esa-sceva/satcom-chunk-collection huggingface dataset:
# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 4 Experiments and Results  
### Datasets and Evaluation Metric  
We evaluated MSDA-Net using four distinct biomedical datasets: the 2018 Data Science Bowl dataset [15] for lung nodule segmentation dataset, ISIC 2018 [16; 17] for dermatology segmentation dataset, CVC-ClinicDB [18] for intestinal polyp segmentation dataset, and a private dataset on Colon Cancer Slice. These datasets contain segmented objects of different sizes and textures. Specifically, the 2018 Data Science Bowl dataset [15] consists of a microscope dataset, which contains a total of 402 training cases, 134 validation cases, and 134 testing cases, which is mainly used for diagnosing lung diseases. ISIC 2018 [16; 17]. This dataset is an application of dermatoscopic images for the diagnosis of melanoma. ISIC 2018 [16; 17] comprises 2594 labeled images. Of these, 1556 were randomly chosen as the training dataset, and the rest of the images were divided into test and validation datasets. CVC-ClinicDB [18] dataset consists of lesion areas of intestinal polyps extracted from colonoscopy videos. CVC-ClinicDB [18] contains a total of 612 images and corresponding masked images with annotations, which contain a total of 368 training data points. Colorectal cancer slice dataset: this dataset is private (images provided under license from partner hospitals) consisting of rectal cancer slices, which are mainly used for rectal cancer diagnostic studies, of which the training dataset contains a total of 407 images.  
\[IoU=\frac{|G\cap Pr|}{|G\cup Pr|} \tag{9}\]  
\[Dice=\frac{2|G\cap Pr|}{|G|+|Pr|} \tag{10}\]  
\[Acc=\frac{TN+TP}{TN+TP+FN+FP} \tag{11}\]  
\[Precision=\frac{TP}{TP+FP} \tag{12}\]  
\[Recall=\frac{TP}{TP+FN} \tag{13}\]  
\(Pr\) represents the foreground effect that the network anticipated, and \(G\) represents the foreground of the actual label. \(TP\), \(TN\), \(FP\), and \(FN\) represent true positive, true negative, false positive, and negative. Precision, denoted by P, refers to the proportion of correctly identified positive samples out of all predictions predictions made, calculated across the entire sample population. R denotes Recall, which represents the proportion of actual positive samples that the model correctly predicts as positive.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 5 Experiments
### Statistical Evaluation of the Detector  
The detection rate of two detectors was statistically evaluated--a detector able to classify objects into one class (drone) and a detector able to classify objects into two classes (drone and bird). The detectors were evaluated on a set of 150 images; 74 of them represented a single drone, and 76 represented a bird. For this evaluation, a new data set was used that was not used in training. The detection rate of the detector trained to one class is shown in Table 4 and the same rate of the detector trained to two classes is shown in Table 5.
# Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks
## 1 Introduction
The authors used a simple equal distance placement scheme and equal power placement scheme and proposed two placement heuristics-based algorithms using a greedy approach. Meta-heuristic optimization techniques are turning out to be useful in designing applications using LWSNs. Some of the major meta-heuristic techniques are single solution-based meta-heuristics and population-based meta-heuristics [25]. Nature-inspired techniques are an important subclass of population-based meta-heuristics. Therefore, several researchers have used nature-inspired techniques in various applications. Recently, [PERSON] [26] used hybrid meta-heuristic techniques to design energy efficient protocols for WSNs. [PERSON] et al. [27], used nature-inspired techniques for node placement and lifetime optimization of a Linear Wireless Sensor Network specifically used in monitoring of oil pipelines. The problem of the optimal number of sensors to be deployed with a given initial energy of each sensor node and message buffering limitations has been discussed. The author used two evolutionary algorithms to solve the deployment problem, i.e., Genetic Algorithms (GA) and Ant Colony Optimization (ACO). The solutions proposed for very long oil/gas pipelines crossing states, countries, and continents cannot be directly applied effectively for managing pipelines inside a building. Moreover, the greedy approach is heuristic in nature, and ACO follows a strictly linear path, which does not consider the crossing nature of pipelines inside a building. In [28], [PERSON] presented the Lion Optimization Algorithm to be used for optimization problems. Considering the nature of pipelines inside a large building, LOA seems to be a good approach for optimizing nodes placement. Therefore, in this paper, an efficient node placement strategy for effective and efficient pipeline management inside a large smart building using LOA has been proposed. The major contributions of the paper are as follows:  
* The paper proposes a solution to manage various pipelines inside a large building effectively. * Lion Optimization Algorithm (LOA) has been used to optimize the node placement. To the best of our knowledge, LOA has not been used in the literature for efficient node placement for LWSN. * An efficient mechanism for data transfer for such pipeline has been designed. * The performance comparison of the proposed scheme with three techniques namely greedy approach, ACO and GA [27] with respect to lifetime, throughput and end-to-end delay has been performed. Figure 1 explains the path to arrive at the proposed approach. The rest of the paper is organized as follows: Section 2 presents the problem statement whereas proposed solution and simulation/result analysis are given in Sections 3 and 4, respectively. Section 5 presents the conclusions of the paper and points out possible future work directions. Figure 1: Proposed Problem and Solution.
# Handwritten Signature Verification Method Based on Improved Combined Features
## 4 Experiment Results and Discussion
### Comparative Analysis  
Experimental results show that using SF-A to combine static and dynamic features is effective for handwritten signature verification. By observing the experimental results of each feature, it is found that for offline images, when the number of samples is 3 and 5, the effect of using GLCM is better than HOG, and when the number of samples is 8 and 10, HOG is better than GLCM. On the whole, the effect of texture features is better than geometric features, which may be related to the dimension of feature vectors. The feature vector dimension of geometric features is smaller. However, the result of combining geometric features and texture features can usually improve the accuracy of verification. It also reduces the FAR and FRR, because the texture feature represents the local texture information of the offline image, while the geometric feature represents the global information of the image. The combined feature vector should be more representative and reliable.  
From the perspective of score fusion, that is, combining static and dynamic features, the experimental results of both SF-L and SF-A are better than those of static or dynamic features alone, indicating that the two fusion methods can improve the complementary performance between the two classifiers. As far as SF-L and SF-A are concerned, the SF-A proposed in this paper has achieved the best results under different training samples by weighting the verification accuracy of the classifier. In particular, the improvement of FAR index is more significant, and in some specific occasions, there are often higher requirements for FAR. Therefore, we can conclude that the proposed SF-A can effectively improve the efficiency of handwritten signature verification.  
Figure 5: Sample distribution when using eight training samples.  
Figure 6: Sample distribution when using ten training samples.
## 4 Numerical Analysis Results
### Effects of Blasting Vibration on the TBM and Ground above the TBM  
Jamming caused by a problem in the TBM or a collapse of the ground above the TBM caused by NATM extended blasting may lead to a significant loss. Numerical analysis has been used to simulate the effects of blasting vibration on a TBM and ground above the TBM to prevent such incidents from occurring. Figure 6 illustrates the effects of blasting vibration on the TBM when the pilot tunnel length is 8 m (11-m point on the graph). The resultant values are lower than those permitted in the Seoul subway in all cases, except when a powder factor of 2.0 kg is applied to hard rock [14]. When the length of the pilot tunnel is 12 m (15-m point on the graph), the measured values are lower than the domestic vibration regulation standards (Seoul Subway Cultural Heritage, Vibration Regulation Standards for Buildings with Precision Machinery), regardless of the rock type and powder factor, which indicates that it is safe to maintain a separation distance of 16 m or more. The results are also lower than the national vibration regulation standards when the powder factor is 1.0 kg or less or when the rock layer is weathered.  
Figure 6: _Cont._Figure 6: _Cont_.  
The effects of blasting vibration transmitted to the ground above the TBM, regardless of the rock type, are greater than the regulated value for shopping complexes, offices, and public buildings when the powder factor is 2.0 kg or more. When the powder factor is 0.75 kg or less or when the length of the pilot tunnel is extended to 12 m, the vibration values are lower than the regulation value applicable to houses and apartments. Therefore, the length of the pilot tunnel can be reduced by selecting an appropriate rock type and power factor, which is expected to reduce construction time.
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Unmanned Aerial Vehicle (UAV) Power Sources  
Gas turbine engines have been widely used in aircraft propulsion systems due to their favorable power-to-weight ratio [67] and extended operational duration [30]. Gas turbine engines operate on the principle of air compression, combustion, and exhaust propulsion. They intake air, compress it, mix it with fuel for combustion, and then release the high-velocity exhaust gases to produce thrust. These engines are known for their high power-to-weight ratio, making them suitable for fast and agile UAVs. However, their optimal performance is limited to high-power ranges [68], typically above 100 hp [69]. As a result, they are not suitable for small-scale UAV applications, as they exhibit drawbacks such as inefficiency in fuel consumption, low overall efficiency, and high noise levels [70]. In contrast, the internal combustion engine (ICE) has traditionally served as the cornerstone of aircraft propulsion systems [71; 72]. These engines are characterized by their energy density, making them suitable for long-endurance missions in UAVs. Compared to electric motors (EMs), ICEs offer advantages in terms of higher fuel energy and power densities, enabling longer flight times and larger payload capacities, which are crucial factors in flight missions. However, the multi-step energy production process in ICEs results in decreased system efficiency [56]. EMs are the preferred choice for UAVs due to their distinct characteristics, including minimal thermal and acoustic signatures, well-established electronic control systems, adaptability to automatic control, self-starting capabilities [37], cost-effectiveness, and higher reliability, which minimizes the risk of crashes caused by motor shutdown or failure. It should be noted that electric propulsion systems may encounter electronic speed controller (ESC) failures due to issues such as overheating and melting of the ESC casing. One potential solution to mitigate this problem is duplicating components [73].  
A research paper [37] introduced a novel hybrid power propulsion prototype for unmanned aircraft systems (UAS), aiming to combine the advantages of thermal and electric engines. Although simulation results showed a 13% improvement in endurance, the system was considered complex and not environmentally friendly. Other researchers [73] have also discussed hybrid parallel powertrain architectures using electric motors (EMs) and internal combustion engines (ICEs). However, the incorporation of ICEs in UAVs is currently not the preferred solution due to limitations in fuel usage and endurance optimization. Therefore, this section will solely focus on electric propulsion-based UAVs and provide a critical evaluation of available electric power sources for UAV applications. Additionally, alternative supply strategies including swapping, laser beam inflight recharging, and tethered UAVs will be discussed, particularly for UAVs relying on a single power source.
## 2 Theoretical Preliminaries  
### PSC Technique  
A significant number of laboratory experiments have been conducted to investigate precursor electrical signals before failure in quasi-brittle and heterogeneous materials. Recording such low-level electrical signals has given enough information about the damage processes involved. The creation of microcracks, during continuous loading until fracture, are sources of electric charges that constitute electric dipoles [18] and most of the time, a more complicated charged system [27]. The existence of such electric dipoles results in the creation of an electric potential along the crack. Therefore, an electric current can be detected and recorded. These electric currents, for which the term pressure stimulated currents (PSCs) was established, are quite weak and, to be detected and recorded, require the use of sensitive electrometers. The order of magnitude of the PSCs ranges from a few pA to nA and is related to the type of the material, the degree of heterogeneity and the complexity of the structure. To record PSCs through sensitive electrometers, a pair of gold-plated electrodes are used as sensors, which are usually attached to the side surfaces of the samples and to some complex structures at strategic points of the specimen/structure under test. An initial physical and theoretical interpretation of the generation mechanism responsible for these currents was attributed to the motion of charged dislocations (MCDs) by [PERSON] [28]. Further development of this model was carried out by [PERSON] and [PERSON] [29; 30]. It should be noted that, according to the MCD model, a relationship has been shown between the PSC and the strain rate [31].  
The PSC technique, after the initial encouraging results that were shown by studying marble specimens [19; 23; 32] and then in cement-based specimens [26], is further developed and is being applied even today, either by the original research team who introduced it [23; 33] or by other researchers [18; 20; 21; 22; 24]. Variations of the PSC technique have been presented by other researchers [17; 34; 35]. By studying the temporal evolution of the PSCsignal, and especially when the specimens approach the fracture area, pre-failure indicators related to those provided by the analysis of AE activity are risen [20; 23; 33; 36; 37].
## 2 Materials and Methods  
Figure 1 shows a schematic of the shingled cell-to-string fabrication process. To prepare the separated cells, we divided a M2-size c-Si solar cell (156.85 mm \(\times\) 156.85 mm, Shingled patterned p-PERC cell, Shinsung E&G, Seongnam, Korea) into five equal pieces to make a shingled string using the laser scribing process. We scribed the solar cells using a nanosecond laser (532 nm, 50 kHz, 20 ns, 10 W, Coherent, Santa Clara, CA, USA) and then broke them mechanically. The separated cells were assembled using electrically conductive adhesives (ECA, XCA-8311, Hankel, Dusseldorf, Germany). We dispensed the ECA on top of the front busbar electrode and connected the back electrode of the next separated cell to create a shingled string structure. An ECA curing temperature of 140 \({}^{\circ}\)C was used to assemble the separated cells.  
Figure 2 shows a schematic of the shingled-type lightweight glass-free solar module for BIPV applications with a description of all the constituent layers. We used ETFE (150 um, Jolywood, Suzhou, China) film as the front sheet and ethylene-vinyl acetate (EVA, 450 um, SVECK, Jiangsu, China) as adhesives. The Al honeycomb sandwich structure was fabricated using glass fiber-reinforced plastic (FRP, FR-4, Keunyung industrial, Seoul, Korea)/Al honeycomb core (10 mm, Hongseong Industrial, Seoul, Korea)/Al plate (1 mm, POSCO, Pohang, Korea) with EVA adhesives. We used a lamination system (BSL2222 OC, Boostsolar, Qinhuangdao, China) to laminate the shingled-type PV module with an Al honeycomb sandwich structure at 140 \({}^{\circ}\)C for 660 s. An electroluminescence (EL) system (Portable EL, 600 W, TNE TECH, Cheongju, Korea) was used to analyze the damage to the shingled strings and modules. The PV cells and modules were measured using a solar simulator (WXS-155S-L2, WACOM, Gajo, Japan) and an I-V analyzer (DKSCT-3T, DENKEN, Yufu, Japan) for AM 1.5 G (100 mW/m\({}^{2}\)) illumination. For the reliability test of the PV modules, we performed a temperature cycle 200 test (TC 200, SEC-4100, ALISTA, Victoria, Australia), damp heat 1000 tests (DH 1000, condensing chamber, ALISTA), mechanical load 2400 tests (ML 2400) in IEC 61215 standards. In the TC 200 test, we applied the maximum power current between \(-40\)\({}^{\circ}\)C and \(85\)\({}^{\circ}\)C and repeated 200 cycles with a  
Figure 1: Schematic of the shingled-type cell-to-string structures and fabrication process.  
constant temperature change to measure the power reduction rate. In the DH 1000 test, we measured the power reduction rate before and after the test after 1000 h in an environment of a temperature of 85 \({}^{\circ}\)C and a relative humidity of 85%. In the ML 2400 test, we measured the power reduction rate before and after the test after applying a load with 2400 Pa on the front and rear of the module.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 3 Results
### Intensive Small Area Mapping by UAV
#### 3.1.5 Comparison of Image Resolutions  
In this section, the effect of image resolution is investigated for the 2 July image of the C1 channel. This is useful to find the minimum required image resolution to classify weed areas in irrigation channels. The UAV imagery had a native resolution of 0.05 m. The imagery was coarsened from 0.1 to 5 m using the GEE resample algorithm. The results are shown in Figure 6. It can be seen that classification accuracy is very good with resolutions less than 1 m. It drops off rapidly above this. This is intuitively expected, as the channel widths are less than 10 m, and weed areas are often small and isolated. The points in Figure 6 increasingly deviated from the line of best fit as pixel size increased. One reason for this is the training and validation sets became small, as there were limited pixels to sample within the validation geometries with large pixel sizes.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation
### Dataset
#### 5.1.1 Training Dataset  
In terms of the room size, four rooms are considered. The size of the room is measured in meters. The four rooms have different sizes except for a fixed height of 3 m. The room sizes are: Room 1 (3 m \(\times\) 5 m \(\times\) 3 m); Room 2 (6 m \(\times\) 8 m \(\times\) 3 m); Room 3 (7 m \(\times\) 10 m \(\times\) 3 m); Room 4 (8 m \(\times\) 9 m \(\times\) 3 m). As shown before, the elevation space and azimuth space are both divided with the interval of 10\({}^{\circ}\). Each class is granted a label. For each DOA class, 125 SMIRs can be obtained by combining different parameters. Eight different speech signals are randomly selected from the Librispeech dev corpus [40] to convolve all the SMIRs. Thus, from the four rooms with different sizes, a total of 4000 samples for each DOA class can be acquired. The signal to noise ratio (SNR) is randomly chosen between 0 and 30 dB. A validation dataset is also needed, whose size is 10% of that of the training dataset.
Because of this importance, there have been several studies that have looked at the positioning capabilities of 5G networks. Most of these works were conducted before commercial 5G networks were widely deployed, so many more recent works are missing. In addition, each study has its own perspective with a fairly specific focus. As a result, there are technologies and methodologies that require more attention. In [15], cellular positioning methods from previous to current standards, their reported performance, and localization perspectives in next-generation cellular networks are presented. The authors of [18] provide a comprehensive overview of the positioning architectures in previous generations of cellular networks and then propose a general positioning architecture for 5G networks, leveraging the new features of emerging technologies. The technology and methods of millimeter Wave (mmWave) and massive Multiple-Input Multiple-Output (MIMO) localization in 5G networks are discussed in [19; 20]. Cooperative positioning technologies and methods are presented in [21]. The security and privacy threats according to the participants of 5G positioning are presented in [22]. An overview of 5G positioning solutions in smart cities is given in [23]. Ref. [24] presents a study on integrated localization and communication (ILAC) in 6G networks. Ref. [25] discusses the technical trends and opportunities in 6G networks to achieve latency and accuracy improvements and low-cost positioning.  
The main goal of this paper is to provide a comprehensive overview of machine-learning-aided and traditional cellular-based positioning techniques and architectures in 5G and 6G networks. To do this comprehensively, we have provided a brief summary on positioning in cellular networks. Furthermore, we surveyed the conventional positioning solutions (which do not use machine learning) for 5G networks, as such overviews were still lacking--in particular, the comparison of the advances in the field. At the end of the chapters on conventional 5G-based positioning and machine-learning-based positioning, we present our key findings to briefly summarize the main results of these long surveys.  
In this paper, we focus on the machine-learning-aided cellular-based positioning techniques and architectures utilizing the improvements of 5G networks. The contributions of this paper are the following:  
* Section 2 is an overview of the characteristics of cellular-network-based positioning--the different types of cellular positioning are summarized here.
* Section 3 describes the capabilities of conventional positioning techniques, and several non-ML positioning solutions are introduced and compared here.
* An extensive study of ML-aided positioning techniques is provided in Section 4, and the different methods are compared to each other based on positioning accuracy.
* Section 5 summarizes the expected advancements of 6G networks in terms of positioning.
* Major results of real-world use cases that have been published within the scientific communities so far are collected in Section 6.  
At the end of the paper, Section 7 concludes this research, summarizing the main findings.
## 3 Data Prediction for Energy Saving in WSNs
### Dual Prediction Scheme
#### 3.2.3 Model Generated in Sensor Nodes  
In this case, sensor nodes start transmitting the data to the CHs, and they also have to produce the prediction model. This scheme requires much more computing power from the sensor node since it is the one producing and transmitting the model. This method allows sensors a certain autonomy because they can decide if a new model is needed based on all the measured data, instead of using only the information that they share with the CH or gateway [33]. An example was presented in [34], where the authors developed a model generated in sensor nodes. The sensors change their internal status based on predictions and the amount of energy they will save.