esa-sceva/satcom-chunk-collection huggingface dataset:
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
## 3 Materials and Methods
### Numerical Optimization  
We focus on optimizing a single second-order section (SOS) whose time-varying difference equation can be expressed as in (3), where \(a_{k}[n]\) and \(b_{k}[n]\) are the time-varying coefficients for \(k\in{0,1,2}\). Over a finite horizon \(H\), these coefficients are allowed to vary from sample to sample, while for \(n>H\) they revert to their stationary values, which ensures long-term stability. The objective function is formulated to minimize the transient response by penalizing deviations of the filter output \(y[n]\) from a desired steady-state target \(y_{\rm ss}\) during the initial \(H\) samples:  
\[J(\theta)=\sum_{n-1}^{H}(y[n;\theta]-y_{\rm ss})^{2}, \tag{5}\]  
where  
\[\theta=\{a_{1}[n],a_{2}[n],b_{0}[n],b_{1}[n],b_{2}[n]\mid n=1,...,H\}, \tag{6}\]  
collectively represents all time-varying coefficients \(a_{k}[n]\) and \(b_{k}[n]\) for \(1\leq n\leq H\). The cost function \(J(\theta)\) encodes the principle that the filter output should converge as quickly as possible to \(y_{\rm ss}\), thereby reducing transient state. Additionally, a set of stability constraints ensures that the time-varying SOS stays a valid candidate. One can find further details about time-varying filter stability in the author's previous works. To solve this constrained minimization problem, we employ the Sequential Quadratic Programming (SQP) algorithm  
Figure 3: Step responses: 6 th-order low-pass elliptic filter decomposition.  
Figure 2: Magnitude responses: 6 th-order low-pass elliptic filter decomposition.  
for its robust handling of nonlinear constraints. We have also proposed a bounding constraint in the form of  
\[-1\leq a_{k}[n],b_{k}[n]\leq 1, \tag{7}\]  
which is a simple yet effective means to prevent runaway filter gains that might otherwise prolong or destabilize the transient.  
Once optimized, the coefficient set is stored and used in real-time operation during known transient events (e.g., a step input or signal onset). After \(H\) samples, the coefficients revert to their stationary (LTI) values:  
\[a_{i}[n]=a_{i}^{LTI}[n],\ \ \ \ \ b_{i}[n]=b_{i}^{LTI}[n]\ \ \text{for}\ n>H, \tag{8}\]  
This hybrid approach maintains the filter's desired long-term frequency response while minimizing initial settling time.
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 4 Applications  
In this section, two examples are given to demonstrate the effectiveness of the proposed method. In the first example of Iris data set classification, which is similar to fault diagnosis, multiple instances are selected as the test set to simulate the selection of the test sample in fault diagnosis, and the identification accuracy is high. In the second application, the fault diagnosis experiment of a motor rotor is carried out to demonstrate the validity in engine fault diagnosis.
# Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
## 1 Introduction
In this final adaptation, melodic supervision was chosen, which utilizes pitch information, particularly the onset of musical notes, to improve the performance. This was accomplished by employing a multi-task learning approach [14] that included pitch detection. The two-stage adaptation process of the SSL model resulted in an improvement, achieving a better performance on multiple levels for Korean phoneme recognition in speech and singing voices. In short, the study explored the usefulness of SSL for phoneme recognition in languages with insufficient data, and validated the effectiveness of melodic supervision in our study. For evaluation, the performance on multi-level error rates based on the Word Error Rate (WER) was determined to prove the validity of our method. The transfer learning improved the phoneme-level error rate of Korean speech by as much as 31.19%. Figure 1: Overview of the SSL-based low-resource language ALT. Additionally, the melodic supervision improved the phoneme-level error rate in singing voices by 0.55%. Therefore, the contributions of this study can be summarized as follows:  
1. An SSL-based detouring method to overcome the insufficient speech and song dataset was explored for the recognition of Korean phonemes in speech and singing voices. The method is quite general for the phoneme recognition of speech and the singing voices of any other language where the labelled data are insufficient. 2. In the proposed model, the HuBERT-type SSL model which was pre-trained on a large-scale English model proved to be useful for capturing the general representation of linguistic phoneme, which is adapted to Korean phoneme in the following stage with a relatively small-scale Korean dataset. 3. In the speech-singing voice adaptation, melodic supervision by employing multi-task learning proved to be useful for better phoneme recognition. The ultimate goal of our work was directed towards the ALT of traditional Korean folk songs, which have been transferred via a mouth-to-mouth manner without text or music score. Thus, the method in the paper may be helpful in preserving the traditional ethnic folk songs of diverse countries. This paper is organized as follows. Section 2 reviews the related prior works, while Section 3 presents a comprehensive discussion of the methodology. Section 4 provides the details of our datasets, elucidates the evaluation metrics, and presents the experimental findings together with the discussion. Finally, Section 5 concludes the paper, while exploring potential directions for future research.
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
## 2 Market Analysis of the PC Industry
### Market Requirements in the PC Industry  
The PC industry has a design strategy to review solutions for products such as all-in-one silicon-on-chips (SoCs) and CPUs with chipsets combined with memory, as well as systems in regular design. As shown in Figure 2, PC systems use PCI Express (PCIe) and PCIe topology to build a system for market demand [17].  
Cloud computing, cloud applications, and cloud storage reduce the requirement for personal equipment. Cloud market demand does not follow traditional market demand for notebooks, desktops, and servers.  
Market Requirements for Security  
More secure services and systems have been reviewed for network and cloud usage. The cloud base environment relies on servers and services through the internet. This process transfers data through different devices and across different routers, which means that servers face challenges such as security, reliability, and safety control processes [18]. Data protection and data security requirements have increased in the past few years. In 2019, the US announced security concerns throughout various industries, which impacted businesses in the US. The reaction worldwide was to face these presented risks that year and continue to enhance security solutions in platforms and services [4].  
Data from Microsoft Azure has indicated there is a rapidly increasing market and consumer base for security. Customers are willing to pay more for security solutions, since cloud environment applications have grown in recent years [19].  
In 5G communication, Function Virtualization (NFV) and Software-Defined Networking (SDN) have faced Distributed Denial-of-Service (DDoS) security risks [20; 21]. 5G applications with network slices featuring various layers face different security risks. There are infrastructure layers with physical devices, backend services with cloud computing, and storage, which create more security considerations. In terms of security considerations, personal data breaches, physical safety, and personal privacy were found to be concerns for 80% of respondents [19].
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 5 Experiments
### Evaluation Criterion
#### 5.2.2 Center Error  
The key to successful harvest is to determine the center of the target apple. Finding the center of apple and guide the actuator to accurately capture the apple. Due to the apple belongs to the quasi-circular rather than the standard round, there is usually a deviation in the determination of the center. The center is a 2D coordinate, so the center deviation can be defined as the distance between two points. The smaller distance represents, the better effect of fitting method, otherwise the worse. In other words, the center error was small, indicating high positioning and recognition accuracy.  
Assume that the actual coordinates of apple center are \((x_{0},y_{0})\), the coordinates of fitting out center \((x,y)\), the center error is defined as \(\Delta E_{r}\)  
\[\Delta E_{r}=\sqrt{(x_{0}-x)^{2}+(y_{0}-y)^{2}} \tag{8}\]
## 3 Distance Vector-Hop (DV-Hop) and Differential Evolution (DE) Algorithms
### Differential Evolution Algorithm  
Differential evolution (DE) algorithm is a new evolutionary computing technology [13], designed to keep the population-based global search strategy of the evolutionary algorithm utilizing real coding, i.e., a simple mutation operation based on difference and one-to-one elimination mechanism to update the population that reduces the complexity of genetic algorithm operation.
# YOLOv4-MN3 for PCB Surface Defect Detection
## 2 Methodology  
### Framework of the Methodology  
The proposed YOLOv4-MN3 for PCB surface defect detection is established based on a cutting edge one-stage detector, YOLOv4, and it consists of dataset building, model training and performance evaluation in three steps, as described in Figure 1. First, all the PCB defect images are collected by a specially designed image acquisition device, after which the augmentation and annotation labeling are conducted. Then, YOLOv4 is modified and trained based on a customized dataset. Finally, the performance of the proposed YOLOv4-MN3 and other SOTA detectors are evaluated and compared.  
Dataset building: The PCB surface defect images were collected by an image acquisition device specially designed and manually labeled by the program labelImg to generate training and image-defect testing datasets with corresponding annotation files.  
Model training: Bumps, broken lines, clutter, scratched, line repair damage, hole losses and over oil-filling, the six categories of the most common surface defects, were selected for the training. The original backbone network CSPDarknet53 of YOLOv4 was replaced by VGG16, Resnet50, Darknet53, MobileNetV2 and MobileNetV3 for the purposes of selecting an appropriate backbone that could decrease memory consumption and computational cost. To better fit to customized defect dataset, the activation functions of the neck and prediction networks in YOLOv4 were replaced with five different activation functions; thus, six different YOLOv4-MN3 detectors were constructed. Finally, fifteen detectors, including ten backbone or activation function-modified YOLOv4 detectors--original YOLOv4, YOLOv3, Faster RCNN, Retinanet and SDD--were trained with PyTorch.  
Figure 1: Framework of the proposed methodology.  
Performance evaluation: The average precision (_AP_), mean average precision (_mAP_) and \(F_{1}\) score were adopted as metrics of detection accuracy, and frames per second (_FPS_) as a speed metric. The models' parameters (_Params_) and _Madds_ were collected to evaluate model complexity.
## 1 Introduction
Basic information about MP excitations in different types of CdTe, GaAs and GaN--based heterostructures and QWs can be found in our previous papers [23; 24; 27; 28; 29; 30; 12] (for a general review concerning the physics of plasmons in a 2 DEG, we recommend Kushvana [31]). Generally, MPs can be excited with radiation of the frequency \(\omega_{exc}\) at magnetic field \(B\) (perpendicular to a 2 DEG) which satisfies the relation:  
\[\omega_{exc}^{2}=\omega_{p}^{2}+\omega_{cr}^{2}, \tag{1}\]  
where \(\omega_{p}\) is the plasmon frequency at \(B=0\), and the cyclotron frequency \(\omega_{cr}=eB/m^{*}\) (\(e\) and \(m^{*}\) is the electron charge and effective mass, respectively). This formula shows that at given frequency of excitation \(\omega_{exc}\), the magnetic field at which a MP is excited is lower than that corresponding to the CR. A difference between these two fields will be called a plasmonic shift. In the Appendix A we give additional information concerning dispersion of magnetoplasmons. In the present paper we are considering plasmonic excitations in CdTe-based quantum structures which were not considered before. First, we focus mainly on samples with two QWs. These wells are separated by a distance of about 44 nm which excludes an overlap of electron wave functions of electrons from different wells. Second, we consider mainly gratings which are etched. As reference samples, we study also unprocessed samples (without any grating) and samples with gratings on wafers with only one QW. Our main experimental technique is measurements of transmission of a monochromatic THz radiation through the samples placed at liquid helium temperature and high magnetic fields. We show that etched gratings form a plasmonic metamaterial whose plasmonic properties at THz frequencies are different from that of metallic gratings. The paper is organized as follows. In Section 2 we describe the samples, the etching technique and the experimental system used. Section 3 is devoted to presentation of results while their discussion is presented in Section 4.
## 2 Materials and Methods
### FT-ICR MS Analysis  
Stock solutions (1 mg/mL) of Bligh-Dyer hydroalcoholic and organic extracts of cricket powder were filtered through 0.45 \(\upmu\)m hydrophobic polypropylene Acrodisc (Sigma-Aldrich, St. Louis, MO, USA) to eliminate debris and then diluted to a final concentration of 0.1-0.2 mg/L in methanol. Formic acid (1% \(v/v\)) and ammonia solution (24% \(v/v\), 2 \(\upmu\)L) were added to all replicates to assist (de)protonation in the positive (ESI (+)) and negative (ESI (-)) polarity mode analyses, respectively, whereas 0.5 \(\upmu\)g L\({}^{-1}\) leucine enkephalin (YGGFL, \(\mathrm{C_{28}H_{37}N_{5}O_{7}}\)) was used as an internal calibrant revealed at \(m/z\) 556.27657 (M\({}^{+}\)H\({}^{+}\)). Further assessment of precision and mass accuracy was achieved by referring to ubiquitous metabolites, such as amino acids and fatty acids, so achieving a maximum mass deviation lower than 0.1 ppm. For each extract, three solutions were prepared as described above and directly infused with a flow rate of 120 mL h\({}^{-1}\) in an electrospray ionization (ESI) source.  
Preliminary ESI (+) MS analyses were performed with a Bruker BioApex Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer (Bruker Daltonics GmbH,Bremen, Germany) coupled with an Apollo I ESI source and a 4.7 T superconducting magnet (FT-ICR lab, Sapienza Universita di Roma). Complementary information for the determination of fatty acids was gathered by using ESI (\(-\)) coupled to a linear ion trap mass spectrometer (LTQ XL, Thermo Fisher Scientific, Waltham, MA, USA). Ultrahigh-resolution mass analyses were carried out on a Bruker SolariX XR FT-ICR equipped with a 7 T superconducting magnet (Magnex Scientific Inc., Yarnton, UK), a ParaCell (Bruker Daltonics GmbH, Bremen, Germany), and an APOLLO II ESI source operated at the Universidade de Lisboa. Spectral analyses, compound identification, and annotation were achieved as already reported [20]. MS spectra were acquired in absorption mode considering a mass range of 100-1000 (resolution of 650,000 at \(m/z\) 400), whereas 200 scans with an acquisition size of 4M were co-added to collect the time domain data. Analyses were carried out in triplicate for each batch.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 4 Implementation and Evaluation Measures
#### Measures for Evaluation  
The proposed equations regarding the optimized protocol extension are Equations (1), (14) and (15). Equations are also implemented in the Java application to measure the cost of associated issues for parameters. However, to assess the effectiveness of the proposed protocol extension, criteria are defined for evaluating the performance of the existing PMIPv6 protocol extensions. For this purpose, a number of quality assessment criteria are defined to assess the effectiveness of the proposed solution in addition to the factors that are affecting the performance of such protocol extensions. Such aspects include the number of nodes, variation in the velocity of MNs, and the effect of RSS on the performance of handover latency, required buffering, and the signaling cost. Based on such aspects, the effectiveness of the existing and proposed scheme is compared. The details of the comparison and obtained results are discussed in detail in the subsequent sections.
## 1 Introduction
The condition of the filter medium, particularly its level of clogging and the number of operational cycles it has undergone, significantly affects performance [25; 26; 27; 28; 29]. Filter press dynamics are inherently complex due to the interplay of multiple non-linear variables, including pressure, flow rate, and cake resistance, which evolve over time. In addition, fluctuations in components, such as membrane pumps, introduce noise, further complicating real-time predictive analysis. Existing data acquisition systems often lack the ability to seamlessly interface with advanced ML algorithms, limiting operational flexibility and optimization potential. According to the work of [PERSON] et al. [6], one problem in using ML in the mining sector is the missing amount of data to train a model. Addressing these challenges requires the integration of real-time data analytics with robust ML capabilities to enable smarter and more adaptive process control. To address these challenges, this work proposes a machine learning-based predictive framework embedded within a digital twin (DT) architecture, a concept that refers to a virtual representation of a physical system, enabling real-time monitoring, analysis, and optimization of its operations through the integration of real-time data and predictive models [30]. The DT is designed to enable real-time estimation of key process variables, specifically pressure and flow rate, while also tracking the condition of the filter medium over successive cycles. By integrating both historical and live data, the approach mitigates the data scarcity issue and enables more robust, adaptive control strategies. The predictive model reduces reliance on manual intervention, enhances process stability, and helps determine the optimal point for filter cloth replacement, improving long-term efficiency and sustainability. By addressing both real-time and historical data, the model can assist operators in determining optimal filter medium utilization, thereby enhancing efficiency and sustainability. Key performance metrics, including root mean square error (RMSE) and mean square error (MSE), are used to evaluate the accuracy of the model in training and validation datasets. The contributions of this work can be summarized as follows. First, a machine learning-based digital twin framework is developed for a chamber filter press, enabling accurate prediction of key operational parameters such as pressure and flow rate across varying experimental configurations. Second, the proposed framework includes predictive modelling of the filter medium efficiency, allowing for proactive maintenance planning and supporting long-term resource sustainability. Finally, the architecture supports seamless data exchange and continuous model updates, ensuring adaptability to evolving operational conditions. In the remainder of the paper, first the methodology is described in Section 2, which contains the experimental setup, parameter selection, and the architecture of the DT, as well as the NN model. Lastly, the results are discussed in Section 3 and conclusions are drawn in Section 4.
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
###### Abstract
These ICTs elements, including sensing devices, monitoring technologies, and digital communications infrastructure, are needed to ensure adequate information exchange between the Smart MG, the consumers, and the Smart Buildings. This interaction is achieved through the Smart Information sub-system which includes the AMI (Advanced Metering Infrastructure) [9]. AMI uses smart meters to gather billing data, track grid status, monitor and control appliances. This system can be classified into smart metering and smart monitoring and measurement system. In which, WSN (Wireless Sensor Networks) provides a communication platform for remote monitoring and system control [9]. Worldwide, buildings consume about 40% of global energy [10]. Therefore, energy-efficient buildings are becoming a must to optimize energy consumption. There are three ways to retrofit a traditional building and make it a Smart Energy-Efficient Building (SEEB): Use highly efficient insulation material, energy-efficient appliances, and energy management system (EMS) [11]. EMSs control appliances and integrate renewable energy sources based on an energy policy that is mandated by the energy department of the MG. ICT is used to execute the energy policy rules and automate the control process in SEEB [12]. The benefits of controlling smart building's loads and Generation Units (GU) are not only reducing energy consumption but also redirecting the energy flow to where it is needed. The outcomes of this project are as follows: First, an intelligent framework that monitors and control loads (heaters) of the building is presented. Second, a database to archive user behavior and environmental data is developed. Third, a software tool to control Heating Ventilation Air Conditioning (HVAC) appliances is implemented [13].
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 6 Future Work  
Currently, the set of anchor nodes is selected statically based on the current drone position, whereby each time the nearest anchor nodes are selected. In environments with many obstacles, the nearest anchor nodes are not always the most reliable due to fading, reflections, absorption, etc. In complex environments, the mobile tag can take into account the quality of the information obtained from anchor nodes. For example, the Channel impulse response (CIR) could be used by a machine learning algorithm to obtain error estimates and to detect line-of-sight information, allowing to dynamically update the set of selected anchors nodes for the next superframe.  
The correct placement and calibration of the anchors is a crucial step in the installation process to get accurate results. One can manually place each anchor and measure the difference to a reference point with a laser meter, or using reference points on the building-scheme to measure distance. However, this can be a lot of work which is not guaranteed to be error-free. Therefore, auto calibration of the anchors can determine their relative position without any installation process. In fact, anchor  
Figure 8: Path loss model for both radio technologies. (**a**) Experimental and simulation-based path loss model for our UWB board. (**b**) Simulated path loss model for the sub-GHz radio [21].  
calibration is an interesting research track which requires a paper on its own to discuss the topic. Many approaches are already documented in literature, e.g., using multilateration, Linear Programming (LP) or Semidefinite programming (SDP).