esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The essential elements of the BMS are sensors that quantify characteristics such as temperature, humidity, light intensity, and occupancy [15]. The energy produced from foot traffic by piezoelectric sensors is one of the methods for power generation. Real-time energy consumption is monitored, providing insights into the operating condition of household appliances using remote monitoring devices [16]. Figure 2 depicts the components and  
Figure 1: Four layer models for IoT. their linkages inside the IoT-implemented smart building. This article analyzes the basis for delivering intelligent services to customers, including energy usage monitoring, market comparison, comparative analysis, and rule-based setup. The suggested system integrates power consumption management and remote on-off control as a demonstrative example [17]. This study seeks to (i) collect relevant articles from the Scopus database utilizing primary keywords such as smart building, (IoT, sensor systems, and energy management; (ii) analyze the gathered data to identify the relationships among these keywords; (iii) assess the IoT communication protocol for sensor deployment in a smart building; and (iv) recognize the applications and challenges associated with the implementation of sensor systems and IoT in a smart building [18]. This study examines the deficiency in incorporating IoT technology inside urban structures by emphasizing the pressing need for efficient resource management amid rising urbanization. It highlights the evolution of conventional BAS into scalable, energy-efficient frameworks via IoT integration. The study enables real-time monitoring of essential environmental factors and optimizes energy management by integrating sensor systems into BMS. Furthermore, it delineates the IoT architecture and its four fundamental levels, establishing a foundation for dependable connection among devices. The project ultimately seeks to improve user comfort, sustainability, and economic development in smart buildings via new energy management methods. The major contributions of this review paper are  
* Performs an exhaustive systematic evaluation of the available literature on IoT applications in smart buildings, essential for comprehending current trends and identifying research needs. * Highlights the crucial impact of IoT in reducing energy consumption, especially within the construction sector, which accounts for more than 40% of worldwide energy use and greenhouse gas emissions. * Emphasizes the uses of IoT while examining the obstacles to its adoption in smart buildings, offering a comprehensive perspective on the issues encountered in this domain. * Proposes a structure for delivering intelligent services to users, including energy consumption monitoring and remote management of equipment, therefore augmenting the practical implementation of IoT technology. * Offers insights and suggestions for prospective study trajectories in IoT applications in construction engineering and management, intended to assist forthcoming researchers.
# Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation
## 2 Recursive Optimal FIR Filter and Adaptive FIR Filter
### Adaptive FIR Filter with Sequential Noise Statistics Estimation
Since the noise statistics estimated in the AFIR filter are obtained by using the measurements a step ahead of the estimation time, the modified sequential noise statistics estimation method in the proposed AFIR filter may provide more adaptive estimation results than results given by previous sequential noise statistics estimation methods in adaptive Kalman filtering.
## 3 The Proposed Coverage Flight Path Planning Algorithm
### Locating Area of Interest on the Modelled Map and Extracting Free Spaces Geometry
#### 3.2.1. Locating Area of Interest on the 3D Modelled Map  
The AOI can be represented with the sequence of \(n\) vertices \(\{v_{1},v_{2},v_{3},\ldots,v_{n}\}\). Each vertex \(v_{i}\) has three co-ordinates values \((v_{x}(i),v_{y}(i),v_{z}(i))\). Considering \(v_{i}\) as the first vertex of the AOI, the next vertex to \(v_{i}\) will be \(v_{next(i)}\), where \(next(i)=i(mod\,n)+1\). The edge \(e_{i}\) connecting two vertices \(v_{i}\) and \(v_{next(i)}\) has length \(l_{i}\), where \(l_{i}=\left\|v_{i}-v_{next(i)}\right\|\). The AOI can contain many obstacles and no-Fly Zones (NFZ) of varying geometries. The AOI boundary is set of collision free vertices and edges. In some cases, the AOI vertices, edges or both can intersect with obstacles as shown in Figure 3.  
Figure 2: Conceptual overview of the proposed coverage flight path planning algorithm.
## 3 Proposed Methodology
### Weighting Schemes
#### 3.3.2 Task-Specific Weighting Scheme  
In the task-specific weighting scheme, weights are determined based on the importance of each task or the dataset's characteristics. Domain knowledge or prior research findings are used to assign appropriate weights to \(loss_{dis}\) and \(loss_{dim}\). For example, if discrete emotion recognition is deemed more critical for a specific application, \(\alpha\backslash\)alpha might be set higher to emphasize the classification task.
# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 3 Material and Method
### Evaluating the Results of Object Detection Algorithms  
Some values are required to understand whether object detection models provide good or bad results and to evaluate their performance. The values used in the performance evaluation were calculated based on the accuracy rates of the models in predicting the objects in the image data in the dataset. If the model detects smoke in the image given to the model for the smoke category, it is considered TP, whereas if the model detects it as not smoke, it is considered FN. If the model detects smoke that does not exist in the image given to the model for the smoke category, it is considered an FP, whereas if the model detects smoke as absent, it is considered a TN. In this case, TPs and TNs show that the model performs the detection process successfully, and FNs and FPs show that the model detects smoke incorrectly.  
Precision measures the ratio of TP values to all positive predictions of the model, as shown in Equation (5).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
Recall is the value that gives the ratio of TP values to all positive situations in a real situation, as shown in Equation (6).  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
F1, which uses recall and precision values as a common evaluation point on a single value, is the value obtained by applying the harmonic mean process to avoid erroneous evaluation of the normal average calculation when the two values are extreme (see Equation (7)).  
\[\mathbf{F_{1}}=\mathbf{2}\times\frac{\mathbf{Precision}*\mathbf{Recall}}{ \mathbf{Precision}+\mathbf{Recall}} \tag{7}\]  
The evaluation data that provide the accuracy value of the model, shown as a single value, is the mAP. When the equation for the mAP value is examined (see Equation (8)), n represents the number of classes and APk represents the AP value of class k.  
\[\mathbf{mAP}=\frac{\mathbf{1}}{\mathbf{n}}\mathbf{\sum_{k=1}^{k=n}\mathbf{AP _{k}}} \tag{8}\]
## 3 Model Structure
### Variational Mode Decomposition  
The key advantage of VMD [34] is its ability to adaptively determine the number of modal decompositions based on the signal's actual conditions. An adaptive search mechanism precisely matches the modes' center frequencies and bandwidths, efficiently separating the intrinsic modal components (IMFs) and segmenting the signal into frequency domains, thereby identifying key signal components effectively [35].  
Figure 1: Overall model architecture.  
Assuming the original time-series data are decomposed into k components, each a modal component with a finite bandwidth and centered frequency, while minimizing the estimated bandwidths' sum, with the constraint that the sum of all modes equals the original signal, the VMD-constrained variational model proceeds as follows in Equation (1):  
\[\begin{split}\underset{\{u_{k},u_{k}\}}{min}\left\{\sum\limits_{k} \left\|\partial_{t}\right[\left(\delta(t)+\frac{j}{\pi t}\right)*u_{k}(t) \right]e^{-j\omega_{k}t}\right\|_{2}^{2}\right\}\\ s.t.\sum\limits_{k}u_{k}=f\end{split} \tag{1}\]  
Here, \(u_{k}=\{u_{1},u_{2},\ldots,u_{k}\}\) represents the modal function, \(w_{k}=\{w_{1},w_{2},\ldots,w_{k}\}\) signifies the central frequency of each mode, \(j/\pi t\) is a complex term where \(j\) denotes the imaginary unit and \(1/\pi t\) is a decay function, and \(e^{-j\omega kt}\) is an exponential function where \(\omega_{k}\) is a frequency variable. Researchers convert the constrained optimization problem into an unconstrained variational problem by applying a quadratic penalty term and the Lagrangian multiplier method. Consequently, they formulate the augmented Lagrangian function as follows in Equation (2):  
\[\begin{split}&\text{L}(\{u_{k}\},\{\omega_{k}\},\lambda):=\alpha \sum\limits_{k}\parallel\partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t} \Big{)}*u_{k}(t)\Big{]}e^{-j\omega_{k}t}\parallel_{2}^{2}\\ &+\parallel f(t)-\sum\limits_{k}u_{k}(t)\parallel_{2}^{2}+ \langle\lambda(t),f(t)-\sum\limits_{k}u_{k}(t)\rangle\end{split} \tag{2}\]  
Here, \(\alpha\) represents the penalty parameter, and \(\lambda\) signifies the Lagrange multiplier.  
The comprehensive algorithmic flowchart of the Variational Mode Decomposition (VMD) algorithm is illustrated in Figure 2 below:  
Figure 2: Flowchart of VMD algorithm.  
In Figure 2, we first transform the constrained optimization problem into an unconstrained variational problem using a quadratic penalty term and the Lagrange multiplier method and subsequently update the generalized function until the specified iterative constraints are satisfied.
## 6 Simulation and Analysis  
### Simulation Parameters  
In this section, we develop a time and power allocation scheme that maximizes system energy efficiency versus three different scenarios of full-duplex dual-antenna energy harvesting, full-duplex single-antenna energy harvesting, and half-duplex single-antenna energy harvesting. The distribution scheme verifies the simulation results and studies the influence of some parameters on the energy efficiency of the system. The specific simulation parameters are shown in the Table 1.
# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 3 The Proposed CoNet-MS Framework and Its Main Modules
### Problem Statement  
In supervised learning, assume that there are \(N\) training samples consisting of the dataset \(D_{tra}=\{\left(x_{i},y_{i}\right)|i=1,\cdots,N\}\), where \(x_{i}\in X\) denotes the \(i\)-th sample, e.g., image, \(y_{i}\in Y\) denotes the label of the \(i\)-th sample, \(X\) is the feature space, and \(Y\) is the label space. The observed label of each training sample is encoded as a one-hot vector with the length \(C\). In addition, we introduce a clean dataset \(D_{val}=\left\{\left(x_{i}^{v},y_{i}^{v}\right)\middle|i=1,\cdots,M\right\}\) without noise, where \(M\ll N\) and the superscript \(v\) denotes that the dataset is the validation set. Our goal is to find a model mapping function \(f:\ X\to Y\) parameterized by \(\theta\), where \(X\) denotes the model input and \(Y\) denotes the model prediction. Typically, the empirical risk \(R(f)\) is computed on the training samples, shown as Equation (1).  
\[R(f_{\theta})=E_{\left(x,y\right)\ \in\ D_{tra}}[L(f_{\theta}(x_{i}),y_{i})] \tag{1}\]  
In Equation (1), the expectation \(E(L)\) denotes the math expectation of the loss function, and \(L(f_{\theta}(x_{i}),y_{i})\) is the cross entropy, shown as Equation (2).  
\[L(f_{\theta}(x_{i}),y_{i})=-y_{i}logf_{\theta}(x_{i}) \tag{2}\]  
The optimal classifier \(f\) parameterized by \(\theta^{*}\) aims to minimize the risk, given as Equation (3).  
\[\theta^{*}=\underset{\theta}{\text{argmin}}R(f_{\theta}) \tag{3}\]  
If the labels of the dataset are exactly correct, the classifier parameter \(\theta\) can be learned by optimizing Equation (3) via the gradient descent method. However, in this study, there exist unknown noise types and distributions in the training dataset \(D_{tra}\), and the classifier can only observe the set of noisy training labels \(y_{i}\). The task involves training a DNN to reduce the negative impact of the noisy labels and predict the true labels. In the following subsections, we describe the training process of the proposed method on noisy datasets.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 3 Proposed VSCL-MAC Architecture  
The design predominantly comprises control and data units operating on TDMA frame cycles and enables multihop communication by maintaining \(k\)-hop neighbor information at every network node. Therefore, the design includes the neighborhood discovery phase at the start of the network and follows with control frames and data transmissions, as shown in Figure 1.  
Given the limitations and importance of expeditious tactical communication, each unit of the protocol operates over TDMA. The neighbor discovery unit runs only once at the start of the network; hence, it is called the \"one-time neighbor discovery\" process. In the first TDMA frame, the initial \(2N\) slots are used to transmit neighbor discovery (NBR_DSCV) packets. Each node transmits the packets in its two-hop vicinity on fixed slots for \(k\)-hop neighbor discovery. After neighborhood discovery, control frames \(C_{i}\) are transmitted for the node's coordination on a non-contention based time-slotted common control channel. These frames are used to send REQ (request), REQ_ACK (request acknowledgment), or BRDSCT (broadcast) packets and perform route establishment for multihop data transmission. In the control unit, the number of control slots is fixed; each corresponds to the node MAC ID. These control slots' duration is specified in bits that depend on the number of radios in the network. In the data unit, DATA packets are transmitted over dynamic time slot vectors to send actual messages, i.e., text, images, or voice. The duration of data slots for a node depends on the type of data the sender wants to transmit.  
The following fundamental elements are used to descend a cross-layer MAC design for tactical SDR-based multihop communication network.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background  
We briefly summarize the models introduced in this survey. Most approaches applied four models. Perceptron receives an input signal and outputs one signal. Each input signal has a weight, and this weight represents the importance of the input signal. Single-layer perceptron have only input and output stages, and multi-layer perceptron add a hidden layer between input and output. Support Vector Machine (SVM) is an algorithm similar to perceptron and is a method of defining a decision boundary to obtain a baseline for classification and maximizing the margin between the decision boundary and the actual data. Recurrent Neural Network (RNN) has input signals, output signals, and weights like perceptron, but has several hidden layers inside, and these hidden layers are not only affected by the current input but are cyclically connected to memorize previous input information. The left side of Figure 1 is the RNN cell. To calculate the hidden state of the current time \(t\), it is calculated as follows using the hidden state \(h_{t-1}\) of the previous time \(t-1\) and the input \(x_{t}\). Next, the calculated \(h_{t}\) value is sent to the output layer and becomes an input value for calculating \(h_{t+1}\) again. Long Short-term Memory (LSTM) [6] is a proposed method to solve the disadvantage that the current hidden layer loses the memory of the previous input information when the input of the RNN becomes long. In order to solve the problem, unnecessary memories are deleted by adding a value called cell state, and things to remember are selected. The  
Figure 1: (**a**) The Architecture of Recurrent Neural Network (RNN) cell; (**b**) The Architecture of Long Short-term Memory (LSTM) cell. right side of Figure 1b is an LSTM cell. LSTM has three gates to obtain hidden state and cell state values. The first forget gate (\(f_{t}\)) is a gate to delete memories. As the second input gate (\(i_{t}\),\(g_{t}\)), it is a gate to remember the current information. Finally, the output gate (\(o_{t}\)) is a gate to calculate the hidden state at the current point in time. The cell state \(c_{t}\) can be obtained by multiplying two values (\(i_{t}\),\(g_{t}\)) calculated at the input gate for each element and adding the memory selected at the input gate to the result of the forget gate. The hidden state \(h_{t}\) can be obtained by performing an entrywise product for the cell state \(c_{t}\) and the output gate (\(o_{t}\)). **3. Review of Machine Learning-Based System Performance Optimization Research**  
This section reviews machine learning-based system performance optimization research. Table 1 is a summary of the paper before surveying the paper.
# Interoperability of the Time of Industry 4.0 and the Internet of Things
## 2 Related Work
### A Semantic Approach for Achieving Intelligent Interoperability in Industry 4.0
According to their findings, none of the ontologies are comprehensive and struggle in documenting all the key concepts for semantically annotating end-to-end IoT applications. Due to the extensive list, the authors did not offer in-depth evaluations of all ontologies. Nevertheless, [PERSON] et al. concluded that due to the influence of use cases or systems for which the ontologies are developed, the ontologies are system specific and not useful outside their own applications. All the presented works outline the fact that the developers of projects in the IoT community design their own ontologies without trying to reuse existing works. This approach harms the attempt of defining a shared understanding of what \"intelligent interoperability\" and clearly outlines the need to reuse existing ontologies. In particular, if we want to follow the guidelines presented by the authors in [10], we need to focus on defining a clear set of key concepts that will represent a core, shared understanding of the representation of a device. [PERSON] et al. [13] initiated this effort and introduced their own key concepts for evaluating existing ontologies; however, their key concepts did not include actuators, which are fundamental for Industry 4.0, and mainly focused on sensing the environment. Another attempt at defining a set of key concepts was performed by [PERSON], [PERSON], [PERSON], and [PERSON] in [6]; in particular, they evaluated LOV-approved generic IoT ontologies on the following key concepts: (i) device; (ii) software agent; (iii) sensor; (iv) observation; (v) actuator; (vi) action; (vii) service; (viii) energy; and (ix) lifecycle. As result, their proposed ontology is considered one of the best for describing IoT devices according to an assessment performed by the authors. In particular, it shows that only five out of the nine evaluated ontologies represented actuators to some degree. The following table, Table 1, shows an overview of the related work on the semantic web for IoT ontologies and ontology development that we believe are relevant for achieving \"intelligent interoperability\" in Industry 4.0 case studies. The research domains of the Internet of Things and semantic web are extremely active and scattered at the same time. Consequently, it is very hard to track progress and initiatives, as also outlined by [PERSON] et al. [16]. We believe that this subset of works can serve as an \"entry point\" for researchers that are not familiar with these domains in order to understand core concepts and grasp future research directions without suffering from information overload.
## 4 Methods
### VAE Dimensionality Reduction and Denoising
#### 4.3.1 Encoder and Latent Space  
The encoder is a neural network that maps the input data \(x\) to the latent space \(z\). VAE assumes that the latent representation of the input data follows a Gaussian distribution and models the latent structure of the data through this distribution. Specifically, the encoder outputs the mean \(\mu(x)\) and standard deviation \(\sigma(x)\) of the latent variable \(z\), as shown in Equation (8):  
\[\mathrm{q}(z|x)=\mathcal{N}\Big{(}z;\mu(x),\sigma^{2}(x)\Big{)} \tag{8}\]  
To sample from this distribution, VAE introduces the reparameterization trick, generating the latent variable \(z\) using Equation (9):  
\[z=\mu(x)+\sigma(x)\odot\epsilon,\epsilon\sim\mathcal{N}(0,I) \tag{9}\]  
where \(\epsilon\) is noise sampled from a standard normal distribution, and \(\mu(x)\) and \(\sigma(x)\) are the mean and standard deviation output by the encoder network. The symbol \"\(\odot\)\" represents element-wise multiplication.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 5 Electro-Thermo-Mechanical Multi-Physics Coupling Analysis
### Coupling Simulation of the Thermo Mechanical Model and Circuit Model  
The dynamic coupling between stresses from the thermo-mechanical model and electrical stresses from the circuit model is central to the reliability prediction of aerospace communication modules discussed in this article. Under thermal cyclic loading, plastic strain accumulation in solder joints leads to a significant increase in contact resistance. For instance, an equivalent plastic strain of 0.00264 in FPGA solder joints, as shown in Figure 19, results in an increase in contact resistance by approximately 8%. Integrating this mechanical degradation into the circuit model causes transient voltage drops in the FPGA core supply, with peak-to-peak voltage fluctuations expanding to \(\pm\)3%, as shown in Figure 20. These voltage fluctuations exacerbate the NBTI effect through dynamic threshold voltage drift. Experiments by [PERSON] et al. [32; 33; 34] indicated that even minor voltage fluctuations, such as \(\pm\)3%, can increase the threshold voltage drift rate in MOS transistors by 12-15%, leading to delays of 8-10 ns in critical FPGA signal paths, which cause periodic data validation errors. Such timing errors force frequent resets of control logic, driving transient current peaks from 185 mA to 210 mA, further intensifying Joule heat accumulation in solder joints. This initiates a closed-loop failure chain from mechanical deformation to electrical degradation and, ultimately, to thermal stress intensification.  
Figure 19: Equivalent plastic strain curve of the critical solder joint.  
Temperature fluctuations also directly impact static power consumption via the leakage current in SRAM solder joints. For example, periodic variations of \(\pm\)5 \({}^{\circ}\)C, as shown in Figure 17, cause an 18% rise in the SRAM leakage current [35,36], increasing static power consumption from 48.5 mW to 54.2 mW. This increased power consumption not only amplifies local temperature gradients, raising the temperature difference from 4.2 \({}^{\circ}\)C to 6.5 \({}^{\circ}\)C, but also exacerbates solder joint strain amplitudes, with a 22% increase in the strain amplitude, as depicted in Figure 19b. This is due to thermal expansion mismatch. Ultimately, this dynamic electrical-thermal-mechanical interaction accelerates fatigue crack propagation. The coupling mechanism revealed here highlights the limitations of traditional single-field models. Ignoring dynamic feedback, such as predictions based solely on electrical stress, leads to overly optimistic life estimations. In contrast, the multi-physics framework presented in this article captures stress interactions in real-time, controlling prediction errors within 10%.