esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Baseline Schemes
#### 5.2.2 Ascn  
ASCN [33] is a scheme utilizing a single deep neural network (DNN) that adaptively adjusts the transmission rate at three levels of SR fading. This adjustment is based on the input image features and time-varying channel state information. Across these SR fading levels, ASCN uses pilots to obtain channel state information and performs semantic matching according to image features, resulting in a significant improvement in image reconstruction quality compared with existing schemes.
## 5 Open Problem and Research Challenges
### Open Challenges
#### 5.1.7 Common Standards  
As of now, there is no common standard for different networks within deep seas to communicate with each other, furthermore creating a gap in communication within the networks. This occurs partly due to the fact that different manufacturers have different standards. Hence, the heterogeneity is lost creating the void problem, especially in shallow waters. In shallow water, the detection of acoustic becomes extremely difficult due to the presence of multiple control systems of heterogeneous nodes and the unique challenges of UWSN.
## 1 Collaborative Distributed Scheduling Approaches for Wireless Sensor Network  
**[PERSON] 1,2 and [PERSON] 1,**  
* State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
* Beijing Special Engineering Design and Research Institute, Beijing 100028, China; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-62796830; Fax: +86-10-62796830.  
_Received: 4 August 2009; in revised form: 4 September 2009 / Accepted: 29 September 2009 / Published: 13 October 2009_  
**Abstract:** Energy constraints restrict the lifetime of wireless sensor networks (WSNs) with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs) based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes' energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs.  
**Keywords:** wireless sensor network; energy, scheduling; collaboration; distribution
###### Abstract  
Due to the massive insertion of embedded cameras in a wide variety of devices and the generalized use of LED lamps, Optical Camera Communication (OCC) has been proposed as a practical solution for future Internet of Things (IoT) and smart cities applications. Influence of mobility, weather conditions, solar radiation interference, and external light sources over Visible Light Communication (VLC) schemes have been addressed in previous works. Some authors have studied the spatial intersymbol interference from close emitters within an OCC system; however, it has not been characterized or measured in function of the different transmitted wavelengths. In this work, this interference has been experimentally characterized and the Normalized Power Signal to Interference Ratio (NPSIR) for easily determining the interference in other implementations, independently of the selected system devices, has been also proposed. A set of experiments in a darkroom, working with RGB multi-LED transmitters and a general purpose camera, were performed in order to obtain the NPSIR values and to validate the deduced equations for 2D pixel representation of real distances. These parameters were used in the simulation of a wireless sensor network scenario in a small office, where the Bit Error Rate (BER) of the communication link was calculated. The experiments show that the interference of other close emitters in terms of the distance and the used wavelength can be easily determined with the NPSIR. Finally, the simulation validates the applicability of the deduced equations for scaling the initial results into real scenarios.  
Keywords:Optical Camera Communication (OCC); Interference; Normalized Power Signal to Interference Ratio (NPSIR); Wireless Sensor Networks (WSN) +
Footnote †: journal: Sensors
## 6 Localization Systems
### Indoor Localization Systems
#### 6.1.2 Bluetooth Localization  
Bluetooth is typically used to connect fixed or moving wireless devices within a specific space. The new BLE technology offers an improved data rate (24 Mbps) and coverage range (70-100 m) with higher energy efficiency than older versions. The protocol is designed explicitly for proximity detection and proximity-based services. The protocol allows a BLE-enabled device to transmit beacons or signals at periodic intervals [179; 182].  
One application of the Bluetooth localization system in basketball involves placing a wearable beacon with receivers on the court's border. Each player wears a small transmitting beacon to enable indoor localization while receivers positioned strategically around the court capture the beacon signals. By analyzing the signal strength or time delay at each receiver, approximate distances to the players can be calculated mathematically. For this technique to be effective, four receiving sites should be deployed around the court [185].
## 4 Validation of the Dominant Frequency Estimation Procedure
In Figure 5, the same frequency analysis is presented for the restored heat flux referred to noise level \(\sigma\) = 0.04 K (noise level of the experimental set-up, as defined in [29]). The scalogram of Figure 5b, when compared to that of Figure 4b, suggests that some low- and high-frequency components are introduced by the restoration procedure. In fact, the scalogram is no longer represented by just a clear, high-power horizontal band, but some other low/average-power spots are perceivable. This effect is clearly noticeable in the power spectrum (Figure 5c), where its maximum decreases of about 20% of the Pw peak in Figure 4c mean that the power of the exact signal has been scattered over a wider range of frequencies. However, the maximum of the power spectrum is still at 1.23 Hz, suggesting that no significant errors are introduced by the heat flux restoration in terms of dominant frequency estimation. Such a remark was verified for all the considered \(q_{exact}\) and  
Figure 4: Exact heat flux over time (\(Amp\) = 2000 W/m\({}^{2}\),\(f\) = 1.2 Hz), used as input for the wavelet method (**a**); corresponding scalogram (**b**) and power spectrum (**c**). \(q_{restoral}\), thus further proving that the presented procedure outcomes are not substantially dependent on the amplitude nor on the natural frequency of \(q_{exact}\).
## 4 Relay Scheme based on FAHP
### Fuzzy Analytical Hierarchy Process
#### 4.1.1 Criteria Selection  
The problem of selecting the relay node can be represented by a three-level diagram as shown in Figure 2. We will consider here three criteria: distance to the gateway, number of hops, and number of neighbors. Additionally, we choose to limit the alternatives to four (or fewer, depending on the number of candidate nodes).  
In a four-level scheme, FAHP can contain sub-criteria under the criteria level. However, in the case of interest, due to the ad hoc network being random, we do not have more criteria relevant than those mentioned, because the topology is unknown until the paths are created.
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 3 Results
### Wisconsin Diagnostic Breast Cancer Data
Data points represented by \(\bullet\) are assigned to the cluster predominantly composed of malignant cases, while those represented by \(\square\) refers to the cluster mainly composed of benign cases. Figure 9: Entropy contribution of each data point in the Wisconsin diagnostic breast cancer data, with dashed horizontal line from uniform noise for reference (**a**). Plot of entropy contribution values \(h_{i}\) for the model without and with the noise component (**b**). Figure 11 shows the scatterplots between pairs of features with data points marked according to the classification provided by the GMM with the noise component reported in Table 2, and ellipses corresponding to the estimated cluster covariances. Data points drawn with the + symbol represent the detected outliers. According to the Maximum a Posteriori (MAP) principle, these are classified as belonging to the noise component due to their highest posterior conditional probabilities compared to the probabilities of belonging to any of the Gaussian components. Note that the two clusters identified by this model appear to be more clearly separated than in Figure 8. Almost all malignant cases are included in the first cluster, which presents the characteristic of having Area_extreme \(>\) 1000. Outliers clearly appear to be located around the edge of the two main groups. Moreover, except for one instance, all the cases belong to the malignant class, suggesting that diseased tissues are more likely to result in abnormal values. \begin{table}
\begin{tabular}{|c|} \hline Gaussian finite mixture model fitted by EM algorithm \\ \hline Mclust EVI (diagonal, equal volume, varying shape) model with 2 components and \\ a noise term: \\ log-likelihood n df BIC ICL Entropy -4457.91356914-9004.64-9077.5937.834645 \\ Clustering table: \\ 1 2 0 142 412 15 \\ Confusion matrix: \\ Cluster \\ Diagnosis noise 1 2 0 356 \\ M 14 142 56 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary and clustering results for the model GMM(EVI,2) with the noise component estimated on the Wisconsin diagnostic breast cancer data. Figure 10: ICL traces from fitting GMMs with the noise component for the Wisconsin diagnostic breast cancer data.
# Simulation-Oriented Analysis and Modeling of
## 6 Micro-Simulation Implementation
### Impact of Distraction on Traffic Flow
#### 6.2.2 Impact of Distraction Fractions on Intersections and Segments on Surface Road  
Based on Section 6.2.1, it is known that distraction leads to poorer efficiency and safety on surface roads, which may be due to the complex road conditions on these roads. Therefore, this section distinguishes between segments and intersections of surface roads to investigate the impacts of distraction on traffic flow. As shown in Table 6, when the distraction fraction increases from 15% to 25%, the impact of distraction on the speed of road segments and intersections is significantly different.  
Figure 8: Average speed and coefficient of variation (COV) of speed by fractions of driving distraction.  
For road segments and intersections, since the expected speeds differ, comparing their speeds may not be very meaningful. Therefore, this section considers the ratio of actual speed to expected speed as the efficiency indicator, while the safety indicator continues to be represented by the COV of speed. From Figure 9a, it can be observed that the efficiency of both intersections and road sections decreases with an increase in the distraction fraction. Additionally, Figure 9a also shows that at the same level of distraction, the throughput efficiency of road sections is significantly higher than that of intersections, and the distribution error of throughput efficiency at intersections is significantly greater than that of road sections. This is due to the fact that signal timing at intersections further affects throughput efficiency. Figure 9b demonstrates that the COV of speed at both intersections and road sections increases with an increase in the distraction fraction, implying that an increase in the distraction fraction reduces safety at both intersections and road sections. Moreover, the figure also reveals that the COV of speed at intersections is significantly higher than that on road sections, indicating that safety at intersections is significantly lower than on road sections. This is because vehicle interaction behaviors at intersections are more complex, thereby increasing the risk of accidents at intersections.
# Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
## 4 Results
### Experimental Results  
#### 4.3.1 Performance Comparisons  
We designed an encoder based on SegFormer and demonstrated the performance changes of NROSegNet (B2) and NROSegNet (B4) during training after the improved model. The 'Step' axis represents the number of training steps and the 'Value' axis represents the mIoU value. All curves are smoothed to clearly show the trend. As can be seen from Figure 5, the B4 encoder converges slowly but performs better in the end, probably due to its stronger high-order feature extraction capability. The B2 encoder converges faster in the early stages of training and achieves higher performance in a shorter number of steps, achieving a good balance between accuracy and convergence speed. In contrast, the unimproved model performs stably in the later stages of training, but the final accuracy is slightly lower.  
As shown in Figure 6, we can clearly see that, compared with PotCrackSeg and other benchmark models, NROSegNet has a significant advantage in the accuracy of edge segmentation and negative obstacle recognition. Especially in complex scenes, the model can more accurately identify the boundaries of negative obstacles and more accurately identify continuous cracks. Moreover, the model has a lower probability of missed detection and false detection, showing stronger robustness. However, in scenes with alternating strong light and shadows, the recognition accuracy of the model is lower than that of normal scenes. Some negative obstacles are misjudged as background. This may be because the image features are unstable due to changes in light, which affects the judgment of the model.  
Figure 5: The process of training NROSegNet. The blue curve indicates B2 backbone, the red curve indicates B4 backbone, and the green curve indicates the unimproved model.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 3 Methodology
### Signal Preprocessing  
EEG signals are often contaminated by electrical interference and artifact noise, which if not removed, it can lead to inaccurate analyses and models. Prefiltering is a fundamental preliminary task to obtain clean and accurate EEG signals [30]. Its effectiveness will largely depend on the type of filter, as well as the control of filter parameters. Excessive filtering can remove much of the noise in a signal, potentially leading to the loss of valuable information. Controlling the amount of removed noise and the level of variation in the signal post-prefiltering ensures a balance between a clean signal and minimal information loss. In this  
Figure 3: Schematic of the proposed method for epileptic seizure detection.  
study, prefiltering is performed using the Savitzky-Golay filter, where the quality of the output signal is quantified using the SNR and the coefficient of linear correlation.  
The digital Savitzky-Golay filter (SGF) is a finite impulse response (FIR) low-pass filter developed by [PERSON] and [PERSON] [32]. It is widely utilized in biomedical signal processing due to its advantages, including smoothing the signal without significant alteration and enhancing the SNR while retaining high-frequency information. It entails convolving a polynomial of order \(N\) with a moving window of the signal \(x[n]\). This window, centered at term \(n=0\), has a frame length of \(2M+1\), where \(M\) represents the number of samples to the right or left of \(n\).  
\[p(n)=\sum_{k=0}^{N}a_{k}n^{k}, \tag{1}\]  
where the coefficients \(a_{k}\) represent the \(k\)-th polynomial [32]. The mean square error (MSE) between the polynomial and the input signal is expressed as:  
\[\epsilon_{N}=\sum_{n=-M}^{M}(p(n)-x[n])^{2}, \tag{2}\]  
where \(\epsilon_{N}\) are the MSE calculation. The coefficients \(a_{k}\) can be estimated by optimizing the polynomial using:  
\[\frac{\partial\epsilon_{N}}{\partial a_{i}}=0. \tag{3}\]  
The performance of the filter will largely depend on the choice of \(M\) and \(N\). A higher order in the polynomial will have a greater impact on the high-frequency response of the filter.
## 4 Experimental Setup and Result Analysis
### Training Details  
During the training process of the wide-angle image correction network, the Adam optimizer was employed to update the network parameters. The exponential decay rates for the first and second moment estimates were set to 0.9 and 0.999, respectively, to ensure stable and convergent gradient estimation.  
In the initial training phase (first 15 epochs), only the TPS control point prediction module was activated and supervised via a key point-based task classifier to enhance its capability in modeling geometric deformation. Following this stage, all modules were jointly optimized in an end-to-end manner during the unified training phase.  
During inference, the proposed method supports distortion correction for arbitrary-resolution images by applying scale-adaptive transformations to the predicted TPS control points and motion residual flow. This design enhances the model's flexibility and adaptability in practical deployment scenarios.  
For learning rate scheduling, a staged strategy was adopted. A linear warm-up policy was applied over the first three epochs to gradually increase the learning rate, followed by a cosine annealing schedule that smoothly decayed the learning rate from \(1\times 10^{-4}\) to \(1\times 10^{-6}\) over the remaining training epochs. The batch size was set to 64, and the input image resolution was fixed at 256 \(\times\) 256 pixels.  
Additionally, several data augmentation techniques--including random cropping, horizontal flipping, and brightness perturbation--were applied to improve the model's robustness and generalization under varying distortion types and image distributions.
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
## 2 Literature Review
This method extends the lifetime of sensor nodes, and they expressed a variety of WSN probabilistic routing protocols to save energy during the data transmissions. Moreover, a mathematical model was proposed to increase the heterogeneity of the clustering algorithms. In order to reduce the energy drain in WSN nodes, the proposed method selects the cluster head according to the residual energy of sensor nodes. [PERSON] et al. [18] introduced a new protocol to choose the optimal cluster heads to save battery energy. The method also extends the network lifetime by utilizing the limited power at the optimum level. In [19], two clustering routing protocols based on distance were proposed for WSNs. The cluster head selection method consists of solutions for two problems. The first problem is the proper distance from the candidate node to the base station, and the second problem is the remaining energy of a candidate node that wants to be a cluster head in the WSN. The performance of the two solutions was evaluated in terms of the number of dead nodes and energy consumption. The authors in [20] proposed a mathematical model that uses energy efficiently. They introduced the cluster head clustering protocol for mobile wireless sensor networks, which maximizes the network lifetime. This method includes the method of changing the cluster head in the cluster, which prevents repeated clustering and regularly distributes the load with the nodes. In [21; 22; 23; 24; 25], the authors proposed several creative methods to select a suitable cluster head by considering several factors. The cluster routing method is the most efficient method to reduce energy consumption, which has been used in recent years. The most important protocol of this method is LEACH. Utilizing this method has led to developing another clustering method named the Base station controlled dynamic clustering protocol. This protocol is more efficient than other protocols in terms of increasing the lifetime. Therefore, in this paper, we use this protocol to propose an efficient approach for cluster head selection.
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 2 Materials and Methods
### Proposed Method
#### 2.3.2 Date-Time Encoding  
In time-series prediction tasks, accurately capturing and modeling the inherent periodicity and dynamic trends of time-series data is one of the core challenges. For most time-series data, there are often certain operating rules and periodicity. Therefore, making full use of the periodic time information in the data can provide strong support for trend prediction. In this paper, the date-time encoding method is adopted. The date and month variables in the time variables are mapped within one period of the sine and cosine functions to become new static covariates. Based on the continuity and periodicity of trigonometric functions, the new encoding ensures uniqueness while maintaining continuity and periodicity, aiming to improve the model's sensitivity to temporal dynamics and prediction accuracy. From the perspective of model input architecture, conventional Transformer models typically adopt element-wise addition between encoded features and embedding vectors, while we introduce encoded multi-dimensional temporal features as independent covariate channels. This feature fusion strategy allows temporal information  
Figure 2: The structure of time series-based multi-scale feature fusion networks, the symbol \({}^{\star}\) denotes element-wise multiplication between tensors of the same shape.  
to participate in feature interactions as explicit covariates, effectively enhancing the model's ability to characterize periodic temporal patterns.  
The specific calculation formula for the date encoding values is shown in Equations (6)-(9), with the encoding range being \([-1,1]\), where \(m\) represents the month and \(d\) represents the day.  
\[DE_{\sin-\text{month}}=\sin(m) \tag{6}\]  
\[DE_{\cos-\text{month}}=\cos(m) \tag{7}\]  
\[DE_{\sin-\text{day}}=\sin(d) \tag{8}\]  
\[DE_{\cos-\text{day}}=\cos(d) \tag{9}\]
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 2 Methodology
### Mathematical Models
#### 2.1.3 Attenuation Due to Rain  
The specific attenuation \(\gamma_{r}\) due to rain is given by [8]:  
\[\gamma_{r}=k\cdot R_{r}^{a} \tag{5}\]  
where \(R_{r}\) is the rain rate, and \(k\) and \(\alpha\) are the frequency and path elevation dependent parameters, as described in Equations (2)-(5) [8].  
Attenuation resulting from rain along a specified line-of-sight path is subsequently determined by substituting the value of \(\gamma_{i}\) in Equation (1) with the value of \(\gamma_{r}\) for the specific path segment.