esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Short-Shot Defects in Injection Molding by Transfer Learning
## 1 Introduction
Five input parameters, namely, mold temperature, melt temperature, holding pressure, injection time, and holding time, were used, and the output parameters were volume shrinkage and warpage. The results indicate that this method achieved the optimization of volume shrinkage and warpage for automobile front panels, leading to an improvement in product quality and a reduction in production costs. In the literature on transfer learning, [PERSON] et al. [16] proposed a neural network model using six parameters as input values--injection time, holding pressure, holding time, mold temperature, cooling time, and melt temperature--to predict product weight. They used simulated data for training and then employed transfer learning to retrain the neural network model with real data. The results show that transfer learning accelerated the network's learning process compared to training from scratch. It also improved the accuracy of the model's predictions while overcoming the challenges of limited real experimental data and high trial run costs. [PERSON] et al. [17] introduced a deep probabilistic transfer regression framework designed to address the issue of missing data in data-driven soft sensors. This framework transfers source domain conditions to the target domain. After validation through various models, the results demonstrated the effectiveness of this approach in handling missing data issues in the target domain and improving the performance of soft sensors. [PERSON] et al. [18] introduced a Long Short-Term Memory Car-Following (LSTM CF) model for Adaptive Cruise Control (ACC) constructed through transfer learning. This model transfers knowledge from the source domain of Human-Driving Vehicles (HV) to the target domain of ACC. They also mentioned that a small amount of ACC data can be used to build highly accurate models. The results indicate that transfer learning is not only an effective approach for modeling with limited data, but also enhances model accuracy. [PERSON] et al. [19] mentioned that transfer learning enhances model accuracy. They utilized a large population's genetic data to understand Single-Nucleotide Polymorphisms (SNPs)causing diseases and then transferred this knowledge to predict genotypes and phenotypes in smaller populations. The results show an improvement in model prediction accuracy ranging from 2% to 14.2%. [PERSON] et al. [20] mentioned that while CAE analysis helps address some issues affecting injection-molded products, on-site adjustments of parameters still rely on human expertise. Therefore, they proposed a Back Propagation Neural Network (BPNN) network model and used CAE simulation data as the training data. The inputs included melt temperature, mold temperature, injection speed, holding pressure, and holding time, while the outputs included EOF pressure, maximum cooling time, \(Z\)-axis warpage, \(X\)-axis shrinkage, and \(Y\)-axis shrinkage. They then retrained the model using real machine data through transfer learning. The results showed higher accuracy compared to conventional network models.
## 2 Related Works
### Wavelet Transform-Based Approaches  
Wavelet transform is a signal processing technique that enables simultaneous analysis in both time and frequency domains and has recently been widely adopted in CNN-based fault diagnosis.  
[PERSON] et al. applied various wavelet-based transforms (CWT, WSST, DWT, etc.) to three-phase motor current signals to generate two-dimensional time-frequency images, which were then input into CNNs for fault diagnosis performance comparisons. Their study highlighted that image conversion utilizing the resolution characteristics of each wavelet method is advantageous for integrating multi-resolution information and achieves a superior diagnostic accuracy and robustness compared to time-domain approaches [13].  
[PERSON] et al. applied DWT to three-phase current and voltage signals to extract fault features at multiple resolutions. Based on 13 extracted energy coefficients, they generated RGB images (224 \(\times\) 224 \(\times\) 3), which were input into a GoogLeNet classifier for fault diagnosis [14].  
[PERSON] et al. proposed a hybrid diagnosis model combining DWT-based feature extraction with CNN to identify winding short-circuit faults in three-phase induction motors. Utilizing detailed coefficients at Level 5 of the Meyer wavelet-based DWT, they demonstrated stable fault classification even under noisy and varying load conditions. By compressing raw time-series data with DWT, this method significantly reduced the size of the training data and effectively shortened CNN training times, offering practical benefits [15].  
[PERSON] et al. proposed a diagnostic method that transforms current time-series data into 2D images using empirical wavelet transform (EWT) and classifies various types of induction motor faults via a CNN model [16].  
[PERSON] and [PERSON] diagnosed early winding short-circuit faults in PMSMs by applying CWT to the negative-sequence component of a three-phase current, generating scalogram images as CNN input [17].  
[PERSON] et al. proposed a fault diagnosis model with a CNN-LSTM parallel structure, where vibration signals were converted into time-frequency images via continuous wavelet transform (CWT) and input in parallel with raw time-series signals. The model extracted spatial-frequencyfeatures from CWT images and temporal features from time-series data, combining them for classification. This approach outperformed the single-input methods [18].  
However, the DWT-based approach's performance may vary depending on the choice of base wavelet function and decomposition level, while CWT demands considerable computational resources to generate high-resolution scalograms and requires manual tuning of scale ranges. These factors necessitate additional experimentation to identify optimal conditions [19; 20; 21].
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 5 Analysis on Corrosion-Fatigue of Structural Steel for Bridges
### Fatigue Limit  
As fatigue cracks can be divided into long cracks and small cracks, [PERSON] et al. [53] claimed that different fatigue limits should be applied for the large-crack and small-crack, respectively [53]. Meanwhile, [PERSON] [16] proposed that the threshold value for crack initiation is smaller than that of the fatigue limit for long cracks. In another world, the crack can also initiate and propagate when the SIF range is between the fatigue limit for a long crack and the threshold for crack initiation. On this end, the decreased fatigue limit recommended in [12] was utilized as shown in Equation (11). Studies [54; 55] indicate that the fatigue limit of steel is affected by the stress ratio, as shown in Equation (16):  
\[\Delta K_{th}=\Delta K_{th,0}(1-\alpha R), \tag{16}\]  
where \(\Delta K_{th}\) is the decreased fatigue limit; \(R\) stands for the stress ratio; \(\Delta K_{th,0}\) is the fatigue limit when \(R=0\) and \(\alpha\) is a material-related parameter.  
According to Equation (16), the threshold value of the two selected materials can be obtained under different stress ratios, as shown in Table 7.
# A Review of Time-Scale Modification of Music Signals +
## 5 TSM Based on the Phase Vocoder (PV-TSM)  
### Overview  
As we have seen, WSOLA is capable of maintaining the most prominent periodicity in the input signal. The next step to improve the quality of time-scale modified signals is to preserve the periodicities of all signal components. This is the main idea of _frequency-domain_ TSM procedures, which interpret each analysis frame as a weighted sum of sinusoidal components with known frequency and phase. Based on these parameters, each of these components is manipulated individually to avoid phase jump artifacts across all frequencies in the reconstructed signal.  
A fundamental tool for the frequency analysis of the input signal is the _short-time Fourier transform_[14] (Section 5.2). However, depending on the chosen discretization parameters, the resulting frequency estimates may be inaccurate. To this end, the _phase vocoder_ (Although the term \"phase vocoder\" refers to a specific technique for the estimation of instantaneous frequencies, it is also frequently used in the literature as the name of the TSM procedure itself.) technique [5; 7] is used to improve on the the short-time Fourier transform's coarse frequency estimates by deriving the sinusoidal components' _instantaneous frequencies_ (Section 5.3). In TSM procedures based on the phase vocoder (PV-TSM), these improved estimates are used to update the phases of an input signal's sinusoidal components in a process known as _phase propagation_ (Section 5.4).
## 5 Performance Study
### AUV Movement Pattern  
In order to gain an insight of the effects of AUVs' movement on the performance of underwater network, we design simple yet practical AUV movement patterns, where AUVs move along an elliptical trajectory. More specifically an AUV trajectory is defined by an equation of ellipse, \(\frac{(x-x_{c})^{2}}{a^{2}}+\frac{(y-y_{c})^{2}}{b^{2}}=1\), where \(a\), \(b\), \(x_{c}\), and \(y_{c}\) are real numbers. Another variable \(\theta\) is used to denote the angle of rotation from the positive Y-axis.  
Note that the network simulator NS2 only supports a linear movement between two positions at a given constant speed. In order to make the designed AUVs' movement pattern work with the NS2 simulator, an elliptical trajectory is partitioned into a large number of small line segments that are represented by a list of (\(x\), \(y\)) positions. Assuming an ellipse is centered at \((0,0)\) and \(\theta=0\) in order to facilitate the discussion, the position of AUV at time \(t\), \((x_{t},y_{t})\), is obtained by using following equation.  
\[\frac{x_{t}^{2}}{a^{2}}+\frac{y_{t}^{2}}{b^{2}}-1-\sqrt{(x_{t}-x_{t-1})^{2}+(y _{t}-y_{t-1})^{2}}+\Delta t*v=0 \tag{1}\]  
where \((x_{t-1},y_{t-1})\), \(\Delta t\), and \(v\) are the previous position of the AUV at time \(t-1\), the unit time in the NS2 mobility trace, and the average speed of AUV respectively. Note that between two solutions of Eq. (1), only one is selected as the next position that corresponds to the current move direction of the AUV.  
Figures 3 and 4 show the AUVs' movement pattern in an area of 25,000 m \(\times\) 25,000 m used for the performance study. In Figure 3, one AUV is considered with four different parameter sets for elliptical trajectories, \(Traj(i)\) where \(1\leq i\leq 4\). The sink node is assumed to be located at the bottom point of the each path. In Figure 4, multiple AUVs' trajectories are shown. Let each movement pattern be denoted by \((a,b,\theta)\). Then, Figure 4(a) shows the trajectory with one AUV, where \((a,b,\theta)=(600,800,0)\). Two AUV case is shown Figure 4(b), where \((a,b,\theta)=\{(500,800,\frac{\pi}{6}),(500,800,-\frac{\pi}{6})\}\). Figure 4(c) show trajectories of three AUVs, where \((a,b,\theta)=\{(400,800,\frac{\pi}{4}),(400,800,0),(400,800,-\frac{\pi}{4})\}\). Finally, four AUVs' trajectories are shown in Figure 4(d), where \((a,b,\theta)=\{(300,800,\frac{3\pi}{10}),(300,800,\frac{\pi}{10}),(300,800,- \frac{\pi}{10}),(300\,,800,-\frac{3\pi}{10})\}\).  
It is worthwhile to note that the trajectories of AUVs can be better optimized with the topology information, in particular, the positions of GWs. More specifically, both positions of the GWs and the trajectories of AUVs can be calculated before deployment such that the performance of the underwater sensors network is maximized.  
Figure 4: Trajectories of multiple AUVs.  
Figure 3: Elliptical trajectories of one AUV.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 6 Performance Evaluation
### Performance Metrics  
We define four metrics to compare the performance of ED-MAC protocol with those of UWAN-MAC and T-Lohi protocols, namely, successful delivery ratio, throughput, energy consumption, and fairness index. The successful delivery ratio is defined as the ratio of the number of packets received successfully to the total number of packets generated in the network. The network throughput refers to the total amount of data successfully transmitted by the network within a given period of time. The energy consumption is obtained by dividing the overall energy consumption in the network by the successfully delivered data packets, which is measured in joules per packet.  
The fairness index is a key performance metric when studying MAC protocols to measure the equality of users to access the media. To evaluate the fairness of underlying protocols, we adopt the Jain's Fairness Index [51]:  
\[\text{Fairness Index}=\frac{(\sum x_{i})^{2}}{(n.\sum x_{i}^{2})}, \tag{13}\]  
where \(x_{i}\) denotes the throughput of node \(i\), \(1<=i<=n\), and \(n\) denotes the number of sensor nodes in the network. This value ranges between 0 and 1 to be used suitably as a measure of fairness. The fairness increases when this value becomes closer to 1. As the disparity increases, fairness decreases and the fairness index becomes closer to 0. Accessing the media is totally fair when the fairness index equals 1.
## 2 GPR Field Survey and Data Processing
### Field Data Acquisition  
Based on the on-site field survey, we established vertically staggered lateral survey line grids (about 47 \(\times\) 8 m, three longitudinal (inline) and five transverse (crossline) survey lines) to effectively conduct a 3D survey while avoiding surface obstacles. A schematic plan view of survey line layout is illustrated in Figure 3, with the legends providing illustrations of surface features for various target bodies for reference. When planning survey lines, if unavoidable surface obstructions (such as manhole covers, rainwater catch basins) were encountered, they were considered as reference points for the cross-section survey, although this leads to increased complexity in subsequent processing and interpretation.  
Figure 2: Images of the test site, including a drone-captured image (**a**) and photos of the target objects such as a square manhole cover (**b**), round manhole cover of underground pipe network (**c**), mouse cavities (**d**), and rainwater catch basins (**e**).  
We selected the MALA GX GPR system equipped with a 450 MHz center frequency antenna to gather data along the arranged survey lines. Data acquisition was carried out using the \"distance mode\" at a sampling frequency of 5120 MHz and a trace interval of 0.02 m, with each A-scan containing a time window set at 108 ns comprising 553 samples. In total, eight GPR B-scan datasets were obtained (three inline profiles and five crossline profiles). During the real data acquisition process, we slowed down the speed at which the radar equipment was manually pulled. We aimed to maintain a 30\({}^{\circ}\) angle between the tie rod and the measurement line to ensure that the radar equipment moved closely to the ground, as the longitudinal line and five cross lines intersect with paving, curbs and lawns, resulting in an uneven surface with raised obstacles.
## 6 Conclusions  
To implement large-scale DR with lower cost, the following steps were taken in this paper: (1) a multi-communication-based DR system implementation routine was researched; (2) a plug-and-play smart terminal meeting multi-time-scale DR requirements was designed; (3) the practicality of the proposed system was validated in a laboratory environment through hardware-in-loop co-simulation.  
The advantages of the proposed system could be concluded as follows: (1) both building and maintaining costs were relatively saved by utilizing existing internet connections and IoT communication technologies; (2) system flexibility and customer initiative were improved through multi-level agents; (3) quick DR tasks were viable by means of \"local detection and action\".  
However, compared with AMI-based implementation methods, the proposed structure and its strategy indeed have some limitations and need additional research on system management and decisions, which can be concluded as follows:  
(1) The proposed regulation method utilizes public communication connections and smart terminals to undertake data collection and signal transmission tasks. This leads to a decrease in data accuracy and timeliness, as well as cyber security challenges.  
(2) Diverse third-party devices are introduced, which cause a significant problem in management. Necessary standards and polices are needed to be formulated to maintain the safety of the system for the above economic activities.  
Conceptualization, Q.W.; methodology, Q.W. and Y.T.; software, H.W.; validation, formal analysis, investigation, resources, and data curation, L.Z. and X.W.; writing--original draft preparation, H.W.; writing--review and editing, Q.W. and Y.T.; visualization, supervision, and project administration, Q.W.; funding acquisition, Y.T.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFB0903000 (Basic Theories and Methods of Analysis and Control of the Cyber Physical Systems for Power Grids), the Science and Technology Project of State Grid Jiangsu Electric Power Company, grant number 1 KJ-17601-DJ (Flexible Control of Distributed Source-Grid-Load in Active Distribution Network), and the National Natural Science Foundation of China, grant number 51707032.  
The authors declare no conflicts of interest.
## 5 Conclusions  
In this article, the use of NEAT along with beamforming was proposed for V2V communications in urban scenarios. The algorithm outperformed other evolutionary algorithms, in particular the GA and MSCPSO, as well as the isotropic antenna in terms of received power and average maximum response time. NEAT also showed similar worse case scenario performance for the maximum response time as MSCPSO, achieving a similar performance to the average values for the isotropic antenna.  
The results also showed that NEAT outperformed the isotropic antenna in terms of received power in every position of the simulation. This indicates that using beamforming with NEAT is a net gain in terms of received power, compared to the common isotropic antenna. NEAT also outperformed GA and MSCPSO in terms of average received power, with a difference of over 14 dBm in every direction.  
As future work, it would be interesting to try more neuroevolution-based algorithms to further increase the performance of the antenna array. The models could also be tested in real-life scenarios, analyzing the difference in performance between the simulations and real scenarios, while also analyzing the feasibility of employing the different models in real-time urban V2V communications. Furthermore, obtaining results in more realistic scenarios, such as different vehicular densities, is important for real-life applications, and thus proposed as future work.  
Conceptualization, H.K.K., S.B., C.A.A.-M., and S.M.-S.; methodology, H.K.K. and R.B.; software, H.K.K.; validation, H.K.K., R.B., C.A.-M., S.B., and S.M.-S.; formal analysis, H.K.K.; investigation, H.K.K. and R.B.; resources, C.A.-M.; data curation, H.K.K. and R.B.; writing--original draft preparation, H.K.K. C.A.-M., and S.B.; writing--review and editing, H.K.K., R.B., C.A.-M., S.B., S.M.-S., and D.Z.-B.; supervision, C.A.A.-M.; project administration, C.A.-M.; funding acquisition, C.A.A.-M. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Maximum response time for the different evaluated models.  
**Funding:** This research was funded by Vicerrectoria de Investigacion y Desarrollo (VID) de la Universidad de Chile Proyecto ENL 01/20.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This research was supported by CONICYT Doctoral Grant No. 21171722 and by ANID/PCHA/Beca de Magister Nacional/2020-22200896.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Statistical Analysis  
#### 2.4.1 Variance Accounted For  
The Variance Accounted For (VAF) was used as a measure of goodness-of-fit to quantify how well the reconstructed EMG data explained the original EMG data. The VAF is defined as \(1-(\text{sum of squared error})/(\text{total sum of squares})\). The overall VAF (\(\text{VAF}_{\text{overall}}\)) and the VAF per individual muscle \(k\) (\(\text{VAF}_{\text{muscle }k}\)) are given by:  
\[\text{VAF}_{\text{overall}}=100\%\cdot(1-\frac{\sum_{k=1}^{m}\sum_{j=1}^{n} \left(\mathbf{M}_{k,j}-\mathbf{W}\mathbf{C}_{k,j}\right)^{2}}{\sum_{k=1}^{m} \sum_{j=1}^{n}\left(\mathbf{M}_{k,j}\right)^{2}}) \tag{3}\]  
\[\text{VAF}_{\text{muscle }k}=100\%\cdot(1-\frac{\sum_{j=1}^{n}\left( \mathbf{M}_{k,j}-\mathbf{W}\mathbf{C}_{k,j}\right)^{2}}{\sum_{j=1}^{n}\left( \mathbf{M}_{k,j}\right)^{2}}), \tag{4}\]where \(k\) represents the \(k\)th of \(m\) muscles and where \(j\) represents the \(j\)th of \(n\) samples of the original measured data set \(\mathbf{M}\) and the reconstructed data set \(\mathbf{WC}\).  
Before performing NNMF, the number of synergies to be extracted needed to be specified based on the VAF [3]. Therefore, one to eight synergies were extracted to decide on the number of synergies needed to sufficiently explain the data based on the criteria for the \(\text{VAF}_{\text{overall}}\) and \(\text{VAF}_{\text{muscle }k}\)[3]:  
\[\text{VAF}_{\text{overall}}\geq 85\% \tag{5}\]  
\[\text{VAF}_{\text{muscle }k}\geq 75\%. \tag{6}\]  
The minimum number of synergies that satisfied both requirements for all subjects and conditions was chosen to be the fixed number of synergies for further analyses.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 3 System Model  
### Machine Learning for Wireless Indoor Localization  
Machine learning algorithms can be categorized into supervised, semi-supervised, and unsupervised learning algorithms. The supervised learning algorithms required a labeled dataset for predictions, and unsupervised algorithms used an unlabeled dataset for their predictions. The typical ML lifecycle has several stages: data pre-processing, model training, hyperparameter tuning, and model evaluation. ML algorithms can be used for classification or regression tasks. In classification, machine learning algorithms classify data into different categories, while regression learns from training data to predict continuous variables. The object's location can be estimated as a numerical value (regression) or as a classification (classifiers). For example, consider a person living inside a house and obtaining the location through an IoT system. The location of the person or object can be estimated geographically with coordinates or can predict the location as an area such as the bedroom, kitchen, etc. In this work, we have selected three algorithms, and each algorithm's brief details are given as follows.