esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Enhanced Spectrogram Analysis for
## 3 Methodology
### Problem Statement  
Accurate classification of biomedical signals obtained from palpation and percussion remains a challenge because of the overlapping frequency components and signal variability among individuals. Traditional diagnostic techniques rely on subjective assessments, leading to inconsistencies in clinical evaluation. This study addresses these limitations by developing a deep-learning-based classification framework that utilizes spectrogram transformations and a fine-tuned ResNet50 model to improve the accuracy and robustness of distinguishing signals from eight anatomical regions. The proposed approach aims to enhance the diagnostic reliability by leveraging advanced feature extraction and machine learning techniques.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Methodology** & **Dataset** & **Key Findings** & **Limitations** \\ \hline \multirow{4}{*}{[PERSON] et al. [19]} & Systematic review of respiratory sound analysis & \multirow{4}{*}{Multiple datasets} & Discussed computer-based classification approaches & No experimental validation of classification accuracy \\  & Analysis of computerized respiratory sounds in COPD patients & Clinical COPD sound dataset & computerizeduscitation for COPD detection & Lack of standardized classification techniques \\  & Acoustic signal processing methods for pulmonary diagnosis & Public respiratory dataset & feature extraction techniques for lung sounds & Limited focus on deep learning-based classification \\  & Review on advances in respiratory sound analysis & Multiple studies reviewed & Characteristics and clinical importance of lung sounds & No implementation of machine learning models \\  & Fundamentals of lung consultation, including classification methods & No dataset used (review study) & Provided insights into the interpretation of auscultation signals & Lacks experimental validation of classification approaches \\  & CNN deep learning model with transfer learning & Cervical cancer diagnosis dataset & Improved classification accuracy using CNN & Not focused on biomedical sound classification \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative Summary of Biomedical Signal Classification Studies.
## 2 Experimental Details
### Slip Load Definition and Slip Factor Calculation  
The load-displacement curve was calculated upon conclusion of the tests. The slip load was defined according to this curve. In the specifications given by the RCSC, the load-displacement curves can be classified into three types [10], as shown in Figure 3. Failure of a slip-critical connection is defined at different levels of slip according to the load-displacement curve type acquired by shear tests [17]. Examining the load-displacement curves, (a) is the type of curve where the slip load occurs at the maximum load. There is a drop in the load after the slip load. This drop in load is related to the major slip [2] and the drop marks the failure of the connection. Next, (b) is the type of curve where the slip load becomes an inflection point and changes suddenly, and the inflection point is also the failure point of the connection. Finally, (c) is the type of curve where the slip rate changes gradually. In this case, the slip load and failure point are a same fixed point, at which the slip equals 0.02 in (\(\sim\)0.5 mm). In this type of curve, minor slippage occurs before the slip load [2].  
Referring to the Chinese technical specification for stainless-steel structures (CECS 410) [18], the slip factor, \(\mu\), is calculated according to the following formula:  
\[\mu=\frac{F}{n_{f}\sum_{i=1}^{m}P_{i}} \tag{1}\]  
where, \(F\) is the slip load; \(n_{f}\) is the number of friction planes; \(m\) is the number of high-strength bolts; and \(P_{i}\) is the pre-tension force of the high-strength bolts, calculated using Formula (2).  
The Chinese standard JGJ82-2011 [19] provides the details of the torque-tension relationship when tightening high-strength bolt assemblies.  
\[T=kdP \tag{2}\]  
where, \(T\) is the torque applied to the nut, \(k\) is the torque coefficient, \(d\) is the nominal bolt diameter, and \(P\) is the specified bolt preload. If the high-strength bolts adopt the same value of torque coefficient, the required pre-tension can be obtained by applying a certain amount of torque to the bolt.  
Figure 3: Three types of slip load curves [10].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material of Connecting Plates** & **Material of Cover Plates (Surface Treatment)** & **Abbreviation of Connection Methods** \\ \hline GFRP (untreated) & GFRP (untreated) & G-G \\ GFRP (untreated) & Stainless-steel (untreated) & G-S \\ GFRP (untreated) & Stainless-steel (660 grit blasting) & G-S600 \\ GFRP (untreated) & Stainless-steel (#24 grit blasting) & G-S24\# \\ GFRP (#60 grit blasting) & Stainless-steel (#24 grit blasting) & G600-S24\# \\ \hline \hline \end{tabular}
\end{table}
Table 3: Abbreviations for each connection method.
# A Self-Calibration Method for Robot End-Effector Using Spherical Constraints
## 2 The Error Model
### Geometric Error
For the robot in this study, when \(i=2\), \(\mathrm{d}d_{i}=0\), and when \(i\
eq 2\), \(\mathrm{d}\beta_{i}=0\). In practical applications, all coordinate system errors must be transformed into the base coordinate system, which can be expressed as Equation (8). \[\Delta_{\mathbf{base}}^{i}= \begin{bmatrix}n_{x}&o_{x}&a_{x}&(\mathbf{p}\times\mathbf{n})_ {x}&(\mathbf{p}\times\mathbf{o})_{x}&(\mathbf{p}\times\mathbf{a})_{x}\\ n_{y}&o_{y}&a_{y}&(\mathbf{p}\times\mathbf{n})_{y}&(\mathbf{p}\times\mathbf{o})_{y}&(\mathbf{p} \times\mathbf{a})_{y}\\ n_{z}&o_{z}&a_{z}&(\mathbf{p}\times\mathbf{n})_{z}&(\mathbf{p}\times\mathbf{o})_{z}&(\mathbf{p} \times\mathbf{a})_{z}\\ 0&0&0&n_{x}&o_{x}&a_{x}\\ 0&0&0&n_{y}&o_{y}&a_{y}\\ 0&0&0&n_{z}&o_{z}&a_{z}\end{bmatrix} \cdot\Delta_{i}=K_{i}\cdot\Delta_{i} \tag{8}\]  
Here, \(K_{i}\) denotes the coordinate system error transmission matrix, and the elements \(\mathbf{n},\mathbf{o},\mathbf{a},\mathbf{p}\) in \(K_{i}\) correspond to the elements \(\mathbf{n},\mathbf{o},\mathbf{a},\mathbf{p}\) of the homogeneous transformation matrix \(T_{i}^{0}\), which describes the i-th coordinate frame relative to the base coordinate frame. For typical industrial robots, the deformations in links and joints are relatively small. Within a limited range, the differential transformations of coordinate frames can be linearly superimposed [17]. Consequently, the errors of all coordinate frames can be accumulated in the base coordinate frame as Equation (9). \[\Delta_{\mathbf{base}}=\sum_{i=1}^{6}\Delta_{\mathbf{base}}^{i}=\sum_{i=1}^{6}K_{i} \cdot\Delta_{i} \tag{9}\]  
\(K_{i}\) is the coordinate system error transmission matrix, and \(K_{1}\) is the identity matrix. By combining Equations (7) and (9), we obtain the following Equation (10). \[\Delta_{\mathbf{base}}=\sum_{i=1}^{6}K_{i}\cdot\Delta_{i}=\sum_{i=1}^{6}K_{i}\cdot G_{i }\cdot\mathbf{X_{i}}=J_{G}\cdot\mathbf{X_{G}} \tag{10}\]  
\(J_{G}\) is the geometric error transmission Jacobian matrix, a 6 \(\times\) 24 matrix composed of known parameters once the robot's pose is determined. \(\mathbf{X_{G}}=\left[\mathbf{X_{1}}^{T}\mathbf{X_{2}}^{T}\cdots\mathbf{X_{6}}^{T}\right]^{T}\) contains all the geometric errors between adjacent links of the robot, forming a 24 \(\times\) 1 vector. These parameters are the ones that need to be identified.
# Natural Language Description of Videos for Smart Surveillance
## 3 Proposed Methodology
### Description Generation Using Bidirectional LSTM  
A recurrent neural network (RNN) and LSTM [26] are the basic sequence modeling blocks in deep learning. However, many variants derived from these basic versions have been used for various tasks, and it is still an open area of research to work out newer and better architectures. The main task of these models is to predict the next member of the incoming sequence data. Due to improved gradient flow properties, LSTM models are preferred over vanilla RNNs, which have exploding and vanishing gradient problems. The main difference in the structure is the presence of an internal cell unit (ct) and a gated structure.  
The role of the gates is to maintain and modify the state of the cell and hidden layer output (h\({}_{\text{t}}\)) according to the previous hidden state and present input. The values of the cell state are invisible to the other network and remain intact unless the forget gate signal applies.  
For video description generation, we have used the bidirectional LSTM architecture. Bidirectional architecture processes the information forward and backward in time and hence can capture better contextual information from the data. We have used two-layered LSTM, each layer having 512 hidden nodes. The embedding vector from the CNN is passed to the first layer together with the attention vector [27], which gives the grouping over the spatial location to concentrate the segment of an image at a certain time. A set of vocabulary has been created by using human handwritten annotations. Every video has been interpreted by 40 human annotators with a varied level of expertise, hence producing a variety of words. A word which occurred less than five times was appointed an unknown <UNK> token. A total of 22,500 sentences were processed. The joint training ensured filling of the gap between visual clues and language.
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 3 Experimental Results
### Ablation Experiments  
To evaluate the method of the CNN-CBAM and CNN, three ablation experiments were performed by using the same configurations for the first 20 frames of each sequence under the four QP values. The results are displayed in Figure 7, where the point values indicate the average of the five types (A-E) of sequences at the four QPs.  
Figure 7a indicates that the coding time saved by CNN-CBAM and CNN was very similar and that there was almost no difference. This is because the introduced CBAM is a lightweight network and does not significantly increase the complexity of the network. From Figure 7b,c, we can see that the CNN-CBAM had a different degree of improvement in RD performance compared to the CNN, which is because the CNN-CBAM network can refine the feature map after adding CBAM, thus improving the representational capability of the CNN. Therefore, the RD performance of CNN-CBAM is more advantageous compared with the CNN.
## 3 System Design Overview
### System Components  
From a technical point of view, the main system components that are deployed are the frontend server, the backend core with the storage module, the matchmaker, the expert profile, the off-chain storage, the IPFS nodes and the blockchain nodes with the smart contracts, as can be observed in Figure 3. All these component deployments contain the conceptual functionalities shown in Figure 1.  
One of the core flows from our proposed system is creating and sending a proposal for collaboration. This flow is showcased in the sequence diagram from Figure 4. There are several main components that interact with each other in this case. Those components are the user's browser, the frontend server, the backend server, the database, the decentralized storage (i.e., the IPFS) and the blockchain network. The other aforementioned communication scenarios follow a somewhat similar flow.  
Figure 4: SPARK-IT sequence diagram for creating and sending a proposal.  
Figure 3: SPARK-IT system components.  
The proposal information is stored encrypted on IPFS, and the proof is on the blockchain network. Periodically, the backend server checks that the proof was properly stored on the blockchain and updates the database status accordingly.
## 3 Method
### Overall Architecture of CAT-IADEF  
As shown in Figure 1, three CNN models make up the overall CAT-IADEF framework.  
First, on the left side of the figure is the window sequence \(W\) obtained by processing the time series data \(T\), which is input into the convolutional auto-encoder to obtain a set of latent variables \(Z\). Afterward, in the middle of the figure, two sets of auto-encoders \(ED_{1}\) and \(ED_{2}\) (the two decoders are composed of encoders, respectively) are trained adversarially to enhance the \(ED_{2}\) model's ability to identify \"slight\" anomalies and output the reconstructed data of the two auto-encoders during the adversarial training phase. By calculating the reconstruction loss \(L\) of the two sets of auto-encoders and performing a weighted average to obtain the anomaly score \(S_{i}\), it is possible to then perform anomaly detection on a multivariate time series. Finally, on the right side of the figure is the anomaly interpretation module, which inputs the anomaly score for each data point and stores it in the label matrix \(R\) by comparing it with a threshold and assigning it a value of 0 or 1. The matrix \(R\) sums each column, and the column with the largest value represents the dimension with the most ones, which is the dimension where the anomaly is most likely to exist.
## 4 Experimental Implementation and Results
### Sensitivity Analysis of Hyperparameters  
To evaluate the robustness of the proposed two-stream CNN-LSTM model, a sensitivity analysis was conducted by systematically adjusting key hyperparameters, including the learning rate, number of epochs, and input sequence length (Table 5). The analysis reveals how these parameters influence classification accuracy, F1-score, and loss, providing insights into the model's stability and generalization capabilities.  
The learning rate had a significant impact on model performance. A rate of 0.0001 achieved the highest accuracy (94.67%) and F1-score (94.78%) with minimal loss (5.33%), demonstrating optimal convergence. In contrast, higher rates (e.g., 0.01) caused unstable training, with accuracy dropping to 43.48% due to erratic weight updates, while lower rates (e.g., 0.00001) led to underfitting (86.09% accuracy) as the model failed to learn efficiently.  
Increasing epochs improved performance up to a saturation point. At 20 epochs, the model achieved 90.43% accuracy but risked premature stopping. Training for 25 epochs improved accuracy to 93.91%, while 30 epochs yielded the best results (94.67%). Beyond this, overfitting was observed, as indicated by diverging training and testing losses (Figure 6), emphasizing the need for early stopping. The input sequence length (number of frames per activity sample) was tested at 10, 15, and 20 frames. A length of 15 frames struck the best balance, achieving 94.67% accuracy. Shorter sequences (10 frames) reduced accuracy by 5% due to insufficient temporal context, while longer sequences (20 frames) introduced computational overhead without significant gains (94.12% accuracy).  
The overall findings confirm that learning rate is the most critical parameter affecting convergence and final accuracy; number of epochs must be optimized to avoid underfitting or overfitting; sequence length has a moderate effect, with an optimal range for temporal coverage. These observations support the reproducibility of the proposed model and demonstrate its robustness when appropriately tuned. The sensitivity analysis enhances the transparency and applicability of the methodology for real-world construction environments.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 4 Results and Discussions
### Custom Loss Combination  
The custom combines the Dice Loss and Focal Loss using a weighted sum. The weights (0.5 for Dice Loss and 0.5 for Focal Loss) determine the contribution of each loss term. The combined loss is 0.5 \(\times\) Dice Loss + 0.5 \(\times\) Focal Loss. The custom loss is a hybrid of Dice Loss and Focal Loss, aiming to benefit from both methods. It addresses class imbalance through Focal Loss and encourages accurate localization through Dice Loss. The specific choice of weights (0.5 for each) reflects an equal emphasis on both components. Adjusting these weights could be explored to fine-tune the model based on the characteristics of your dataset and task.
## 2 Materials and Methods
### Metabolite Identification and Multivariate Data Analyses  
Identification of volatile and non-volatile silylated components was performed by comparing their retention indices (RI) in relation to n-alkanes (C6-C20), mass matching to NIST, WILEY library database and with standards if available. Peaks were first deconvoluted using AMDIS software (www.amdis.net (accessed on 28 November 2019)) [12] before mass spectral matching. Peak abundance data were exported for multivariate data analysis by extraction using MET-IDEA software ([PERSON], [PERSON], [PERSON], [PERSON], & [PERSON], 2006). Data were then normalized to the amount of spiked internal standard (Z)-3-hexenyl acetate and then subjected to principal component analysis (PCA), hierarchical clustering analysis (HCA) and partial least squares discriminant analysis (OPLS-DA) using SIMCA-P version 13.0 software package (Umetrics, Umea, Sweden). Markers were subsequently identified by analyzing the S-plot, which was declared with covariance (p) and correlation (pcor). All variables were mean-centered and scaled to Pareto variance. Model validation was assessed by computing the diagnostic indices, viz. Q2 and R2 values, and permutation testing.
# Manufacturer Channel Encroachment and Evolution in E-Platform Supply Chain: An Agent-Based Model
###### Abstract  
Manufacturer channel encroachment is a common phenomenon in the current e-commerce supply chain, which has been well studied. This study develops a multi-agent-based model of the e-platform supply chain to analyse manufacturers' channel encroachment strategies and supply chain evolution. Through both direct sales channels and e-commerce platforms, manufacturers can sell two complementary products of varying quality. Consumers who have preferences compare the pricing information gathered from manufacturers through different channels before selecting the one that generates the best utility. At the end of each period, the manufacturers make a price adjustment using the genetic algorithm. We look at the supply chain evolution process through multi-period simulations and discuss the factors that influence encroachment decisions. We find that manufacturers' channel encroachment is detrimental to the profitability of the e-commerce platform. Consumers' channel preferences and quality preferences benefit the e-commerce platform and can discourage manufacturers' encroachment decisions. In addition, increases in encroachment costs and commission rates can reduce manufacturers' propensity for channel encroachment.  
agent-based model; e-commerce; channel encroachment; supply chain evolution Article
## 5 Experimental Methodology
### PVA Detection
#### 5.2.2 Methodology  
As with the leak-detection data, we first train the normal model using the training dataset. The training data is split 70-20-10 into a training set, a validation set and test set, respectively. The training and validation sets are used to tune the hyperparameters of the normal model, with the test set then providing an unbiased measure of the normal model's forecast accuracy.  
We will again examine multiple candidates for the normal model architecture. Our first candidate is the 1D CNN, for the same reasons as in Section 4.1. Given the fact that ventilator waveforms are complex time-series signals, recurrent neural networks, such as LSTM, are again also sensible candidates. However, it is known that LSTM is relatively slow compared to some more recent architectures [53]. Hence, we will instead explore the more recent Gated Recurrent Unit (GRU) networks [65], both with and without dropout [66]. In addition, bidirectional recurrent networks have shown considerable promise, as learning both a sequence and its reversal has the potential to highlight key invariants in the data (much as image reversals are used in data augmentation for modern image classifiers). They are commonly used in natural language processing, for example. Our final normal model candidate is thus a bi-directional GRU [53]. All of these candidate models are implemented in Tensorflow using the Keras libraries.  
The OC-SVM algorithm is selected as our AD component. As an unsupervised algorithm, it is trained using the full training set obtained from the normal model (3862 actual vs. predicted observations). We will also explore adding synthetic data produced by our resampling algorithm (a further 3862 actual vs. synthetic observations). The algorithm is then evaluated using the test data from the normal model (i.e., this is a single-split design). We evaluate our AD with the traditional measures of sensitivity and specificity. Sensitivity is another name for the True Positive Rate (\(TPR\)) of Equation 3. Specificity is also known as the True Negative Rate (\(TNR\)) and is computed as follows:  
\[TNR=\frac{TN}{TN+FP} \tag{5}\]  
using the symbols of Equations (3) and (4). Specificity can also be computed as 1-FPR for binary classification problems. In this case, the Positive class is defined as a breath exhibiting PVA.
## 6 Discussion and Comparison  
(1) The accuracy of surface roughness estimation can be significantly enhanced by the skew correction preprocess. As presented in introduction part, measuring direction errors could lead to frequency change compare with idle direction. Related research also reports that direction inclination can directly affect the classification accuracy [27].  
Figure 17: Confusion matrix.  
Figure 18: Filter characteristic.  
As the comparison, the authors have tested a similar method without texture skew correction preprocess. The results show an inferior accuracy of 74.62%. Moreover, a CNN model of over 30 layers without skew correction preprocess is also explored and the accuracy was found to be 69.73%. The above comparison results are listed in Table 5. In contrast, with the texture skew correction process, the classification accuracy reveals a remarkable improvement in accuracy.  
The sources of these differences may be caused by the convolutional kernel weights. Once the convolutional kernel weights are trained, the kernel weights are fixed and not able to respond to directional changes. Therefore, proper skew correction is definitely indispensable for the surface roughness estimation via CNN model.  
(2) Proper image filtering is beneficial to reduce the training model complexity. As described in relevant sections, the shallow layers mainly focus on the brightness contrast ratio adjustment and feature enhancement. Therefore, instead of feeding the network with raw data, properly processed image (based on the prior knowledge) is helpful to reduce the model complexity.  
As the comparison, the authors also explored a contrasting method without image filtering preprocess and acquired an accuracy rate of only 79.88%. While the classification accuracy rises to 91.14% when the layer number is set to be 18. The above comparison results are listed in Table 5.
## 1 Introduction
The main contributions of this work are as follows:  
The implementation of an entropy-based information mapper and demapper enabling the transformation of a binary signature into a watermark (and vice versa) for embedding and extracting the watermark in a video signal. The modification of the watermarking algorithm in terms of extending the loss function and changing the learning procedure to ensure the high transparency and robustness of the method. The laboratory testing of the watermarking algorithm for watermark capacities from 4 to 16,384 bits with the individual selection of optimal algorithm hyperparameters to confirm the efficiency and effectiveness of the method. The remainder of the article is organized as follows: Section 2 presents the currently employed approaches for watermarking static images and video signals, Section 3 describes the method used and the modifications introduced, Section 4 presents the results of the experiments performed, and Section 5 summarizes the results of this work and provides directions for further research.