esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FTIR Analysis

Table 8: Absorbability (A), water absorption (WA), softening point (SP), brittleness (B), apparent density (d), and content of closed cells (CC). and -COOH groups. The presence of these groups was confirmed by the FTIR analysis (Figure 3). This analysis revealed the presence of a wide -OH band in the wave region of about 3346 cm\({}^{-1}\)) and -COOH bonds (about 1077 cm\({}^{-1}\) and 2928 cm\({}^{-1}\)). The unidentified residue was most likely cellulose with -OH groups. The increase in A and WA was also caused by the opening of cells after the addition of cinnamon extract to the foams; see Table 8. Increase in absorbability (A) and water absorption (WA), was also observed for foams modified with ground coffee [45]. Opening of the cells was confirmed by the SEM method (Figure 5c,f). The increase in brittleness (B) in CU_0t foams (about 42%-45%) was caused by the lack of flame retardant. Antilbaze was embedded between the walls of the C_0t series foams and stiffened the structure by gluing it together. Its absence caused a decrease in the stiffening of the foam structure and thus increased brittleness. Figure 5: SEM micrographs of foams: (**a**) W_0t, (**b**) W_1t; (**c**) C15_0t; (**d**) C15_1t; (**e**) CU15_0t; and (**f**) CU15_1t. The addition of cinnamon clearly affects the opening of the foam cells. This causes an increase in absorptivity (A). Both closed-cell and open-cell foams show suitability in insulation [25; 36]. If the increase in the volume of bubbles (and reducing the thickness of their walls) is not strictly synchronized with the increase in the viscosity of the mixture, the gas escapes to the outside. The opening of cells during the synthesis was probably influenced by the elongation of free rise time and string gel time (Table 3). The CO\({}_{2}\) formed in the cells caused the cell to break before it gelled. The opening of the foam cells was influenced by an increase in the heat transfer coefficient \(\lambda\), which for all foams ranged from 0.0353 W/(m\(\cdot\)K) to 0.0356 W/(m\(\cdot\)K). For standard foams W_0t and WU_0t was 0.026 W/(m\(\cdot\)K). Open cell foam has lower strength than closed cell [36]; see Table 6.
## 3 Design of Hybrid Control Scheme
### DOB-based Compensator
At the same time, the frequency responses of sensitivity functions with the DOB should below enough to ensure sufficient disturbance attenuation besides the constraints of the traditional DOB, because the plant uncertainty is negligible in the low-frequency region. It is well known that the key of the DOB design is the design of \(Q(s)\). Once the nominal model is chosen, the upper bound of the uncertainty against the nominal model is confirmed, and the upper limitation bandwidth of \(Q(s)\) is decided. The tracking performance is enhanced by increasing the order and the bandwidth of \(Q(s)\), but the excessive order and bandwidth of \(Q(s)\) can lead to resultant destruction of the stability condition due to plant uncertainty in the high-frequency region. The design of \(Q(s)\) is a trade-off between disturbance suppression and robustness. If \(Q(s)\) is designed appropriately, the disturbance observer not only suppresses the rest disturbance, but also fulfills robustness. Apparently, solving the model mismatch in an overall frequency band through the DOB loop compensator is unrealistic. Therefore, the DOB-based inner loop compensator is designed mainly to solve the suppression problem of the extraneous force, other low-frequency external interference and model mismatch at low frequencies, as well as taking some proper consideration of the system model mismatch at middle and high frequencies. The model mismatch in the middle- and high-frequency range can be considered in the outer loop control.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 2 Related Work
### Polarimetric and Spatial Features of PolSAR Image
#### 2.1.2 Spatial Features  
The spatial information in the PolSAR image is captured by using the morphological transformation-based methods, which have been proved to be powerful tool for analyzing remote sensing images [26; 27]. Recently, morphological profiles (MPs) has attracted the attention of researchers in image processing due to its excellent ability to describe spatial information. However, little attention has been paid to the use of MPs. To construct the spatial feature space \(\mathcal{F}^{S}\), a morphological profile (MP) is built for a remote sensing image by using a combination of several morphological operations and a set of structural elements (SEs). Some commonly used morphological operations include erosion, dilation, opening, closing, opening by reconstruction, and closing by reconstruction. Erosion can remove burrs and bumps in images. Dilation can enlarge profiles, filling holes whose size is lower than SEs. The process of opening entails erosion first and dilation second. Closing, on the contrary, entails dilation first and erosion second. Opening and closing can both smoothen an image. Detailed information about morphological operations can be seen in [28]. A summary of morphological operations considered in this paper can be seen in Table 2. Moreover, SEs can have different shapes and sizes, and these different structures present can be captured in images. By combining different morphological operations and SEs in the process of the input image, different structures are emphasized in the resulting images. Since structures essentially reflect the spatial relationship between pixels, spatial information is captured in this process. To build the MP for a PolSAR image, we apply morphological transformations to the _SPAN_ image, which is obtained by [2]  
\[SPAN=|s_{hh}|_{2}+2|s_{hv}|_{2}+|s_{vv}|^{2} \tag{1}\]  
If only multi-looking PolSAR data is available, we generate the base image by computing the trace of the coherence matrix \(\mathbf{T}\) or covariance matrix \(\mathbf{C}\), which is \(\langle|s_{hh}|\rangle^{2}+2\langle|s_{hv}|\rangle^{2}+\langle|s_{vv}|\rangle ^{2}\). The _SPAN_ image is the non-coherent superposition of three polarimetric channel images and can be considered as the total backscattering power. Compared to each single-channel image, the speckle noise in the _SPAN_ image is effectively reduced, which helps to make the actual structure of ground objects more clear. Therefore, the _SPAN_ image is more suitable to be taken as the base image to build MP than single-channel images. There is another reason that we do not apply morphological transformations to each channel image. It is shown that, for a multi-channel image, applying
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 6 Conclusions  
In this paper, we have analyzed energy consumption of UASNs and investigated reasons of rapid energy depletion. We have proposed two new routing protocols, EBET and EEBET. These protocols overcome the energy inefficiency and unbalancing deficiencies in existing UASN routing protocols. The use of Energy Levels help in balanced energy consumption of sensor nodes. The techniques are proved to be efficient for large scale UASNs in terms of network lifetime prolongation and throughput maximization. The EEBET technique improved the energy balancing and increased the network lifetime. The EEBET incorporates depth threshold to limit the number of nodes towards sink and eradicate backward data transmissions. We evaluated the performance of our proposed protocols by comparing them with BTM and UWDAR in terms of network lifetime, transmission loss, and throughput. We also checked the relative scalability of our protocols by varying the number of nodes and the network radius. Simulation results show the improved performance of our proposed techniques than their counterpart schemes in terms of the selected performance metrics.  
The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this research through Research Group Project No. RG#1435-051.  
[PERSON] and [PERSON] proposed main idea. [PERSON] and [PERSON] performed simulations. [PERSON] and [PERSON] performed mathematical modelling. [PERSON] and [PERSON] provided overall supervision and manuscript's write-up and refinement.  
The authors declare no conflict of interest.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 3 Results
### Further Optimization of the DSCNN-GRU Surrogate Model
#### 3.3.1 Optimization of Convolution Layer  
To determine the optimal number of convolutional layers for the proposed DSCNN-GRU model, this study experimented with three, four, and five layers. As the number of convolutional layers changed, it was found that excessive pooling layers were unnecessary. Thus, when only three or four convolutional layers were used, the number of units in both convolutional and pooling layers (as shown in Figure 6) was correspondingly reduced to three or four. Table 1 demonstrates minimal changes in MSE values across the three models during extended training, likely due to slight differences in the number of convolutional layers. However, reducing the number of convolutional layers resulted in an increase in training time, from 2858 s with five layers to 3064 s with three layers. Despite maintaining constant regularization parameters, batch size, and other model parameters settings, several possible reasons could explain this abnormal behavior: (1) Insufficient parallel optimization of the GPU, possibly due to the inappropriate scale of the model, limiting GPU utilization and slowing down training. (2) The reduction in the number of layers may have decreased the caching efficiency of data and model parameters in memory, resulting in longer training times. (3) With fewer convolutional layers, feature extraction capability is diminished, necessitating more time for the model to learn the same feature representation.  
As the training duration increased, the MSE value in the test set also rose. Specifically, the MSE value was 0.0062 for the model with five layers and 0.0096 for the model with three layers, indicating a decline in prediction accuracy. The R\({}^{2}\) value exhibited a similar trend, decreasing from 0.954 for the model with five layers to 0.948 for the model with three layers. These findings suggest that increasing the number of convolutional layers enhances both the computational efficiency and accuracy of the model. Both the models in Sections 3.3.2 and 3.3.3 utilize five convolutional layers.
# Research on Human Exposure to Transport-Induced Vibration in Buildings
## 1 Introduction
An important outcome of the project was the establishment of a guideline on the evaluation of adverse effects of vibration, which include exposure-response relationships for annoyance and some general conclusions on the influence on sleep [21]. The CargoVibes project focused on vibrations generated by freight trains. This article presents selected research results on the impact of passenger rail transport, including trans, in dense urban development. The main purpose of this article is to demonstrate the correct measurement and interpretation methodology of the impact of vibrations on people who passively perceive these stimuli in buildings. The article also presents a historical outline of vibrational comfort to illustrate how this topic has evolved. Additionally, new trends that have recently emerged in this field are highlighted, and it is proposed to consider vibration comfort as a critical factor in building design and evaluation. Frequent errors in the assessment of vibrations' impact on building occupants often arise from misunderstandings of existing standards and guidelines. The paper highlights the need for more uniform guidelines to ensure accurate and reliable results across different regions. Transport-induced vibration has been identified as the dominant source of vibration in urban areas. These vibrations significantly impact both the structural integrity of buildings and the comfort of their occupants. The main novelties presented in the paper are as follows:  
* This research underscores the necessity of accurate measurement methodologies, including the appropriate selection of measurement points and the use of suitable equipment. Proper sensor placement and data collection are critical for obtaining reliable vibration data. Incorrect methodologies can lead to significant errors in assessing the impact of vibrations. * Different evaluation methods, such as the Root Mean Square (RMS) and Vibration Dose Value (VDV) methods, are compared in the paper. Each method has its advantages and applications. The VDV, for example, is more sensitive to peak values and may be more effective in certain contexts, providing a more nuanced understanding of vibration impacts. * This study examines the role of different weighting functions in vibration analysis. It finds significant differences in results depending on the weighting function used. Combined functions like Wm tend to provide substantially lower values compared to others. This finding highlights the importance of choosing the correct weighting function for accurate analysis. By addressing these key areas, the paper aims to enhance the accuracy and reliability of vibration assessments and promote better standards and practices in structural engineering and urban planning.
## 3 Data Analysis  
### Spectral Pre-Processing  
The objective of the preprocessing of spectral data is to remove phenomena in the spectra, in order to improve subsequent multivariate classification. The general purpose of spectral preprocessing is to correct random noise in the spectra, length variation of the light direction, and light scattering generated by instruments. The pre-treatment of obtained data by appropriate mathematical analysis is important for enhancing crucial information from the sample and removing unwanted variations from the spectral data. Therefore, to obtain smoothing without artifacts or physical variations, pre-processing of the spectral data is required [23]. In this study, normalization, smoothing, multiplicative scatter correction (MSC), the standard normal variate (SNV), and the Savitzky-Golay first and second derivative preprocessing methods were used to eliminate noise and undesired elements from the seeds' spectral data. Figure 3 shows the spectral data before and after pre-processing treatments, where the red color represents the almonds and blue represents the apricots. These pre-treatment techniques were utilized to improve the spectral data by removing irrelevant information and retaining valuable spectra, in order to provide a better performance for the multivariate classification model that was developed in this study.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 2 Data and Methodology  
### Overview of the Yangtze River Estuary (YRE)  
The Yangtze River Estuary is China's most populated and industrially productive area. The country's four major drinking-water reservoirs (Qingcaosha, Baogang, Chen Hang, and Dongfeng Xisha Reservoirs) are located in the YRE (Figure 1). These reservoirs provide abundant water sources for the cities in the estuary area, including those of Jiangsu Province and Shanghai Municipality.  
The Dongfeng Xisha Reservoir is located on the north side of the upper reach of the SB, with a total capacity of up to 9.762 million m\({}^{3}\). The Baogang Reservoir is located at the south bank of the SB. Its storage volume has been decreased due to sedimentation in the reservoir. The Chenhang Reservoir is located in the downstream area of Lihekou and has a comparatively small capacity. The Qingcaosha Reservoir, which is located in the northern part of Changxing Island, has an area of 70 km\({}^{2}\), and a capacity of 0.44 billion m\({}^{3}\). The water quality in the Qingcaosha Reservoir is considered to be very high (Category I or II) according to Chinese surface water standards, which makes the Qingcaosha Reservoir one of the most efficient reservoirs in China at this time.  
Figure 1: The range of the Yangtze River Estuary (YRE).  
The saltwater intrusion path of the YRE has been determined to be as follows: The upper section of the North Branch of the YRE has a trumpet-shaped topography, resulting in an only 5% runoff into the sea, and the branch's access being located far offshore [12]. In particular, during the dry seasons, the high-salinity water in the NB of the YRE can reach as far as Qinglonggang Harbor. The tides of the NB may rise across to Chongming Island and head into the SB of the YRE. The South Branch (SB) and NB then become inundated, as shown in Figure 1. The lower part of the SB will be then be mainly affected by the ocean tides and form a saltwater intrusion path inward from the river's mouth.
## 3 Experiments and Results
### Influence of Structural Parameters on Cavitation Performance
#### 3.1.4 Tooth Bevel Angle  
When the tooth bevel angle (as shown in Figure 1) is 75\({}^{\circ}\) or below, interference occurs between the moveable and fixed teeth, preventing the occurrence of cavitation. Therefore, 80\({}^{\circ}\), 85\({}^{\circ}\), 90\({}^{\circ}\), 95\({}^{\circ}\), 100\({}^{\circ}\), and 105\({}^{\circ}\) are chosen as six different movable tooth inclination angles to study the influence of different angles on the cavitation performance of the hydrodynamic cavitation generator.  
From Figures 16 and 17, it can be observed that as the tooth surface inclination angle increases, the velocity between fixed teeth increases, and the velocity distribution becomes more stable, leading to the generation of more cavitation bubbles. With the increasing angle, the velocity on the outer side of the moveable teeth flow region increases, while the velocity on the inner side decreases, creating a noticeable velocity difference. This enlargement of the low-pressure region between moveable teeth creates favorable conditions for the generation of cavitation bubbles.  
Figure 14: Cloud plot of cavitation bubble volume fraction distribution for different cylinder diameters.  
Figure 15: Variation in cavitation bubble volume fraction with time for different fixed tooth outer diameter.  
The velocity cloud in Figure 18 reveals that, as the rotated teeth rotate, the shear velocity within the fluid domain between the rotated and fixed teeth varies with different degrees of overlap. When the rotated and fixed teeth are on the same axis, the shear velocity is lower (indicated by a smaller red area in the cloud map). When the rotated and fixed teeth are completely off the same axis, the shear velocity increases (indicated by a larger red area in the cloud map). It can be inferred that the relative motion between the rotated teeth and the fixed teeth is not affected by the rotation of the solid angle.  
Figure 16: Variation rule of cavitation bubble volume fraction with tooth inclination angle.  
Figure 17: Velocity cloud for different tooth level angles.  
and fixed teeth causes a variation in shear velocity, i.e., the greater the shear velocity, the larger the volume fraction of cavitation bubbles. Generally, the generation of cavitation bubbles is influenced by the shear velocity between the rotated and fixed teeth.
## 1 Introduction  
Solar loading thermography has gained recent scientific attention [1, 2, 3, 4] and shows the potential to become a proven non-destructive testing (NDT) method for large-scale outdoor structures and infrastructure. As natural heat sources are inherently erratic (affected by cloud coverage, wind gusts and ambient temperature fluctuations), evaluation algorithms must be reviewed on a case-by-case basis. The range of evaluation algorithms discussed in this work is constrained to conventional active thermography approaches, namely, lock-in thermography (LIT) [5], step-heating thermography (SHT) [6] and linear effusivity fit (LEF) [7]. Each of these may be used to determine thermal material properties, in this case, the thermal effusivity of an excited object. Thermal effusivity is a measure of how strongly a material exchanges heat with its surroundings: higher effusivity means a slower response to heat input, whereas lower effusivity means a quicker response. Thermal effusivity is also the main reason why different materials feel cold or hot to the touch, despite being at the same temperature initially.  
Determining thermal effusivity using solar loading lock-in thermography was shown in our previous work [1], but empirical calibration was needed to do so. In this work, we propose an alternative approach using a pyranometer to measure the insolation (solar irradiance) directly. This allows the experimenter to directly assess the irradiation shape and amplitude at each point in time, which, in turn, allows the creation of analogies to conventional, laboratory-scale active thermography methods.
## 3 Results and Discussion  
### Church Microclimate  
To assess the action of aggressor agents, the indoor microclimate of the Ascension Church, e.g., temperature, humidity, ambient lighting, and CO\({}_{2}\) concentration, was monitored monthly during one year.  
As can be seen from Figure 6, the church inner space is characterized by a higher than optimum humidity during spring, autumn, and winter, a phenomenon that becomes very intense at lower temperatures. Along the summer months, inside the church, the temperature never exceeds 22 \({}^{\circ}\)C while the relative humidity remains relatively high due to inadequate ventilation. The illumination remains all the time at about 10 to 100 times lower than 50 lx, the value considered optimal inside a museum [29], but this is a characteristic of the majority of Byzantine stile churches.  
At its maximum value, the CO\({}_{2}\) concentration, whose source could be the presence of a great number of people attending the Holy Liturgy, showed to fluctuate between 0.5 and 5 ppm during the summer while, in the winter time, due to properly adjusted gas stoves, its concentration never exceeded 15 ppm. As these values fall within the technical regulations, we consider that CO\({}_{2}\) could generate only minor alterations [30].  
According to oral testimonies, the main pollutants indoor of the church over the years were carbonaceous particulate matter resulting from burning candles and incense, especially during the Holy Liturgy. As a result, various combustion compounds such as olefins, aromatic hydrocarbons, carbonyl and carboxylic moieties, and carbonaceous particles were released into the atmosphere during religious services [31].  
It is worth mentioning that microclimate conditions, coupled with a high bio-receptivity of the lime wood, could favor the fungal attack too [31]. Also, in our opinion, the mentioned aggressor agents can stimulate over time a weathering phenomenon responsible for the physical and chemical transformations of the historic materials, resulting in visible aesthetic changes (Figure 2 insets) [32; 33; 34].  
At a first visual inspection, the \"Imperial Doors\" present some of the most representative effects of decay and degradation due to the combined action of aging agents and weathering (Figures 2-5), which will be further analyzed and discussed separately for the wooden support and gilded layers.