esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
This manuscript presents a method for predicting areas of relevant change, within the GeoCDX system [2]. This system combines automated change detection processing with human-in-the-loop rapid triage of change detection results. While the GeoCDX system is agnostic to the type of change detected, human judgment is used to conclude whether a tile should be tagged as containing \"relevant\" change depending on the analyst's task. As a user interrogates change detection results presented by GeoCDX, we showed that we were able to use the change/no-change annotations of the imagery analyst to help predict whether subsequent tiles contained relevant change or not. These predictions ultimately lead to decreased analysis time for the user.  
Four different classification algorithms were used to perform the prediction; in general, the random forest classification algorithm performed the best. We also explored various schemes to construct a well-diversified training dataset that included areas of change and areas without change to ensure that the makeup of the training dataset reflects that of the entire dataset [25]. Generally, training datasets that included samples from all of the GeoCDX change clusters produced the best classifiers. We demonstrated that with an appropriate training dataset, we can produce a random forest classifier that can typically predict relevant change with an accuracy of greater than 70% and even up to 97%. The classifiers that are produced generally favor precision over recall, meaning that there will be relatively few false positive change indications.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Columbia**} & \multicolumn{3}{c}{**Las Vegas**} & \multicolumn{3}{c}{**Natanz**} \\ \cline{2-10}
**Dataset** & \(F_{0.1}\) & \(F_{1}\) & \(F_{10}\) & \(F_{0.  
In future work, we plan to investigate using more granular features extracted from the imagery to predict changes at a finer scale. We recognize the limitations of using the features extracted from 256 by 256-meter tiles used by GeoCDX, but were generally pleased with the results that could be achieved with those features. Additionally, we plan to incorporate gaze tracking information gathered from system users [18] to better identify precisely which portions of the image are important for making decisions about relevant change _versus_ irrelevant change _versus_ no change. Using this eye tracking information along with more fine-grained image features will improve future change predictions. Finally, additional experiments should be performed to gauge the improvement in performance offered by using change/no change annotations from an imagery analyst to predict the existence of relevant change in other, unseen portions of the image. We anticipate significant efficiency improvements by using our semi-automated approach to suggest whether relevant change has occurred or not; however, this should be verified experimentally.
## 3 Method
### Crop Condition Assessment Result
These drops are a reflection of the NDVI profiles for the five provinces. Henan province suffered from both unseasonable snow fall in early November 2009 and rainfall in early February 2010, which is reflected by the two drops in its profile. Anhui and Jiangsu were  
Figure 9: Crop condition map of NCP using MODIS NDVI (**a**) and UALR-adjusted NDVI; (**b**) for early May 2011. The maps show the condition of the crop compared to the previous year. affected by the rainfall in early February 2010, while Hebei and Shandong suffered from heavy snow in early November 2009, which is reflected in single drops in their profiles. However, all of the growth profiles have some similar basic patterns. Average UALR-adjusted NDVI values are all higher to some degree than average MODIS NDVI. Furthermore, winter crop phenology in the 2010-2011 growing season was around ten days ahead compared with 2009-2010 due to the dry and warmer winter in 2010-2011, significantly more so for Shandong, Hebei and Henan provinces. Crop growth profiles for Jiangsu and Anhui provinces for 2010-2011 were well below that of 2009-2010, because of severe drought throughout the growing season. For example, for Anhui, the peak of UALR-adjusted NDVI in the 2010-2011 winter growing season was 5.5% lower and the peak of MODIS NDVI was 6% lower than in 2009-2010. The peak of MODIS NDVI and UALR-adjusted NDVI for other provinces also differ significantly. Figure 10: Crop growth profiles for North China Plain (**a**) and five provinces: Anhui (**b**), Hebei (**c**), Henan (**d**), Jiangsu (**e**) and Shandong (**f**).
## 4 Performance Results and Discussion  
In this section, we describe the evaluation results on the reliability aspects in the three setups (cf. Section 3) with and without using FRER. All the measurements were conducted on an industrial shop floor to validate the use case prototype in a production environment. The tests have been performed with a 100-byte message size and a cycle time of 10 ms.
## 1 Introduction  
Generally, heavy metals refer to metals whose densities are greater than 5 g/cm\({}^{3}\), such as cadmium (Cd), chromium (Cr), mercury (Hg), lead (Pb), iron (Fe), manganese (Mn), and cobalt (Co), while the metalloid arsenic (As) is usually classified as a heavy metal due to similarities in chemical properties and environmental behavior [1; 2; 3]. Concerns over heavy metals are due to their high toxicity, long durability, persistent bioavailability, stability, and profusion [4; 5; 6]. Heavy metal pollution not only degrades the quality of the soil, crops, atmosphere, and water bodies, but also threatens human health through the food chain [7; 8; 9]. Soils are important sinks for heavy metals discharged into the environment [10; 11; 12]. Therefore, heavy metal pollution in soils has become a serious issue that has attracted much attention worldwide [13; 14; 15].  
Since the 1980s, the economy of China has developed rapidly with the acceleration of industrialization and urbanization, while heavy metal pollution in soil has become increasingly concerning due to competing pressures between the population, resources, and environment [16; 17; 18]. Due to the high geochemical backgrounds and activities of mining and smelting in southwest China, the study of heavy metals of soils has been a hotspot of environmental evaluation [19; 20].  
The assessment methods commonly used for heavy metal pollution can be grouped into two types. One is on heavy metals with established criteria such as Cd, Cr, Hg, Pb, and As with the criterion GB15618-2018 for soil of agricultural land in China [21; 22; 23; 24]. The other is on heavy metals without specific criteria such as Fe, Mn, and Co in which the evaluation can be conducted through pollution indices such as single indices and integrated indices [25; 26; 27; 28].  
In this paper, the heavy metals of surface soils in the Xinping area of Yunnan Province, China are first surveyed on a scale of 1:50,000 along with some crop samples. Then, new single and integrated pollution indices are described and proposed to assess the pollution status of soils and crops on Chinese criteria of GB15618-2018 [21] and GB2762-2022 [29], respectively. Finally, the assessment results between soils and crops are compared and discussed.
## 2 Materials and Methods
### Material Definition  
In short-term deformation cycles, material can be treated as an anisotropic hyperelastic model with no viscous loss. The characterization of reversible materials in an isothermal process, with no energy dissipation, is evaluated according to the continuum mechanics with a strain density function. Considering a Cartesian system represented by an orthonormal basis, the position of a material point in the initial and deformed configuration can be denoted respectively by the vectors \(\mathbf{X}\) and \(\mathbf{x}\). The material deformation gradient is expressed with the two points tensor \(\mathbf{F}=\frac{\partial\mathbf{v}}{\partial\mathbf{X}}\) where the determinant of this tensor, expressed as \(J=det(\mathbf{F})\), considers the volumetric changes of the initial volume. For incompressible materials, \(J=1\). The deformation gradient can be expressed as \(\mathbf{F}=J^{\frac{1}{3}}\;\overline{\mathbf{F}}\), where \(\;J^{\frac{1}{3}}\) represent the volumetric deformation and \(\overline{\mathbf{F}}\) the deviatoric deformation. The Right Cauchy Green deformation tensor \(\mathbf{C}=\mathbf{F}^{T}\mathbf{F}\) can be expressed by the tensorial product of the transpose of \(\mathbf{F}\), denoted as \(\mathbf{F}^{T}\), and \(\mathbf{F}\) and the modified Right Cauchy Green tensor can be expressed as \(\overline{\mathbf{C}}=\overline{\mathbf{F}}^{T}\overline{\mathbf{F}}\) considering the deviatoric deformation tensor and its transpose. Strain energy density was divided into a volumetric, depending on the Jacobian determinant \(J\) and isochoric component according to [PERSON], [PERSON] et al. [34], as can be observed in Equation (1). The isochoric component was divided into an isotropic and anisotropic component. The isotropic component depends on the modified Right Cauchy Green tensor \(\overline{\mathbf{C}}\), that considers randomly distributed fibers and the extracellular matrix. The anisotropic term, considers the structural tensors \(\;\mathbf{M}=\mathbf{m}_{0}\otimes\mathbf{m}_{0}\;\;\mathbf{N}=\mathbf{n}_{0} \otimes\mathbf{n}_{0}\) taking into account the fibers' preferential orientation, defined by the vectors \(\mathbf{m}_{0}\) and \(\;\mathbf{n}_{0}\), in accordance with the results observed in the X-ray techniques [24]. According to these results, Nasal-Temporal and Superior inferior directions were considered as preferential orientations. This work did not include density variation across the thickness. \[\psi=\psi_{\mathrm{volumetric}}(J)+\;\overline{\psi}_{\mathrm{Isochoric}}( \overline{\mathbf{C}},\;\mathbf{M},\;\mathbf{N}) \tag{1}\]  
Equation (1) can be evaluated in terms of invariants as seen in Equation (2)  
\[\psi=\psi_{\mathrm{volumetric}}\;(J)+\;\overline{\psi}_{\mathrm{Isochoric}}( \overline{I}_{1},\overline{I}_{2},\overline{I}_{4},\overline{I}_{5},\overline{ I}_{6},\overline{I}_{7},\overline{I}_{8},\overline{I}_{9}) \tag{2}\]Considering the definition of the trace of the modified Right Cauchy-Green deformation tensor \(tr\) (\(\overline{\textbf{C}}\)), the invariants can be expressed according to the expressions contained in Equations (3) and (4).
## 3 Results
### Scenario Simulation Results  
The simulation was performed in a scenario considering the measurement range of the aneurysm diameter, height, neck angle, and iliac angle based on the CT data of patients with AAA. Figure 6 illustrates the stress distributions of the loaded maximum blood pressure in the systole of the cardiac cycle based on the scenario. To avoid the concentration of stress, the PWS was measured in the AAA sac; the total PWS for the scenario is presented in the Appendix A. In particular, in this study, the maximum principal stress values were compared to examine the variation in the PWS according to the maximum systolic blood pressure in the cardiac cycle.  
The PWS tended to increase as the aneurysm diameter increased. In particular, when the aneurysm diameter values were 80 and 120 mm, the PWS increased by 30.2% and 36.9% on average as the height increased, respectively. This can be attributed to the changes in the curvature of the AAA model. When the aneurysm diameter was 40 mm and the neck angle increased, the PWS tended to increase slightly; however, this trend was not observed when the aneurysm diameter values were 80 and 120 mm. Similarly, the PWS did not exhibit any trend when the value of the iliac angle was changed, as shown in Figure 7. It was confirmed that at given values of the aneurysm diameter and height, the PWS significantly changed when the iliac angle and neck angle were changed; however, no trend was exhibited, as shown in Figure 8. Further, it was verified that the values of PWS ascended in the order of intima, adventitia, and media, as shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Diameter (mm)** & **Neck Angle (\({}^{\circ}\))** & **Height (mm)** & **Iliac Angle (\({}^{\circ}\))** \\ \hline Minimum & 41.1 & 7.8 & 36 & 11.7 \\ Maximum & 118.1 & 136.9 & 188 & 143.5 \\ Average & 63.4 \(\pm\) 14.2 & 49.3 \(\pm\) 22.5 & 93.4 \(\pm\) 28.6 & 68.34 \(\pm\) 22.1 \\ \hline \end{tabular}
\end{table}
Table 2: Abdominal aortic aneurysm AAA geometrical measurements.  
Figure 6: Maximum principal stress contour in inner wall. **(a)** Diameter: 40 mm; height: 110 mm; **(b)** diameter: 40 mm; height: 190 mm; **(c)** diameter: 80 mm; height: 110 mm; **(d)** diameter: 80 mm; height: 190 mm; **(e)** diameter: 120 mm; height: 110 mm; and **(f)** diameter: 120 mm; height: 190 mm.  
Figure 8: Maximum principal stress according to (**a**) iliac angle and (**b**) neck angle.  
Figure 7: Maximum principal stress according to (**a**,**b**) diameter; (**c**) iliac angle; and (**d**) neck angle.
## 4 Results and Discussion
### Prediction of Bond Strength
#### 318-19:  
To predict bond strength between natural stone aggregate concrete and rebar, the ACI 318-19 [46] proposed the following equation:  
\[\tau=\frac{A_{b}f_{y}}{\pi d_{b}l_{d}} \tag{5}\]  
where \(A_{b}\) = area of the rebar, \(f_{y}\) = yield strength of steel, \(d_{b}\) = diameter of rebar, and \(l_{d}\) = development length of the specified bar that can be calculated using the following equation:  
\[l_{d}=\bigg{(}\frac{f_{y}\psi_{t}\psi_{e}\lambda}{2.1\sqrt{f_{c} \epsilon^{\prime}}}\bigg{)}d_{b} \tag{6}\]  
The details of all parameters used in this equation may be found in reference [46]. In this study, steel bars of diameter 12.7 mm were used. The yield strength of the rebar was 415 MPa, and \(\psi t\) and \(\psi e\) were assumed to be one based on the specified conditions of the experiment.  
**Australian standard AS 3600:**  
According to Australian standard AS 3600 [69], following Equation (7) can be employed to calculate the bond strength between the natural stone aggregate concrete and the rebar, where \(f_{c}^{\prime}\) = concrete compressive strength, \(c\) = concrete cover, and \(d_{b}\) = diameter of the rebar.  
\[\tau=0.265\sqrt{f_{c}^{\prime}}\bigg{(}\frac{c}{d_{b}}+0.5\bigg{)} \tag{7}\]  
**CEB-FIP model:**  
According to the CEB-FIP model [70], bond strength depends upon the type of failure and bond condition, as given in Table 3. The equations proposed by the CEB-FIP model for the measuring bond strength of reinforcing bars embedded in natural stone aggregate concrete are also proven in this table. For all samples tested in this study, pull-out failure mode was observed as a dominant failure type, and a good condition of the bond was assumed to calculate bond strength, and accordingly, model (e) was used to calculate bond strength.  
A comparison of experimentally obtained bond strength and predicted bond strength using existing models is presented in Figure 13. This comparison revealed that the bond strength predictions of the [PERSON] et al. [45] model are in good agreement with the experimental data on the bond strength of RAC mixes containing 100% fine RBAs and varying percentages of coarse RBAs in place of coarse NAS (Figure 13a) and are in fair agreement with the experimental bond strength of RAC mixes containing 100% fine RCAs and varying percentages of coarse RCAs in place of coarse NAS (Figure 13b). However, the bond strength determined using the model proposed by ACI 318-19 was almost half of the experimental values of RAC mixes. On the contrary, compared to experimental results, the AS 3600 and CEB-FIP models overestimated the bond strength for RAC mixes containing RBAs or RCAs, as shown in Figure 13.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 3 Sensor Localization
### Localization Techniques  
In localization service, two possible scenarios are present: (i) Anchor-Based: This is a network formed in part by Anchor nodes that are aware of their position, having established a system of predefined coordinates. These nodes can transmit their position to all the nodes of the network that do not have their own coordinates, called Unknowns (or targets) and that use the position of the Anchors to determine their own position within the absolute reference system, i.e., a reference system that uniquely associates a node to a position. (ii) Anchor-Free: In a network of this type, all nodes are unaware of their position. In this case, it is no longer the Anchors that communicate their position to the Unknowns, but selected Unknowns, elected as relative Anchors, transmit their position in a reference system created by them. The Unknowns nodes are then located within a relative reference system, which is a reference system that associates, in a non-unique way, a node to a position.  
Both \"families\" have their advantages and disadvantages: an Anchor-Based network has the unquestionable advantage of determining the position of a node uniquely and can also communicate this information to other localization systems, but at the same time, it has disadvantages from the implementation point of view. All Anchors must be aware of their position, so these nodes must be positioned correctly within the network, and at the same time, Unknowns have to be positioned in a certain range to be located. On the other hand, an Anchor-Free network does not have problems with node arrangement. The devices that make it up can be randomly positioned within an area; obviously, the disadvantage is that this can change over time, and this information cannot be used outside.
# Numerical Simulation Analysis Method for Rockburst Control in Deeply Buried Caverns
###### Abstract  
Investigating the mechanism and measures of controlling rockburst in deeply buried underground caverns is a prerequisite for ensuring the safety of personnel, reducing the damage to construction equipment caused by the rockburst, and making the project economical and efficient. Efficient analysis methods for evaluating rockburst control in underground caverns are crucial for qualitatively selecting rockburst control methods and quantifying the effectiveness of rockburst measures. Based on the mechanism of energy accumulation in rock mass and the occurrence of rockburst as well as the active methods for controlling rockburst, we propose a mathematical model for simulating borehole stress relief using implicit borehole elements. In this modeling approach, we combine vector superposition in different borehole directions to analyze the effect of borehole stress relief on improving the external environment of the rock mass. Based on the interaction between the anchor support and the surrounding rock as well as the implementation of passive rockburst control measures, we propose a mathematical model that utilizes the implicit anchor column elements to simulate the reinforcement of the surrounding rock with anchors. By combining system anchors into arches and suspending anchors to reinforce the caverns, the overall stiffness of the surrounding rock can be improved and the stress accumulation can be alleviated. The coordinated deformation between the surrounding rock and the anchor provides support to prevent the rockburst. The effects of two rockburst control measures, borehole tealesement and anchor reinforcement, are analyzed using both the strength theory and the energy theory. Engineering examples are provided to illustrate that the use of borehole elements to simulate local stress relief can effectively improve the external environment of the rock mass to control the occurrence of rockburst. Additionally, the mechanism of using anchor column elements in combination with the surrounding rock elements to enhance the overall stiffness of the rock mass and prevent rockburst is explored.  
borehole release; anchor rod support; implicit element; rockburst control Article
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Non-Repudiation, Accountability, and Auditability
#### 3.4.1. Signed Message  
The Signed Message pattern ensures a message's authenticity, integrity, and non-repudiation. The message sender may sign the message using a digital signature, and in that way, the signer is securely associated with the generation and/or transmission of the message. Digital signatures use Public Key Infrastructures (PKIs) to provide the highest levels of security along with universal acceptance. They are a specific signature technology implementation of electronic signature (eSignature).  
Three algorithms are involved with the digital signature process:  
* Key generation: This algorithm provides a private key along with its corresponding public key.
* Signing: This algorithm produces a signature (typically on the digest of the message) upon receiving a private key and the message that is being signed.
* Verification: This algorithm checks the authenticity of the message by verifying it along with the signature and the public key.  
Figure 10 shows the processes that take place and the communications between the involved parties. The signer uses their private key to sign the message, while the verifier uses the corresponding public key to verify the signature and, by extension, the message. The signature is valid when the two hash values (i.e., message digests), the one stored and the one calculated, are equal.