esa-sceva/satcom-chunk-collection huggingface dataset:
### Contributions  
The main contributions of this study can be summarized as follows:  
* First research in the field of insurance policy loans: We propose an explainable deep learning model based on a CNN architecture to predict the success of outbound telemarketing using insurance policy loan data. To the best of our knowledge, the present work is the first in the field.
* New dataset configuration: We utilize newly constructed data to predict the success of outbound telemarketing of insurance policy loans. This dataset comprised 153 variables extracted from 44,412 customers.
* Information loss minimization: We used high-dimensional insurance policy loan data consisting of more than 200 input dimensions without feature selection, which allowed the advantages of a deep learning model to be exploited by extracting features from input data and minimizing information loss due to feature selection.
* Performance superiority and feasibility in practice: We confirmed that the proposed XmCNN model significantly outperformed the machine learning and deep learning models used for comparison. In particular, an ensemble model built with the proposed deep learning model showed the lowest false positive rate and the highest F1-score. Therefore, the experimental results indicate that our proposed model can reduce negative outbound telemarketing outcomes, which are detrimental to customer experience.
* Improvement of model explanatory power: The proposed interpretable model exhibited the ability to identify important variables applicable in practical operations.  
The remainder of this study is organized as follows: Section 2 summarizes the characteristics of outbound telemarketing related to the research topic and prior studies related to predicting the success of outbound telemarketing. In addition, the concepts of DNN and CNN are briefly explained along with some relevant prior works. Section 3 describes the architecture and components of the proposed CNN-based prediction model. Section 4 presents an analysis of the experimental results and important features. Section 5 reportsthe research results, and Section 6 discusses the contributions as well as the theoretical and practical implications of the present work. Finally, Section 7 presents our conclusions and outlines possible directions for future research. In addition, detailed explanations of the variables and abbreviations used in this study are provided in the Appendix A.
## 3 Results and Discussion
employed vacuum-assisted stir casting, the use of ultrasonics in our method effectively reduced porosity, ensuring better matrix-particle bonding and improved mechanical properties. Additionally, the T6 heat treatment applied to our AMCs significantly enhanced ductility (elongation increased by 60.3%), whereas [PERSON] et al. reported a moderate elongation improvement. A direct comparison with the work by [PERSON] et al. [58] highlights significant distinctions in the approach and outcomes of this study. While [PERSON] et al. explored the reinforcement of aluminum matrix composites using TiC particles at higher weight percentages (~2-10 wt%), this study uniquely focuses on ultrafine microsilica particles as a minor reinforcement (~0.36 wt%). Notably, the ductility of the composites in this study (elongation up to 60.3%) surpasses that reported for TiC-reinforced composites, where increased hardness often compromises ductility. Our results, achieved with 0.36 wt% microsilica as a reinforcing material, demonstrate a tensile strength of 73 MPa and an elongation of 60.3%, showing superior ductility compared to [PERSON] et al. 's [59] composites (AA 5083/7.5 SiC), which attained higher strength (tensile strength exceeding 170 MPa) but lower elongation (~8%).
# Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model
## 2 Analysis of the Game Process in Collaborative Allocation of En-Route Network Resources
### Game Construction and Principle Analysis
Other trajectory time slot allocation plans are not considered, so as to ensure that each airline can still meet the overall relatively optimal state of the system after strategy selection, which means satisfying the requirements of ATC. 3. The initial allocation plan derived by ATC is made public to all airlines, and all airlines simultaneously make their choices after ATC issues the guidance information. All airlines are aware of the available trajectory and slot resources and the payment function (the resulting delay situation) of other airlines when choosing plans. This game is a complete information static game based on this assumption.
## 2 Measuring System
### Measurement System Stability Test  
A stability test was conducted to calibrate and validate the zero-point stability of the measurement system. The antenna remained stationary for three consecutive windless nights, with a fixed azimuth angle of 70\({}^{\circ}\) and elevation angle of 90\({}^{\circ}\). Ambient temperature data from these three nights were recorded, as depicted in Figure 9.  
The external temperature conditions remained highly consistent over the course of three consecutive nights. To verify system stability, we conducted the following tests: on the first and third nights, the temperature control switch was activated, while on the second night, it was deactivated. The inclinometer data were recorded and are presented in Figure 10.  
It is evident that the zero drift of the inclinometer on the second night, when compared to that of the first and third nights, becomes more pronounced upon turning off the temperature control switch. This observation confirms that external environmental factors do indeed impact the stability of the inclinometer zero point. The root mean square (RMS) values of the inclinometer measurements taken during the three nights are presented in Table 2. The zero-point RMS value of the inclinometer on the first and third nights is significantly lower than that of the second night. It is evident that activation of the temperature control module significantly mitigates zero-point drift in the inclinometer,  
Figure 9: The ambient temperature data of the three nights.  
thereby affirming that the environmental control box ensures comparable repeatability for outdoor measurements as those conducted in laboratory settings.
# Thiokol with Excellent Restriction on the Shuttle Effect in Lithium-Sulfur Batteries
###### Abstract  
Commercial application of lithium-sulfur (Li-S) batteries is still greatly hampered by several issues, especially the shuttle effect of polysulfides. In this work, we proposed a simple but effective method to restrain the shuttle of the soluble polysulfides by adopting a novel binder of Thiokol in the sulfur cathode. Compared to the battery with conventional polyvinylidene fluoride (PVDF) binder, the initial discharge capacity for the battery with the Thiokol binder were increased by 42%, that is from 578 to 819 mAh/g, while the capacity after 200 cycles were increased by 201%, which is from 166 to 501 mAh/g. Besides, according to the rate capability test cycling from 0.1 to 1 C, the battery with the Thiokol binder still released a capacity amounting to 90.9% of the initial capacity, when the current density returned back to 0.1 C. Based on the UV-vis and ex situ XRD results, it is reasonably proposed that the reactions with polysulfides of the Thiokol help to restrain the shuttle effect of polysulfides. It is therefore suggested that the novel Thiokol binder holds promise for application in high-performance lithium-sulfur batteries.  
Keywords:lithium-sulfur batteries; binder; Thiokol; shuttle effect +
Footnote †: journal: Applied Sciences
The combination of both InSAR and the permanent ground monitoring network with additional extensive geodetic benchmark campaigns has been essential to constrain models and improve our understanding of the inner architecture and long-term eruptive behavior of Piton de la Fournaise volcano (Section 3). Figure 2: The ground deformation monitoring network of OVPF plotted onto the shaded relief of Piton de la Fournaise volcano with the approximate location of the three main rift zones (shaded areas). Permanent GNSS stations are marked as black triangles, tiltmeters as blue squares, extensometers as yellow stars, and stainless-steel benchmarks as red dots. Station names ending with “G” are indicative of GNSS stations, station names ending with “I” are indicative of tiltmeters, station names ending with “X” are indicative of extensometers; the first three letters of a station name represent the station location. Two GNSS stations were offline at the time of writing and have italicized labels (OVPF-IPGP). All Synthetic Aperture Radar (SAR) data described in this publication cover the entire Enclos Grand Brule collapse structure, except for COSMO-SkyMed (CSK) and TerraSAR-X (TSX) SpotLight (SPT) data, the spatial coverages of which are indicated by solid (ascending, ASC) and dashed (descending, DSC) orange and light green frames, respectively. Throughout this publication, all maps feature the exact same map extent (wherein North is up), except for Figure 8, which covers the area indicated by the white solid frame.
###### Abstract  
From a conceptual perspective, beyond-5G technologies promise to deliver very low latency, even higher data rates, and ultrareliable connections for future generations of communication systems. Modulation schemes based on orthogonal frequency-domain multiplexing (OFDM) can accommodate these requirements for wireless systems. Several hybrid OFDM-based systems, such as the time-interleaved block-windowed burst-OFDM (TIBWB-OFDM), are capable of achieving even better spectral confinement and power efficiency. This paper addresses the implementation of the TIBWB-OFDM system in more realistic and practical wireless link scenarios by addressing the challenges of proper and reliable channel estimation and frame synchronization. We propose to incorporate a preamble formed by optimal correlation training sequences such as the Zadoff-Chu (ZC) sequences. The added ZC preamble sequence is used to jointly estimate the frame beginning through signal-correlation strategies and a threshold decision device, and acquire channel-state information (CSI) by employing estimators on the basis of the preamble sequence and transmitted data. The employed receiver estimators show that it is possible to detect the TIBWB-OFDM frame beginning and highlight the robustness of the TIBWB-OFDM technique to imperfect channel estimations by showing that it can provide comparatively close BER performance to the one where the CSI is perfectly known.  
windowed OFDM; time interleaving; frame detection; frequency-domain equalization; channel estimation +
Footnote †: journal: Applied Sciences  
1  
Joint Channel Estimation and Synchronization Techniques for Time-Interleaved Block-Windowed Burst OFDM
# Bond Strength Assessment of Concrete-Corroded
###### Abstract  
Bond strength assessment is important for reinforced concrete structures with rebar corrosion since the bond degradation can threaten the structural safety. In this study, to assess the bond strength in concrete-corroded rebar interface, one of the machine learning techniques, artificial neutral network (ANN), was utilized for the application. From existing literature, data related to the bond strength of concrete and corroded rebar were collected. The ANN model was applied to understand the factors on bond property degradation. For the input in the ANN model, the following factors were considered the relative bond strength: (1) corrosion level; (2) crack width; (3) cover-to-diameter ratio; and (4) concrete strength. For the cases with confinement (stirrups), (5) the diameter/stirrups spacing ratio was also considered. The assessment was conducted from input with single parameter to multiple parameters. The scaled feed-forward multi-layer perception ANN model with the error back-propagation algorithm of gradient descent and momentum was found to match the experimental and computed results. The correlation of each parameter to the bond strength degradation was clarified. In cases without confinement, the relative importance was (1) \(>\) (2) \(>\) (4) \(>\) (3), while it was (2) \(>\) (1) \(>\) (3) \(>\) (5) \(>\) (4) for the cases with confinement.  
Keywords:rebar corrosion; bond strength; concrete cover thickness; artificial neural network +
Footnote †: journal: Applied Sciences
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 1 Introduction  
The mobile robot maintains a grid map with the help of multi-sensor information. The grid map describes the distribution of obstacles and supports the mobile robot to achieve basic functions, such as obstacle avoidance, navigation, and positioning [1, 2]. However, some more complex tasks, such as map compression [3] and map optimization [4, 5], rely on deep information in grid maps, such as straight lines. Many studies in the field of mobile robots treated grid maps as images [6, 7, 8]; therefore, image-based methods are used to obtain straight lines from grid maps of mobile robots. As an important research direction in the field of robot vision, researchers have proposed numerous straight-line extraction methods, which are mainly divided into two categories: methods based on Hough transform and methods based on perceptual grouping [9].
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 4 Experimental Verification
### Case Study 2: Application to the Lab-Measured Bearing Datasets
#### 4.2.3 Feature Learning Capability  
To visually analyze the feature learning effect of the proposed method on the unbalanced datasets, the test samples in Dataset C are input to the model and the middle layer features learned by the model are extracted. Then, the features are reduced from high-dimension to two-dimension and visualized using t-SNE. The results are shown in Figure 12, in which the middle part is the network structure of the model. Different colors in Figure 12a-f represent different samples, and each point represents one sample. It can be seen from Figure 12a that due to the influence of noise, redundancy, and other factors, various samples are mixed together. After the input signals are extracted by the first and second convolution pooling layers, the sample distribution of each category is improved, but it is still difficult to distinguish, as shown in Figure 12b,c. After further feature extraction of the third convolution pooling layer, the normal samples (blue) and the outer ring raceway fault samples (red) are obviously gathered, as shown in Figure 12d. After the fourth convolutional pooling layer feature extraction, the normal samples (blue) and the outer race raceway fault samples (red) are completely separated, and the other three types of samples are gathered, as shown in Figure 12e. After the fully connected layer, the samples of the five different categories are all separated, and the samples of the same category are completely gathered in the same area, as shown in Figure 12f. We can also see that the normal samples (blue) are separated first because they contain the largest number of samples (10,000 samples). The second sample separated is the outer ring fault samples (red), which contain the second largest number of samples (500 samples).  
From the above analysis, the number of samples has a great impact on model training. When the data are unbalanced, the recognition accuracy of the minority class samples  
Figure 12: Visualization of features learned in different layers.  
is easily affected by the smaller number of samples. The results further show that the proposed method overcomes this shortcoming, obtains high accuracy, and can learn the essential data features hidden in the raw vibration signals.
## 4 Mathematical Formalism of Consensus Through Path-Laplacian Matrices  
Consider a simple undirected network \(G=(V,E)\) with \(n=|V|\) nodes (vertices) and \(m=|E|\) edges. Two nodes \(i,j\in V\) are said to be _adjacent_if an edge \((i,j)\in E\) is connecting them. The _adjacency matrix_\(A\) of \(G\) is the \(n\times n\) matrix with entries \(A_{ij}=1\) if \((i,j)\in E\) (and \(A_{ij}=0\) otherwise). Let \(\deg(i)\) denote the degree of node \(i\) (number of neighbors). The diagonal _degree matrix_ is \(D=\text{diag}(\deg(1),\ldots,\deg(n))\). We recall that the _Laplacian matrix_ is defined as  
\[L\;=\;D\;-\;A, \tag{3}\]  
which is a symmetric positive semi-definite matrix of size \(n\times n\) that has real, non-negative eigenvalues \(0=\lambda_{1}\leq\lambda_{2}\leq\cdots\leq\lambda_{n}\)[39; 40]. When modeling, we assume that a node represents an agent, and its adjacent nodes will be connected to it with edges. Classical consensus models assume that each agent is influenced only by its nearest neighbors (direct connections). In what follows, we introduce a generalized Laplacian formalism that allows for incorporating longer-range interactions beyond immediate neighbors. This generalization is based on graph paths and will lead to an extended family of Laplacian matrices capturing \(k\)-hop relationships in the network.
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 4 Challenges and Limitations
### Biocompatibility  
To ensure the safety, stability, and efficacy, GFET biosensors must demonstrate compatibility and environmental stability, particularly for implantable devices that interact continuously with complex biological environments. To address this, systematic studies of graphene material stability, such as coating and storage techniques, are constantly being developed. Graphene-based nanomaterials have a large surface area and are remarkably biocompatible with specific biomolecules, including antibodies, enzymes, DNA, and cells [273]. However, existing research demonstrates conflicting results regarding the biocompatibility of graphene and its derivatives [274], with several factors influencing cell viability, such as the graphene dose, number of layers, lateral size, surface chemistry, hydrophilicity, and exposure time [275, 276, 277].  
Although GFET sensors have shown great sensitivity in detecting a variety of biochemical analytes, there needs to be more investigation into their use in cell culture and amine NT detection. Some studies suggest that graphene's high electrical conductivity promotes neural cell adhesion [274], although findings regarding its cytotoxicity are inconsistent [278, 279, 280, 281, 282, 283, 284]. However, the gate electrode of GFET sensors can be functionalised using biocompatible materials such as Nafion for DA detection [157].
## 3 Results and Discussion
### Results of 3D CFD Modeling of Liquid-Gas Ejector
#### 3.1.5 Mixing Shock and Location of Its Occurrence  
In the annular entrance zone, no changes are essentially found in both streams, whereas a gas boundary layer begins to develop on the wall and interfacial shear forces act between the surface of the jet and the gas annulus. A critical magnitude is reached for the wave disturbances on the jet inside the throat, but prior to the mixing zone, high-velocity droplets are shed from the jet into the annular space. The position of the mixing shock zone is an important factor for evaluating the performance of the eject. By knowing the location of mixing shock, it is possible to know in which area of the ejector the liquid jet is broken. The plot of static pressure profile of the ejector (along its centerline) and contour of volume fraction of air in the ejector are shown in Figure 11a,b, respectively. As it is evident from these figures, due to the mixing shock, a decrease and an increase are observed, respectively, in the volume fraction of air and static pressure at the end of the mixing tube; therefore, mixing shock comes about in this region and liquid jet breaks up just at the end of the mixing tube. It shows that the nozzle is situated at an optimum position.  
Figure 11: _Cont._  
Figure 10: The effects of liquid flow rate on negative pressure, (**a**) flow rate: 2.49 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s, (**b**) flow rate: 2.64 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s.  
3.1.6. The Influence of the Mass Flow Rate on the Mixing Shock Phenomenon  
In Figure 12, pressure contours along the ejector for the specific mass flow rates of (a): 2.49\(\times 10^{-4}\) and (b): 2.64 \(\times 10^{-4}\) m\({}^{3}\)/s are depicted. As mentioned in Section 3.1.5, mixing shock occurs where a decrease in pressure is observed (red arrows). As is crystal clear, with increasing the mass flow rate of the liquid, the mixing shock is transferred from the ejector throat to the diffuser side. In this position, increased mass flow rate causes an enhancement in the velocity of the gas film around liquid jet. Therefore, the location of the mixing shock that depends on the velocity of the annular gas flow is transferred downwards, and a considerable amount of kinetic energy that is related to this flow is lost in this shock.  
Figure 11: (**a**) The contour of volume fraction of the air in the ejector. (**b**) The plot of static pressure profile of ejector (along the center line of the ejector).
## 3 Materials and Methods  
### Breast Cancer Thermodynamics Dataset  
The research utilizes the Database of Mastology Research Infrared Images (DMR-IR), a thorough collection developed in conjunction with the Antonio Pedro University Hospital in Brazil. The DMR-IR is publically open access benchmark dataset for breast cancer detection using thermograms [47]. The database was created in accordance with strict ethical guidelines, following approval by institutional review boards and individual patient consent. The original dataset includes female individuals aged 21 to 80 years, with confirmed diagnoses. The dataset consists of thermal photographs from individuals with healthy breasts and confirmed breast abnormalities. Blurry images were removed. As a result, only 1000 breast images (500 normal and 500 abnormal) were incorporated into the final dataset. The images are classified as either \"normal\" or \"abnormal\" according to clinical and diagnostic evaluations. The images are classified as either \"normal\" or \"abnormal\" according to clinical and diagnostic evaluations. The term \"abnormal\" in this context extends beyond malignant breast cancer cases; it includes a wider range of breast pathologies, encompassing both malignant conditions and benign entities. This classification signifies the clinical aim of promptly identifying any breast abnormalities that may require additional examination, regardless of their cancerous nature. The incorporation of in situ cancer instances is not explicitly mentioned in the dataset but can be deduced within the context of the 'abnormal' classification. In other words, specific lesion type subclassifications (e.g., benign, in situ, invasive) are not distinctly identified in the publicly accessible metadata.  
On the contrary, the \"normal\" category pertains to subjects who display no obvious pathological findings during clinical examination and imaging. This group is devoid of both benign and malignant breast abnormalities, with the thermographic images serving as the baseline for healthy breast thermal patterns. The publicly available DMR-IR dataset lacks detailed clinical labels and histopathological confirmations for each individual image. Consequently, the proposed CAD utilized the binary classification established by the dataset curators, who classified the images based on a review of medical imaging examinations and clinical evaluations at the time of collection. Additionally, the publicly accessible DMR-IR dataset lacks comprehensive clinical annotations, including tumor size, stage, and histopathological validation, which are either absent or reported inconsistently.  
The study employed frontal thermal photos obtained with a high-quality FLIR SC-620 infrared camera at a dimension of 640 \(\times\) 480 pixels. This rigorously gathered database consists of 500 normal and 500 abnormal breast photographs, showcasing a broad range of breast anatomy differences and offering a distinct appearance of infrared breast scans, as depicted in Figure 1.