esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Metals Analysis  
Copper, arsenic, cadmium, mercury and lead are heavy metals that bioaccumulate in the human body. They can only be partially excreted with the metabolism [28]. In particular, copper is an essential element in the right doses. Its excess can lead to excessive stimulation of the nervous system and to problems related to the organs where it tends to accumulate more: the liver, brain and reproductive organs [29]. Arsenic is a certain human carcinogen. Its accumulation beyond reference thresholds leads to bladder, lung and skin tumors [30]. Cadmium is a possible human carcinogen. The accumulation of it in excessive amount leads to considerable damage to the kidneys, liver, bones and reproductive system [31]. Mercury is another possible human carcinogen. Factors that determine the type and severity of the produced consequences are: its form (the organic form is more toxic than the inorganic one), the quantity, the duration and the way of exposure and the age of the exposed individual [32]. Regarding lead, the most affected apparatus for the accumulation of it in an excessive amount is the central nervous system, but the renal, gastrointestinal, hematopoietic and reproductive systems are also involved [33].  
Table 3 reports metals concentration (mg/kg) in investigated food salt samples, as obtained through ICP-MS analysis. As reported, for Cu, As, Cd, Hg and Pb, the experimental values are lower than the contamination thresholds set by [34] (2.0 mg/kg, 0.5 mg/kg, 0.5 mg/kg, 0.1 mg/kg and 2.0 mg/kg, respectively), for all analyzed samples.  
Therefore, they can not be considered pollutants. As a consequence, they do not cause objectionable effects and do not constitute a risk to human health.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multicolumn{5}{c}{**Metals Concentration (mg/kg)**} \\ \cline{2-6}  & **Cu** & **As** & **Cd** & **Hg** & **Pb** \\ \hline S1 & 1.7 & 0.03 & 0.01 & 0.03 & 0.5 \\ S2 & 1.2 & 0.02 & 0.01 & 0.05 & 0.2 \\ S3 & 1.8 & 0.04 & 0.005 & 0.02 & 0.2 \\ S4 & 0.8 & 0.02 & 0.01 & 0.04 & 0.2 \\ S5 & 0.6 & 0.01 & 0.01 & 0.02 & 0.5 \\ S6 & 0.7 & 0.03 & 0.02 & 0.02 & 0.5 \\ S7 & 1.3 & 0.01 & 0.02 & 0.09 & 0.3 \\ S8 & 0.9 & 0.1 & 0.01 & 0.02 & 0.6 \\ S9 & 1.5 & 0.01 & 0.01 & 0.02 & 0.3 \\ _Contamination threshold_ & _2.0_ & _0.5_ & _0.5_ & _0.1_ & _2.0_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metals concentration (mg/kg) in investigated food salt samples.
## 5 Results and Discussion
### Measurement Accuracy
The standard deviation of the WIDIM-DPIV algorithm was \(1.39\times 10^{-3}\) m/s and that of the IM-WIDIM-DPIV algorithm was \(0.56\times 10^{-3}\) m/s, which indicates that the volatility of the IM-WIDIM-DPIV algorithm was smaller than that of WIDIM-DPIV algorithm. The maximum deviation of the WIDIM-DPIV algorithm was \(5.77\times 10^{-3}\) m/s under the condition of \(51.9\times 10^{-3}\) m/s-90% and that of the IM-WIDIM-DPIV algorithm was \(2.65\times 10^{-3}\) m/s under the condition of \(42.4\times 10^{-3}\) m/s-70%. The maximum relative deviation of the WIDIM-DPIV algorithm was 16.8% under the condition of \(14.1\times 10^{-3}\) m/s-90%, and that of the IM-WIDIM-DPIV algorithm was 9.4% under the condition of \(9.4\times 10^{-3}\) m/s-90%, which indicates that the deviation degree of result that calculated by the IM-WIDIM-DPIV algorithm from the actual value was lower than that of the WIDIM-DPIV algorithm. As shown in Figure 10, there is jumping data at specific points with different water cut, and the major differences between the two PIV approaches occur at relatively high flow velocity. This may be caused by the following four reasons. (1) Oil droplets are used as tracer particles in the experiment. With the increase of flow velocity and water cut, the average size of oil droplets increase due to the stacking effect. The minimum visible diameter of oil droplets is \(\sim\)2 mm, the maximum diameter is \(\sim\)9 mm, and the average diameter is \(\sim\)4.5 mm. In the case of the same flow velocity and different water cut, the oil droplets distribution in shooting area is different. When the water cut is high, the oil droplets distribution in the shooting area is sparse compared with that of low water cut, and the number of matched oil droplets is small. This results in larger errors. (2) Reversal flows induced by buoyancy driven turbulence. (3) With the increase of flow velocity, the velocity gradient in the pipe increases, and DPIV interrogation window's following performance becomes worse. However, the interrogation window following performance of IM-WIDIM-DPIV algorithm is better than that of WIDIM-DPIV algorithm. Therefore, the measurement accuracy difference is obvious at high flow velocity. (4) As the flow velocity and water cut increases, the distribution of oil droplets gradually became denser; when the droplets are too dense, oil droplets shield each other seriously. This results in inaccurate calculation in DPIV calculation, so the accuracy of DPIV reduces. In conclusion, from both qualitative and quantitative perspectives, the IM-WIDIM-DPIV algorithm had a higher precision than the WIDIM-DPIV algorithm, with a maximum measurement accuracy of 97.5% and an average measurement accuracy of 94.8%.
## 2 Material and Methods
### Preparation of Data  
The min/max transformation technique was used to normalize the collected data. The objective of this transformation was to restrict the inputs' possible advantages to those with noticeable numerical values, over those with small values. Handling large-value inputs can be challenging and complicated due to the fact that the kernel quantity relies on vectors' internal multiplication of inputs. Therefore, conquering mathematical complications throughout calculation procedures is another crucial aspect of input normalization. The data were transformed using Equation (1) and the normalized data ranged from zero to one.  
\[a_{i}^{n}=\frac{a_{i}-a_{min}}{a_{max}-a_{min}} \tag{1}\]  
where the input vectors with the calculated observation points are denoted by \(a_{i}\). \(a_{min}\) and \(a_{max}\) refer to the lowest and highest values that relate to the calculated data set. \(a_{i}^{n}\) is the transformed variant of \(a_{i}\).
## 3 Materials and Methods
### New Feature Extraction Method  
Due to the limitations of conventional convolution operation, the method based on the fusion of CNN and transformer has attracted wide attention. It solves the problems of poor interaction between CNN and global and contextual semantic information, and the loss of features of transformer. However, most of these methods ignore the rich context information between adjacent keys. In view of these problems, CoTNet integrates local context information and global context information on the basis of traditional self-attention learning to better enhance visual expression ability. Compared with the commonly used conv, it has better feature extraction ability.  
Figure 1: Schematic diagram of deep separable deconvolution.  
Dilated convolution inserts 0 between each parameter of standard convolution. On the basis of guaranteeing constant standard convolution parameters, the perception field is expanded so that each convolution kernel can obtain a larger range of information, thus avoiding the loss of spatial location information caused by the pooled layer. The convolution of holes with smaller expansion rates can extract small object information from the image, which is helpful for accurate classification of small objects and enhances the model's ability to detect small objects.  
To solve the problems of the large resolution of existing remote sensing images, small convoluted sensing fields in the network, and insufficient utilization of local context information, we propose a new feature extraction method, namely DCoTNet. It replaces the conventional convolution in CoTNet (Contextual Transformer Networks) [29] with dilated convolution, which can better extract the feature information of small objects in remote sensing images and improve the detection performance of the network. The formula for the new method is as follows:  
\[Y=\ K+\ XE_{V}*[K\,,\ X]C_{r}C_{o} \tag{1}\]  
where \(Y\) is the output result and \(X\) is the characteristic channel of the input, \(K\) is the local context information obtained by the dilated convolution, \(E_{V}\) is the embedded matrix, and \(C_{r}\) and \(C_{o}\) are two \(1\times 1\) convolutions with and without relu activation function, respectively. Figure 2 shows the schematic diagram of DCoTNet. The width, height, and channel number of input and output have not changed.
## 2 Evaluation Criteria
### Mobility  
The mobility of rescue robots is paramount for effective disaster relief efforts. Only with robust mobility can they adeptly navigate the complex disaster environment, expeditiously and efficiently execute rescue tasks, and provide timely assistance to trapped personnel. When designing a robot for rescue missions, it is crucial to consider its ability to operate in confined spaces, such as collapsed buildings, caves, or rugged terrains. Factors like debris, uneven terrain, and narrow passageways impose limitations on the physical design of the rescue robot; hence, it must be compact. Consequently, optimizing the size and mass of the rescue robot becomes essential for maneuvering through narrow spaces and overcoming obstacles [28]. To ensure effective operation in these environments, designers need to meticulously evaluate the positioning of the rescue robot's center of gravity [29]. Specifically, when crossing obstacles, maintaining a low center of gravity is imperative to ensure optimal traction capability [30; 31]. This precautionary measure prevents tipping over or loss of balance that could hinder task completion. Therefore, designers must thoughtfully consider both structure and weight distribution to achieve an appropriate center of gravity position. In addition to the aforementioned factors, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Criteria** & **Corresponding Criteria** & **Literature Sources** \\ \hline Survivability & Continuous working time with an independent power supply & [20; 21; 22; 23; 25] \\ Continuous walking distance with an independent power supply & [20; 21; 22; 23; 26] \\ Success rate of continuous start, working, stop & [24; 27] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The survivability evaluation criteria.  
continuous advancement and widespread use of re-configurable modular rescue robots have made the mechanical transformation success rate a crucial factor affecting the mobility of such robots. This is because their ability to smoothly adapt and transform into different forms in complex environments directly impacts their task completion success [32; 33]. Meanwhile, the rescue robot deployed in disaster sites must possess exceptional climbing, obstacle-crossing, running, and turning capabilities to effectively adapt to diverse terrain environments [34; 35]. Moreover, it is imperative for the rescue robot to demonstrate flexible control proficiency and robust load-bearing capacity in order to efficiently transport rescue equipment and materials, promptly reach locations where individuals are trapped, and execute rescue operations [36; 37]. Therefore, the mobility evaluation criteria are presented in Table 2, providing comprehensive details for analysis.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 4 Future Research Prospects
### Security of Data in Industrial NB-IoT  
Protecting data from unauthorized access and corruption throughout its life cycle is a huge concern to every industrial management team. Therefore, securing NB-IoT data generation in an industrial environment incorporates a complex set of industrial processes and practices that needs to accommodate both reliability and safety requirements [50]. These requirements are not limited to but include the following: confidentiality and integrity of data and users. This can be achieved through a protection policy that allows secured, encrypted, version control, error detection, backup and recovery procedure mechanisms, password management, two-factor authentication, biometric verification, and access control for users. This is because confidentiality and integrity in data allows accurate, complete, and unmodified data to be accessed by only approved users, a requirement that ensures the continued survival of industrial practice. Service availability is a requirement that ensures easy access to NB-IoT data whenever they are needed. To meet this requirement, NB-IoT data must incorporate a mechanism that supports off-sight back-ups or mirroring, a quick and safe data recovery system, and a redundancy mechanism to avoid complete network failover. However, to avoid data breaches and failures, a constant and proper monitoring policy needs to be put in place by industrial management. Data freshness is another requirement paramount to NB-IoT data security, owing to the limitation in the memory capacity of these modules and the need to have up-to-date data for decision making using the generated data. To overcome this challenge, a secured Cloud Internet of Things framework can be deployed to provide this memory space requirement.
## 6 Applications to Cardiovascular Medicine
### The Unloaded Reference Configuration in Cardiovascular Mechanics
(**b**) Relative stress error of the in vivo loaded configuration and solution of the forward inflation problem from the estimated unloaded configuration (roundtrip solution). (**c**) Relative displacement error the in vivo loaded configuration and solution of the forward inflation problem from the estimated unloaded configuration (roundtrip solution). (**d**) Colormap representation of the maximum principal stress distribution of the loaded configuration on top of the estimated unloaded configuration in gray shade. (**e**) Colormap representation of the maximum principal strain distribution on top of the estimated unloaded configuration in gray shade. (Reprinted/adapted with permission from Ref. [100]. 2018, Elsevier). of an independent deformation gradient. This method was used by [PERSON] et al., to study the diastolic stress distribution of three patient-specific AAA geometries derived from CT scans with population-averaged pressure loads and material constants for an isotropic Neo-Hookean constitutive equation. In this work, the outcomes of the modified updated Lagrangian formulation are compared to direct solutions with inverse FEM, concluding that both methods yield similar diastolic stress distributions although the latter seemed more prone to solution multiplicity and buckling [242; 243]. However, iterative methods may require suboptimal convergence times, and on some occasions, convergence could fail altogether [100]. It is important to highlight that even at an unloaded configuration, cardiovascular tissue is not truly stress-free. This fact has been widely proven by opening angle experiments at different arteries and layers of the heart wall. The residual stress responsible for this recoil effect exists without any distending pressure, being the possible result of non-uniform growth and remodeling over the patient's entire lifespan. The latter implies that residual stress cannot be resolved solely from load-deformation data. Indeed, the most common technique for the estimation of residual strain relies on the quantification of the opening angle after a stress-relieving cut. Some specialized studies have collected opening angle data from multiple locations of the cardiovascular system through experimental tests on human cadaveric tissue. These experiments have shown that the opening angle, and thus the preexisting residual stress, depends on specific tissue location and individual factors such as age and health conditions. Consequently, generic or averaged opening-angle derived residual stress can hardly be used for patient-specific analyses, especially, in pathological cases. The constrained mixture theory provides a consistent framework for the estimation of residual stress through the modeling of growth and remodeling and could be the key, along with the image-based resolution of tissue composition, for a truly patient-specific estimation of a stress-free reference configuration [32; 244].
## 3 Results and Discussion
### Case Study 2: Bronze Objects
#### 3.2.4 FT-IR Analysis  
The soil samples were comprised of small amounts of incoherent material that had adhered to the walls of the fragments themselves. Fourier transform IR analysis was carried out because this requires only a few milligrams of material. As Figure 7 shows, the spectra of the two different soils, i.e., the soil from the belt and helmet fragments, overlap perfectly, which indicates a probable homogeneity of composition. The IR spectra revealed both absorption bands of silicates [38; 39]. However, through the FTIR analysis, it was not possible to give a specific indication of the silicate minerals present in the analyzed soils since the absorption peaks are common to several silicate minerals having similar molecular structures [38]. In particular, the main absorptions of IR radiation were found between the following wavelength ranges: 1250/1000 cm\({}^{-1}\), which is associated with the asymmetrical stretching vibration of the Si-O-Si bond; 820/760 cm\({}^{-1}\), which is associated with the Si-O symmetrical stretching vibration; 705/685 cm\({}^{-1}\), which is associated with the Si-O symmetrical bending vibration; 545/435 cm\({}^{-1}\), which is associated with the out of the plane bending vibrations of the O-Si-O bond. However, despite the limitations of the IR technique in identifying exactly the mineralogical composition of soil samples (it should be used in combination with other techniques [38]), it allowed a homogeneity of composition to be established which seems to confirm the hypotheses of the same provenance between the two types of finds.  
Figure 7: (**A**) IR spectra of the two burial soils compared with silicate absorption bands (**B**).
## 4 Less Regular Connectivity Models
### Hybrid Models including Wired Infrastructure  
Driven in part by results showing that the capacity per node decreases in large networks [26], many researchers have considered hybrid networks which contain not only peer nodes (sensors), but also some wired base stations, which can communicate to sensors via single or multi hops. Percolation in a hybrid network has also been studied. [PERSON] _et al._[63] and [PERSON] _et al._[64] studied algorithms for base station placement to cover Poisson distributed nodes and the impact of base stations on the connectivity in two-dimensional sparse networks respectively. The results suggest that base stations help increasing the network connectivity significantly, when the node density in one dimension is much higher than the density in the other dimension.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 2 The Direct (Physical) Model of Radiation Thermometry
### Input Data of the Physical Model  
#### 2.3.1 Object's Emission and Environmental Radiation Reflection  
Equation (6) represents the emission of the surface in the direct model as the spectral radiance of a black body \(M_{\text{BB,obj}}\Big{(}\lambda,T_{obj}\Big{)}\) from Equation (2), reduced by the spectral emissivity factor \(\epsilon(\lambda)\).  
\[M_{\text{emission}}(\lambda)=M_{\text{BB,obj}}\Big{(}T_{obj},\lambda\Big{)} \epsilon(\lambda) \tag{6}\]  
According to Equation (4), the only complementary process of emission for real opaque surfaces is reflection. The radiation (7) reflected from the object is considered as being the raw environmental spectral radiance \(M_{\text{env}}(\lambda)\), reduced by the spectral reflectivity factor \(\rho(\lambda)\) (8).  
\[M_{\text{reflection}}(\lambda)=M_{\text{env}}(\lambda)\rho(\lambda) \tag{7}\]  
\[\rho(\lambda)=1-\epsilon(\lambda) \tag{8}\]  
The directional dependence of any influence parameter is omitted from this (simplified) model and is considered accounted for in the raw environmental radiation \(M_{\text{env}}(\lambda)\) data. This simplification does not affect the performance of the model, as long as the environmental radiation data are obtained in agreement with the spatial reflectivity characteristic of the surface in the given direction of observation.  
The directional spectral emissivity of any material can be measured with IR spectroscopy. Databases exist that cover the directional spectral emissivities of various materials, such as ECOSTRESS [25] (formerly ASTER [26]) and USGS Spectral Library Version 7 [27]. The normal (perpendicular) spectral emissivity of Topaz from the USGS Spectral Library is used as an example in this research (Figure 3).
## 2 Data and Methods
### Methods Overview  
To integrate the T1-weighted MR images into the ResNet50 architecture, they were uniformly resized to 224 \(\times\) 224 pixels. Figure 1 illustrates representative MR images acquired from SCH and BD patients. While the T1-weighted MR volumes encompassed 216 axial slices per participant, our analysis focused on the central 32 slices to optimize computational efficiency and maintain image quality. Subsequent to slice selection, all images underwent normalization to standardize intensity values. To mitigate overfitting, data augmentation techniques were employed, carefully considering the reliability of medical images. Specifically, rotation and brightness adjustments were incorporated into the augmentation pipeline, ensuring that the generated images remained consistent with the characteristics of real MR scans. Finally, all images were scaled to a range of 0 to 1 for numerical compatibility within the neural network.
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
###### Abstract  
Thanks to the increasing number of permanent GNSS stations in Europe and their long records, we computed position solutions for more than 1000 stations over the last two decades using the REPRO3 orbit and clock products from the IGS CNES-CLS (GRGS) Analysis Center. The velocities, which are mainly due to tectonics and glacial isostatic adjustment (GIA), and the annual solar cycle have been estimated using weighted least squares. The interannual variations have been accounted for in the stochastic model or in the deterministic model. We demonstrated that the velocity and annual cycle, in addition to their uncertainties, depend on the estimation method we used and that the estimation of GPS dracontic oscillations minimises biases in the estimation of annual solar cycle displacements. The annual solar cycle extracted from GPS has been compared with that from loading estimates of several hydrological models. If the annual amplitudes between GPS and hydrological models match, the phases of the loading models were typically in advance of about 1 month compared to GPS. Predictions of displacements modelled from GRACE observations did not show this phase shift. We also found important discrepancies at the interannual frequency band between GNSS, loading estimates derived from GRACE, and hydrological models using principal component analysis (PCA) decomposition. These discrepancies revealed that GNSS position variations in the interannual band cannot be systematically interpreted as a geophysical signal and should instead be interpreted in terms of autocorrelated noise.  
GNSS; IGS REPRO3; tectonic velocity; hydrological loading; principal component analysis Article Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models  
11 October 2021  
134523. [[https://doi.org/10.3390/rs13224523](https://doi.org/10.3390/rs13224523)]([https://doi.org/10.3390/rs13224523](https://doi.org/10.3390/rs13224523))  
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