esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Lanthanum chromite (LaCrO\({}_{3}\))-based perovskite phases possess high melting points, large electronic conductivity and stability in high-temperature reducing and oxidizing atmospheres, which makes them promising candidates for heating elements of muffle furnaces and current-conducting electrodes in magneto-hydrodynamics, especially interconnectors of solid oxide fuel cell (SOFCs) [1; 2; 3; 4; 5; 6]. The interconnections of SOFCs have a dual function of separating reducing and oxidizing gases and providing a short-circuit current between individual cells. Therefore, the interconnection of SOFCs must be tight. Nevertheless, LaCrO\({}_{3}\) is difficult to sinter to high densities in air, due to appreciable volatilization loss of chromium oxide at high temperatures in oxidizing atmospheres [7; 8; 9]. Therefore, a considerable amount of research effort has been performed on improving the sintering properties of the ceramic materials [6; 7; 8; 9; 10; 11; 12], and the results have been demonstrated the advantage of alkaline earth metal (AE = Ca, Sr) substituted LaCrO\({}_{3}\), due to a transient liquid phase (e.g., CaCrO\({}_{4}\) and SrCrO\({}_{4}\)) formed in the early stage of sintering and suppressing the vaporization of Cr-O species to some extent [10; 11; 12]. However, the application of excess AE substituted LaCrO\({}_{3}\) may lead to the long-term operation stability of the system [13; 14].  
It is well known that the sinterability of the ceramics could be enhanced by highly reactive powders synthesized by wet chemical methods. Moreover, physical properties of the ceramics, such as electrical transport and thermal expansion, may be affected by the characteristics of the powders synthesized by the methods, possibly due to different microstructures of the sintered ceramics. In this work, we synthesized La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) powders by a combustion method in comparison with the high temperature solid state reaction (SSR) method, and investigated the influence of preparation methods on sinterability, electrical transport and thermal expansion of the ceramics.
# Contactless Ultrasonic Wavefield Imaging to Visualize Near-Surface Damage in Concrete Elements
## 2 Materials and Methods
### Concrete Sample and Damage Implementation  
A mature concrete sample with dimensions of 533 mm \(\times\) 152 mm \(\times\) 152 mm was tested. The concrete sample was produced with the mixture design proportion shown in Table 1. The concrete sample was tested over a year after it was cast. Hence, the concrete sample was assumed to reach its ultimate material properties (i.e., Young's modulus and Poisson's ratio) already. The type of ultrasonic waves generated and detected from the concrete sample are Rayleigh surface waves (hereafter called surface waves). To visually understand the interaction between incident surface waves and surface (or near-surface) damage of concrete, impact damage points were introduced on the top surface of the concrete sample using a low-power nail gun (Windsor probe system manufactured by James Instruments, Inc., Chicago, IL, USA). We assume that a mechanical impact introduced by the nail gun locally crushes the concrete sample and forms a fine microcrack network in the vicinity of the impact points. Four locations of impact damage were sequentially introduced, from D1 to D4 on the top surface of the sample, as shown in Figure 1. Ultrasonic wavefield data were collected for every damage case including the undamaged case. The tested sample cases are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Contents** & **Unit Weight [kg/m\({}^{3}\)]** \\ \hline Cement & 406.5 \\ Water & 192.7 \\ Fly ash & 71.7 \\ Coarse aggregate \({}^{*}\) & 953.5 \\ Fine aggregate & 663.5 \\ \hline \hline \end{tabular} \({}^{*}\) The nominal maximum aggregate size is 25.4 mm.  
\end{table}
Table 1: Mixture design proportions for the concrete sample.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **The Number of Impact Damage Points** \\ \hline
0 & 0 (Pristine) \\
1 & 1 (D1 only) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The pristine and damaged sample cases considered for testing.
# Experimental Study on Physical and Rheological Properties of Trinidad Lake Asphalt Modified Binder
###### Abstract  
Mastic asphalt (MA) has been recognized as one of the most deformation-resistant and thus durable materials for bridge pavement. The performance properties of MA are highly dependent on the physical and rheological properties of the binder applied in the MA mixture. To modify the binder properties to obtain the expected performance of the MA mixture, Trinidad Lake Asphalt (TLA) is often applied. In this study, the TLA-modified binders to be used in mastic asphalt bridge pavement systems were evaluated to develop the optimum material combination using conventional and performance-related testing. Physical and rheological tests were carried out on TLA-modified binders with the different modifier content in the range of 10-50% on a weight basis. The tests revealed that the TLA modifier addition to the 35/50 base bitumen should be close to the value of 20%. Higher concentrations of TLA may make the binder very stiff and could induce low-temperature cracks in mastic asphalt.  
asphalt binder; Trinidad Lake Asphalt; modification; physical properties; rheological properties; mastic asphalt +
Footnote †: journal: Applied Sciences  
shear viscosity) is the key factor to properly select the TLA-modified binder for the optimal MA mixture regarding performance. Consequently, a detailed assessment of the physical and rheological properties of TLA-modified binders should be carried out for optimal MA pavement performance [14; 15; 16].  
In this study, the TLA-modified binders to be used in mastic asphalt (MA) bridge pavement systems were evaluated to develop the optimum material combination using conventional and performance-related testing. However, it is very important to emphasize that these TLA-modified binders are evaluated in the context of using them in the mastic asphalt (MA) mixture. Since the 35/50 penetration grade bitumen is the only one recommended for MA bridge pavements by the Polish road administration, it was chosen as a base bitumen in this study [17]. Moreover, 35/50 bitumen is also the most popular in Poland for other types of road pavements. Based on the available studies [9; 12; 18; 19] and the authors' initial research [20], the TLA content by bitumen weight up to 20% was chosen to modify the base bitumen properties towards the durable MA applications on bridges. The paper describes the evaluation of the physical and rheological properties of TLA-modified binders depending on the TLA content as well as the determination of the optimal content of TLA for the bitumen modification. The low-temperature properties of TLA-modified binders are compared to those of base bitumen to check the potential MA deformation resistance. Physical (conventional) tests and rheological tests were conducted on TLA-modified binders to achieve the mentioned objectives.
## 2 Weather Data Super-Resolution Algorithm Based on CBAM-SRResNet
### Mixed Attention Mechanism
#### 2.2.1 Channel Attention  
The channel attention mechanism improves deep neural network performance [38] by enabling the model to automatically learn channel relationships in the feature map and dynamically adjust the importance of different channels, thus enhancing both performance and generalization. The basic idea of the channel attention mechanism is to perform global statistical analysis on the input feature maps and then dynamically allocate the attention between channels based on this statistical information. In the channel attention mechanism, an attention weight vector is usually obtained by learning to adjust the feature response of each channel. Its structure is shown in Figure 5.  
As illustrated in Figure 5, the input weather feature map passes through two parallel Max Pool and Avg Pool layers, reducing the size from C * H * W to C * 1 * 1, before being fed into the Shared MLP module. Next, two results are generated using the ReLU activation function. These outputs are then elementwise summed, passed through a Sigmoid activation, and multiplied by the original image to restore the size to C * H * W. The calculation is given in Equation (7).  
\[M_{C}(F)=\sigma(MLP(AP(F))+MLP(MP(F))) \tag{7}\]  
where \(AP\) is represented by average pooling, \(MP\) is represented by maximum pooling, \(F\) is represented by the input feature map, and \(\sigma\) is represented by the weight of the mapping.  
Figure 4: Mixed attention module.  
Figure 5: Channel attention module.
## 5 Methodology for Anomaly Detection and Mitigation
### Ensemble Learning  
Usually, an artificial intelligence model is used to help in decision making. Adversarial attacks can attack this single model, generating adversarial samples. However, ensemble learning uses two or more models. Using multiple different models is called ensemble learning. In practice, ensemble learning gives better accuracy and stronger robustness. Predicting the results will make the attack difficult, and the attack cost will be greatly increased. Furthermore, ensemble learning can not only increase the difficulty of the attack but also improve the accuracy of the model prediction, which is a win-win solution. Although this method increases the difficulty of the attack and reduces the probability of being attacked, it does not mean that the system will not be attacked at all. If the attacker has enough time and rich resources, the attack can still be successful, so in the next section, a second method is provided.
# Dual Quaternion Embeddings for Link Prediction
## 4 Our DualQuatE Model
### Multiple Relations between the Entities  
To address the challenge of having multiple relations between head and tail entities, we embed knowledge graph into dual quaternion space. \(\mathbf{h},\mathbf{r},\mathbf{t}\) denote vector of entity embeddings and relation embeddings, each element of entity embeddings \(h_{i}\) or \(t_{i}\) is pure quaternion and every dimension of relation embeddings \(r_{i}\) is unit dual quaternion. We expect to model relation embeddings \(\mathbf{r}\) as **interaction** of **rotation** and **translation** from head entity embeddings \(\mathbf{h}\) to tail entity embeddings \(\mathbf{t}\) as shown in Figure 2a. Specifically, each true triple \((h,r,t)\) satisfies:  
\[\mathbf{r}\otimes(1+\epsilon\mathbf{h})\otimes\mathbf{r}^{\circ}=(1+\epsilon \mathbf{t}) \tag{9}\]  
where each dimension of \(\mathbf{r}\) is a unit dual quaternion satisfying Formula (4). We define a quaternion \(\mathbf{m}=\cos\frac{\theta}{2}+\mathbf{u}\sin\frac{\theta}{2}\) to represent a rotation about pure unit quaternion \(\mathbf{u}\) through \(\theta\) and a pure quaternion \(\mathbf{n}=n_{1}\mathbf{i}+n_{2}\mathbf{j}+n_{3}\mathbf{k}\). Furthermore, we define a unit dual quaternion by:  
\[\mathbf{r}=\mathbf{m}+\frac{\epsilon}{2}\mathbf{n}\mathbf{m} \tag{10}\]  
With Formula (10), we can deduce the transformation of DualQuatE in Formula (9):  
\[\mathbf{r}\otimes(1+\epsilon\mathbf{h})\otimes\mathbf{r}^{\circ} =1+\epsilon(\mathbf{m}\mathbf{h}\mathbf{m}^{*}+\mathbf{n}) \tag{11}\] \[=(1+\epsilon\mathbf{t})\]  
where the geometric meaning of \(\mathbf{m}\mathbf{h}\mathbf{m}^{*}\) is shown in Formula (2). As shown above, DualQuatE transforms head entity \(h\) to tail entity \(t\) by relation \(r\) which combines **rotation** (i.e., \(\mathbf{m}\)) and **translation** (i.e., \(\mathbf{n}\)). Unlike previous models learned similar representations of relations \(r_{1},r_{2},r_{3}\) shown in Figure 2c,d, our model learns combinations of different translations and rotations to represent various relations between head and tail entities.  
We define score function by:  
\[f_{r}(h,t)=-||\mathbf{r}\otimes(1+\epsilon\mathbf{h})\otimes\mathbf{r}^{\circ }-(1+\epsilon\mathbf{t})|| \tag{12}\]  
where \(||\cdot||\) represent \(L_{2}\) norm of a vector. With the score function we want head entity to be as close to tail entity as possible after the transformation of the relation.
# Characterizing the Tensile Behaviour of Woven and Composite Fabrics under UV Exposure
## 4 Results and Discussions
### Microstructure Analysis
Such conditions under the outdoor weathering test have been supported by an experimental study previously conducted [28]. The degradation observed from the SEM micrographs of woven fabric was relatively consistent with the tensile test findings, where the loosening of fibre alignment caused a progressive reduction in tensile properties after each month of outdoor exposure. For composite fabrics, the presence of microcracks on the polymer surface after the second month provided a shorter pathway for UV penetration, which accelerated polymer degradation through the evidence of greater microcracks formed thereafter. The substantial development of microcracks were observed on the surface of composite fabrics, which was not well supported by significant tensile strength loss. The tensile strength was primarily governed by the load-carrying component (i.e., woven fabric). In this case, the non-woven geotextile overlay, which acted as a sacrificial layer, protected the woven geotextiles in composite fabrics to the extent of minimal to no damage. UV radiation has thus been identified to cause engineered fabric degradation that is associated with tensile property loss. Figure 6: SEM micrographs of woven fabrics at (**a**) Month 1, (**b**) Month 2, (**c**) Month 3, (**d**) Month 4, (**e**) Month 5, and (**f**) Month 6 of exposure.
# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
### Infrared Thermography
#### 4.2.3 Thermography Issue and Critical Aspects  
The infrared thermometry measurements suffer from several critical points, which may affect negatively the accuracy of the results. [PERSON] et al. (2016) found many important deficiencies in the general applications of CWSI. In particular deficiencies included (i) the absolute temperature ranges may be small in humid environments; (ii) the canopy temperature was negatively affected when crop surface target accidentally includes areas such as soil, sky, and trunks; (iii) there existed a different distribution of temperatures of sunlit and shaded parts of the canopy; and (iv) the sunlit part of the canopy showed a wider range of temperatures when viewed normal to the rows or down the rows.  
[PERSON] et al. (2015) reported some other critical points including that (i) the signal was not stable during cloudy periods; (ii) the lower base line and upper limit were difficulty to determine and (iii) no uniqueness of these limits. Others problems were related to the first growth stages, when soil background was visible and it was more difficult to measure the canopy temperature.
###### Abstract  
A new statistical method for the quality control of the positional accuracy, useful in a wide range of data sets, is proposed and its use is illustrated through its application to airborne laser scanner (ALS) data. The quality control method is based on the use of a multinomial distribution that categorizes cases of errors according to metric tolerances. The use of the multinomial distribution is a very novel and powerful approach to the problem of evaluating positional accuracy, since it allows for eliminating the need for a parametric model for positional errors. Three different study cases based on ALS data (infrastructure, urban, and natural cases) that contain non-normal errors were used. Three positional accuracy controls with different tolerances were developed. In two of the control cases, the tolerances were defined by a Gaussian model, and in the third control case, the tolerances were defined from the quantiles of the observed error distribution. The analysis of the test results based on the type I and type II errors show that the method is able to control the positional accuracy of freely distributed data.  
Keywords:ALS data; non-normal errors; positional accuracy; quality control; multinomial distribution +
Footnote †: journal: Applied
## 3 Test Results and Analysis
### Horizontal Shear Performance
#### 3.2.1 Shear Strain  
The designed vertical pressure of 3400 kN was smoothly exerted in the three bearings and remained unchanged during the whole test process. Under the frequency of 0.01 Hz, horizontal sinusoidal excitation waves were loaded with the shear strains of 25%, 50%, 75%, and 100% in succession, until unloading was completed in all cycles under all shear strains. The data acquisition instrument recorded the data during the whole loading process.  
The designed vertical pressure of 3400 kN was smoothly exerted to the three bearings and remained unchanged during the whole test process. Under the frequency of 0.01 Hz, horizontal sinusoidal excitation waves were loaded with the shear strains of 25%, 50%, 75% and 100% in succession, until unloading was completed in all cycles under all shear strains. The data acquisition instrument recorded the data during the whole loading process.  
The horizontal shear performance of each bearing can be seen in Figure 12. As the shear displacement aggardies, the slopes of the curves gradually reduce, due to the reason that the compression area of the bearing core decreases, which can lessen the constraint of steel plates on the internal rubbers. The hysteresis curves of the bearings become larger as the shear strain grows, indicating that their energy dissipation capacity is strengthened. It should be noted that after multiple loads, points of contraflexure appear on the load-displacement curves, as seen in Figure 12b,c, and the degree of contraflexure on the curves gradually increases as the loading displacement grows.  
Figure 12: _Cont._The test values of horizontal equivalent stiffness and the equivalent damping ratio of the bearings are displayed in Figure 13. It can be seen that under the same shear strain, the horizontal equivalent stiffness of LRB and TLRB are adjacent throughout the test, and the stiffness of the former is slightly larger than that of the latter, while that of HDRB-LSF is the smallest. TLRB has most significant horizontal equivalent damping ratio, followed by LRB and HDRB-LSF, according to priority. The shear strain has an unnoticeable effect on HDRB-LSF and TLRB, but has an obvious impact on LRB whose growth of shear displacement is faster than the increase of the hysteretic curve area [14].
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Monitoring of Fetal Distress and Neonatal Critical Care  
Fetal inspection is vital and must precisely mirror embryonic well-being, identify the most plausible development process, and correctly envisage critical perinatal consequences. Thus, the primary goal of fetal monitoring is to offer precise and efficient care units for an early diagnosis of the onset of potentially life-threatening diseases. The fetus's ability to survive, thrive, and progress into the neonatal stage depends on proper blood pressure, volume, and fluid dynamics. Prenatal cardiovascular control disturbances can increase fetal and neonatal mortality and morbidity.  
Neonatal care is equally important to avoid unintentional complications. Patients in a Neonatal Intensive Treatment Unit (NICU) require specialized assistance, the efficiency of which is heavily reliant on the functioning of the medical equipment. The development of surveillance and imaging techniques improves predictive tactics and aids in preventing some complications. An HRV analysis has considerably aided neonatal care in neonatal sepsis detection, neonatal necrotizing enterocolitis monitoring, predicting preterm birth, avoiding possible complications, etc. For example, [PERSON] et al. (2019) examined the changes in HRV as a potential marker of the time and severity of acute hypoxia-ischem (HI) [176]. The authors created umbilical cord occlusion of chronically instrumented fetal sheep to simulate HI and monitored the HR and HRV features for 72 h. It was found that the HI groups exhibited lower VLF power and increased sample entropy in the initial 3 h. Also, the spectral power change towards a higher frequency band was observed, suggesting autonomic dysfunction after HI. According to the findings, the authors of the study proposed that the changes in HR patterns can have predictive usage in the initial hours of HI onset. In a study by [PERSON] et al. (2020), the prognostic capability of HRV features, respiratory factors, and ECG-derived estimates of infant motion to predict neonatal sepsis were examined [177]. A total of 22 features were extracted, and an NB classifier was employed to differentiate between the control and septic states. The efficiency of the proposed system was analyzed utilizing the region underneath the receiver functioning feature (AUROC) curve and the true positive rate (TPR). When the time of sepsis came closer, HRV demonstrated massive decelerations. Moreover, it was observed that the HR could not be increased in response to pathological HR slowing down. On the basis of the outcomes, the writers recommended that the projected technique can be utilized as an early indicator of sepsis.
# Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing
###### Abstract  
Conventional synchronization signal detection methods rely on linear correlation function analysis with fixed thresholds, which are insufficient for handling the nonlinear characteristics of practical wireless communication systems. In such environments, the usage of a long synchronization signal is beneficial for ensuring sufficient correlation information and enhancing detection robustness. To address these problems, this paper proposes a novel framework that combines Hankelization-based preprocessing with the operation of a neural network (NN). The proposed method enhances feature extraction through the inverse Fourier transform and Hankel matrix construction, followed by singular value decomposition (SVD) to preserve dominant signal features and suppress noise components. Leveraging the ability of NNs to learn nonlinear patterns, the proposed method eliminates the need for fixed thresholds and achieves robust synchronization signal detection. The simulation results demonstrate superior accuracy in various environments compared to conventional methods, underscoring the potential of Hankelization-based preprocessing in future wireless communication systems.  
synchronization acquisition; neural network; Zadoff-Chu sequence; binary classification; Hankelization +
Footnote †: journal: Applied sciences  
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