esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 6 Conclusions  
This paper presents a method for scene classification of high-resolution remote sensing images. For the problem of limited data of the existing high-resolution remote sensing datasets, the multi-view scaling strategy is adopted for data amplification, so as to improve the classification accuracy. At the same time, the structure of RFPNet is constructed in the paper, which is characterized by the adoption of the residual block and the fusion strategy of pooling layer feature maps. On one hand, the problems of gradient disappearance and gradient explosion can be avoided, and on the other hand, the phenomenon of information loss in the pooling process can be solved. In addition, dropout, parameter norm penalty, and the moving average model are adopted to optimize the network structure of RFPNet.  
In order to verify the effectiveness of the proposed method, experiments were performed on the UC Merced dataset and the SIRI-WHU dataset. In order to comprehensively evaluate the proposed method, the evaluation indexes in this paper include accuracy, precision, recall, and F1-score. Compared with the traditional methods and deep learning methods, the proposed method has great advantages.  
Different from the object-centered image classification tasks, scene classification often relies on high-level semantics of the whole image for image classification. The multi-view scaling strategy proposed in this paper adopts the random cropping method, which may crop out some useful or even useless information, resulting in a relatively low classification result. Therefore, in future research work, we plan to solve the above problems by means of supervised data augmentation with the help of image tag information.  
[PERSON] wrote the draft; [PERSON] gave professional guidance and edited; N.Z. and D.X. gave advice and edited; B.C. gave advice.  
The authors declare no conflict of interest.
## 1 Introduction  
The human microbiome changes with age [1] and health affects the composition of the microflora. It has been suggested that aging is accompanied by an underlying inflammatory state [2] that interacts with the microbiota of older adults and makes them more susceptible to age-related diseases [3; 4; 5]. Changes in the gut microbiome are explained by diseases, including metabolic changes and inflammatory conditions. Studies of intestinal microbiota in the elderly show that the microbiome affects a variety of clinical problems, including physical weakness, _Clostridium difficile_ infection, colitis, vulvar vaginal atrophy, colorectal cancer, and atherosclerosis [6]. Other research has focused on the metabolism of nutrients. The microbiota has been shown to correlate with the declining metabolism of essential amino acids by aging [7].  
In addition to the intestinal microbiome, the oral microbiome has been intensively studied. Both are affected by development, aging, and the state of oral disease. The formation of the first oral flora is strongly influenced by the mother [8; 9; 10]. Breast milk provides a source of bacteria that act as an inoculum for newborns [11; 12; 13; 14]. The production and excretion of metabolites by pioneering colonies, such as _Streptococcus_ and _Actinomyces_, alter the anaerobic oral environment. Under such conditions, anaerobic bacteria such as _Veillonella_ and fusobacteria colonize [8; 15]. With development, the microbial community evolves and microbial diversity increases [16; 17]. Current knowledge shows that adult-like stability is reached around age two [15]. The established oral microbiota is disturbed by oral diseases. The development of dental caries has been associated with changes in microbial composition over time [18]. Differences in bacterial communities on the phylogenetic level were observed between healthy people and patients with periodontal disease [19; 20]. The oral microbiota includes some pathogenic bacteria for systemic diseases. Therefore, the oral microbiome affects health and varies with health or disease [21]. Oral diseases, especially periodontal disease, can be the main risk factor for several systemic diseases [22; 23; 24; 25; 26; 27]. Periodontal bacteria and their surface lipopolysaccharideaccharides were the agents of systemic diseases [28]. Recent advances of research on systemic diseases and oral health showed that there exist protective host factors for systemic diseases in relation to periodontal diseases [29; 30; 31]. The number of studies on the oral microbiota of community-dwelling older persons is limited [32; 33; 34; 35; 36; 37].  
In this study, denture plaque and tongue surface samples were obtained from people over 100 years old. The samples were analyzed by high-throughput sequencing of 165 rRNA through a metagenomics approach. We describe the commonly prevalent and highly abundant species of four female centenarians and compare them with the healthy oral microbiome proposed in previous studies. In addition, differences in sampling sites and the co-prevalence of species were analyzed.
## 4 Discussion
The initial accuracy was 80% for Young's modulus and 72% for Poisson's ratio. By adding more layers and feature maps in the convolutional layers and units in the fully connected layers, our network had more trainable parameters and could learn more details and extract more features about images and their following material properties. It must be noted that overfitting can occur in a network with more layers, although more layers can increase the performance to a certain extent. The optimal number of layers and nodes in a given configuration should be decided to avoid falling into the situation of overfitting. The tuned model in this work consists of learnable kernels applied to the input images for extracting convolved features. These convolved features were computed through a rectified linear unit (ReLU) function and passed on to the following layers. With 512 different kernels (the sizes of 3 \(\times\) 3, using a stride of 2 in both \(x\) and \(y\) directions) at the first layer, similar procedures were repeated in layers 2, 3, 4, and 5 with 256, 128, 64, and 32 kernels, respectively. The last convolutional layer was then connected to the first fully connected layer as the input in the vector form. In the fully connected layers, learnable weights were calculated for each layer. We used the dropout technique after every fully connected layer. The last layer was a linear function for Young's modulus and a sigmoid function for Poisson's ratio. To minimize the prediction error, the kernels mentioned above were learned and will be activated when a similar feature appears in the input. This study utilized two-dimensional RVE datasets. However, the framework could be expanded to predict the three-dimensional mechanical properties of BG-COL. Additional properties of the composite hydrogel, such as thermal conductivity, thermal expansion coefficients [27], fatigue life, toughness, and stress-strain curves [23], could be predicted using our framework. Moreover, it must be mentioned that this work did not consider the impact of COL crosslinking, which is typically done for these hydrogels [3]. When the COL and BG are crosslinked, the effect of BG on the mechanical properties of COL can change depending on what COL crosslinking method was used and how much crosslinking was done. For example, COL crosslinking can mask the effect of BG such that the BG incorporation does not impact the mechanical properties of the COL hydrogels.
# A Method for Extracting Acoustic Water Surface Waves Based on Phase Compensation
## 2 Models  
### The Model of an Acoustic Water Surface Wave  
Underwater transmitters emit sound waves that are transmitted to the water-air interface. Due to the mismatch of acoustic impedance at the water-air interface, the sound waves can excite the water surface to produce slight vibrations, namely acoustic water surface waves [15]. The mathematical model of acoustic water surface waves is given by Equation (1). The vibration frequency is equal to the frequency of the sound waves [16]. The amplitude of acoustic water surface waves decays exponentially with spatial distribution.  
\[\eta=\frac{2p_{i}}{\omega\rho c}e^{-\alpha\sqrt{x^{2}+y^{2}}}cos\bigg{(}k\sqrt {x^{2}+y^{2}}-\omega t\bigg{)} \tag{1}\]  
Among them, \(\omega\) is the angular frequency of sound waves, \(\rho\) is the water density, and \(k\) is the wavenumber. \(A=2p_{i}/\omega\rho c\) is the center amplitude and \(p_{i}\) is the sound pressure level (SPL). The sound pressure level is defined as 20 times the logarithmic value of the ratio of the measured sound pressure to the reference sound pressure, and the reference sound pressure is taken as \(2\times 10^{-5}\) Pa.  
\[p_{i}=SPL=20 log\frac{P}{P_{ref}} \tag{2}\]  
\(\alpha\) is the attenuation coefficient of acoustic water surface waves on the water surface.  
\[\alpha=\frac{4\mu k^{2}\sqrt{kg+\sigma^{\prime}k^{3}/\rho}}{\rho g+3\sigma^{ \prime}k^{2}/\rho} \tag{3}\]  
Among them, \(\mu\) is the viscosity of the water medium, \(g\) is the gravitational acceleration, and \(\sigma^{\prime}\) is the surface tension coefficient. According to Equation (1), the amplitude of acoustic water surface waves is inversely proportional to the frequency of the sound source. The higher the frequency, the smaller the amplitude, and the amplitude decreases from the center outwards. Based on the data in Table 1, a three-dimensional model of acoustic water surface waves was simulated, as shown in Figure 1.  
Figure 1: Three-dimensional model of acoustic water surface waves.
# Bridge Damping Extraction Method from Vehicle-Bridge Interaction System Using Double-Beam Model
## 4 Bridge Damping Ratio Extraction Method  
The method proposed in this study can be used to extract bridge damping ratios by removing the vehicle-bridge coupling effect from the dynamic characteristics (frequency and damping ratio) of a VBI system. In bridge health monitoring, the structure is usually monitored using a bridge health monitoring system that requires the frequency \(\omega\) and damping ratio \(\xi\), which can be calculated using the OMA [29; 30; 31] method. Because of the presence of traffic on the bridge during the monitoring period, we obtained the dynamic parameters of the VBI system using the monitoring data rather than the dynamic parameters of the bridge. The damping ratio of the bridge structure was extracted using Equation (17) after simplifying the VBI system by calculating the equivalent vehicle flow parameters using Equation (1) with known information on the mass, stiffness, and the vehicle damping coefficient.  
In general, many bridge health monitoring systems are installed with Weigh-In-Motion (WIM) systems, which can obtain information on the number and mass of vehicles. Since vehicle stiffness and damping coefficient usually remain unknown while using WIM monitoring systems, it is difficult to determine the equivalent traffic flow parameters. The authors of this study proposed a method to estimate the equivalent traffic flow parameters and then validated this estimation model using numerical simulations.  
Figure 7: Damping ratio for a VBI system.  
The assumption that the average vehicle stiffness is \(k_{v}\) and the average vehicle damping is \(c_{v}\) remain constant over time is acceptable because traffic volumes and vehicle types in a traffic route usually have a certain statistical pattern. Under this assumption, the equivalent traffic flow parameters only vary with the known number of vehicles \(n\). As a result, it is only necessary to determine the constants \(k_{v}\) and \(c_{v}\) to calculate the equivalent traffic flow parameters.
###### Abstract  
The increasing demand for high-frequency applications and the widespread use of electromagnetic (EM) waves in communication systems necessitate the development of effective electromagnetic interference (EMI) shielding materials. This study investigates the structural and shielding effectiveness properties of novel polyaniline (PANI)-based NiO/ZnNb\({}_{2}\)O\({}_{6}\) composites for sub-8 GHz applications. NiO and ZnNb\({}_{2}\)O\({}_{6}\) were synthesized via conventional solid-state reactions and combined with PANI to form composites with varying compositions. X-ray diffraction (XRD) confirmed the successful formation of single-phase NiO and ZnNb\({}_{2}\)O\({}_{6}\), while scanning electron microscopy (SEM) revealed well-defined microstructures, contributing to enhanced shielding efficiency. Shielding effectiveness (SE) measurements were performed across the 0-8 GHz frequency range using a vector network analyzer. Among the compositions tested, the epoxy-based NiO-ZnNb\({}_{2}\)O\({}_{6}\) (75-25% by weight) with a 1:1 PANI ratio exhibited the highest SE value of \(-41.16\) dB (decibels) at 6.24 GHz, demonstrating superior attenuation of EM waves. The observed shielding efficiency is attributed to multiple reflection effects, dipole interactions, and the conductive network formed by PANI. These findings highlight the potential of NiO/PANI/ZnNb\({}_{2}\)O\({}_{6}\) composites as cost-effective, high-performance EMI shielding materials for next-generation microwave communication and electronic applications. Further optimization and hybridization approaches are recommended to enhance performance for broader frequency bands.  
EMI shielding; polyaniline composites; NiO/ZnNb\({}_{2}\)O\({}_{6}\), microwave shielding; electromagnetic pollution 2025
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on iLIDS-VID Dataset  
The iLIDS-VID dataset, with its long video sequences and controlled environment, provides an ideal test to evaluate GAF-Net's ability to integrate skeleton-based gait features with appearance information. Our analysis of this dataset encompasses the performance of different fusion techniques, comparisons with state-of-the-art methods, and an examination of optimal fusion factor settings. These results serve as a basis for understanding GAF-Net's effectiveness in scenarios where gait information can be reliably extracted and utilized.
## 2 The Proposed VA-RAIM
### Va-Raim
#### 2.3.2 Fault Detection  
The fault detection method can be classified as snapshot method [22,23] or filtering method [24]. The former is generally evaluated with the current observations only, while the latter uses both the current and historical measurements. Compared with the filtering method, the snapshot scheme is more widely used due to its faster response to sudden failures [25,26]. Given the measurement Equation (19), the weighted least-squares solution for the estimation of \(\mathbf{x}_{(k)}\) is given by:  
\[\tilde{\mathbf{x}}_{(k)}=\mathbf{A}_{I(k)}\mathbf{z}_{I(k)} \tag{24}\]  
Then, the residual vector is defined as:  
\[\mathbf{r}_{I(k)}=\mathbf{S}_{I(k)}(\mathbf{e}_{I(k)}+\mathbf{b}_{I(k)}) \tag{25}\]  
The test statistic of the snapshot RAIM at discrete time \(k\) is given by:  
\[SSE_{I(k)}=\mathbf{r}_{I(k)}^{T}\mathbf{\Sigma}_{I(k)}^{-1}\mathbf{r}_{I(k)} \tag{26}\]  
As discussed, e.g., in [27], it is well known that the test statistic \(SSE_{I(k)}\) follows a noncentral chi-squared distribution with \(N_{I(k)}-4\) degrees of freedom and noncentrality parameter \(\lambda_{kk\odot}^{2}=\mathbf{b}_{R\odot}^{T}\mathbf{\Sigma}_{k\odot}^{-1}\mathbf{S}_{I(k )}\mathbf{b}_{I(k)}\). The integrity warning is outputted when \(SSE_{I(k)}\) is larger than a detection threshold \(T(N_{I(k)}\), \(P_{FA})\). Or, it will output the position \(\tilde{\mathbf{x}}_{(k)}\) if \(SSE_{I(k)}\) is less than \(T(N_{I(k)}\), \(P_{FA})\).
_Appl. Sci._**2015**, \(5\), 555-565; doi:10.3390/app5030555  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
Intensity Correlation Analysis on Blue-Violet Femtosecond Pulses from a Dispersion-Compensated GaInN Mode-Locked Semiconductor Laser Diode  
**[PERSON] \({}^{\star}\), [PERSON], [PERSON], [PERSON] and [PERSON]**  
Compound Semiconductor Development Department, Semiconductor Device Development Division, Sony Corporation, 4-14-1 Asahi-cho, Atsugi-shi, Kanagawa 243-0014, Japan;  
E-Mails: [EMAIL_ADDRESS] (R.K.); [EMAIL_ADDRESS] (H.W.);  
[EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (H.N.)  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-50-3140-9782; Fax: +81-50-3809-1311.  
Academic Editor: [PERSON]  
_Received: 29 July 2015 / Accepted: 31 August 2015 / Published: 10 September 2015_  
**Abstract:** We investigated the spectral and temporal characteristics of blue-violet femtosecond optical pulses generated by a passively mode-locked GaInN laser diode in a dispersion-compensated external cavity. The output optical pulses at 400 nm were analyzed in detail by intensity auto- and cross-correlation measurements using second harmonic generation on the surface of a \(\beta\)-BaB\({}_{2}\)O\({}_{4}\) crystal. The obtained results clarified wavelength-dependent chirp characteristics of the optical pulses. The analysis suggested that a large frequency shift due to saturation in the saturable absorber and gain sections played an important role in the generation of femtosecond optical pulses.  
**Keywords:** GaN; mode-locked semiconductor laser diode; gain saturation; saturable absorption; self-phase modulation; soliton-like mode locking
## 3 Numerical Simulation
### Results
#### 3.3.1 Mechanical Characteristics  
The force-displacement curve recorded by numerical simulation was compared with that of the experiments, as shown in Figure 13. The numerical model fit the force-displacement curve very well in the particle contact test. The maximum ball-plane contact force of the numerical model was 6.989 kN, and the equivalent stiffness was approximately 8.160 kN/mm. It can also be seen from the curve that the local breakage of the numerical result was not evident at the initial stage. The reason could be that the model was an aggregate of a finite number of rigid elementary particles, which limited the degree of fragmentation at a relatively small displacement. However, the advantage of this model on the simulation of particle contact was appreciable when it was compared with previous studies using a parallel-bond model [18], the result of which had an unrealistically large displacement.  
Figure 12: Original numerical model.  
Figure 13: Force–displacement curve in numerical simulation.
## 3 Results
### Sensory and Color Evaluation
The color parameters of the control and fortified wafers were also assessed with the instrumental method in the CIELab system. The results are presented in Table 3. With the increasing addition of juice, the value of the L parameter decreased, which evidences the decreasing brightness of the color. The a parameter slightly increased with the increasing concentration of raspberry juice in the wafers, which indicates a greater share of the red color. Significant differences were noted for the RJ50, RJ75, and RJ100 variants compared to the control. The b parameter had a significantly lower value as the content of raspberry juice in the wafers increased, which means that the yellow color decreased. The chroma value (C*) is used to indicate the degree of saturation of a color, and is proportional to its strength. The higher the chroma values, the higher the color intensity of samples perceived by humans. The highest C* value was calculated for the CW wafers. The C* value for all juice-fortified wafers was significantly lower than in the control. On the hue circle, \(h=0^{\circ}\) denotes redness, \(h=90^{\circ}\) denotes yellowish, \(h=180^{\circ}\) denotes greenness, and \(h=270^{\circ}\) denotes blueness. The hue angle (\(h^{\circ}\)) decreased with increasing raspberry juice addition in the wafers. This means that the shade of the color changed from yellowish to redder. Even a 20% addition of juice caused a significant change in the hue angle compared to the control. The lowest value (i.e., the closest to red) was noted for the RJ100 variant. \(\Delta E\geq 3\) indicates that the color is perceivable by humans [37]. The results of the \(\Delta E\) value of the wafers with the addition of raspberry juice ranged from 9 to 37, which means that the color differences between the control wafers and the enriched wafers were noticeable and statistically significant, and increased with the addition of juice. There was no statistically significant difference between RJ50 and RJ75.
_Applied_  
_sciences_  
_Article_  
**Influence of Surroundings on Radiant Tube Lifetime**  
**in Indirect-Fired Vertical Strip Annealing Furnaces**  
**[PERSON]\({}^{\star\copyright}\), [PERSON], [PERSON] and [PERSON]\({}^{\copyright}\)**  
Department for Industrial Furnaces and Heat Engineering, RWTH Aachen University, Kopernikusstrasse 10, 52074 Aachen, Germany; [PERSON]ADDRESS] (N.K.K.); [PERSON]ADDRESS] (N.S.); [PERSON]ADDRESS] (H.P.)  
\({}^{\star}\) Correspondence: [PERSON]RESS]  
Received: 20 December 2019; Accepted: 28 February 2020; Published: 4 March 2020  
**Featured Application: Towards the development of a full-fledged model for the prediction of radiant tube lifetime in strip annealing furnaces, to serve its end-user through integration into the process-and control-strategies.**  
**Abstract:** The effects of surrounding radiation--emanating from radiation exchange with neighboring partners in indirect-fired vertical strip annealing furnaces, such as the other radiant tubes, the passing strip, and the enclosing furnace chamber--on the radiant tube lifetime were studied. In-house developed and validated numerical models were used to calculate the thermomechanical behavior, especially creep deformations and the corresponding stresses as lifetime indicating parameters. Different setups of recirculating P-type radiant tubes were investigated, including a reference case of an isolated tube. The investigations could be broadly classified into the study of the effects of different tube arrangements, burner operations (synchronous/asynchronous on/off firing), and changes of strip parameters (width/temperature). Results showed higher creep deformation of the central radiant tube in the setup with three tubes arranged horizontally in a row compared to three tubes stacked in a vertical column, even though the respective characteristic temperature values in a firing cycle were similar. Furthermore, the cases with asynchronous burner firing resulted in lower creep rates than other cases, where the burners were operating in synchronous on/off firing modes. In addition, the change of strip width had a higher impact on radiant tube lifetime compared to locally changing strip temperatures across the furnace. Alternating temperatures, caused by burner operation or process changes, such as change of strip's speed or cross-section, and local temperature gradients were observed to be the main factors influencing the tube's service life.  
**Keywords:** radiant tube lifetime; metallic radiant tube; annealing furnace; industrial furnace; thermomechanical stress; creep; radiation; galvanizing line; CGL