esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Experimental Results
### Elastic Modulus Strengthening Effect at Different Initial Average Stresses  
Figure 10 shows the number of cycles of siltstone specimens under different initial average stresses versus axial strain. The relationship between the cumulative axial strain and cyclic number can be divided into two cases: one is that when the initial average stress is small, no significant damage occurs in the siltstone specimen after completing 1000 cyclic disturbances and the other is that when the initial average stress is large, the siltstone specimen is damaged before completing 1000 cyclic disturbances or before the cycle begins. When the initial average stress was increased from 5.48 MPa to 10.05 MPa, the axial strain of the siltstone specimen increased slightly under cyclic disturbance, and then remained basically constant. When the initial average stress was 10.96 MPa, the axial strain increased with the increase in the number of cycles, and destabilization damage occurred at 219 cycles. When the initial average stress was 11.87 MPa, the specimen was damaged before the cycle started. In short, when the initial average stress does not reach a specific value, the specimen would not be damaged even if it is cyclically loaded multiple times, which indicates that there is an upper threshold for the damage of the siltstone specimen during cyclic loading.  
Figure 11 shows the relationship between the elastic modulus of the siltstone specimen and the different initial average stresses, where A, B, C, D, E, Fand G represent the initial average stresses of 5.48 MPa, 6.39 MPa, 7.31 MPa, 9.13 MPa, 10.05 MPa, 10.96 MPa and 11.87 MPa, respectively. Taking the specimen with an initial average stress of 5.48 MPa as the reference, when the initial average stress was 6.39 MPa, 7.31 MPa, 9.13 MPa, 10.05 MPa,  
Figure 9: Acoustic emission position.  
10.96 MPa, and 11.87 MPa, the elastic modulus of the specimen increased by 15.5%, 19.6%, 49%, 59.2%, 64.5%, and 72.7%, respectively. The average of the elastic modulus of each group is used for data fitting, which shows that the elastic modulus increases as the initial average stress increases. Under the cyclic disturbance load, the pre-existing cracks in the siltstone specimen are closed, resulting in an increase in the stiffness and elastic modulus of the specimen.  
The percentages corresponding to the different initial average stresses of the siltstone specimens are 30%, 35%, 40%, 50%, 55%, 60%, and 65%, and their corresponding maximum load relationship curves are shown in Figure 12. The testing process of the specimens can be divided into three stages, and the maximum load shows a trend of first decrease, then increase, and finally decrease, and the fitting curves are polynomial.  
Figure 11: The relationship between siltstone sample and elastic modulus under different average stress.  
Figure 10: Number of cycles and axial strain curves in siltstone specimens at different initial average stresses.
## 4 Results and Discussion
### Comparison of the Responsivity Temperature Dependence Results and the RTDC Effect
In fact, the temperature change rate on the photosensitive surface of the detector was smaller on orbit than in this experiment, and temperature correction  
Figure 17: Comparison between the detector-level and instrument-level experimental results. \(G_{\text{det}}\)(T) shows nonlinear change with PST, and the relative change in responsivity is greater compared with \(G_{\text{ins}}\)(T). This difference may be that the detectors used in the two experiments were not from the same batch of products. Using \(G_{\text{det}}\)(T) to correct changes in PSAC responsivity within a large temperature range may cause deviation. mitigated most variations. For this, when RSTC is applied to remote sensors operated in an environment with rapid temperature changes, the thermal resistance between the thermistor and the target should be reduced as much as possible to avoid deviations in the correction results caused by temperature hysteresis.
# A Bunch of YBCO Josephson Generators for the Analysis of Resonant Cold-Electron Bolometers
## 1 Introduction  
An important task for the study of cryogenic narrow-band bolometric systems is to develop a broadband radiation source with the possibility of continuous frequency tuning in the selected frequency range from tens to hundreds of GHz. It is also desirable that the emitting system is located inside the cryostat to avoid reflections from screens and background illumination from open windows, as well as to decrease the internal noise of an oscillator due to the lower operating temperature. As sensitive receivers, we use Cold-Electron Bolometers (CEBs) [1, 2] with direct electron cooling of the absorber, that demonstrate photon-noise limited operation [3, 4, 5] and can potentially reach single-photon level sensitivity in the cm wavelength range [6]. Earlier, it was reported on the frequency response measurements of CEBs performed using a mercury arc lamp source [7], broadband thermal sources [3, 4, 5, 8], and external room temperature sources of THz range [9, 10]. These solutions are not very suitable in the case of a study of resonant narrow-band structures in various frequency ranges. Therefore, we develop the concept of the source of the sub-THz frequency range based on a bunch of long YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\delta}\) (YBCO) Josephson junctions (JJ).  
High critical temperature YBCO bicrystal long Josephson junctions [11, 12, 13, 14, 15, 16, 17] and arrays [18, 19] can be used for spectral calibration of sensitive low temperature receivers, such as semiconductor single-photon detectors [20], bolometers [21, 22, 23, 24] or integrated superconducting heterodyne receivers [19, 25]. Josephson YBCO generators are convenient to use for the mentioned goals since their working frequency is connected with the voltage by the Josephson relation and at the same time they have a broadband continuous frequency tuning in the THz range [23].  
In this work, for broadband frequency tuning, we use the so-called \"travelling wave\" or \"flux flow\" regime that occurs in a long JJ [23]. If a JJ is rather long with length \(L\gg\lambda_{J}\), where \(\lambda_{J}\) is the Josephson penetration depth, the train of Josephson vortices (fluxons)is created due to the effect of the magnetic field and radiated at an output edge after propagation along the junction.  
The aim of the current work is to compare two setups of Josephson junction placement and to test various narrow-band detecting systems with Cold-Electron Bolometers to demonstrate the feasibility of the suggested technique.
# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
###### Abstract
This study proposes a CNN-based MFA system using feature-level concatenation, and it is shown that the objectives were achieved, according to the experimental results. The three factors used for learning were a face image, a securely converted password image, and a user-specified auxiliary image. The contributions of this study are as follows:  
1. This study presents a mobile multi-factor CNN (MMCNN) with three inputs, containing only two convolutional layers and two FC layers. With the MMCNN, the proposed CNN-based multi-modal MFA system exhibited an accuracy of 99.6% in the experiment. The three factors used for learning were a face image, an image securely converted from a password, and a specific image designated by the user. These contributions are introduced in Section 3.1, Section 3.2, and Section 4.1. 2. This study presents an algorithm to create a secure transformed password image, called the Secure Password Transformation (SPT) algorithm. To ensure that a password image is safe even if it is exposed, the pixel location of the password bit among the image pixels is configured differently for each user. This algorithm is introduced in Section 3.3. 3. This study also presents a method for demonstrating that the three considered factors have the same weight regarding the accuracy of authentication, which is confirmed by assuming that one or two factors include fake information. It is found that the weight value of each factor on the authentication system can be determined by judging their FAR values. In this research, the three factors were adjusted to equal weights which were then shown through the FAR values. The experimental results of the system are described in Section 4.2.
# Research on Thermal and Heat Insulation Properties of Aerogel Heat-Insulating Reflective Coatings
## 1 Introduction  
The proportion of energy consumption in the operation phase of buildings to the national total energy consumption is 21.3%, of which heating and air-conditioning energy consumption accounts for 50-70% of the total energy consumption of building operation. Therefore, improving the heat preservation and insulation capacity of building walls is of great significance to realize building energy conservation and promoting the \"dual-carbon\" strategy [1; 2; 3]. Currently, the construction industry mainly uses materials with low thermal conductivity to achieve thermal insulation in buildings [4; 5; 6; 7]. But only reducing the thermal conductivity of the wall does not meet the higher demand for building energy efficiency [8; 9]. Therefore, some scholars have applied reflective materials to building walls [10; 11] to reduce the heat absorption on the wall surface through the reflection effect, as shown in Figure 1a,b for the thermal insulation schematic diagram of barrier materials and reflective materials. Silica aerogel is a strategic cutting-edge new material, and its unique silica chain structure is characterized by high porosity, high reflection, and low thermal conductivity [12; 13]. The aerogel heat-insulating reflective coatings (hereafter referred to as aerogel coatings) prepared as functional fillers also have the characteristics of high reflection and low thermal conductivity, which can effectively reduce the heat transfer within the wall. Some scholars [14; 15; 16; 17] conducted thermal insulation tests on the prepared aerogel coatings, and the results showed that the aerogel coatings could effectively block heat transfer. However, the research on aerogel coatings focuses more on preparing coatings, and the research on thermal properties needs to be more specific. It cannot be compared effectively with traditional thermal insulation panels.  
For this reason, this paper describes study designs of thermal performance comparison tests for thermal insulation boards and aerogel coatings. It explores the enhancement of the thermal insulation performance of building walls by the use of aerogel coatings from the principle of heat transfer calculations. Thermal parameters such as insulation temperature differences, attenuation multipliers, time delays, and the insulation power of the specimens are compared and analyzed.
# Low-Pass Filtering Method for Poisson Data Time Series
Digital processing of Poisson data, including the Gaussian filtering application, can be outside of experimental physics, for example, in medical technology for imaging blood vessels and tumor therapy with particle beams, in measuring technology for tribological studies of the surfaces of metal parts, in astronomy for gamma telescopes, in muon tomography for recognizing cavities in rocks, and building structures and many other applications. One of the applications of the designed filter proposed here is the digital processing of the data from the URAGAN muon hodoscope (MH) designed by NRNU MEPH [10; 11]. The MH is a computerized measuring device that estimates the intensities of muon fluxes by counting the number of elementary particles--muons--registered in its detector for a set of solid angles with a set time step. Within the framework of this article, MH can be interpreted as a distributed set of muon counters, consisting of primary and secondary information converters. From each primary MH transducer, the initial Poisson data--time series of random non-negative integers \(N(Tk)\)--the quantities of Poisson-distributed events recorded in a given solid angle at time intervals \((T(k-1),\quad T(k-1)+T_{0k}),\;k=1,2,\ldots,k_{0}\), where \(T=1\) minute. Due to the features of the MH design, registration intervals \(T_{0k}\)are random with a uniform distribution law in the range \(T_{0\,\text{min}}\leq T_{0k}\leq T_{0\,\text{max}}<T\). From each secondary MH transducer, the 1-minute-sampled normalized Poisson data \(Y(Tk)\) are generated for a given solid angle by reducing to one second and calculating the averaged normalized Poisson data \(Y(T_{0}n)\) with an hourly discreteness according to the following relations:  
\[Y(Tk)=N(Tk)/T_{0k},Y(T_{0}n)=\frac{1}{60}\sum_{k=1+60(n-1)}^{k=60n}Y(Tk),n=1,2,\ldots,T_{0}=T\cdot 60. \tag{1}\]  
Data resulting from (1) are produced for the whole set of solid angles; next, they are placed into time series of matrix MH data in the database [12].
# Decorrelation-Based Deep Learning for Bias Mitigation
###### Abstract  
Although deep learning has proven to be tremendously successful, the main issue is the dependency of its performance on the quality and quantity of training datasets. Since the quality of data can be affected by biases, a novel deep learning method based on decorrelation is presented in this study. The decorrelation specifically learns bias invariant features by reducing the non-linear statistical dependency between features and bias itself. This makes the deep learning models less prone to biased decisions by addressing data bias issues. We introduce Decorrelated Deep Neural Networks (DcDNN) or Decorrelated Convolutional Neural Networks (DcCNN) and Decorrelated Artificial Neural Networks (DcANN) by applying decorrelation-based optimization to Deep Neural Networks (DNN) and Artificial Neural Networks (ANN), respectively. Previous bias mitigation methods result in a drastic loss in accuracy at the cost of bias reduction. Our study aims to resolve this by controlling how strongly the decorrelation function for bias reduction and loss function for accuracy affect the network objective function. The detailed analysis of the hyperparameter shows that for the optimal value of hyperparameter, our model is capable of maintaining accuracy while being bias invariant. The proposed method is evaluated on several benchmark datasets with different types of biases such as age, gender, and color. Additionally, we test our approach along with traditional approaches to analyze the bias mitigation in deep learning. Using simulated datasets, the results of t-distributed stochastic neighbor embedding (t-SNE) of the proposed model validated the effective removal of bias. An analysis of fairness metrics and accuracy comparisons shows that using our proposed models reduces the biases without compromising accuracy significantly. Furthermore, the comparison of our method with existing methods shows the superior performance of our model in terms of bias mitigation, as well as simplicity of training.  
decorrelation; deep learning; DcCNN; DcANN; bias mitigation; fairness metrics; distance correlation; hyperparameter; bias; features +
Footnote †: journal: 2022, 14, 110. [[https://doi.org/10.3390/fi4040110](https://doi.org/10.3390/fi4040110)]([https://doi.org/10.3390/fi4040110](https://doi.org/10.3390/fi4040110))
## 2 Related Work
Key features such as colour, contour, and texture were extracted from these images to minimize background interference. The SAGAN model was then enhanced by deepening its network structure and adjusting loss functions to handle the small initial dataset effectively. The focus was on generating realistic X-ray images. An evaluation using GAN-train, GAN-test metrics, and the FID score demonstrated that the model successfully enlarged the image dataset and improved data quality for training more accurate automatic detection models. [PERSON] et al. [38] introduced a method to enhance the detection of prohibited items in X-ray baggage scans using advanced machine learning techniques. They proposed a novel image generation approach utilising generative adversarial networks (GANs), specifically through a custom CT-GAN model, to produce realistic X-ray images of prohibited items. This method allowed for increased diversity and quantity in training datasets, essential for training effective convolutional neural networks (CNNs). By improving the CGAN model to vary the poses, positions, and scales of the generated images, they further refined the training process. [PERSON] et al. [39] developed a foreground and background separation (FBS) framework for X-ray prohibited item detection, addressing the challenge of object overlap in X-ray security screening. Their method involved separating prohibited items from irrelevant objects by designing a target foreground and using recursive training to fine-tune the detection. An attention module was also integrated to enhance focus on the foreground. The framework was tested on synthetic and public datasets, showing that it significantly outperformed existing solutions and required only a minimal number of foreground and background ground truths for effective training. This approach offered a more accurate and efficient solution for detecting prohibited items in complex X-ray images. [PERSON] et al. [40] proposed a novel method for waste inspection using X-ray imagery, addressing the inefficiency of traditional manual or RGB image-based methods. They introduced the concept of instance-level waste segmentation in X-ray images and created a comprehensive dataset of over 5000 X-ray images with detailed annotations. To tackle the challenges of heavy occlusions and penetration effects typical in X-ray images, they developed an innovative instance segmentation method, Easy-to-Hard Instance Segmentation Network (ETHSeg), which incorporated a global structure guidance module and an easy-to-hard disassembling strategy. This approach significantly enhanced the ability to segment and analyse overlapped objects in X-ray waste images, demonstrating the potential to revolutionize efficient waste inspection processes. Table 1 outlines key generative models used in X-ray image augmentation; the table highlights differences in model types, publication years, and datasets used.
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
## 2 Experimental Procedures  
The main material used in the experiments was coal slime discharged in the coal washing process from Shuilandong coal mine, Binxian County, Shaanxi Province, China. Three materials, namely, the high-water-content materials, cement, and fly ash were also selected as additives to prepare the cemented backfill material together with coal slime. Therein, the cement was a 42.5# ordinary Portland cement; the fly ash was the secondary fly ash discharged from a power plant. Preliminary tests showed that specimens showed favorable strength characteristics and economy when they were prepared following these mix proportions: mass concentration of coal slime was 28.75% to 40.00%, and the proportions of the high-water-content materials, cement, and fly ash remained at 2.4% to 3.9%, 3.3% to 10%, and 3.3% to 10% of the total mass.
# Characterization of the Intelligibility of
## 1 Introduction  
Despite the significant incidence of hearing loss in older adults and the severe consequences of this condition on communication and quality of life, age-related hearing loss is still largely underdiagnosed [1]. The majority of individuals with hearing loss tend to live with their hearing problems for several years before seeking help until they experience communication problems and disability due to the slow progression of hearing loss [2, 3]. One way to help prevent or limit the progression of disabling hearing loss is to raise awareness about the importance of early detection in adults and promote adult hearing screening [4]. In the field of adult hearing screening, there is increasing interest in easy-to-use, user-operated tools that are able to detect hearing difficulties and can be administered at a distance [5, 6, 7, 8]. The inability to understand speech in noise is one of the most common complaints of older adults with hearing loss, even at early hearing impairment stages [9]. As such, speech-in-noise tests may reflect real-life communication performance, and they may be helpful in promoting awareness, early detection of hearing loss, and supportingpatient motivation to seek care [10]. In particular, hearing tests delivered remotely may help increase access and facilitate regular monitoring.  
In comparison to pure-tone audiometry, speech-in-noise tests can be performed in noisy environments outside the clinic, and they can be self-administered in the absence of calibrated equipment and with no supervision by hearing healthcare providers [9; 10; 11; 12; 13]. The increasing use of speech-in-noise tests raised awareness of the limitations of diagnostic validity in specific testing conditions and applications. For example, appropriately serving underrepresented populations with special attention to racial, ethnic, and linguistic minorities [14]. It is worth noting that several mobile health apps have been recently introduced to support hearing screening in adults, as well as self-testing for education purposes or for hearing aid fitting [15]. However, hearing screening at a distance may open new challenges in hearing health care. For example, remote testing using pure-tone audiometry has limitations in terms of output levels calibration and sensitivity to environmental noise [16]. Speech-in-noise testing can help overcome this limitation, but language-specific test stimuli may lead to issues with non-native listeners [17].  
Examples of speech-in-noise tests viable for adult hearing screening include, but are not limited to: the Speech Perception Test [18] and the Earcheck [19; 20], based on consonant-vowel-consonant (CVC) words, the digits-in-noise test, based on digit triplets [13], and the Speech Understanding in Noise test, based on nonsense vowel-consonant-vowel (VCV) stimuli [12; 21; 22]. All the above-mentioned tests were originally developed in a single language and then adapted to different languages, (i.e., by translation of stimuli and by using recordings from native language speakers).