esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
###### Abstract  
Anomaly detection of remote sensing images has gained significant attention in remote sensing image processing due to their rich spectral information. The Local RX (LRX) algorithm, derived from the Reed-Xiaoli (RX) algorithm, is a hyperspectral anomaly detection method that focuses on identifying anomalous pixels in hyperspectral images by exploiting local statistics and background modeling. However, it is still susceptible to the noises in the Hyperspectral Images (HSIs), which limits its detection performance. To address this problem, a hyperspectral anomaly detection algorithm based on channel attention mechanism and LRX is proposed in this paper. The HSI is feed into the auto-encoder network that is constrained by the channel attention module to generate a more representative reconstructed image that better captures the characteristics of different land covers and has less noises. The channel attention module in the auto-encoder network aims to explore the effective spectral bands corresponding to different land covers. Subsequently, the LRX algorithm is utilized for anomaly detection on the reconstructed image obtained from the auto-encoder network with the channel attention mechanism, which avoids the influence of noises on the anomaly detection results and improves the anomaly detection performance. The experiments are conducted on three HSIs to verify the performance of the proposed method. The proposed hyperspectral anomaly detection method achieves higher Area Under Curve (AUC) values of 0.9871, 0.9916 and 0.9642 on HYDICE urban dataset, AVIRIS aircraft dataset and Salinas Valley dataset, respectively, compared with other six methods. The experimental results demonstrate that the proposed algorithm has better anomaly detection performance than LRX and other algorithms.  
remote sensing images; hyperspectral; anomaly detection; auto-encoder; channel attention mechanism +
Footnote †: journal:  
Article
# Fuel-Water Emulsions as an Alternative Fuel for Gas Turbines: A Project Summary
## 3 Research Results and Discussion
### CO Emissions
In Figure 13, the grouping of studies is taken from [4], while the year corresponds to the date of the earliest publication in a given group. It includes research conducted on gas turbines and combustion chamber test stands. Research conducted on gas turbines is additionally marked with an arrow and the name of the unit. Since 1989, research on the 9000B turbine has been dominated by observations showing no negative impact of the fuel-water emulsion on CO emissions, but despite this, it is difficult to find a common denominator in these studies that translates into such results. It is worth emphasizing that in none of the analyzed studies was the entire combustion chamber designed to be optimized in order to maximize the combustion efficiency of the emulsion fuel. Therefore, the vast majority of experiments concerned determining the impact of water addition on the operation of units in a configuration adapted to the combustion of the base fuel. Taking into account the research conducted on the GTM120 gas turbine [40; 41] and the experimental research conducted on the combustion chamber test stand where the flame was observed through optical windows [62], as a result of which significant changes in the distribution of the reaction zone in the combustion chamber were found, it can be concluded that similar situations could also occur in other cases. However, considering that the combustion chambers were not adapted to this type of change, it can be concluded that their impact on CO emissions was largely accidental, and that the use of emulsion fuel does not have to worsen CO emissions, as long as the design of the combustion chamber does not result in the reaction zone being in an unfavorable position from the point of view of CO emissions. As the size of the turbine (combustion chamber) increases, it can be expected that this effect (improving the uniformity of temperature distribution) will have less and less impact on changes in CO emissions due to a more effective mixing process and, therefore, a more uniform combustion process. Figure 13: Changes in CO emissions resulting from the use of fuel–water emulsions depending on the year of publication of test results (based on data from [4]).
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
###### Abstract  
Aiming at the scarcity of Low Earth Orbit (LEO) satellite spectrum resources, this paper proposes an algorithm of interference signal feature extraction and pattern classification based on deep learning to further improve the stability of satellite-ground communication links. The algorithm can successfully predict the interference signal pattern, start-stop time, frequency change range and other parameters, and has the advantages of excellent interference detection performance, high detection accuracy and small parameter prediction error, etc. It can be applied in the field of channel monitoring of communication satellite-to-ground communication links, and realize the repeated and efficient utilization of spectrum resources. Experiments show that the precision and recall of the algorithm for detecting five kinds of interference signals are all close to 100%, the prediction error of starting and ending time is less than 4 ms, and the prediction error of starting and ending frequency is less than 6 KHz.  
LEO satellite; deep learning; interference signal; feature extraction; pattern classification Article
## 3 Materials and Methods
### Wireless Communication Technologies for IoT and Cloud-Based Solutions
Another notable feature of WiGig is its low latency, making it ideal for applications that require real-time data transfer, such as gaming and virtual reality [55]. It also supports multiple-input, multiple-output technology, which enables multiple antennas to transmit and receive data simultaneously, improving the overall performance and efficiency of the network. In the context of 5G networks, WiGig can be used as a complementary technology to provide high-speed local area network (LAN) connections for mobile devices and IoT devices. The 60 GHz frequency band has a limited range, but it can support high data rates over short distances, making it suitable for applications, such as augmented and virtual reality (AR/VR), wireless HD video streaming, and cloud gaming [56]. In addition, WiGig can be used as a backhaul technology for small cells in 5G networks, enabling high-speed data transfers between small cells and the core network. This can help improve the performance and capacity of 5G networks, especially in densely populated urban areas where there is high demand for data services. Regarding IoT services, WiGig can enable high-speed local area connections between IoT devices, allowing them to share data quickly and efficiently. This can be especially useful for applications, such as smart homes, where multiple IoT devices need to communicate with each other in real time.
## 5 Conclusions  
This study compared three estimation models for electricity consumption at the district level in Eastern Thailand and evaluated the usefulness of remotely sensed data extraction as well as a general socioeconomic dataset as model features. The result revealed that the Decision Tree (regression method) algorithm is the best-fitting model with the highest accuracy at the lowest MAE, RMSE, and fastest total time than the other models. The findings from this study also highlighted the benefits of utilizing remotely sensed extracted variables, including lit area, sum of light intensity, and built-up area. Specifically, the nighttime light data were significant for estimating electricity consumption at the district level. They can help understand human activity and reflect the economic status and its impact in the industrial parks and neighborhood areas. These data also represent an excellent proxy indicator to measure economic activity, which is valuable for developing countries with a limited budget and data, such as Thailand.  
Nevertheless, the nighttime light data capture an artificial light that varies by the intensity of the source of light generated by the bulbs. LEDs have increasingly replaced regular light bulbs in the past decade because they require less energy and provide the same brightness. The changing of light bulb types may reflect the utilization of nighttime light intensity for estimating electricity consumption. Starting from 2015, Thailand's government has promoted LED usage within the 20 years of saving energy plan, which is still ongoing [46]. Thus, this paper, covering the period from 2012 to 2018, assumed little impact from the policy and has yet to consider the effect of different light sources.  
Conceptualization, S.H. and D.C.; Data curation, S.H.; Formal analysis, S.H.; Methodology, S.H. and D.C.; Supervision, D.C.; Writing--original draft, S.H.; Writing--review and editing, D.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant.  
**Acknowledgments:** The authors would like to express their gratitude to the Provincial Electricity Authority (PEA), Geoinformatics and Space Development Agency (GISTDA), National Statistical Office (NSO), Office of the National Economic and Social Development Council (NESDC), and Ministry of Higher Education, Science, Research, and Innovation (MHESI), Thailand, for data and research funding. We also highly appreciate [PERSON] and [PERSON], who dedicated valuable time to proofreading the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Single-Shot Global and Local Context Refinement Neural Network for Head Detection
## 3 Our Approach
### One-Stage Object Detector
#### 3.1.1 Heatmap Branch  
The heatmap aims to estimate the position of the head center. In particular, for our problem, the shape of the heatmap is \(1\times H\times W\). The value of a location in the heatmap is anticipated to be 1 if it corresponds to the ground-truth head center and 0 for background pixels. As the distance between the heatmap location and the head center increases, the response within the radius exponentially decays, and the background outside the radius is 0. Following [34], we set the radius of each head center point to a constant 2.  
For each ground truth box \(\mathbf{b}^{k}=(x_{1}^{k},y_{1}^{k},x_{2}^{k},y_{2}^{k})\), the center of it is \(\mathbf{c}^{k}=(c_{x}^{k},c_{y}^{k})\), where \(c_{x}^{k}=\frac{x_{1}^{k}+x_{2}^{k}}{2}\) and \(c_{y}^{k}=\frac{y_{1}^{k}+y_{2}^{k}}{2}\). Then its location on the headmap can be computed by dividing the output stride; here, the output stride is set to 4. Therefore, \(\mathbf{\hat{c}}^{k}=(\hat{c}_{x}^{k},\hat{c}_{y}^{k})=(\lfloor\frac{c_{x}^{k} }{4}\rfloor,\lfloor\frac{c_{y}^{k}}{4}\rfloor)\). After that, the heatmap target value at the location \((x,y)\) is obtained as follows:  
\[Y_{x,y}=\max_{k\in[1,N]}\left(\mathbb{1}_{\left\{\|(x-\frac{c_{x}^{k}}{2})^{2} +(y-\frac{c_{y}^{k}}{2})^{2}\right\|_{2}\leq radius\right\}}\exp^{-\frac{(x- \frac{c_{x}^{k}}{2})^{2}+(y-\frac{c_{y}^{k}}{2})^{2}}{2^{2}}}\right).\]  
Here, \(\mathbb{1}_{\left\{\|(x-\frac{c_{x}^{k}}{2})^{2}+(y-\frac{c_{y}^{k}}{2})^{2} \right\|_{2}\leq radius\right\}}\) is the indicator function, which is 1 if the distance between position \((x,y)\) and the object location \((\hat{c}_{x}^{k},\hat{c}_{y}^{k})\) is less than or equal to the radius, and 0 otherwise. \(N\) stands for the number of heads, and \(\delta\) is the standard deviation related to the radius. Specifically, \(\delta=\frac{radius}{2}\).  
The heatmap's loss function is the pixel-wise focal loss [16]:  
\[L_{heat}=-\frac{1}{N}\sum_{xy}\left(\frac{(1-\widetilde{Y_{x,y}})^{\alpha} \log\left(\widetilde{Y_{x,y}}\right)}{(1-Y_{x,y})^{\beta}(\widetilde{Y_{x,y} })^{\alpha}\log(1-\widetilde{Y_{x,y}})}\right.\qquad\left.\begin{array}{l}Y_{ x,y}=1\\ (1-Y_{x,y})^{\beta}(\widetilde{Y_{x,y}})^{\alpha}\log(1-\widetilde{Y_{x,y}}) \end{array}\right. \tag{1}\]  
where \(\alpha\) and \(\beta\) are predetermined parameters, and \(\widetilde{Y}\) is the predicted heatmap.
## 5 Experimental Results and Discussion
### Discussion
#### 5.5.3 Recommendations and Future Directions  
To overcome the problems mentioned above, further research should focus on improved preprocessing strategies that are specific to the issues of dialectal variations and implicit hate speech. Using domain-specific lexicons in hate speech, tuning the embeddings on hate speech-relevant corpora or using different embeddings for different Arabic dialects could minimize the misclassification errors emerging from dialectal differences. Another approach could be to introduce other models using transformers such as AraBERT or utilize hybrid models that encase both CNN and transformer layers to approach the problem of CNN, which does not capture long or complex texts' global context. These models penetrate better in tasks that necessitate a better understanding of textual relations and may generate a good synergy with CNNs that are optimized for local feature extraction.  
Concerning class imbalance, by using oversampling or improving cost-sensitive learning, the performance of the model for sorting underrepresented types of hate speech, such as political speech, can be improved. This is likely to result in a more uniform performance of the exemplary schools at all classes. Third, extending the model for the analysis to several modalities, and the combination of textual data with image or video processing, would greatly improve the model's reliability and versatility. As mentioned earlier, many hate speech messages on social media are image dependent, and it is thus necessary to incorporate such data.
## 3 Integrated Model
### Maintenance Situations
The cost at time \(t_{i+1}\) is \(C_{i+1}=C_{i}+C_{m}+C_{s}\), \(t_{mt}^{i+1}=t_{i+1}^{\prime}\), no change in the number of PM as \(r_{i+1}=r_{i}\), no change in the number of CM as \(f_{i+1}=f_{i}\). 2. The system performs PM only: \(L_{p}\leq X^{\prime}(t_{i+1})=\lambda({t_{i+1}}^{b}-{(t_{mt}^{i})}^{b})<L_{f}\). The cost at time \(t_{i+1}\) is \(C_{i+1}=C_{i}+C_{m}+C_{s}+C_{p}\), \(t_{mt}^{i+1}=t_{i+1}^{\prime}+\triangle t_{pm}\). The number of PM changes to \(r_{i+1}=r_{i}+1\), no change in the number of CM as \(f_{i+1}=f_{i}\). 3. The system performs CM: \(X^{\prime}(t_{i+1})=\lambda({t_{i+1}}^{b}-{(t_{mt}^{i})}^{b})\geq L_{f}\). The cost at time \(t_{i+1}\) is \(C_{i+1}=C_{i}+C_{m}+C_{s}+C_{f}+C_{l}+\triangle t_{s}\cdot C_{\min}\), \(t_{mt}^{i+1}=t_{i+1}^{\prime}+\triangle t_{cm}\), the minimal repair cost is \(\triangle t_{s}\cdot C_{\min}\). The number of CM changes to \(f_{i+1}=f_{i}+1\), no change in the number of PM as \(r_{i+1}=r_{i}\). The total production and maintenance cost for the integrated model is \(TC=C_{n}+C_{H}\). Inventory holding costs need to be calculated for the entire production and maintenance process. According to the notation and assumptions, the \(Q_{s}\) is the demand for the \(s-\text{th}\) product and \(N\) is the number of production cycles (lots). Therefore, we know that the \(s-\text{th}\) product produces \(Q_{s}/N\) in each production cycle and the consumption rate is \(d_{s}=Q_{s}/[N\sum\limits_{s=1}^{m}\ (\triangle t_{s}+\triangle t_{s}^{s}+ \triangle t_{mt}^{s})]\). The maximum inventory of product \(s-\text{th}\) can be obtained by multiplying the difference between productivity and consumption rate by the production time as \((\mu_{s}-d_{s})\triangle t_{s}\). Assume that both productivity and consumption rate are fixed, so the total inventory holding rate of product \(s-\text{th}\) in a production cycle is \([(\sum\limits_{s=1}^{m}\triangle t_{s})(\mu_{s}-d_{s})\triangle t_{s}]/2\). Therefore, we can obtain the total inventory holding cost as \(C_{H}=N\bigg{[}(\sum\limits_{i=1}^{m}\triangle t_{i})\times[\sum\limits_{s=1 }^{m}\ (\mu_{s}-d_{s})\triangle t_{s}C_{h}^{s})]\bigg{]}/2\). We can obtain the sum of the gross profit of all products  
\[\sum\limits_{s=1}^{m}R_{s}\cdot Q_{s} \tag{1}\]  
The net profit is  
\[ER=\sum\limits_{s=1}^{m}R_{s}\cdot Q_{s}-TC \tag{2}\]Therefore, to maximize the profit per unit time, the optimal decision variables \(L_{p}\) and \(N\) can be obtained. \[ \begin{split}\mathit{UR}(L_{p},N)=\frac{\sum\limits_{s=1}^{m}R_{s} \cdot Q_{s}-TC}{N\sum\limits_{s=1}^{m}\left(\triangle t_{s}+\triangle t_{s}^{ \prime}+\triangle t_{mt}^{s}\right)}\end{split}  \tag{3}\]
## 5 Simulation Results  
### Evaluation in MATLAB  
To evaluate the link performance of the Sheil Road deployment, we first simulate the network layout using MATLAB as shown in Figure 4, based on the information identified in the previous section. The MATLAB simulator is built to accurately model the properties and parameters of IEEE 802.11 ad networks and closely represents the deployment in Liverpool 5G Create in terms of node position and alignment. Specifically, in Figure 4 there are three links used to connect three IEEE 802.11 ad access points (APs), i.e., nodes 2-4, with node 1 acting as a gateway according to the 5G Create deployment illustrated in Figure 3. Moreover, links that are configured to work on the same channel have the same colour in the figure, i.e., red for Channel 2 at 60.48 GHz and blue for Channel 3 at 62.64 GHz. Table 2 summarizes the main simulation parameters [32; 33; 34].  
We next present the performance of the longest link, i.e., Link 2 from node 2 to node 3 in Figure 4, which is 94.4 m long, and one of the shortest links, i.e., Link 3 from node 3 to node 4, which is 18.35 m long. As introduced in the previous section, the PER of these links was computed as a function of the SNR and for different MCS values as shown in Figure 5 for Link 2 and Figure 6 for Link 3, respectively. As can be noted in both figures, the PER represented in log scale decreases as the SNR value increases for all MCS types. In addition, higher MCS can be achieved for a targeted level of PER when the SNR of the considered link increases. For instance, in the case of very low targeted levels of PER, in Link 2 MCS 11 can be achieved when the PER in percentage is 1.3% and the SNR is 20 dB, while MCSs 8 and 9 can be considered in the case of targeting a PER of 1% and 0.2%, and SNR of 14 dB and 15 dB, respectively. In Link 3, in the case of low targeted levels of PER, MCSs 11, 8 and 9 can be achieved when the PERs are 2.5%, 0.8% and 0.57%, and the SNRs are 21 dB, 16 dB and 18.5 dB, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network Standard & IEEE 802.11 ad \\ \hline Radio Channels & 60.48 GHz, 62.64 GHz \\ \hline Pathloss Model & Street Canyon Hotspot \\ \hline MCSs & 7–12 \\ \hline Maximum number of packet errors for computation of PER & 100 \\ \hline Maximum number of packet for computation of PER & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters [32; 33; 34].  
Figure 4: Network Layout.
## 1 Introduction
### Mechanism of Arc Reignition Overvoltage  
Before the arc burning, the resistance of the pantograph-catenary gap is large (approximately 1 M\(\Omega\)). At the moment of arc burning, the gap resistance exponentially decreases to around 0.5 \(\Omega\) within a few microseconds [14]. In this process, the sudden change of arc current and voltage will cause high-frequency electromagnetic oscillation in the system, and then form operating overvoltage, which is generally 2-3 times the size of the system voltage. When considering arc reignition during pantograph dropping, high-amplitude and high-steepness overvoltage, which is up to 9 times the system voltage, will occur in the later stage of arc development process [15]. Figure 2 shows the results of a test of the operating overvoltage of a dropping pantograph waveform during an HX\({}_{\text{D}}\)1 electric locomotive's operation (test system used a high-precision oscilloscope with a minimum sampling rate of 500 MHZ and is linear), where multiple arc reignitions are observed. The amplitude of overvoltage increases with the number of arc burning steps, reaching peak during the final burning.  
Figure 1: Three stages of arc development.  
Figure 2: Multiple arc reignitions in dropping pantograph operating overvoltage scenario.  
The condition of AC arc reignition is shown in Figure 3. It illustrates two situations when the arc crosses the zero point: reignition or permanent extinction. \(u_{1}\) represents the dielectric strength and \(u_{2}\) denotes the recovery voltage; both units are kV. The arc reignites depending on whether the recovery rate of the dielectric strength \(u_{1}\) is greater than the recovery rate of the gap voltage. At point A in Figure 3a, \(u_{2}\) is greater than \(u_{1}\), which can cause arc reignition, and the gap voltage transitions to arc voltage. Figure 3b shows that there is no intersection between \(u_{1}\) and \(u_{2}\), meaning it fails to meet the conditions for arc reignition, so the arc undergoes permanent extinction [16].  
By introducing the variable capacitance of the pantograph-catenary arc, the repeated breakdown of the pantograph-catenary gap during the pantograph dropping process can be simulated more accurately [17]. The model can simulate a situation of arc reignition after extinguishing and complex RC oscillation between the pantograph and the catenary.
## 3 Materials and Methods
### Model Evaluation  
The model performance was evaluated using accuracy, precision, recall, and the F1-score. These metrics provide a comprehensive assessment of the models' classification capabilities. The following formulas were used for the evaluation metrics:  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FN}+\text{FP})\]  
\[\text{Recall}=(\text{TP})/(\text{TP}+\text{FN})\]  
\[\text{Precision}=(\text{TP})/(\text{TP}+\text{FP})\]  
\[\text{F-measure}=2\ \times(\text{Precision}\times\text{Recall})/(\text{Precision}+\text{ Recall})\]  
where: TP = true positive; TN = true negative; FP = false positive; FN = false negative.
# Analysis of Electroencephalograms Based on the Phase Plane Method
## 1 Introduction  
The expansion of the functions and tasks of information exchange is one of the most-important trends in the modern development of society. At the same time, the role of information systems that ensure the transmission, reception, and processing of information with the required quality and in specified time intervals increases significantly. The use of a unified energy information carrier--the electromagnetic field--significantly increases the importance of radio engineering systems for receiving and processing signals operating in a wide range of frequencies, both in pulsed and continuous modes.  
The complexity of the problem is significantly aggravated due to a sharp increase in the number of signal sources, which leads to an increase in the integral background of electromagnetic interference. This complicates the task of the useful signal standing out. Along with natural noise, which includes the internal noise of radio receivers, atmospheric and industrial interference, signal fluctuations, reflection from the underlying surface, etc., in the channels of radio engineering systems, several radio systems for various purposes operate simultaneously on the same or adjacent sections of the frequency range. Based on this, the search for new methods of signal processing is relevant.  
Despite the high information content and compactness, the phase plane did not become a tool for analyzing processes in radio engineering and telecommunication systems. The phase plane is a less-commonly used method in signal processing than the amplitude spectrum. However, there are applications of this method to EEG, audio signals, etc. [1; 2; 3; 4].  
This work aims to expand the application of this method and to show its advantages in the visualization and determination of the SNR, also in the case of EEG.