esa-sceva/satcom-chunk-collection huggingface dataset:
# Robotic System for Post Office Package Handling
## 3 Architecture
### Hardware Setup
#### 3.1.2. Depth Sensors  
The information about the dynamically changing environment, specifically the grasping pose of randomly dropped objects, which can vary in six dimensions, was acquired using a depth sensor. Two different depth sensors (Zivid One M and Intel RealSense D415) that vary in such parameters as precision, field of view, working range, weight, and dimensions were considered.  
After examining initial experiments and the nature of computer vision techniques required to handle post office packages, it was determined that the system relies on a high precision of the depth information. The common point precision of an Intel Realsense D415 is 1-3 mm, whereas, for a Zivid One M depth camera, it is around 60 \(\upmu\)m. For this reason, the Zivid camera was chosen to be used in this robotic system. Zivid uses structured light for 3D information capture. It operates with a resolution of 1920 \(\times\) 1200 at a frame rate of up to 12 frames per second and utilizes the USB 3.0 SuperSpeed as the connection interface. The Zivid camera is ideal for high-precision tasks when the camera can be mounted statically in a position in the workspace, where its field of view covers most of it, as the comparatively large dimensions (226 mm \(\times\) 86 mm \(\times\) 165 mm) and weight (2 kg) of this camera would restrict some movements for the industrial robot if mounted on it.  
Figure 3. Physical setup of the system.
# Feature Normalization Reweighting Regression Network for Sugar Content Measurement of Grapes
## 2 Materials and Methods
### FNRR Network  
In this experiment, we propose an innovative deep learning framework FNRR, shown in Figure 4, for the non-destructive prediction of the sugar content of grapes. The framework is mainly composed of two modules: (1) The feature extraction module, which obtains the feature vector to obtain the result of the normalized vector through convolutional neural networks or transformer models, and (2) the normalized function, which produces a categorical distribution from the previous layer's output. The entries of the labeled vector are the average labels of each category. The result was achieved through the expectation of the normalized vector and labeled vector. The process of training is described in Algorithm 1 (The proposed algorithm for training the feature normalization reweighting regression models. Epoch is the number of iterations during training).  
Due to the non-uniform distribution of the data, the direct use of a multiple linear regression model will generate data a priori; the model will predict the data in the sparsely dense interval as if it were in the denser interval. The characteristics of the predicted data have a greater similarity to the data in the adjacent interval, and we can enrich the characteristics of the sparse interval data to a certain extent using grouping. We divided the grape image dataset into 14, 9, 7, and 6 categories with sugar content intervals of 0.4%, 0.6%, 0.8%, and 1%, respectively, and labeled each category with an appropriate sugar content value. We used typical deep learning-based models to obtain the features. A feature map is obtained by the convolution layer or attention mechanism. The convolution layer is composed of the kernel size, stride, padding, input channels, and output channels. For details, see Section 2.3.1. The image is operated with layers of encoder blocks based on the self-attention mechanism to obtain the feature maps. The self-attention mechanism is described in Section 2.3.3. The last layer of the model is a fully connected layer whose size is the number of the categories. The output result of the fully connected layer is passed through a normalized function, and a probability value is given to each category label; this is calculated as the expected value with the vector of the category label.  
Figure 4: Overview of the feature normalization reweighting regression (FNRR) model architecture.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### Performance Metrics  
The evaluation metrics used were micro-average Precision (P) (Equation (1)), micro-average Recall (R) (Equation (2)), and micro-average F1-score (F1) (Equation (3)).  
The equations are given for an example of a problem with two classes, class 1 and class 2. TP, FP and FN stand for True Positive, False Positive and False Negative, respectively.  
The use of these metrics is justified when having an imbalanced dataset, where no class is more important than the others [27].  
\[\text{P}=\frac{TP1+TP2}{TP1+TP2+FP1+FP2} \tag{1}\]  
\[\text{R}=\frac{TP1+TP2}{TP1+TP2+FN1+FN2} \tag{2}\]  
\[\text{F1}=\frac{2\times R\times P}{R+P} \tag{3}\]
As the characteristic of the adaptive gate is gate size, which is dependent on the predicted weight of each component, the adaptive gating strategy shows better performance than the standard elliptical gating strategy at a low value of gate probability. For linear Gaussian multi-targets tracking systems, we use the adaptive gating strategy to reduce computation load in PHD and CPHD algorithms. At the same time, we put forward the novel detection probability in GM-PHD and GM-CPHD algorithms with application in underwater target tracking. Here, we name the proposed algorithms as the Pd-GM-PHD algorithm and Pd-GM-CPHD algorithm. Simulation shows that the Pd-GM-PHD algorithm and Pd-GM-CPHD algorithm can realize the estimation of the time-varying number of targets and their state more accurately in dense clutter environments. The paper is organized as follows. An overview of the MTT based on RFS, PHD and CPHD algorithms and its Gaussian Mixture implementation are described in Section 2. In Section 3, the adaptive gating strategy and proposed algorithm are shown in details. Simulation results showing the performance compared with existing algorithms are shown in Section 4. Finally, some conclusions are provided in Section 5.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 2 Methods
### Classification Based on Clustering and Thresholding
#### 2.3.1 Clustering-Based Dynamic Classification  
Data classification is the process of categorizing data into different levels based on their importance, sensitivity, scope of use, and potential impact on the organization. This enables us to implement targeted security measures during data storage, transmission, access, and processing [23]. By applying protection at the data classification level, differentiated security strategies can be employed for data of varying sensitivity, thereby enhancing the effectiveness of data leakage prevention.  
In current data classification practices, most of methods primarily rely on static classification, in which sensitivity levels are determined by manually set thresholds. Although such methods are instructive and operable, they also reveal many limitations in practical application. First, static classification is inefficient. Especially when facing large-scale, multi-format railway data, static classification is not only time-consuming and laborious but also prone to classification errors due to differences in subjective judgment. Second, static classification lacks flexibility. Even when automated approaches based on sensitivity calculation are employed, they typically require predefined sensitivity thresholds to distinguish between classification levels. However, these thresholds often depend on domain expertise, making them difficult to standardize and challenging to adjust dynamically in real-world scenarios.  
To address the above issues, we propose a clustering-based dynamic classification strategy. It leverages the sensitivity values of data attributes and employs unsupervised clustering algorithms--such as K-means, k-medoids, or hierarchical clustering--to analyze the attributes within a data table. It thus enables the automatic classification of attributes into multiple sensitivity levels. The highlight is that it classifies attributes automatically without the need to define preset thresholds, offering strong adaptability while enhancing classification efficiency.  
Additionally, to ensure that the results of sensitive attribute classification better align with practical requirements, we also developed a set of sensitive attribute classification rules (see Section 2.4.2 for more information) based on domain expertise. These expert-defined rules serve as a supplementary mechanism to enhance the clustering-based classification results, making classification more accurate.  
The clustering-based dynamic classification Algorithm 4 is as follows:  
```
Input:\(S^{\prime}_{i},k\) Output:\(Y=\{y_{1},y_{2},\ldots,y_{k}\}\) \(Y=k\_means(S^{\prime}_{i},k)\) // Clustering the set \(S^{\prime}_{i}\) results in \(k\) subclusters.; return\(Y=\{y_{1},y_{2},\ldots,y_{n}\}\);
```  
**Algorithm 4**Attribute dynamic classification algorithm
# Energy Efficient Policies for Data Transmission in Disruption Tolerant Heterogeneous IoT Networks
## 3 System Model and Definitions
Equation (3) presents the state transition cost as described above,  
\[g(s_{k},u_{k})=u_{m}\cdot[(1-u_{k}^{m})E_{l}+(1-u_{k}^{p})E_{w}+(1-u_{k}^{p})( 1-u_{k}^{w})E_{d}]. \tag{3}\]  
We are interested in minimizing the total energy consumption accumulated over an horizon, i.e., an number of stages, expressed as follows,  
\[J_{\pi}(s_{0})=\lim_{N\to\infty}\underset{k=0,\dots}{E_{W_{k}}}\left\{\sum_{k =0}^{N-1}\gamma^{k}g(s_{k},u_{k})\big{|}s_{0}\right\}, \tag{4}\]  
where \(s_{0}\) is the initial state of the system, expectation is taken with respect to the joint probability mass function of the random variables comprising \(W_{k}=(W_{k}^{m},W_{k}^{\Delta x},W_{k}^{\Delta y})\) and \(\gamma\) is a discount factor, i.e., \(0<\gamma<1\), indicating that the importance of energy consumption decreases with time. Finally, \(\pi\) represents a policy, i.e., a sequence of functions \(\pi=\{\mu_{0},\mu_{1},\dots\}\), where each function \(\mu_{k}\) maps states to controls for the \(k\)-th stage. For a policy \(\pi\) to belong to the set of all admissible policies \(\Pi\), functions \(\mu_{k}\) must satisfy the constraint that for stage \(k\) and state \(s_{k}\) controls are selected exclusively from the set \(U(s_{k})\). In order to minimize Equation (4) we must apply an appropriate control \(u_{k}\) at each stage \(k\) given state \(s_{k}\). However, decisions cannot be viewed in isolation since one must balance the desire for low cost at the current stage with the avoidance of high future costs due possibly unfavorable state transitions. For example, the mobile node might avoid to transmit packets at the current stage and state, a decision that will always result in zero energy consumption, yet, due to its random walk, at the next stage it may find itself in a cell without network connectivity and a full backlog due to new packet arrivals. As a result of its last decision the mobile node will now have to drop packets, possibly at an excessive cost.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 5 Discussion and Analysis
### Compare with Other Frame Selection Methods
#### 5.2.2 Maximum Temperature Elevation Method  
[PERSON] [6] adopted the maximum temperature elevation method (MTEM) to obtain a frame image from a sequence of thermal image for image processing. He thought that only the image acquired when the temperature reaches its maximum elevation is considered, i.e., at the time \(t_{max}\) corresponding to the maximum of transmitted heat and therefore a maximum of information. Using MTEM, Frame 385 was selected based on the peak of the curve of temperature elevation over time, which is presented in Figure 27. However, at Frame 385, the image has obvious heat diffusion and the defect tip has disappeared, so it is obviously not a suitable frame. Therefore, this method is not suitable for processing our sequential images.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Defect Number** & **Frame** & **Threshold** & **Defect Pixel** & **Relative Error** \\ \hline \#1 & 188 & 0.42 & 3525.5 & 4.52\% \\ \#2 & 196 & 0.44 & 3191.9 & 4.42\% \\ \hline \end{tabular}
\end{table}
Table 4: Statistics of defect parameters using the maximum temperature difference method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{
\begin{tabular}{c} **Defect** \\ **Number** \\ \end{tabular} } & \multicolumn{4}{c}{**MSDSMM**} & \multirow{3}{*}{**MTDM**} \\ \cline{2-2} \cline{4-7}  & \multicolumn{2}{c}{**Single Selection**} & \multicolumn{2}{c}{**Multiple Uniform Selection**} & \multicolumn{2}{c}{**Multiple Random Selection**} & \multirow{2}{*}{**Frame**} & \multirow{2}{*}{**Error**} \\ \cline{2-2} \cline{4-7}  & **Frame** & **Error** & **Frame** & **Error** & **Frame** & **Error** & **Frame** & **Error** \\ \hline \#1 & (158, 193) & [0.25\%, 4.94\%] & 164 & 0.42\% & (164, 173) & [0.24\%, 1.06\%] & 188 & 4.52\% \\ \hline \#2 & (160, 203) & [1.05\%, 5.37\%] & 182,183,186 & [0.94\%, 2.44\%] & (173, 193) & [0.47\%, 3.15\%] & 196 & 4.42\% \\ \hline \end{tabular}
\end{table}
Table 5: Compared the results of MSDRM with those of maximum temperature difference method (MTDM) for PVC specimen.  
Figure 26: The curves of average temperature difference between the defect area and sound area over time.
## 3 Materials and Methods
### Hybrid Anomaly Detection Model  
Transformer-Based Autoencoder for Anomaly Detection  
The Transformer-based Autoencoder [21] was implemented using Python and the TensorFlow 2.x library with the Keras API [22]. The model is designed to capture temporal dependencies in multi-sensor time series and detect deviations from normal behavior. This subsection details the architecture, training parameters, and tools used to ensure full reproducibility. The Transformer-based Autoencoder was implemented using Python 3.11.11 (Python Software Foundation, Wilmington, DE, USA) and TensorFlow 2.18.0 (Google LLC, Mountain View, CA, USA) with the Keras API [22].  
* Input shape: \((10,15)\), representing 10 time steps of 15 engineered features per window. These features include raw sensor readings (temperature, smoke, sound, and light), their first-order differences (the rate of change), rolling window variability (the standard deviation), and z-score normalized values. The full feature list is detailed in the Feature Engineering subsection.
* A GRU layer with 64 units and ReLU activation (return_sequences=True) to capture short-term dependencies.
- An LSTM layer with 32 units and ReLU activation (return_sequences=False) for long-term feature encoding.
* Bottleneck: a RepeatVector(10) layer expands the latent representation across the time dimension for decoding.
* An LSTM layer with 32 units (return_sequences=True).
- A GRU layer with 64 units (return_sequences=True).
- A TimeDistributed(Dense) layer to reconstruct the original feature dimensions.
* Loss function: the Mean Squared Error (MSE), computed between the input and the reconstructed output.
* Optimizer: the Adam optimizer with a learning rate of 0.001.
* Training Configuration: Fifty epochs with a batch size of 32.  
* Training and Prediction: The Autoencoder was trained on sequences of 10 time steps to learn the normal operating conditions of the sensors. Once trained, it was used to predict sensor readings, and the reconstruction error was computed.
## 2 Remaining Useful Life (RUL) Prognostics Methodologies
### RUL Prognostics Methodologies Based on Filtering Techniques
A time-varying temperature and a random variable current have only partly been considered in the actual process of lithium battery operation, and the predicted results are inconsistent with the actual results. At the same time, the prediction result is the RUL point estimation or the average estimate, which cannot provide the prediction probability density analytic function form and does not easily guide later maintenance.
## 4 Simulations Results and Discussion with Performance Evaluation  
In this section, the performance of the proposed protocols is evaluated, i.e., SPF-Three and BFS-SPF-Three. Furthermore, the comparative analysis of existing and the proposed protocols is presented. In both proposed and existing protocols, the packets are forwarded from source node towards sink (sonobuoys on the surface of the sea in a greedy manner). The main difference is that the existing protocols only sense the next 2-hop neighbors or 3-hop neighbors to find the best potential-forwarder (with a low probability of avoiding void hole regions). Whereas, the proposed protocols sense the next 3-hop neighbors to find the best potential-forwarder. Meanwhile, choose the best shortest path from that forwarder towards the destined sonobuoy by completely avoiding the void hole regions. This shortest path selection results in minimum delay with minimum EC. The proposed protocols eliminate the chance of packet loss in the void holes. In the next phase, the experimental setup of implemented protocols is discussed.
# A New, Fast Pseudo-Random Pattern Generator for Advanced Logic Built-In Self-Test Structures
## 5 Pseudo-Random Pattern Generator DT-LFSR-TPG for LBIST Circuits with the STUMPS Architecture
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{8}{c}{**Type I LFSRs**} & \multicolumn{8}{c}{**Type II LFSRs**} \\ \cline{2-13}  & \(h_{1}\)**(x)** & \(h_{2}\)**(x)** & \(h_{3}\)**(x)** & \(h_{4}\)**(x)** & \(h_{5}\)**(x)** & \(h_{6}\)**(x)** & \(h_{1}\)**(x)** & \(h_{2}\)**(x)** & \(h_{3}\)**(x)** & \(h_{4}\)**(x)** & \(h_{5}\)**(x)** & \(h_{6}\)**(x)** \\ \hlineThe maximum operating frequency of DT-LFSR-TPG units exceeds the corresponding frequency for LFSR + PS solutions given in [34] by 20% to 191%. The discrepancy between operation frequencies for both test pattern generators tends to increase in pace with the number of scan paths fed by these TPG units. The higher operating frequency of a TPG unit enables the reduction of testing time, which, in turn, contributes to a reduction in expenses for the entire testing process of integrated circuits. However, the benefit is paid by much higher hardware overhead associated with the use of test pattern generators of the DT-LFSR-TPG type, where such an overhead may be higher by 17% to 223% than the LFSR + PS modules given in [34]. However, one has to keep in mind that the silicon area assigned for a network of interconnections between flip-flops and gates included in these circuits is not taken into consideration for both solutions of test pattern generators. The delay (propagation) time introduced by these interconnections is also ignored. For pseudo-random pattern generators of the DT-LFSR-TPG type, this is permissible since the layout of interconnection networks in such TPGs is regular and the networks are of a local nature, in particular when the topology of components that make up the circuits is carefully designed. This is why only a slight impact of these interconnections on the overall circuit surface and the maximum operating frequency can be assumed. On the contrary, the network of interconnections in test pattern generators of the LFSR + PS is much more chaotic with numerous branches. It particularly refers to LFSR + PS modules with a large number of output channels, where the area occupied by interconnections takes a significant portion of the entire area assigned to the test pattern generator. Consequently, propagation times down these interconnections also have a substantial impact on the maximum operating frequencies of such TPGs.
## 2 Related Work
However, this scheme relies more on the accuracy of bandwidth estimation, and the performance is degraded in a highly dynamic network environment. In another important work in the field of multi-stream optimization is the definition of video perceptual quality, [PERSON] et al. [10] used a linear combination of screen size and resolution as the definition of perceptual video quality. In addition, VMAF and PSNR use video content-specific features to calculate perceptual quality scores and thus are often used in the field of video streaming optimization. The QoE definition in this paper can support any video quality metric.
# COVID-19 Diagnosis from Crowdsourced Cough Sound Data
## 3 Proposed Method
### Audio Features  
The feature set was created by extracting audio features from an audio chunk file by cutting only the cough from the original data. MFCC is one of the most commonly used features in the field of speech recognition. It has been widely used in studies into the diagnosis of COVID-19 from cough sounds [11; 13; 14; 15; 23]. Spectrograms were also frequently used in this study, and were considered necessary for high accuracy. In this study, in addition to the MFCC and spectrograms used primarily in existing papers, we added the following audio features:  
* 13 MFCCs;
* 5 spectral features: spectral centroid, spectral bandwidth, 7 spectral contrast features, spectral flatness, and spectral roll-off;
* 12 chroma features: 12-dimensional chroma vector.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cough\_Detection\_Score**\(\geq\)**0.8** & **Cough\_Detection\_Score**\(\geq\)**0.9** \\ \hline Healthy & 5608 & 4702 \\ Symptomatic & 1135 & 949 \\ COVID-19 & 547 & 441 \\ Total & 8295 & 6092 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of data having a coughing sound detection score of 0.8 or higher, and 0.9 or higher in COUGHVID.  
Both the spectral features and the chroma features have been widely used as effective features in studies related to speech. Therefore, the feature sets were combined by selecting features based on preliminary research. All features were extracted using the librosa [24] package with a sampling frequency of 24 kHz. In addition, 36-dimensional feature sets were constructed using the average value of each extracted feature. Figure 6 shows spectrogram  
Figure 4: An example of the original data.  
Figure 5: Segmented cough sounds.  
images extracted from the segmented cough sound data of male and female participants who were positive and negative for COVID-19.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 2 Requirement Analysis
### Non-Technical Requirements  
There are other non-technical requirements or limitations that should be taken into account when evaluating the suitability of a certain relative positioning system. In commercial passenger and freight vehicles, the price plays an important role. The cost of a certain relative positioning solution is not only the direct price of the device that needs to be equipped in the car, but also secondary costs need to be considered, including installation and maintenance costs, processing power, weight and power consumption, noise and heat creation. In cooperative solutions that use infrastructure-based communications, such as cellular communication, running costs in the form of monthly or annual fees for maintaining the infrastructure and the use of the licensed frequency band can be expected.