esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 3 Problem Statement and Modeling
### Edge Environment Model  
We propose a system model for the VNF placement problem that aims to minimize:  
* The total latency from the moment of the user's request until the moment the user receives the result of the requested service;
* The energy consumption at the edge tier;
* The total cost of computing and network resources required to run the VNFs. The model assumes that the VNF placement problem is performed at the edge tier. It also considers that each user is associated with only one edge node and the service demands come from users directly connected to an edge node. In addition, in our model, each VNF instance can handle a maximum number of requests. Such instances are prohibited from consuming more resources than the allocation defined during the VNF instance creation. The system model represents the following entities and attributes. The physical infrastructure is represented as an undirected graph, \(G=(H,L)\), where \(H\) is the set of edge nodes that host virtual machines where VNF instances can be deployed; and \(L\) is the set of links between edge nodes. Each link between two edge nodes \(h_{i}\in H\) and \(h_{j}\in H\), represented by \(l_{ij}\in L\), has a bandwidth \(b(l_{ij})\), a delay \(d(l_{ij})\), and a loss rate \(w(l_{ij})\). The edge nodes comprehend a set of resources \(\mathcal{A}~{}=~{}\{CPU,Memory\}\). Each resource is identified by the variable \(R^{\alpha}(h)\) with \(\alpha\in\mathcal{A}\). The set of users is represented by \(U\). According to our assumption, each user \(u\in U\) is associated with only one edge node \(h\in H\). This association is characterized by the following properties: bandwidth \(b(l_{uh})\), delay \(d(l_{uh})\), and loss rate \(w(l_{uh})\) between user \(u\in U\), and host \(h\in H\). The set of VNF types is represented by \(V\). Each VNF type \(v\in V\) has attributes \(r^{\alpha}(v)\) which represents the CPU and memory allocated in host \(h\) during the creation of a VNF instance. We also define the set of VNF instances of a given type \(v\) as \(l_{v}\). The attribute \(max_{v_{inst}}\) defines the maximum number of SFC requests that the VNF instance \(v_{inst}\) can accept. The VNF instance is non-shareable if \(max_{v_{inst}}=1\). The entity SFC type \(s\in S\) is composed of a chain of VNF types, where \(S\) is the set of SFC types. Accordingly, we have instances of SFC types, which are represented by the set \(l_{s}\) of SFC instances of type \(s\). Table 2 summarizes the entities and attributes of our approach. In the following discussion, we present the delay, energy, and QoS models that are considered in our VNF placement problem solution.
## 3 Materials and Methods
### Chemical Analysis
#### 3.4.3 Analysis of Tocopherols via HPLC-FLD  
Tocopherol analysis was conducted using high-performance liquid chromatography with fluorescence detector (HPLC-FLD). The system employed was an HP1200L liquid chromatograph (Agilent Corporation, Palo Alto, CA, USA) equipped with an autosampler, an FLD, and a C18 Lichrosorb column (5 \(\upmu\)m, 250 \(\times\) 4.6 mm i.d., Interchim, Montlucon, France). Mobile phases: (A) acidified water (pH 3.2, adjusted with formic acid), and (B) acetonitrile (CH\({}_{3}\)CN). The elution program was as follows: 95% B at the start, maintained for 5 min, then increased to 100% B over 1 min and held for 24 min, followed by a return to 95% B over 2 min, making a total run time of 32 min. Flow rate, 0.8 mL/min. Injection volume: 20 \(\upmu\)L of a sample solution from 80 mg of oil in 1 mL of isopropanol. Fluorescence chromatograms were recorded at excitation and emission wavelengths of 295 nm and 325 nm, respectively [58]. Quantification was performed using a calibration curve of \(\alpha\)-tocopherol (0-8.76 \(\upmu\)g).
# A Survey of Machine Learning Methods for Time Series Prediction
## 4 Deep Learning Architectures
### Feed-Forward Neural Networks  
The term Feed-Forward Neural Network (FFNN) is often used interchangeably with other terminologies in the literature, including a Artificial Neural Network (ANN), Neural Network (NN), Multilayer Perceptron (MLP), Back Propagation Neural Network (BPNN) [45; 65], Deep Neural Network (DNN), and Deep Feed-Forward Neural Network (DFFNN). These networks are characterized by a unidirectional flow of information from input to output nodes, often traversing one or more hidden layers (for a comprehensive overview of the implementation details and mathematical foundation of FFNNs, see [85]).  
Although the various terms for the FFNN are used interchangeably in the surveyed literature, there are small architectural differences that exist between these variants, namely in their depth. General FFNN models do not require any hidden layers, whereas MLP models typically contain one or two hidden layers. DNNs usually incorporate multiple hidden layers, allowing them to capture more complex patterns in the data. Despite their widespread use in time series analysis, these architectures require careful feature engineering to incorporate temporal information effectively and struggle to learn long-term patterns within the data. These limitations create the need for more specialized  
Figure 2: Architecture of an FFNN, including the modifications needed to transform it into a CNN (labeled in orange) or an RNN (labeled in red). Solid arrows represent connections consistent across all architectures, and dashed arrows represent deviations for specific architectures.  
Convolutional Neural Networks (CNN) are a type of FFNN that distinguish themselves in their architectural design through convolutional layers. The biggest benefit of these convolutional layers is to systematically scan input data to extract various features, making them well-suited candidates for handling image data as they can detect local features, combine them into complex patterns, and still maintain spatial relationships between features (for a comprehensive examination of CNN architectures, including detailed mathematical formulations and implementation considerations, see [86]). Although this can be useful in certain datasets which contain a time series of images, CNN models lack the ability to remember information over longer periods of time, such as Recurrent Neural Network (RNN) models and Transformer models.
###### Abstract  
Conventional sensor nodes are often battery-powered, and battery power limits the overall lifetime of the wireless sensor networks (WSNs). Wireless charging technology can be implemented in WSNs to supply power to sensor nodes and resolve the problem of restricted battery power. This type of mixed network is called wireless rechargeable sensor networks (WRSNs). Therefore, wireless charger deployment is a crucial task in WRSNs. In this study, the method of placing wireless chargers to efficiently extend the lifetime of the WRSNs is addressed. Owing to the data forwarding effect in WSNs, sensor nodes that are closer to the data collection or sink node drain more power than nodes that are further away from the data collection or sink node. Therefore, this study proposes a novel hybrid search and removal strategy for the power balance charger deployment method. The wireless chargers are placed in the chosen nodes of the WRSNs. The node-chosen problem we address is called the dominating set problem. The proposed hybrid search and removal strategy attempts to discover the minimum number of chargers required to cover all sensor nodes in the WRSN. The proposed algorithm considers the charging power of the wireless directional charger when arranging its placement to maximize the charging capacity in a power-balanced prerequisite. Therefore, the proposed deployment strategy preserves the awareness of the presence of the sink node that could result in unbalanced power distribution in WRSNs. The simulation results show that the proposed strategy spares more chargers and achieves better energy efficiency than other deployment approaches.  
wireless rechargeable sensor networks; hybrid method; wireless charger deployment +
Footnote †: journal:  
Article
## 3 CSI Estimation Process
#### Problem Formulation  
We minimize the MSE based on TS \(\mathbf{S}_{\mathrm{tr}}\) in the DL m-MIMO systems with FDD transmission mode equivalent to the optimization problem defined in (13) under a total power constraint, i.e.,  
\[\begin{split}\underset{\mathbf{S}_{\mathrm{tr}}}{\text{minimize}} &\ \mathrm{tr}\Big{(}\mathbf{R}_{\mathrm{tot}}^{-1}+\mathbf{S}_{\mathrm{tr}} \mathbf{S}_{\mathrm{tr}}^{\mathrm{H}}\Big{)}\\ &\ \mathrm{subject}\ \mathrm{to}\ \mathrm{tr}(\mathbf{S}_{\mathrm{tr}}^{ \mathrm{H}}\mathbf{S}_{\mathrm{tr}})\leq\rho_{\mathrm{tr}}.\end{split} \tag{13}\]  
The MSE corresponds to a function of the subspace that depends on the superposition of the user channel covariance matrices \(\mathbf{R}_{\mathrm{tot}}\) and the pilot matrix \(\mathbf{S}_{\mathrm{tr}}\), which implicitly depends on the training time and power.  
In m-MIMO systems, where the BS antennas are closely spaced by half a wavelength and located within a limited physical area, the channel coefficients may be strongly correlated. As a result, the CCMs will have a significant spread of eigenvalues. Furthermore, a significant amount of energy would be concentrated in a small number of strong directions, known as eigenmodes, rather than being distributed uniformly in all directions.  
Consequently, it is essential to allocate more power to these strong spatial directions while assigning zero power to the weaker directions. The authors were motivated by this observation to investigate the exploration of the superposition covariance matrix \(\mathbf{R}_{\text{tot}}\) in the training design. This was done with the aim of reducing the overhead of the DL TS and optimizing the allocation of power. To this end, this paper proposes a computationally efficient TS design using a waterfilling algorithm, described in detail in Section 4, when the users have distinct correlations. The algorithm proposed is intended to achieve a high rate of performance with a simplified design process.
# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 5 Conclusions  
According to this article, we propose a self-training physical model of the Fourier ptychographic microscopy reconstruction method (STPM-FPM). The reconstruction method trains the weight of the physical model with a custom training code, and then completes the reconstruction. When training the network, the effects of different loss functions on the data set collected by the Fourier ptychographic microscopy were compared, and the loss function suitable for this network was selected. In the experimental stage, the validity, generalization, and robustness of the reconstruction method in this paper were verified by using the same noise data set, a different noise data set, and a fine-tuning data set with some real data. The classical reconstruction algorithms G-S, A-S, [PERSON] et al.'s method based on neural networks, and [PERSON] et al.'s method based on deep convolutional neural networks were compared. The reconstruction quality of the reconstruction algorithm was proved through this article, and two evaluation indexes were used to quantitatively and qualitatively analyze the reconstruction results from different perspectives.  
The method used in this paper is the FPM reconstruction method of a self-training physical model, which effectively solves the decisive factors of different network models for reconstruction quality. At the same time, the idea of transfer learning and deep learning can fully improve the quality of reconstructed images. Compared with other traditional and neural network-based reconstruction algorithms, this paper has a more obvious denoising effect on noise. However, the deep learning reconstruction method is affected by its own reconstruction network, such as the adjustment of hyperparameters, the selection of loss function, the structure of the network, and other factors. Therefore, how to improve the stability of the deep learning reconstruction method remains to be studied. At the same time, the cycle time of training based on the deep learning method is long, and how to lighten the model is one of the key research directions for the future.  
Conceptualization, X.W., Y.P., T.X. and J.L.; methodology, X.W. and Y.J.; validation, X.W.; formal analysis, X.W.; investigation, X.W.; resources, X.W.; data curation, X.W. and Y.J.; writing--original draft preparation, X.W.; writing--review and editing, X.W., Y.P., Y.J., J.L., Z.L. and J.C.; visualization, X.W. and Y.J.; supervision, T.X.; project administration, X.W. and J.L.; funding acquisition, Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jilin Provincial Department of Science and Technology (YDZJ202102 CXJD062).  
Not applicable.  
We would like to thank all editors and reviewers for their valuable comments for improving this manuscript.  
The authors declare no conflict of interest.  
Figure 19: Reconstruction results under real data acquisition.
## 5 Findings and Analysis
### Implementation Insights and Results
This innovative architecture is particularly well-suited for IoT applications designed to assist individuals with disabilities, as it effectively balances the need for performance and minimal energy use, making it an ideal choice for devices that must operate efficiently over extended periods. This enhancement is crucial for devices like assistive technology tools and remote health monitoring solutions, as it safeguards user information--preventing serious consequences for privacy and safety--while also ensuring energy efficiency to prolong device usage. Furthermore, the design's compact nature enables its seamless integration into small, user-friendly IoT devices tailored for disabled individuals, fostering intuitive interactions and facilitatingfeatures like real-time location tracking and personalized assistance services that can greatly benefit users in their daily lives. This innovative multiplier not only enhances security in resource-constrained IoT edge devices, but also significantly improves the overall daily experiences of disabled individuals. By fostering greater independence and enhancing overall quality of life, the design of the multiplier serves to deliver secure assistive technologies that are specifically tailored to address individual needs. This targeted customization enables users to navigate their environments with increased ease and confidence, ultimately contributing to their enhanced autonomy.
# High Performance Receiver Design for RX Carrier Aggregation
###### Abstract  
Carrier aggregation is one of the key features to increase the data rate given a scarce bandwidth spectrum. This paper describes the design of a high performance receiver suitable for carrier aggregation in LTE-Advanced and future 5 G standards. The proposed architecture is versatile to support legacy mode (single carrier), inter-band carrier aggregation, and intra-band carrier aggregation. Performance with carrier-aggregation support is as good as legacy receivers. Contradicting requirements of high linearity and the low noise is satisfied with the single-gm receiver architecture in addition to supporting carrier aggregation. The proposed cascode-shutoff low-noise trans-conductance amplifier (LNTA) achieves 57.1 dB voltage gain, 1.76 dB NF (noise figure), and \(-6.7\) dBm IIP3 (Third-order intercept point) with the power consumption of 21.3 mW in the intra-band carrier aggregation scenario. With legacy mode, the same receiver signal path achieves 56.6 dB voltage gain, 1.33 dB NF, and \(-6.2\) dBm IIP3 with a low power consumption of 7.4 mW.  
low noise amplifier (LNA); carrier aggregation; LTE-advanced; single-input multi-output (SIMO); multi-input multi-output (MIMO); multi-band +
Footnote †: journal: Low Power Electronics and Applications
## 5 Validation of the Proposed CARLA+
### Experimental Setup  
In this section, we discuss the experimental setup of the proposed CARLA+ in detail. To validate the proposed CARLA+ solution, all the experiments are conducted in CARLA Town10 map. The motivation behind choosing Town10 is a complex urban setting with detailed high-quality realistic textures and a city comprised of streets, avenues, and pedestrian walkways with various environments. The map layout and the illustration of the simulation environment are shown in Figure 6. Furthermore, the details of different hardware, software, and libraries used to design and develop the CARLA+ are listed in Table 3.  
Figure 6: (**A**) A map of Town 10 in CARLA. (**B**) Illustration of Town 10 simulation environment.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 4 Experiments
### Experiments on UC Merced Dataset  
We evaluated the proposed approach on the UC Merced dataset [71], a benchmark dataset for remote sensing scene classification. This dataset comprises 21 land-use categories, with each category containing 100 scene images of size \(256\times 256\times 3\) pixels.  
We compared the proposed method with IB-CNN [72], which explores the application of bilinear pooling in remote sensing scene classification. For a fair comparison, we adopted the experimental settings described in [72] and used ResNet-34 as the backbone network. Additionally, 50% of the images were randomly selected as the training dataset, while the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Backbone** & **Accuracy** & **IS (ms)** & **TT (h)** \\ \hline Plain GCP & & 70.0 & 149.9 & 31 \\ LN & & 70.8 & 155.8 & 31 \\ iSQRT-COV & ResNet-18 & 75.2 & 159.3 & 29 \\ DropCov & & 75.2 & 149.9 & 31 \\ RegCov (Ours) & & 75.3 & 149.9 & 29 + 4 \\ \hline Plain GCP & & 73.5 & 201.0 & 54 \\ LN & & 75.2 & 202.8 & 54 \\ iSQRT-COV & ResNet-50 & 78.2 & 211.5 & 38 \\ DropCov & & 78.2 & 201.0 & 54 \\ RegCov(Ours) & & 78.2 & 201.0 & 38 + 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of our method RegCov with several normalization methods on the ImageNet1K dataset. On the RTX-3090 GPUs, accuracy, training time (TT) and inference speed (IS) for one mini-batch (256 images) are listed. Please note that training time (TT) for RegCov has two values: training time spent for the first stage + training time spent for the first stage.  
remaining images were reserved for testing. The classification performance was evaluated using two metrics: overall accuracy (OA) and the Kappa coefficient (\(\kappa\)). To ensure statistical reliability, each experiment was repeated five times, and the average results were reported as the final accuracy.  
Table 3 summarizes the classification results. In addition to IB-CNN, the results of fine-tuned CNN (F-CNN) and bilinear CNN (B-CNN) are also included in the table. Compared to F-CNN, B-CNN achieves better performance, demonstrating that bilinear pooling improves classification accuracy. By leveraging the proposed joint pooling method, IB-CNN generates more compact bilinear representations while preventing an increase in the parameter size of the subsequent fully connected layer, which mitigates overfitting and thereby improves network performance. In contrast to IB-CNN, our RegCov method incorporates normalization, and its superior results highlight the robustness of our approach across different classification tasks. These findings further emphasize that the normalization plays a crucial role in significantly boosting performance.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 6 Applications of AMI in Heathcare
### Case Studies for Smart Healthcare
#### 6.1.2 Automatic Identification and Localization of Colorectal Cancer Lesions  
CRC ranks among the most common and deadly cancers worldwide. The key to saving lives lies in early detection and accurate localization of cancerous lesions, which can dramatically improve prognosis and survival rates. Abdominal computed tomography (CT) scans are often the first step before conducting a more invasive colonoscopy. Yet, when these scans are performed for reasons other than colorectal evaluation, the intricate and winding structure of the colon makes thorough analysis exceptionally challenging. The ability of CT scans to detect colorectal cancer also varies greatly with tumor size, leading to a troubling number of incidental colon cancers being missed, much to the concern of clinicians and radiologists [136]. Furthermore, traditional diagnostic methods such as colonoscopy depend heavily on the skill of clinicians, resulting in inconsistent detection rates and the potential for small or subtle lesions to be overlooked. This highlights an urgent need for innovative automatic solutions which could increase detection accuracy and reduce the manual burden on doctors.  
The procedure described in Figure 8 starts with the patient undergoing a CT scan for gastrointestinal disease. The AI analyzer integrated with the CT scan machine then detects any abnormalities and localizes probable CRC lesions, displaying the results directly on the visualization console. This automation significantly reduces the manual burden on doctors, as a typical CT scan comprises 250 to 300 slices. Acting as a guiding tool, the AI highlights potential CRC lesion regions, enabling quicker decision-making for treatment strategies. However, DL models can generate false positives, especially for small tumors, tumors obstructed by fecal material, or incorrect predictions [136]; therefore, while automated analysis provides valuable guidance, the final diagnosis and treatment decisions remain in the hands of the physicians. The visualization images shown here were obtained from [136].  
Figure 7: Pictorial framework of smart transdermal drug delivery system for diabetic patients.