esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 5 Proposed Algorithm
### The Complete Procedure of the Improved Localization Algorithm Is Outlined as Follows  
To address the issue of poor distance estimation accuracy caused by non-uniform node distribution in the IoT, the BGWO algorithm is incorporated into the localization phase for anchor node selection. Each anchor node is initially encoded using \"0-1\" coding. Anchor nodes with large distance estimation errors are excluded based on changes in the fitness values of the grey wolves. In the multilateration localization phase, only the anchor nodes selected by BGWO are used for localizing unknown nodes, and the GWO algorithm replaces the least squares method for estimating the unknown node's position. While retaining the advantages of the original DV-Hop algorithm, its third phase is optimized and enhanced. The complete flowchart of the improved DV-Hop localization algorithm is presented in Figure 2.
###### Abstract  
Developing modern products involves numerous domains (controlling, production, engineering, etc.) and disciplines (mechanics, electronics, software, etc.). The products have become increasingly complex while their time to market has decreased. These challenges can be overcome by Model-Based Systems Engineering (MBSE), where all development data (requirements, architecture, etc.) is stored and linked in a system model. In an MBSE system model, product requirements at the system level can lead to numerous technical variants with conflicting objectives at the parameter level. To determine the best technical variants or tradeoffs, Multidisciplinary Analysis and Optimization (MDAO) is already being used today. Linking MBSE and MDAO allows for mutually beneficial synergies to be expected that have not yet been fully exploited. In this paper, a new approach to link MBSE and MDAO is proposed. The novelty compared to existing approaches is the reuse of existing MBSE system model data. Models developed during upstream design and test activities already linked to the MBSE system model were integrated into an MDAO problem. Benefits are reduced initial and reconfiguration efforts and the resolution of the MDAO black-box behavior. For the first time, the MDAO problem was modeled as a workflow using activity diagrams in the MBSE system model. For a given system architecture, this workflow finds the design variable values that allow for the best tradeoff of objectives. The structure and behavior of the workflow were formally described in the MBSE system model with SysML. The presented approach for linking MBSE and MDAO is demonstrated using an example of an electric coolant pump.  
model-based systems engineering; mbse; multidisciplinary analysis and optimization; MDAO; centrifugal pump; automotive coolant pump; development; design; test; optimization Article
## 3 User Study
### Devices
#### 3.2.1 Virtual Reality (VR) Interaction Interface  
In the realm of Virtual Reality (VR), the interaction interface primarily revolves around specialized motion controllers. These controllers are designed with precision and functionality in mind, typically consisting of ergonomic handheld devices equipped with buttons, triggers, and sensors. Users can manipulate virtual objects within the digital environment through direct physical gestures. This includes actions such as pointing, grabbing, rotating, and scaling virtual objects. Importantly, VR motion controllers often provide tactile feedback to users, enhancing immersion by simulating the sensation of touching and interacting with objects. Users can physically reach out and grasp objects in the virtual world, offering a high degree of control and precision in spatial interactions. However, it is essential to acknowledge that the learning curve associated with VR motion controllers may vary among users, particularly those new to VR technology. The initial unfamiliarity with the controllers can lead to a brief adjustment period, which can affect the user experience during the early stages of interaction.  
Figure 3: Experimental setup.
## 2 Overview of Drive-by Condition Assessment of Railway Bridges
The success of most modal parameter-based methodologies was only proved via numerical simulations, and their results are quite susceptible to external factors such as temperature variations, wind, the mass ratio between vehicle and bridge, and presence of other vehicles on the bridge, among others. In contrast, non-modal parameter-based methods do not explicitly seek the computation of bridge modal parameters, but instead focus on the bridge deflection under passing loads (e.g., apparent profile or change in curvature) or on the dynamic response of the vehicle crossing the bridge, which is also the method examined in this paper. Numerous research groups have conducted both numerical and experimental investigations to explore various methodologies for assessing the condition of railway bridges using vehicle scanning (drive-by) techniques. These studies evaluated the potential of detecting diverse types of damage and devising strategies to address operational and environmental interferences associated with this assessment approach. Part I of this review article provides a detailed overview of several of these recent studies identified by the authors. The article's layout is illustrated in Figure 1 through a comprehensive flowchart. To enhance clarity, the article was divided into subsections based on the two main categories into which the applications of drive-by methodologies in railway bridges are divided. Section 3.1 discusses the application of drive-by methodologies in railway bridges for the extraction of modal parameters. This involves, specifically, the use of sensors mounted on vehicles to estimate the bridge's natural frequencies, mode shapes and damping ratios. Section 3.2 focuses on the aspects related to railway bridge damage identification through drive-by techniques, namely feature extraction and machine learning (ML) methodologies. This clear and concise categorization provides a structured approach to better understand the contents presented in the following sections of the article. It is worth noting that although some of the studies presented in Section 3.1 do not specifically refer to the application of the drive-by methodology in the context of railway bridges, the importance of including these studies in this literature review lies in the fact that, due to their analytical or numerical nature, these works provide insight into the premises of the drive-by methodology and, to a large extent, can be generalized to the bridge infrastructure for railway transportation systems. Similarly, some of the works reviewed in Section 3.2.2 present feature extraction methodologies that can be applied to railway bridges, although they were not originally studied in this context.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 2 Materials and Methods
### Classification Methods  
To build and evaluate the performance of different classification models, the MATLAB Classification Learner App was used, allowing quick training, testing, and validation of several classifiers. The methods used were Linear Discriminant Analysis (LDA) [43], decision trees (DT) with a maximum number of splits of 20 [44], Kernel Naive Bayes [45],KNN with K = 10 [46], linear Support Vector Machines (SVM) [47], and Ensemble Bagged Tree classifiers [48] with default parameters for screening. The previously obtained features were used as inputs and the labels of each segment or scattered window as target. Later, the classifiers with the best performance in terms of accuracy and F1-score were tested with PCA-obtained features.
## 2 Materials and Methods
### Assessment of Clinical Image  
Regarding the clinical data, results from a previous study were used, with permission [22]. The clinical research methodology is described below. This study was conducted in accordance with the Declaration of Helsinki and received approval from our institutional ethics committee (Approval No. 2021-1-1059). Informed consent was obtained from all participating patients. A total of 40 patients scheduled to undergo unilateral THA with a Ti-Nb-Sn stem were enrolled in the Ti-Nb-Sn group. The inclusion criteria included patients over 20 years of age with a diagnosis of osteoarthritis, avascular necrosis, or rheumatoid arthritis. Patients were excluded if they had previously undergone surgery on the affected hip, including hip arthroplasty, osteotomy, or tenotomy, had bilateral hip disorders, or had rheumatoid arthritis classified as Charnley category C (multiple joint involvement or other conditions severely limiting mobility) [29], a history of deep venous thrombosis or pulmonary embolism, metal allergy, severe obesity (body mass index > 35.0 kg/m\({}^{2}\)), uncontrolled diabetes mellitus, and infection around the hip joint. Patients with unavailable data in medical records and poor-quality radiographs were excluded.  
Figure 4: Boundary and loading conditions of the FEM analysis. The distal end was fully constrained. P0 represents the peak load during walking, while P1, P2, and P3 account for the effects of surrounding hip muscles. Von Mises stress and micromotion were evaluated under these conditions.  
Figure 3: Schematic representation of the FEM analysis model. The FEM of [PERSON]’s modulus-graded Ti-Nb-Sn stem was implanted into patient-specific femoral data reflecting bone density. A champagne-flute-shaped femoral canal from a female patient in her 60s was selected for the simulation.  
Anteroposterior radiographs of both hips and lateral radiographs of the affected hip were obtained preoperatively, as well as immediately after surgery, and at 1 and 3 years postoperatively for both the Ti-Nb-Sn and control groups. Stress shielding was evaluated at 1 and 3 years postoperatively using [PERSON]'s classification [30]. Additionally, the distribution of stress shielding at the 3-year follow-up was analyzed according to Gruen zones 1-7 [31]. The radiographic evaluation of stress shielding was independently performed under blinded conditions by two orthopedic surgeons who were not involved in the implantation procedures.  
The baseline demographic characteristics of the 40 patients included in the Ti-Nb-Sn group are summarized as follows: The mean age was 64.2 \(\pm\) 10.7 years, with 36 females (90%) and 4 males (10%). The mean body mass index (BMI) was 24.6 \(\pm\) 4.1 kg/m\({}^{2}\). Bone mineral density (BMD) changes were not assessed in this study. No matched control group was included; thus, clinical and radiographic outcomes were evaluated independently within the Ti-Nb-Sn stem cohort.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Evaluating Parameters of Simulation
#### 3.16.4 Energy Consumption  
The downlink connection between the mRSU and the vehicle is considered in this study utilizing only one channel. We suppose that the time is split into equal-length time intervals \(\tau\). The mRSU assigns each time slot to a vehicle for communication at a consistent data rate, independent of the vehicle's location. As a result, the mRSU-to-vehicle link is subjected to power regulation [53]. Any vehicle that arrives transmits to the mRSU its current location and speed information. The cost of communication energy may be approximated based on the location of each vehicle. As a result, the mRSU distributes time slots to each node in such a manner that their energy usage is minimized [53]. Let \(E(i,t)\) denote the energy usage for communication between the RSU and a vehicle \(i\) at time \(t\), as calculated using a simplified path loss, and be equal to  
\[E(i,t)=P_{tx}(t)\tau=\frac{P_{rx}(i,t)\tau}{P_{o}[\frac{d_{o}}{d_{(i,t)}}]\tau }=\frac{N\ (2^{\frac{D}{n}}-1)\tau}{P_{o}[\frac{d_{o}}{d_{(i,t)}}]\gamma} \tag{21}\]  
Thus, in this equation, \(P_{tx}(t)\) is the power transfer of the mRSU at the particular time slot \(t\), while the received power of vehicle \(i\) is \(P_{rx}(i,t)\), the reference distance is denoted by \(d_{o}\), \(P_{o}\) is the path loss at the reference distance, [54] the distance between the mRSU and vehicle \(i\) at time slot \(i\) is expressed by \(d_{(i,t)}\), the path loss exponent is \(\gamma\), D is the data rate, B is the bandwidth of the channel, and N is the noise power.
# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 2 Materials and Methods
### Dataset  
The dataset used in this study was from the public database Osteoarthritis Initiative (OAI). Most of the patient samples in the OAI dataset include an X-ray image; however, many do not have an accompanying MRI sequence. For this study, we used a subset of the OAI data with 1100 knees, with each knee having both MRI and X-ray available. The 3D DESS MRI data for each knee contain a sequence of 160 2D images, while there is one X-ray image containing both knees from a patient. The dataset was selected with an equal distribution among different OA severity levels (0-4) measured by the Kellgren and Lawrence (KL) grades.  
A common practice in machine learning is to split the available dataset into three subsets known as the training set, validation set, and testing set [26]. Machine learning models learn from the training set with the validation set being used during the training process to tune parameters [27]. The testing set is not seen during the training process but rather is held back until the end of the study. The available dataset was randomized and then split into groups balanced by the KL grade with 800 training samples, 200 validation samples, and 100 testing samples. Each set contains a balanced number of samples from each of the five KL categories. Table 1 shows the distribution of the data.
## 3 Design, Implementation and Test of the DXN Engine
### The Problem Statement
#### 3.2.2 Bridging Algorithm  
The fundamental protocol for DXN is the addition of features to each SN LoRaWAN protocol:  
1. Every SN will wake up as intended/scheduled to try sending its message to the GW in normal Class A mode (maintaining normal Class A frequencies and duty cycle restrictions). If unsuccessful after three tries (no Acknowledgment message received), it will switch itself to a Class C mode (listening mode frequency). We label such blocked SN node as an EN.
2. If any SN is successful in reaching the GW, then (after transmitting its own message on Class A mode and receiving the corresponding successful Acknowledgement message from the GW) it will transmit a \"rescue message\" in the Class C frequency, in the hope that a nearby EN would receive it and reply within its normal Class C single receive window. We label such a node as an MN. This rescue message will include a chosen Class A frequency that an EN is expected to transmit its message on, and which this MN will be listening to during the \"receive window\".
3. If steps 1 and then 2 are complete, and if an EN does receive a rescue message on the Class C frequency, it will transmit its message immediately within the normal \"receive window\" timing on the frequency stated in the rescue message. It will then stay in listening mode at this frequency until it receives an acknowledgement message via MN. Otherwise, if nothing is received within the user-defined timer (depending on the network transmission intervals), or if the MN is permanently removed from the network, the EN will start fresh again from step 1 (i.e., go back being SN, being in Class A, etc.) as this MN will be considered as an unreliable bridge.
4. If the MN does receive an EN message within the \"receive window\", it will forward it to the GW as a normal Class A message and later it will also forward the Acknowledgment message back to the EN once received, using the pre-agreed frequency stated in the rescue message.
5. Both MN and EN nodes will go back to sleep and will wake up in Class A for the next scheduled time as in step 1.
## 4 Discussion
### Socio-Economic Impacts
#### 4.2.3 Potential Use Cases in Academia and Industry  
SPARK-IT's versatile design makes it highly applicable to both academic and industrial contexts. In academia, the platform can facilitate research collaborations by connecting researchers with industry experts, mentors, or funding opportunities. It provides a secure environment for sharing ideas, receiving feedback, and co-developing innovative solutions. In industry, SPARK-IT is effective in accelerating startup development. It connects entrepreneurs with experienced mentors and investors and helps foster partnerships across sectors. Its ability to adapt to various use cases ensures relevance across diverse domains, making it a valuable resource for driving economic growth and societal progress.  
Through these impacts, SPARK-IT contributes to creating a more inclusive, trusted, and efficient global innovation ecosystem.
# Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification
## 2 Multiple Fault Classification and Fault Severity Determination
### The Scheme of the Proposed Method  
We mainly focus on diagnostics based on Electrical Signature Analysis, but we also utilize traditional vibration data. The analysis starts with the elimination of the components that do not contribute to the system from the acquired stator phase current signals. After, the filtered signal is used for extracting the fault features affecting the machine health. Then, the trained multi-label classifier receives the new feature vectors and performs the multi-label prediction. The flowchart of the method is depicted in Figure 1 below.  
For both of the misalignment and unbalance faults, the two targets can be treated as logical labels, i.e., each output is a binary value, indicating whether a label is associated with the fault or not.  
For evaluating the fault severity, we can inject additional classes or alternatively we can generate a second classifier that is executed parallel. Acquired vibration data is sufficient for proper evaluation. The fault severity can be determined easily according to the ISO 2372 Standard [119; 120] for vibration severity of machines operating between 600 to 1200 rpm range. The severity classes are shown in Table 1.
## 4 Discussion
The increased EMG of VL is consistent with higher knee extension torque during the loading response. However, the increased EMG of GMAX did not directly support the non-significant change in hip extension torque. This discrepancy might be due to the inverse dynamic approach not accounting for co-contraction of agonist and antagonist muscles. Older adults demonstrate higher antagonist coactivation during level walking compared to young adults [37,38]. Increased GMAX muscle activation could be accompanied by co-contraction of hip flexor muscles, which was not measured in this study. Future studies are needed to determine if VGRF biofeedback affects co-contraction of lower extremities in older adults. There are some limitations in this study. First, we included healthy older adults who walked without any assistive devices in the community. Thus, the results may or may not be generalizable to frail older adults. Future research should consider including clinical populations with limb loading deficiencies during early stance to determine the applicability of VGRF biofeedback for individuals with specific mobility challenges. Second, we used a fixed walking speed of 1.2 m/s, which may not reflect the comfortable speed of all participants. On average, participants' comfortable walking speed (CWS) overground was 1.35 \(\pm\) 0.31 m/s, slightly higher than the fixed speed, while their treadmill CWS was 0.9 \(\pm\) 0.17 m/s, which was lower than the fixed speed. This discrepancy suggests that 1.2 m/s may have been slightly above their natural treadmill walking speed, potentially causing discomfort or subtle adjustments in gait mechanics. Future studies should examine how deviations from a fixed speed influence biofeedback outcomes. Third, the study had a relatively small sample size of 13 older adults. While previous studies have used similar sample sizes to examine the effects of aging on walking [9,14], future research should include larger cohorts to enhance the robustness of the findings. Additionally, this study only involved a single session of biofeedback training, limiting our ability to assess the long-term effects of this intervention. We also did not measure muscle fatigue and cognitive load, both of which could impact the feasibility of force biofeedback. While biofeedback may help increase knee extension torque, it could also elevate fatigue risk, necessitating careful rest period management. Future studies should incorporate measures of muscle fatigue and energy expenditure to better understand the trade-offs between biofeedback benefits and the risks of fatigue in older adults. Finally, this study did not examine muscle co-contractions in relation to increasing the first peak of VGRF. Future research should explore these aspects to provide a more comprehensive understanding of the biomechanical changes induced by VGRF biofeedback during gait.
###### Abstract  
This paper aims to explore an alternative reversible digital watermarking solution to guarantee the integrity of and detect tampering with data of probative importance. Since the payload for verification is embedded in the contents, algorithms for reversible embedding and extraction, imperceptibility, payload capacity, and computational time are issues to evaluate. Thus, we propose a reversible and imperceptible audio information-hiding algorithm based on modified integer discrete cosine transform (intDCT) coefficient expansion. In this work, the original signal is segmented into fixed-length frames, and then intDCT is applied to each frame to transform signals from the time domain into integer DCT coefficients. Expansion is applied to DCT coefficients at a higher frequency to reserve hiding capacity. Objective evaluation of speech quality is conducted using listening quality objective mean opinion (MOS-LQO) and the segmental signal-to-noise ratio (segSNR). The audio quality of different frame lengths and capacities is evaluated. Averages of 4.41 for MOS-LQO and 23.314 [dB] for segSNR for 112 ITU-T test signals were obtained with a capacity of 8000 bps, which assured imperceptibility with the sufficient capacity of the proposed method. This shows comparable audio quality to conventional work based on Linear Predictive Coding (LPC) regarding MOS-LQO. However, all segSNR scores of the proposed method have comparable or better performance in the time domain. Additionally, comparing histograms of the normalized maximum absolute value of stego data shows a lower possibility of overflow than the LPC method. A computational cost, including hiding and transforming, is an average of 4.884 s to process a 10 s audio clip. Blind tampering detection without the original data is achieved by the proposed embedding and extraction method.  
a _Applied_ **2024**, 14, 2757. [[https://doi.org/10.3390/app14072757](https://doi.org/10.3390/app14072757)]([https://doi.org/10.3390/app14072757](https://doi.org/10.3390/app14072757))  
Academic Editor [PERSON]
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
### Challenge  
Although great progress has been made in the research of 6D pose estimation in recent years, there are still some challenging problems to be solved in practical application. When the background is messy, the viewpoint and illumination change greatly, or the scene has less texture, the accuracy and the robustness of 6D pose estimation needs to be improved. Learning-based approaches are relatively robust in these conditions. However, 2D-information-based approaches and 3D-information-based approaches do not perform well. However, for learning-based approaches, adequate training data and training time are required, and challenges related to the requirement of offline training and practicality are also problems to be solved.