esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### SVM-ML Model Configuration
Therefore, the hyperparameter pair (\(\gamma=2^{1}\), C = 23) from the 21-channel EEG was selected as the optimal RBF kernel hyperparameters. After the RBF kernel function was determined, the k-fold CV was also configured using the RSP features of the 21-channel EEG as inputs to improve generalization of the SVM-ML model. Compared to the simple train/test split, the k-fold CV ensures that each sample from the original dataset has the chance of appearing in the training and testing set, which results in less biased evaluations [61]. Since the partition of k folds is random, the k-fold CV was performed 10 times to calculate the mean estimate to decrease the variance of accuracy estimations of the one-shot k-fold CV [62; 63; 64]. Typically, the configuration of k is 5 or 10, as these values have been shown to be the bias-variance trade-off for model evaluation [61; 65]. In our experiment, different selections of k from 2 to 10 were employed to compare the influence of k on model performance. In addition, the leave-one-out CV, where k is the number of samples in the dataset, was used as a complementary comparison to different k-fold CV. Although the leave-one-out CV is more computationally expensive compared to the above strategies, i.e., five-fold and ten-fold CV, it offers an unbiased  
Figure 2: Configuration flowchart for the SVM-ML model. RSP: relative spectral power; SVM: support vector machine; RBF: radial basis function; Acc: accuracy; LOO: leave-one-out, CV: cross validation. evaluation of the model performance as each sample is given the opportunity to represent the entirety of the test dataset [61]. The accuracies of the SVM-ML model for distinguishing between the UD, SA, and SU groups with different k-fold CV strategies in the different frequency bands are shown in Table 3. The model achieved the highest accuracy of 75.4% in the gamma band by six-fold CV. For the leave-one-out CV, the model obtained the highest classification accuracy of 74.4% in the gamma band as well. Therefore, the six-fold CV was selected as the optimal evaluation strategy for the model when using the RSP features of the 21-channel EEG as inputs. Figure 3: Grid search results of \(\gamma\) and C in the SVM-ML model with RBF kernel using the 62-channel EEG RSP features of the delta, theta, alpha, beta, and gamma bands. Acc: accuracy. Peak Acc: highest classification accuracy of the SVM-ML model in the predefined range of (\(\gamma\), C); Peak Loc: location (\(\gamma\), C) corresponding to the highest classification accuracy of the SVM-ML model.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
###### Abstract  
Accurate localization of robots in unstructured environments poses challenges due to low localization accuracy and local trajectory oscillation caused by complex feature points when using Euclidean-based filtering methods. In this study, we propose a novel 3D robot localization method named LIF-M that leverages a manifold-based approach in conjunction with an unscented Kalman filter (UKF-M). Additionally, a relocalization algorithm is designed to ensure localization stability. The proposed method addresses the limitations of conventional Euclidean-based filtering methods by incorporating manifold-based techniques, providing a more comprehensive representation of the complex geometric features. We introduce the manifold concept, where the relevant definition is defined and utilized within the LIF-M framework. By combining left and right invariants, we effectively reduce noise uncertainty, resulting in improved localization accuracy. Moreover, we employ sigma points as a matrix representation of the state points' space in order to seamlessly transition between the matrix space and the vector representation of the tangent space. Experimental tests and error calculations were conducted to evaluate the performance of various algorithm frameworks, and the results demonstrated the importance of the manifold-based approach for accurate attitude estimation. Compared to the standard UKF, the manifold space equips LIF-M with better robustness and stability in unstructured environments.  
Keywords:unstructured environment; robot localization; manifold; unscented Kalman filter +
Footnote †: journal:
# Limitations on the Use of Recycled Asphalt Pavement in Structural Concrete
## 3 Quality and Homogeneity of RAP Aggregates
### Definition and Origin/Production of RAP Aggregates
By influencing water demand, aggregates' shape plays a major role in the strength of hardened concrete. Another factor that influences the bond between aggregates and cement is the surface texture, whether it is rough or smooth [32; 74; 75]. For rough surfaces, the bond between cement and aggregates is strong and leads to good mechanical properties [32]. In the case of aggregates with smooth surfaces, it is necessary to wash it well and to clean the aggregates before use to avoid a poor bond between aggregates and cement [74; 75]. One of the most important physical characteristics of RAP aggregates is the crushing value (or resistance to fragmentation), which is a relative measure of the resistance of an aggregate to crushing under a gradually applied compressive load [4; 8; 16; 44; 45; 46]. As an alternative to the crushing value, the aggregate impact value gives a relative measure of the resistance of an aggregate to a sudden shock or impact [33]. Properties like crushing, impact and abrasion resistance do not usually exceed the limits imposed by the standards expected for the use of recycled aggregates when compared to virgin aggregates, especially if they are used for concrete pavement applications [33]. According to [PERSON] [4], the British testing method prescribed in BS 812-3 [76] to determine the crushing and impact value for assessing the strength of aggregate is not suitable for RAP. A more adequate testing method should be developed to define the strength value of RAP in order to not overestimate or underestimate one of the most important characteristics of the RAP aggregate [4]. The same problem could be faced when the European testing method for crushing and impact value (EN 1097-2 [77]) is used, since it is comparable to that proposed by the British standard. Grading of aggregates is another important aspect as it influences various properties of concrete such as cohesion, water demand, workability, and strength [3; 75]. RAP aggregates should be well graded and consistent in their grading, especially because gradation is related to the percentage of the substitution of natural aggregates by recycled aggregates in concrete, which, in turn, it is strongly dependent on by the exposure class and the strength class of concrete for the majority of countries, as will be shown in the following sections.
## 5 Challenges, Open Research Problems, and Future of WBANs
### Reliable Transfer of Data  
The low transmission power and small sized antennae of wireless sensor devices can cause reduced signal to noise ratios that cause a higher bit error rate and reduce the reliable coverage area. However, reliable data transfer of data in WBANs and medical monitoring systems is crucial. Thus, the error of network coding schemes is resilient for medical data transfer must be developed to improve network reliability. [PERSON] _et al_. [83] proposed a network coding technique for a TDMA protocol. This allows each sensor to transmit data via two relays and relay nodes XOR packets before sending. Although they showed improvement in the rate of packet by simulating real deployments to measure physiological signals such as electrocardiograms and electroencephalography and can improve the proposed system, respectively, remain as future work. The reliable data transmission should be investigated for low power body sensor networks which still a challenge for BASNs.
# Sound Localization Framework for Construction Site Monitoring
## 5 Experiments
### Microphone Array Shape for 2D Localization  
Two array shapes--a triangular array with three microphones and a square array with four microphones--were tested for estimating sound-source locations in a 2D space [32; 38]. In the square array, the data transmission increased when a microphone was added. However, the size of the dead zone, which refers to the space not included in the internal space of the microphone array, decreased, enabling the system to monitor a larger area.  
The triangular array was designed as an equilateral triangle to minimize errors that could occur because of the differences in the distance between microphones. The microphones were installed at points (0, 0), (2.70, 0.00), and (1.35, 2.624), as shown in Figure 5. Sound-source locations were estimated 20 times each at randomly selected points within the internal space of the microphone array.  
In the square array, microphones were installed at points (0, 0), (0, 4.2), (5.4, 4.2), and (5.4, 0), which corresponded to the maximum size of the laboratory environment, as shown in Figure 6. The tests were performed within the internal space of the array.  
Figure 5: 2D localization results with a triangular array.  
Considering that the developed framework presents its results as x, y coordinate values, the validity of the system was verified depending on how close the estimates were to the sound source by using the average estimation error in the x and y directions and the distribution chart. In the triangular array, the average error was 0.10 m and 0.33 m in the x and y directions, respectively (Table 1). Although there was a larger error in the y-direction, a similar distribution point cloud was formed.  
However, in the square array, the average error value was 0.56 m and 0.34 m in the x and y directions, respectively, and the distribution of the estimation results was wider (Table 2). Compared to the square array results, the triangular array results had a more clustered distribution [37]. Therefore, the triangular array, which has uniform distances between the microphones, is more suitable for localization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Test Run** & **Avg \& **x (m)** & **Avg \& **y (m)** \\ \hline (0.9, 0.9) & 20 & 0.06 & 0.26 \\ (2.25, 1.35) & 20 & 0.16 & 0.28 \\ (1.35, 0.00) & 20 & 0.08 & 0.45 \\ Average & & 0.10 &
## 3 Vehicular Communications: Core Concepts and Challenges
### DSRC Communication  
Dedicated Short-Range Communication (DSRC) serves as a wireless protocol specifically designed to facilitate high-speed communication over short distances, both between vehicles and between vehicles and infrastructure. Traditional cellular technologies like LTE and GSM are limited to the unique advantages of DSRC: high-bandwidth and low-latency capabilities, especially in difficult weather circumstances or high-speed situations. Unlike GSM or LTE, DSRC is the international Wireless Access in Vehicular Environments (WAVE) initiative standard. To illustrate, DSRC's protocol stack is designed to handle the rapid changes and high mobility in the system topology characteristics of vehicular networks. It operates in a dedicated spectrum--the 5.9 GHz band--explicitly allocated for vehicular communication. This allocation blocks interference from other wireless devices, ensuring uninterrupted and reliable communications needed for safety-critical applications.  
The primary objective of DSRC is to enable the instantaneous exchange of critical information, with a particular focus on applications like collision avoidance and traffic management. Operating within a range of 300-900 m, DSRC allows vehicles to communicate effectively when in close proximity [33]. However, a notable challenge arises in offline conditions where the absence of nearby RSUs complicates or renders impossible the standard verification process for received messages. Despite this challenge, DSRC remains pivotal in advancing safety and efficiency within connected vehicle systems.
# Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model
## 5 Experimental Results
### Dataset  
We used the dataset from the 2017 MICCAI Liver Tumor Segmentation Challenge (LiTS) for the experiments. The 2017 MICCAI Liver Tumor Segmentation Challenge collected 131 abdominal CT images from multiple medical institutions in China. The volumes of the livers present a normal distribution similar to the known type, ranging from 0 to 75 in number and from 38 to 349 in size. The subjects include liver cancer, primary liver cancer, secondary liver cancer, colorectal cancer, breast cancer, lung cancer, etc. The contrast of the lesions varies, such as with high- and low-contrast enhancements. Due to the differences in the CT scanning equipment and acquisition standards, there is a significant variation in the resolution and image quality of the CT images. The in-plane resolution ranges from 0.6 mm \(\times\) 0.6 mm to 1.0 mm \(\times\) 1.0 mm, and the slice thickness ranges from 0.45 mm to 6.0 mm. All axial slices have a size of 512 \(\times\) 512 pixels, and the number of slices obtained in each scan ranges from 42 to 1026. Figure 3 shows four samples from the LiTS dataset.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS with RGB Camera  
#### 3.1.1 FAS Combining Handcraft and DL  
It can be seen from the reviews [15; 22] that the research on FAS prior to 2018 primarily focused on traditional manual feature extraction, with its primary strategy being to identify deception clues from the prostheses, which required extensive prior knowledge. Some methods were based on random motion, such as gaze tracking [33; 34], eye blinking [35; 36],  
Figure 4: Topology of FAS based on DL. nodding and smiling [37], lip reading [38], etc. Some methods focused on life information, for example, remote Photoplethysmography (rPPG) [39; 40; 41], optical flow [42; 43], and face micro-movements [44; 45]. Moreover, texture features were extracted for FAS, including local binary pattern (LBP) [46], local binary pattern on three orthogonal planes (LBP-TOP) [47], color distortion texture [48], scale-invariant feature transform (SIFT) [49], speed-up robust features (SURF) [50], histogram of oriented gradients (HOG) [51], difference of Gaussian (DoG) [52],and so on. Some researchers have paid attention to image quality features, including specular reflection, color distribution and sharpness [26; 53]. In addition, some sensors were used for FAS, such as infrared images [54; 55], light fields [56; 57], and depth maps [58]. Typical handcrafted methods are shown in Table 2. As shown in Table 2, these algorithms are categorized based on the types of attacks they are generally effective at detecting, along with an analysis of their advantages and disadvantages. Despite their effectiveness in specific scenarios, these traditional methods often require improvement, such as necessitating user cooperation, being susceptible to external conditions, and needing additional hardware. With the development of deep neural networks (DNNs), many researchers have begun to use DNNs to extract the features of face images for FAS. [PERSON] et al. [59] introduced DL to FAS for the first time in 2014, demonstrating the positive impact of CNN on face liveness detection. Since then, researchers have integrated traditional features with DNN to enhance the performance of FAS. By combining handcrafted features like LBP [46] and HOG [51] with DNN, these algorithms use methods like cascade fusion, feature-level fusion, and to improve spoofing detection. Traditional features capture detailed textures, while DL-based models identify complex patterns, improving the algorithm's accuracy and robustness. However, this may increase computational complexity and processing time. Cascade model fusion: [PERSON] et al. [60] proposed a layered neural network to fuse shearlet-based image quality and optical flow-based motion cues for face liveness detection. [PERSON] et al. [61] fully utilized the differences in reflective characteristics between real faces and 3D masks. [PERSON] et al. [62] combined optical flow with video frames as CNN input, employing spatial and channel attention for adaptive classification. [PERSON] et al. [63] leveraged VGG
# Global Power Dynamics in the Contemporary Space System
## 4 System Actors--Space Powers
### Major Space Actors
#### 4.2.7 Emerging Middle Space Powers
Turkish Air Pilot [PERSON] became Turkey's first astronaut as part of the Axiom Mission 3, which launched on 19 January 2024 [73]. Additionally, Turkey is an active member of the UN space system and, since 2006, a member of the Asia-Pacific Space Cooperation Organization. Finally, South Korea is another salient example of space development in the last few decades. Despite the country's economic success in certain sectors, such as electronics and automotive manufacturing, its space program lags behind those of other key Asian states,including India, China, and Japan. In the last two decades, South Korea has demonstrated a resolute commitment to developing an autonomous and robust space program, primarily driven by economic and national security imperatives [26] (p. 136). In October 2021, South Korea successfully launched the NURI-Rocket KSLV-2, becoming the seventh nation capable of placing a satellite weighing at least one ton into orbit. In August 2022, the Korea Pathfinder Lunar Orbiter was launched, and it was successfully inserted into orbit around the Moon in December 2022. In recent years, South Korea has notably accelerated its space development initiatives. In May 2024, President [PERSON] inaugurated the Korea Aerospace Administration (KASA) [74]. In May 2021, the South Korean conglomerate SK joined NASA's Artemis program, which aims to return humans to the lunar surface. To further develop its space capabilities for military applications, South Korea has established a task force and a space policy-oriented think tank. Furthermore, South Korea is developing plans to construct its own satellite navigation system and a 6G communications satellite network, with the ambitious goal of sending a spaceship to the Moon by 2030 [75].
# Advanced Micro/Nanocapsules for Self-Healing Coatings
## 4 Classification of Self-Healing Coatings Based on the Polymeric Matrix
### Thermoset Polymers
#### 4.1.1 Epoxy-Based Self-Healing Coatings  
Epoxy stands out as a highly effective and dependable polymer matrix and has been employed in self-healing polymer coatings for several years. Its exceptional thermal, mechanical, and adhesion properties set it apart from other polymer matrices. In the extrinsic self-healing mechanism, healing agents or inhibitors are typically encapsulated within micro/nanocarriers alongside the self-healing polymer matrix. When employing this micro/nanoencapsulation technique, it is crucial to consider that the self-healing agents or inhibitors should possess low viscosity to facilitate seamless encapsulation and timely release as needed [40]. [PERSON] et al. [41] formulated a multifunctional anticorrosion coating system using waterborne epoxy and nanofillers, specifically vanadium oxide modified by the polyaniline polymer PANI and tannic acid (V2O5@pani@TA-Fe). In this system, the polyaniline-modified vanadium oxide particles play a dual role, providing both corrosion passivation and inhibition effects. Additionally, the tannic acid contributes to the inhibition activity by actively releasing inhibitors in response to pH changes. The combined dual-action capability of the system significantly enhances its self-healing anti-corrosion  
Figure 3: The classification of self-healing coatings based on the polymeric matrix.  
properties. The self-healing performance was assessed using electrochemical impedance spectroscopy (EIS) and salt spray tests (SST). The results demonstrated that the inhibition value of the water-based epoxy reinforced with V2O5 modified by polyaniline and tannic acid complex (WEP/VPC) coating remained higher than 10\({}^{8}\) X/cm\({}^{2}\) even after 80 days of immersion, surpassing the performance of pure water-based epoxy (WEP) coatings [42]. Water-based epoxy coatings were fabricated by the team of [PERSON] and demonstrated anti-corrosion and self-healing properties [43]. These coatings had several additives, such as organic microcapsules, including 8-hydroxyquinoline as a corrosion inhibitor. Robust superhydrophobic coatings combing chemically reactive epoxy groups and low surface-energy materials were produced by [PERSON] et al. [44]. In the work of [PERSON] and his group, epoxy coatings, including montmorillonite modified with ionic liquids and zinc-based salts acting as anti-corrosion additives, were synthesized and applied onto steel substrates [45]. In the study of [PERSON] et al. [46], a wear-resistant epoxy coating was fabricated that demonstrated self-lubricating properties and displayed self-healing action under photothermal effect.  
However, more work is needed, including the introduction of novel healing techniques, engineering design optimization, cost reduction, and a faster transition from laboratory work to useable real-world applications in a variety of fields and industries. Additionally, the creation of long-lasting self-healing polymeric coatings is essential for prolonged environmental exposure.
## 5 Experimental Evaluation and Analysis
### Experimental Results
#### 5.2.4 Total Energy Consumption of Nodes  
Network life is one of the main factors to evaluate the network performance. Network life time is directly related to the energy consumption of the nodes. Thus, the presented protocol should decrease the energy consumption of the sensor nodes. As shown in Figure 13, the energy consumption of the nodes in the SFRRP protocol is mostly less than that of the Flooding protocol. The reason is that much more redundant packets are generated in the Flooding protocol, while in the SFRRP protocol, only a single packet is sent to the base station without any copied packets. The simulation results show that the network life in the SFRRP protocol is higher than that of the Flooding protocol. Total energy consumption in the proposed protocol will be zero after a short period, because all generated packets are transmitted to the base station in less time. However, the total energy consumption in Flooding is high most of the time, because much time is needed to transmit all generated packets to the base station. Furthermore, as the remaining energy of the node affects the number of live nodes and network life, the  
Figure 12: Impact of node initial energy, (**a**) average delivery rate; (**b**) average delay; and (**c**) network life.  
residual energy of the node in the proposed protocol at simulation termination time is depicted in Figure 14.