esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and The Things Network
###### Abstract
Looking ahead with an entrepreneurial and innovative spirit, he adopts the concern of contributing to a more sustainable future through the integration of the legitimate desires of the communities in which it operates with respect for the environment [9]. Therefore, we have set as the primary response to the question posed, to monitor, using information and communication technologies, the health status of elderly adults while moving within their homes and peripheries, living in areas away from urban centers. In this way, support institutions, family members, support teams, or other entities can follow and monitor, almost in real time, the state of health of these people. Furthermore, as the systems geared to monitoring and managing people's data are undergoing significant evolution, we hope to improve the quality of life of people, particularly the low-income elderly population. To do that, we propose using the LoRa (Long Range) communication networks and their interconnection to The Things Network (TTN). TTN is an open Internet of Things infrastructure supported by a global ecosystem of thousands of developers, IT integrators, hardware manufacturers, universities, and governments, supported by LoRaWAN, the secure messaging protocol, etc. [10]. On the TTN, we have registered a test application Available online [10]. Given the use of the proposed technology and system, which proves viable, we intend to establish a protocol with a private entity operating in the telecommunications area. We already have a partnership for a possible implementation of the supporting infrastructure. Of course, this entails the reorganization and redefinition of the electronic components. Thus, they become more adapted to real-life conditions, miniaturizing all the equipment used by the elderly in a non-invasive and non-intrusive way. Our focus is on validation, testing and complete development, and some machine learning models. In addition to the introduction, the remainder of this paper is organized as follows. In the Section 2, we address studies developed in this area that we consider to be relevant. Next, we show the research and development method in the Section 3 and present the system support data model. Section 4 covers the materials and methods of system development, including the description of components, development boards and their characteristics, and the connectivity scheme of the developed models. The Section 5 discusses the results and shows how a machine learning model can detect abnormal events, using accelerometer data, quickly and in a simplified manner. Finally, Section 6 concludes the article.
# Study and Investigation on 5G Technology: A Systematic Review
## 3 Preliminary Section
### Commercial Service Providers of 5G
Samsung provides several hundred times faster data transmission than standard 4G for core 5G mobile communication systems. The company achieved a lot of success in the next generation of technology, and it is considered one of the leading companies in the 5G domain. **Qualcomm:** Qualcomm is an American multinational corporation in San Diego, California. It is also one of the leading company which is working on 5G chip. Qualcomm's first 5G modern chip was announced in October 2016, and a prototype was demonstrated in October 2017. Qualcomm mainly focuses on building products while other companies talk about 5G; Qualcomm is building the technologies. According to one magazine, Qualcomm was working on three main areas of 5G networks. Firstly, radios that would use bandwidth from any network it has access to; secondly, creating more extensive ranges of spectrum by combining smaller pieces; and thirdly, a set of services for internet applications. **ZTE Corporation:** ZTE Corporation was founded in 1985. It is a partially Chinese state-owned technology company that works in telecommunication. It was a leading company that worked on 4G LTE, and it is still maintaining its value and doing research and tests on 5G. It is the first company that proposed Pre5G technology with some series of solutions. **NEC Corporation:** NEC Corporation is a Japanese multinational information technology and electronics corporation headquartered in Minato, Tokyo. ZTE also started their research on 5G, and they introduced a new business concept. NEC's main aim is to develop 5G NR for the global mobile system and create secure and intelligent technologies to realize 5G services. **Cisco:** Cisco is a USA networking hardware company that also sleeves up for 5G network. Cisco's primary focus is to support 5G in three ways: Service--enable 5G services faster so all service providers can increase their business. Infrastructure--build 5G-oriented infrastructure to implement 5G more quickly. Automation--make a more scalable, flexible, and reliable 5G network. The companies know the importance of 5G, and they want to connect more than 30 billion devices in the next couple of years. Cisco intends to work on network hardening as it is a vital part of 5G network. Cisco used AI with deep learning to develop a 5G Security Architecture, enabling Secure Network Transformation.
## 3 Decentralized Optimal Scheduling of OMEF for IEGS
### Decentralized Framework  
There are two common methods for regional topology separation: (1) Node tearing method. By node tearing, the node is divided into two to form two boundary nodes. (2) Branch tearing method. Introduce a virtual node at the midpoint of the interconnection branch, and divide the virtual node.  
Both topological separation methods must satisfy the boundary coupling condition, that is, the boundary nodes of the sub-regions must meet the consistency of variables.  
In the IEGS, the power system and the natural gas system are connected together through P2 Gs and gas turbines. This article uses the node tearing method to decouple the entire system, as shown in **Figure** 2. The consistency of boundary variables can be described as follows:  
\[\begin{array}{l}\mathbf{x}^{e}=\left[\mathbf{\alpha}_{\text{P2G}}\mathbf{P}_{\text{P2G}} /\mathbf{C}_{\text{g}},\overset{\mathbf{\wedge}}{\mathbf{q}}_{\text{GT}}\right]\\ \mathbf{x}^{\text{g}}=\left[\mathbf{q}_{\text{P2G}},\mathbf{q}_{\text{GT}}\right]\\ \mathbf{x}^{e}-\mathbf{x}^{\text{g}}=0\end{array} \tag{48}\]  
where \(x^{e}\) and \(x^{\text{g}}\) are the boundary variable vectors of power system and natural gas system.
## 3 Net Impact and Capturing Risks
### Dynamic Risks  
#### 3.3.1 Translational Dynamics Risks  
When the net impacts the space debris object, the contact forces (Figure 6) are fed into Equation (9) leading to the target's translational dynamic reaction. For the purpose of this study, only the velocity is shown in Figure 8, with the addition of the analytical estimate of momentum conservation (for the \(y\) direction).  
As expected, the object's velocity seems to oscillate around the analytical estimation of 32 mm/s of momentum exchange around the along-track direction. The artificial extension seems to result in a slightly underestimated final velocity of 28 mm/s, which would lead the Envisat to vary its \(y\) position by 0.40 m after 15 s. Although small, it is essential to limit the deviation in position with the use of a robust control system to avoid significant positional corrections. However, from this point of view, translational dynamics seem to result in limited risks.
## 4 Results and Discussion
### Experimental Setup
#### 4.1.3 Baseline  
In the field of time series prediction, mainstream models such as Support Vector Machines (SVMs) [38], Random Forest [39], Recurrent Neural Networks (RNNs) [40], and Long Short-Term Memory (LSTM) networks [41] are widely applied across various domains including finance, meteorology, and traffic. SVM excels in handling small samples due to its powerful classification capability and high predictive accuracy, making it particularly suitable for high-dimensional data processing. However, SVM has limited capacity for modeling nonlinear relationships, often requiring the integration of kernel functions to enhance its performance. Random Forest improves prediction accuracy and stability by aggregating multiple decision trees, making it especially suitable for data with complex feature relationships. The structure of RNN enables it to process sequential data, capturing information over time, which makes it appropriate for modeling time series data such as speech and text, although it is prone to gradient vanishing issues. To address this, LSTM, as a variant of RNN, effectively captures long-term dependencies by introducing memory cells and gating mechanisms, particularly excelling in long-term predictions for financial market trends and weather changes.  
In the domain of image classification, models such as AlexNet [42], GoogLeNet [43], ResNet [44], and EfficientNet [45] have garnered widespread attention in recent years. AlexNet gained prominence in the 2012 ImageNet competition with its deep convolutional neural network architecture, marking the rise of deep learning in the field of image recognition. By employing ReLU activation functions and dropout regularization, it effectively reduces the risk of overfitting and achieves high classification accuracy. Subsequently, GoogLeNet introduced the Inception module, which enhances the network's expressive power by computing features in parallel with convolutional kernels of different sizes while maintaining a low computational complexity. ResNet addresses the gradient vanishing problem in training deep networks through residual learning, allowing the network to reach significant depths and markedly improving image classification performance. Finally, EfficientNet employs a compound scaling strategy in its model design, effectively balancing the model's depth, width, and resolution, resulting in excellent performance in terms of efficiency and accuracy, making it a popular choice in the current image classification domain. By conducting an in-depth analysis of these mainstream models, we can better understand their strengths and weaknesses, enabling us to select the best solutions for specific application scenarios and enhance model performance in complex tasks.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 4 Experimental Comparison
### Undisturbed Scenario  
#### 4.1.1 Experimental Results of Loosely Coupled and Tightly Coupled Systems  
Experimental results of the loosely coupled closed-loop system and tightly coupled system were obtained under the same experimental conditions, as shown in Figure 8.  
Figure 7: Neural network framework combining CNN and LSTM.
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Common Challenges for Time Synchronization  
Time synchronization is a broad area and many researches have been involved in this area over the past few decades [23]. Several algorithms and mechanisms have been proposed and used. Node A sends a message to Node B with its current time in order to synchronize with Node B. If there is no delay at all in delivering and receiving the message, Node B can immediately calculate the difference between the clocks and adjust its time according to the Node A clock [24]. However, in a real wireless network, several types of delays from the sources described above affect the delivery of messages which make time synchronization much more difficult. In order to estimate the relative clock skews and offsets among nodes, a series of timing messages can be transmitted, and therefore, time synchronization can be regarded as the process of removing the effects of delays from timing message transmissions.  
However, in WSNs, it is not suitable to use the current synchronization techniques because of WSNs' unique characteristics such as limited battery power, limited bandwidth availability, and limited computational resources and storage space. These characteristics make the traditional time synchronization schemes, i.e., the network time protocol (NTP) and the global positioning system (GPS), unsuitable for WSNs [23]. The following challenges need to be considered to achieve time synchronization [23; 25]:
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 5 Measured and Predicted Results
### Reflection Coefficient  
The measured reflection coefficient of Ant#3 is shown in Figure 17, together with the predicted results. Excellent agreement is observed between the measured and the predicted results. The \(-6\) dB (VSWR \(<\) 3) reflection coefficient bandwidth of the prototyped antenna (Ant#3) is 2.63 GHz (1.37-4 GHz), which covers the two LSA, 16 FDD LTE, 11 TDD LTE, UMTS, DCS/GSM1800, PCS/GSM1900, 2.4 GHz WLAN, and 3.3 GHz WiMAX frequency bands.
# Visual Analytic Method for Students' Association via Modularity Optimization
## 6 Qualitative Analysis of Association among Students
### Discovering Student Communities Based on Modularity Optimization
#### 6.2.1 Definition of Modularity  
[PERSON] highlighted the property of community structure and proposed the algorithms to divide the network into communities based on modularity optimization [9; 27; 28]. Modularity in a weighted network is defined as Equation (6) [29], where \(w(i,j)\) is the weight of the edge-connecting nodes \(i\) and \(j\), and \(k_{i}=\sum_{j}w(i,j)\) is the sum of the weights of edges connected to node \(i\), and \(c_{i}\) is the community to which node \(i\) is assigned. The function \(\varphi(c_{i},c_{j})\) is 1 if \(c_{i}=c_{j}\) and 0 otherwise, and \(m=\frac{1}{2}\sum_{ij}w(i,j)\). The modularity \(Q\) is between \(-1\) and 1, and it can be used as an objective function to look for the divisions with high modularity over all possible partitions:\[Q=\frac{1}{2m}\sum_{ij}[w(i,j)-\frac{k_{i}k_{j}}{2m}]\varphi(c_{i},c_{j}). \tag{6}\]
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ4--Edge Intelligence Application Domains
#### 4.4.6 Energy Management  
In recent years, energy management systems (smart grid [51], smart buildings [24], smart plugs [50] and so on) have received huge research and industrial attention with the explosive development of smart cities [38]. As one of the most critical urban services, electric power systems play a vital role in supporting our society and economy. Therefore, it is incumbent on smart cities to propose solutions to energy management by ubiquitous monitoring and reliable communications.  
IoT manufacturers and application developers are devoting themselves to developing frameworks to store sensed data in edge nodes and perform data processing via analytics at the edge of the network.  
Within the wide scope of this field, the smart grid is a proposition to improve the performance of the standard electric network and manage the various distributed energy resources by continuously monitoring the dynamics of electricity consumption through a considerable number of sensors scattered over the network.  
In a narrower scope of smart homes, there is a typical IoT device, smart plugs (or smart outlets) [161]. A smart plug can monitor the power usage of the appliance plugged into it in real-time, transmit it as well as other measurements to the processing server (edge or cloud), and receive remote commands or settings to control the operation of the connected device, turning conventionally passive devices into \"smart\" ones [50].
## 2 Methodology
### The Proposed Hybrid Models  
In this subsection, the four proposed hybrid models, which contains the WPT-PSO-BPNN model, the EMD-PSO-BPNN model, the ITD-PSO-BPNN model and the VMD-PSO-BPNN model, are developed on the basis of the methodology mentioned above to forecast the day-ahead prices of the wheat, corn and soybean futures which will be described concretely in the next section; the basic structure of these models is given in Figure 3.  
Weight and threshold are two important parameters in the BPNN which would have an influence on the forecasting accuracy. The PSO algorithm, with its advantages of fast rate and high efficiency, is regarded as a widespread optimization tool based on the swarm intelligence. Therefore, we apply this approach in optimizing these two parameters. Furthermore, agricultural commodity futures prices are nonlinear and nonstationary time series data with relatively obvious volatility, which can  
Figure 3: Basic structure of the proposed hybrid model.  
be handled through the decomposition techniques to some extent. Thus, the hybrid models are established. It is necessary to note that, in our study, all components decomposed are normalized into the range of [0, 1] using linear transference method before entering the next step, for better convergence of the BPNN. Certainly, the output data should be rescaled back by reversing the normalization in order to compute the forecasting accuracy on the basis of the original scale of the data. All calculating and modelling processes involved in this paper are realized in MATLAB R2015b.
## 3 Materials and Methods
### Evaluation Metric Calculation  
Various skin lesion classification metrics are used to evaluate the optimized CNN model, including the Dice similarity coefficient (DICE), Accuracy (ACC), Sensitivity (SN), Specificity (SP), Precision (PREC), and F-score to assess the performance of the proposed model. These metrics have been used widely in several studies and they are considered the most common evaluation metrics for image segmentation and classification. More details about these metrics are discussed as follows.  
DICE measures the level of similarity or overlap between the actual result and the predicted outcomes [41]. This measure is defined as follows.  
\[\mathbf{DICE}=\frac{2 TP}{FP\cup 2 TP\cup FN} \tag{4}\]  
where the number of false positive, false negative, and true positive pixels are represented by \(FP,FN,\) and \(TP\), respectively.  
The ACC is a metric representing the ratio of correct results (both positive and negative) to the overall number of samples studied. [41], and has the following definition  
\[\mathbf{ACC}=\frac{TP\cup TN}{TP\cup TN\cup FP\cup FN}, \tag{5}\]  
where true negative pixels are presented by \(TN\).  
The SN, also known as recall or true positive rate (TPR), is a metric that quantifies the number of true positive predictions relative to the total number of positive cases. It is calculated as the ratio of true positive predictions to the number of positive cases. The calculation of SN is given  
\[\mathbf{SN}=\frac{TP}{TP+FN}. \tag{6}\]  
The SP calculates the accuracy of negative predictions by dividing the number of true negatives by the total number of negatives. It is also referred to as the true negative rate (TNR).  
\[\mathbf{SP}=\frac{TN}{TN+FP}. \tag{7}\]  
Precision (PREC), also known as the positive predictive value (PPV), is calculated as the ratio of the number of true positive predictions to the total number of positive predictions made.  
\[\mathbf{PREC}=\frac{TP}{TP+FP} \tag{8}\]  
Furthermore, the F-score is used to evaluate the model since it is the harmonic mean of precision and recall and can provide better insight into model performance. It is given  
\[\mathbf{F-score}=2\cdot\frac{PREC\cdot SN}{PREC+SN}. \tag{9}\]
# Surface Defect Detection Methods for Industrial Products: A Review
## 2 Traditional Feature-Based Machine Vision Algorithm for Surface Defect Detection
### Texture Feature-Based Method  
The texture feature reflects the homogeneity phenomenon in the image and can reflect the organization structure and arrangement properties of the image surface through the gray distribution of the pixels and their nearby spatial neighborhoods. Methods based on texture feature can be further divided into four categories: statistical method, signal processing method, structural method, and model method [14; 15].  
For the statistical method, the main idea is to treat the gray value distribution on the surface of an object as a random distribution, analyze the distribution of random variables from the perspective of statistics, and describe the spatial distribution of gray value through histogram feature, gray level co-occurrence matrix, local binary pattern, autocorrelation function, mathematical morphological, and other features.  
For the signal processing method, the main idea is to treat the image as a two-dimensional signal and analyze the image from the perspective of signal filter design, so it is also called the frequency spectrum method; the signal processing method includes  
Figure 2: Traditional feature-based machine vision algorithm for industrial product surface defect detection.  
Fourier transform method, Gabor filter method, wavelet transform method, and other specific methods.  
For the structural method, its theoretical basis is the texture primitive theory. The texture primitive theory states that the texture is composed of some minimal patterns (called texture primitives) that appear repeatedly in space according to a certain rule.  
For the model method, the commonly used models for detecting surface defects of industrial products include MRF model and fractal model.  
For the method based on texture features, this paper summarizes some recent application examples in industrial product surface defect detection, which are arranged according to the classification order in Figure 2, and the details are shown in Table 1.
###### Abstract  
A spatial modulation (SM) scheme has been developed as a hopeful candidate for spectral and energy-efficient wireless communication systems, as it provides a great judgment for the system performance, data transmission rate, receiver complexity, and energy/spectrum efficiency. In SM, the data is conveyed by both habitual M-ary signal constellations and the transmit antennas indices. Therefore, the system data rate improvement due to the side information bits transmitted, encapsulated in indices of the transmit antennas, improves the SM transmission efficiency compared to the different MIMO players. The information bits transmitted over the antenna index and data symbol constellation using M-ary signal performance have different levels of bit error rate (BER) performance. This paper proposes unequal error protection (UEP) scheme for image transmission over the Internet of Underwater Things (IoUTs) using SM. The Set Partitioning in Hierarchical Trees (SPIHT) coders encode the underwater image and classify the encoded bits in two categories: critical and uncritical bits. The critical bits are transmitted over the SM index bits and have a low BER while the uncritical bits are transmitted over high order M-ary signal constellation to resolve the underwater acoustic channel bandwidth limitation problem. The proposed SM-UEP technique has been developed carefully with enough justification and evaluation over the measured underwater acoustic channel and the simulated channel. The simulation results show that the proposed SM-UEP can increase the average peak signal-to-noise ratio (PSNR) of the reconstructed received image considerably, and significantly.  
IoUTs; spatial modulation; underwater communication; unequal error protection; SPIHT coder; image processing +
Footnote †: journal: Sensors