esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty Analysis and Simulation  
### Uncertainty Analysis  
In general, the uncertainties of a power system mainly come from two aspects: the power sources and the load demand. For the uncertainties from the load demand side, the load prediction deviation can be described by a normal distribution function. Then, the distribution functions for load demand will also follow a normal distribution function. During the day-ahead scheduling, HS, TPP, and ESD could all provide a reserve service for the load demand. Furthermore, daily weather forecasting technology is mature, so the think power system can better cope with the uncertainty of the load  
Figure 1: The basic structure of the multi-energy complementary system (MECS) operation.  
demand. In particular, as smart grid technology becomes more and more advanced, the demand response will be another resource that can be used to overcome the uncertainty from load demand.  
The uncertainties from the power source side are mainly caused by uncontrollable clean energy sources, such as wind power and photovoltaic power. The outputs of WPP and PV are not controllable because the natural wind speed and solar irradiance are influenced by the weather conditions. The output of HS is uncertain from the perspective of the seasonal characteristics, and it is controllable from the perspective of the complementary energy characteristics. If the TPP output is sufficient to offset the insufficient output of HS, the uncertainty of the HS output can be ignored. Therefore, this paper mainly discusses the uncertainties due to WPP and PV.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Laser-Induced/Scribed Graphene ECG Electrodes
#### 2.4.2 Water-Stable LSG ECG Electrode  
To produce graphene devices on a large scale, it is very convenient to process graphene materials in liquid. However, as a hydrophobic material, graphene easily agglomerates in solution, and it is not easy to disperse it evenly, which increases the difficulty of the graphene liquid phase process [63]. Besides, the high cost of high-quality graphene remains an important limitation for its application. Therefore, GO plays an important role as a low-cost and water-dispersible graphene material. It can be processed as a suspension and then reduced to conductive rGO by several means after the device's previous processing. Traditional thermal or chemical reduction methods may release toxic and harmful substances, require large energy consumption, and take a long time. By using a laser to reduce GO, the conductive electrode can be prepared quickly and energy-efficiently with customizable patterns. Based on this method, [PERSON] et al. proposed a water-stable and flexible LIG electrode for ECG monitoring [64]. They used polyethylene terephthalate (PET) as the substrate for the GO suspension's drop-casting. After drying on a hot plate, a thin-film GO layer was prepared on the surface of PET. Then, a 405 nm laser with a maximum power of 1 W was used to induce the rGO conductive pattern (Figure 3a). The resulting LIG electrode shows excellent stability in solutions with different pH and realizes a long-term wearing over 100 h. This work demonstrates that the LIG electrode can maintain certain stability when exposed to human sweat, ensuring long-term monitoring of ECG signals.
## 3 Dry-Low Emission Gas Turbine
### Case Study in DLE Gas Turbine  
A case study reported the tripping problem of 6 years that was faced by a 4.4 MW single-shaft DLE gas turbine at the Gas District Cooling plant. As per the documentation, the trip is frequent during the first or second year of the installation and reduced in the following year. As the gas turbine was installed in 2010, the highest recorded trips were in the second year, 2011. As the year progressed, fewer trips were recorded, with the minimum in 2014.  
Of the total trips, 77% are critical trips where the equipment is shut down abruptly without any allowable period for personnel to bring the operation back into a normal state. The trip report was analyzed and summarized in the pie chart as illustrated in Figure 18. It is found that 25% of the trips are rooted from LBO error during DLE mode that is implemented to reduce NOx and \(CO_{x}\) emission. This severe fault might lead to high maintenance costs due to unplanned downtime. Therefore, LBO is considered the superior problem faced in DLE gas turbines, which will be discussed in detail in the next section.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Trip Faults** & **Causes** & **Prevention** & **Ref.** \\ \hline \multicolumn{4}{c}{**DLE gas turbine**} \\ \hline \multirow{4}{*}{Lean Blowout} & Too lean zone that exceed the blowout limit & Controlling the air and fuel ratio to keep the gas turbine in its operating region & [79; 80; 81; 82; 83; 84] \\ \cline{2-4}  & Combustion reaches the auto-ignition temperature & Well designed premix ducts to accomplish enough mixing at periods less than the typical ADT & [85] \\ \hline \multirow{2}{*}{Flashback} & High burning velocity & Advanced cooling techniques & [86; 87] \\ \hline \multirow{2}{*}{Instability} & Pressure oscillations at low equivalence ratio of combustion & Utilization of dynamic pressure transducer to control the flow of air and fuel & [69] \\ \hline \multicolumn{4}{c}{**Conventional gas turbine**} \\ \hline \multirow{4}{*}{Blades Fault} & \multirow{4}{*}{Fatigue failure} & Application of coatings resistant to oxidation and corrosion at high temperatures & [70; 72] \\ \hline \multirow{2}{*}{Sensor Fault} & Mechanical failure or improper calibration & Routine calibration and instrumentation checking to ensure the reading ability & [73] \\ \hline \multirow{2}{*}{High Vibration} & \multirow{2}{*}{Dynamic forces} & Continuous monitoring and spectrum analysis to detect the vibration sources & [74] \\ \hline \multirow{4}{*}{Compressor Surge} & Inside pressure is lower than incoming air pressure & Implementing surge active control & [75] \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### Assessment Strategies
#### 3.3.1 Spot and Long-Term Measurements  
One of the most widely used techniques used to assess the exposure in indoor environments involves spot measurements of the EMF levels in selected locations by narrowband and broadband methods. Broadband measurements give the estimation of the whole field level due to all the sources in the observed radiofrequency band, whereas narrowband measurements highlight the contribution of each type of source to the overall exposure level. Spot measurements in indoor environments have been carried out in the range from several MHz to 10 GHz, with many studies focused on exposure in the frequency range of mobile telecommunications (base stations).  
Large-scale measurement campaigns based on spot and long-term RF monitoring systems have been carried out, but, even when the same tools are used, measurements protocol used in different studies were often completely different. First, the locations where to conduct measures were selected with different approaches depending on the study: some studies established some representative positions for each indoor environment [28], with fixed distances from the indoor sources or following a predefined measurement path [29], while other approaches based their investigation on a random sampling [19; 30].  
Second, as the level of exposure for a certain location is usually obtained averaging the values recorded during a time interval, the sampling time used by the instrument and the total temporal duration of the measurements in each locations represent other factors of variability among results of different studies [24]. The way in which the measurement of the exposure level took into account the time variations could lead to significant variabilities among the obtained values especially when considering sources emitting quasi-stochastic signals like the wireless local area networks WLAN [31]. Moreover, also the part of the day (morning/afternoon) and the day of the week (working days vs. weekend) play their role in exposure assessment, considering that they are both known to influence the exposure [5; 6; 24].  
Third, a last source of variability among different studies were the exposure metrics (electric field amplitude vs. power density) and the descriptive statistics used to represent the results, such as mean, median, maximum or minimum values, or percentiles of the distribution. Moreover, some studies dealt with both indoor and outdoor environments, sometime presenting separately the results of the two different cases, but, in other cases, reporting them in a mixed form, making difficult the comparison among results from different studies.
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 3 Improved Genetic Algorithm
### Improved GA
#### 3.2.1 Encoding Mechanism  
The encoding mechanism is essential, and the theoretical principle of IGA is implemented based on it. Traditional encoding mechanisms include the binary, the floating, the integer, and the symbol. The binary mechanism provides convenient encoding and decoding operations when solving discrete or continuous optimization problems. The floating mechanism provides results with high precision when solving continuous optimization problems with multidimensional functions. The integer mechanism is usually employed for solving combinatorial optimization problems such as the Traveling Salesman Problem [40]. The symbol mechanism contributes to the combination of GA and other algorithms. In IGA, the integer encoding mechanism is employed for the waypoint sequence, while the binary encoding mechanism is employed for the sequence of joint configurations and the special value corresponding with the cubic coefficients of the arguments, which is sketched in Figure 5.
## 3 Probabilistic Topology Control Algorithm
### Parameter Deviation Calculation  
In an underwater cooperating AUVs network, the underwater AUVs are always powered by batteries. Moreover, once the energy is exhausted, the AUVs become non-functional, which has a great effect on network performance. Similarly to the energy, the buffer space of the underwater AUVs is limited, too. Thus, in case the memory space is occupied completely, the nodes cannot handle the incoming data packets, which makes the packet loss ratio increase. The occupation of the buffer space can be evaluated by queue length (in this paper, the queue length is defined as the number of data packets to be transmitted in AUV's buffer space). Therefore, in this paper, the residual energy, the queue length, the transmission power, and the AUV's degree will be taken into account to determine the transmission power adjustment probability for each AUV.  
Based on the analysis in Section 1, we define the parameter deviation in Definition 1. The deviation of a parameter relates to the optimal solution or the constraint of this parameter.  
**Definition 1**.: _The deviation of parameter \(x\) which relates to its optimal solution or constraint \(x^{*}\) is defined as the ratio of the difference between these two values to the value of the optimal solution or the constraint, which can be expressed as:_  
\[D=\frac{|x-x^{*}|}{x^{*}}, \tag{14}\]  
According to Definition 1, to transmission power, when the optimal transmission power of AUV \(s\) is \(P_{s}^{*}\) which is calculated by the optimization algorithm, the deviation of transmission power can be calculated as:  
\[D_{P_{s}}=\frac{|P_{s}-P_{s}^{*}|}{P_{s}^{*}}, \tag{15}\]  
Similarly to the transmission power, for the queue length (in this paper, the queue length is defined as the number of data packets to be transmitted in AUV's buffer space), assuming that the maximum queue length allowed in AUV is \(Q_{s}^{*}\), and the current queue length of AUV \(s\) is \(Q_{s}\), then according to Equation (14), the deviation of queue length is expressed as:  
\[D_{Q_{s}}=\frac{|Q_{s}-Q_{s}^{*}|}{Q_{s}^{*}}, \tag{16}\]  
The total energy of AUV is \(E_{s}^{*}\) and the residual energy of AUV \(s\) is \(E_{s}\), then the deviation of residual energy is:  
\[D_{E_{s}}=\frac{|E_{s}-E_{s}^{*}|}{E_{s}^{*}}, \tag{17}\]  
Figure 8: The network model of PTC algorithm.  
Assuming that the needed degree of AUV for guaranteeing network connection is \(n_{s}^{*}\) and the current degree of AUV is \(n_{s}\), then the deviation of AUV's degree can be calculated as:  
\[D_{E_{s}}=\frac{|n_{s}-n_{s}^{*}|}{n_{s}^{*}}, \tag{18}\]  
Note that in Equation (18), the AUV degree needed for guaranteeing network connections can be calculated based on the conclusion in [33]. In [33], the authors have proved that for a wireless network, if the number of neighbors of a node is larger than \(5.1774\ \log n\), then the network will be connected with probability \(1\); where \(n\) is the total number of nodes in network, so in this paper, considering the energy consumption, we choose \(n_{s}^{*}=5.1774\ \log n\) as the needed AUV degree.
## 4 Experiment
### Data Preparation  
#### 4.1.1 Original Dataset  
For this case study, an area in the United Kingdom (UK) was chosen because its legal framework allows the collection of Street View Images (SVIs). A random sample of 2000 buildings was then drawn from London's North-West region, as illustrated in Figure 11. The sampling process relied on the national mapping agency's website ([[https://osdatabub.os.uk/](https://osdatabub.os.uk/)]([https://osdatabub.os.uk/](https://osdatabub.os.uk/)) (accessed on 25 November 2024)). Next, these addresses were used to query the GSV API, facilitating the retrieval and download of building images.  
Various GSV parameters were manually adjusted to facilitate the clear identification of buildings. The field of view (FoV), controlling the zoom level or scene scope, was set to between 12 and 48. The pitch, indicating the camera's vertical angle relative to the street-view vehicle ranged from 22 to 28, while the heading--the camera's horizontal orientation--varied from 31 to 52. All images were captured at a resolution of 640 \(\times\) 640 pixels, the maximum resolution currently available from GSV. Figure 12 shows representative images from London.  
Figure 11: Geographic scope of data collection area.  
Figure 12: Examples images in London.  
Nevertheless, many GSV images proved unsuitable for accurate detection. Some lacked a visible building, others were partly obscured by trees or fences, and certain images showed interiors rather than facades. In some instances, the address query returned an error instead of an image. Figure 13 also highlights examples of these unusable images, which were discarded after a manual review. Of the original 2000 addresses, 1232 (61.6%) yielded images deemed appropriate for building dimension estimation.  
Each region's dataset was subsequently split into training (60%), validation (20%), and test (20%) subsets, resulting in 740, 246, and 246 images, respectively. This random allocation helped preserve an unbiased distribution across all three subsets.
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 4 Experimental Results and Analysis
### Verification of the Channel Calibration Performance
#### 4.2.2 Results and Analysis  
The simulated non-ideal echoes of the scatter-point target in Figure 6 was generated by multiplying the spectrum of the target's ideal LFM echo signal with the channel transfer function estimated by real satellite echo data in a specific CPI. The channel transfer function for calibration is estimated by real satellite echo data in another adjacent CPI. The ISAR two-dimensional image without channel calibration is shown in Figure 9a. The two-dimensional ISAR image with channel calibration based on the single period echo and echoes of 1000 consecutive periods are shown in Figure 9b,c respectively. For comparison, Figure 9d shows the image of the target without adding non-ideal channel characteristics. As shown in Figure 9a, the image is defocused and the scatter points are split and broadened, due to the channels non-ideal characteristics and no channel calibration. Because of the low SNR using a single period echo, in Figure 9b the calibration coefficient cannot fully reflect the channel characteristics. Even if the channel calibration is conducted, the image quality cannot be improved effectively. On the contrary, the image is deteriorated due to the influence of noise. In Figure 9c, we coherently accumulated the calibration satellite echoes of consecutive 1000 periods and obtained the calibration coefficient. After channel calibration, the image quality was improved. Compared with Figure 9d, the image Figure 9c is similar to the ISAR image without adding non-ideal channel characteristics, which indicates that the proposed method effectively eliminates the influence of the non-ideal channel.  
Figure 8: Scatter-point model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter Name** & **Value** & **Parameter Name** & **Value** \\ \hline Mean bistatic angle & 96.2\({}^{\circ}\) & Integration angle & 2.1\({}^{\circ}\) \\ Range resolution & 0.187 m & Cross range resolution & 0.623 m \\ Pulse number of CPI & 400 & Range alignment & Cross-correlation accumulation \\ Phase compensation & phase gradient auto-focus & Image algorithm & Range Doppler \\ \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
To analyze the performance of proposed calibration method quantitatively, Table 4 shows the image contrast of the four images in Figure 9. The image contrast of the ISAR image using proposed channel calibration method was better than the image without channel calibration and channel calibration using single period echoes. It was close to the image with an ideal channel, which demonstrates the effectiveness of proposed channel calibration method based on accumulated calibration satellite echoes.
# A New Approach to Border Irregularity Assessment with Application in Skin Pathology
###### Abstract  
The border irregularity assessment of tissue structures is an important step in medical diagnostics (e.g., in dermatoscopy, pathology, and cardiology). The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, to distinguish between benign and malignant skin lesions. We propose a new method for the segmentation of individual border projections and measuring their morphometry. It is based mainly on analyzing the curvature of the object's border to identify endpoints of projection bases, and on analyzing object's skeleton in the graph representation to identify bases of projections and their location along the object's main axis. The proposed segmentation method has been tested on 25 skin whole slide images of common melanocytic lesions. In total, 825 out of 992 (83%) manually segmented retes (projections of epidermis) were detected correctly and the Jaccard similarity coefficient for the task of detecting retes was 0.798. Experimental results verified the effectiveness of the proposed approach. Our method is particularly well suited for assessing the border irregularity of human epidermis and thus could help develop computer-aided diagnostic algorithms for skin cancer detection.  
Keywords:image analysis; border irregularity; feature detection; pathology; epidermis; morphometry; skin +
Footnote †: journal: Applied
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 4 Methodology
### LogEDL  
In this section, we present LogEDL, a semi-supervised log representation framework for anomaly detection. An overview of the proposed LogEDL framework is shown in Figure 3. It consists of three main components, Transformer Encoder, ENN head, and uncertainty. Transformer Encoder is used after the log sequence is tokenized to extract the contextual information of the log sequence and transform it into semantic vectors. In the second step, ENN head is used to convert the semantic vectors into evidence of the logs, which is the core of LogEDL, and finally, uncertainty is applied to make the judgment of anomaly detection based on the computed uncertainty. For this purpose, we build an evidential deep learning anomaly detection model. It mainly solves the problem of how to extract evidence of separable features with contextual information, which means that normal and abnormal sequences have boundaries in the feature space. Finally, the uncertainty of the extracted evidence is computed for anomalous log sequence detection. We describe each component in detail in the following subsections.  
Figure 3: The framework of LogEDL.
# Carcass Yield and Meat Quality of Broiler Chicks Supplemented with Yeasts Bioproducts
## 3 Results
### Sensorial Evaluation  
The dietary yeast supplemented broiler breast and thigh raw meat sensorial evaluation is presented in Figure 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Group** & **SC** & **RM** & **Hard** & **Adhe** & **Cohe** & **Spri** & **Gumm** & **Chew** & **Resi** \\ \hline \multicolumn{10}{c}{**Main effect—SC levels**} \\ \hline
**0** & & 4073.62 & 0.460 & 0.323 & 2.650 & 1652.71 & 28.97 & 0.200 \\
**0.6** & & 4227.12 & 0.429 & 0.319 & 2.561 & 1697.97 & 29.76 & 0.205 \\
**1** & & 4030.75 & 0.436 & 0.334 & 2.668 & 1695.86 & 29.73 & 0.205 \\
**1.3** & & 4017.31 & 0.480 & 0.321 & 2.595 & 1617.03 & 28.34 & 0.195 \\
**SEM** & & 174.099 & 0.027 & 0.011 & 0.060 & 70.049 & 1.228 & 0.008 \\ \hline \multicolumn{10}{c}{**Main effect—RM addition**} \\ \hline
**No** & 4045.66 & 0.457 & 0.331 & 2.612 & 1683.54 & 29.51 & 0.204 \\
**Yes** & 4128.73 & 0.446 & 0.317 & 2.624 & 1648.23 & 28.89 & 0.199 \\
**SEM** & & 123.107 & 0.019 & 0.008 & 0.042 & 49.532 & 0.868 & 0.006 \\ \hline \multicolumn{10}{c}{_p_ **value**} \\ \hline \multicolumn{10}{c}{SC—_Saccharomyces cerevisiae_, g/kg feed; RM—_Rudotural melaginous_, 0/0.3 kg/t of feed; SEM—standard error mean; SC \(\times\) RM—interaction between the dietary supplements; Adhe—adhesiveness; Cobe—cubesiveness; Spri—springiness; Gumm—gumminess; Chew—chewiness; Resi—resilience;} \\ \end{tabular}
\end{table}
Table 7: _Cont._  
Figure 1: Yeasts supplemented broiler’s breast (**A**) and thigh (**B**) meat sensorial attributes. 1—Negative control group–fed with basal diet (Corn-soybean meal); 2—Basal diet + SC 0.6 g/kg feed; 3—Basal diet + SC 1.0 g/kg feed; 4—Basal diet + SC 1.3 g/kg feed; 5—Positive control group–fed with basal diet (Corn-soybean meal) + RM (0.3 kg/t feed); 6——Basal diet + SC 0.6 g/kg feed + RM (0.3 kg/t feed); 7—Basal diet + SC 1.0 g/kg feed + RM (0.3 kg/t of feed); 8—Basal diet + SC 1.3 g/kg feed + RM (0.3 kg/t feed).  
Compared with the control, similar values for the broiler breast meat appearance and odor were obtained. Significant differences (\(p<0.05\)) were shown for the broilers' raw breast meat fat appearance, consistency, juiciness, elasticity, and tenderness. The highest evaluation fat appearance score was found in the control group, and the lowest in the experimental group five (RM positive control). The same trend was observed for the meat juiciness attribute. Meat consistency was found to be influenced by the dietary SC linear increasement (0, 0.6, 1, and 1.3 g/kg feed), showing a direct influence on the evaluation average score, having the lowest values in the control groups (one and five), and the highest in groups four and eight. Similar score evaluation was found in the broiler thigh meat appearance, consistency, elasticity, and meat tenderness. Although, fat appearance, odor, and juiciness were significantly influenced by the dietary SC supplementation.