications like ocean sampling network. Monterey bay experiment is one of them which has already shown the improved ability to observe and predict ocean environment using more delicate vehicle and reliable network [2]. Uwsn can also be deployed for finding mineral mines. Researchers have developed a Uwsn which can be deployed along with Remotely Operative Underwater Vehicle (ROV) incorporated with wireless sensor communication module facilitating vision based monitoring system and monitors. This can be used in detecting and measurement of underwater manganese crust [12, 13]. Moreover, researchers have developed a Uwsn combining underwater mobile networks along with underwater acoustic network for large scale deep-sea scans thus finding mineral mines and dig out [12, 14].