esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Monitoring Results and Analysis
### Contact Pressures between Surrounding Rocks and Primary Liniings
According to the formulas above, the calculated surrounding rock pressure at the left or right tunnel vault is 0.14 MPa (see Equation (6)), which is far lower than the field measured value 0.30 MPa. In this case (see Figure 15, case 1), the left and the right tunnel arches are regarded independent of each other, which is clearly not in accord with that actual situation. As is known, during the construction of the double-arched tunnel, the left and the right tunnel arches interact with each other and both have significant influence on the mechanical behaviors of each other. Therefore, when calculating the surrounding rock pressures of the double-arched tunnel, both the left and the right arches should be taken into consideration. At present, since there is no clear calculation method, based on the balance arch theory, a simplified calculation model is established to estimate the surrounding rock pressures of the double-arched tunnel. That is to consider these two arches as an entire single-arched structure, as shown in the Figure 15, case 2. In this case, the calculated value is 0.25 MPa (see Equation (7)). As is shown, although both the calculated values in these two cases are lower than the field measured value 0.30 MP, the calculated value in case 2 is much closer to the measured value than that in case 1, which indicates that both the left and the right arches should be considered when calculating the surrounding rock pressures through the balance arch theory. In addition, the difference between the theoretical calculated value and the field measured value reflects the complexity of surrounding rocks during the construction of the double-arched tunnel, which in turn illustrates the necessity to carry out on-site monitoring projects during the excavation of the double-arched tunnel. Figure 11:0  
Figure 11:1 Schematic diagram of the tunnel balance arch curves of the double-arched tunnel in two cases.
## 4 Discussion
### Strontium Release  
The correlation between the functionalization of the ceramic with the strontium ion and the expected effects of osteoinduction may lie in its ability to release the ion in the intercellular medium ([PERSON] et al., 2019). The release of strontium ions (Sr) from calcium phosphate-based biomaterials is a critical factor influencing their biocompatibility and therapeutic effectiveness. In our systematic review, we observed that 29 out of 52 included studies provided detailed records on the Sr release from the biomaterials. This section discusses the release  
Figure 2: Expected biological effects of different ranges of strontium ion incorporation into calcium phosphates, according to the evidence from literature.  
profiles of these materials and their implications for biological responses, particularly in bone regeneration.  
Strontium release profiles varied significantly across different studies, reflecting the diverse nature of the biomaterials and their preparation methods. For instance, [PERSON] et al. [31] reported that Sr-HA discs released between 0.2 to 1.0 ppm over four days, while [PERSON] et al. [40] observed releases ranging from 5.05 to 19.46 ppm in media after 24 h. This variability indicates that the structural properties and composition of the biomaterials play a crucial role in determining the release kinetics of Sr ions.  
The controlled release of Sr is essential for achieving the desired biological effects without causing toxicity. Studies such as those by [PERSON] et al. [37] highlighted that Sr-HA coatings on PET artificial ligaments released 6330 to 20,260 ppm over 30 days, which was beneficial for sustained therapeutic outcomes. Conversely, high concentrations of Sr release, such as the 38-58 ppm observed by [PERSON] et al. [12] in Sr-\(\beta\)-TCP cement, could potentially lead to cytotoxic effects if not carefully regulated.  
The beneficial effects of Sr release on bone formation and cellular responses are dose-dependent. For example, controlled releases of Sr in the range of 2-5 ppm have been shown to stimulate osteoblastic activity and enhance mineralization, as demonstrated by multiple studies. On the other hand, excessive release, such as 20-100 ppm, has been associated with inhibitory effects on calcification and potential cytotoxicity.  
Interestingly, only a few studies, such as [PERSON] et al. [23], investigated the dynamic release of Sr in the presence of osteoclasts, showing an increase from 200 ppm to 1000 ppm when in contact with these bone-resorbing cells. These findings underscore the importance of considering the biological environment in which the biomaterials will be used, as the presence of specific cell types can significantly alter the release dynamics and subsequent biological effects. Therefore, the release profile of strontium from calcium phosphate-based biomaterials is a critical parameter that needs careful optimization. Ensuring a controlled and sustained release of Sr can maximize the therapeutic benefits for bone regeneration while minimizing potential adverse effects.
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 3 Typical Case: Integrated Demonstration of Xinqiao Iron Mine
### Measures for the Transition from Open-Pit to Underground Mining at Xinqiao Mine  
As outlined above, the transition from open-pit to underground mining at Xinqiao Mine involves a large-scale operation, high-capacity integration demands, and the extraction of deep ore bodies. The open-pit mining operation at Xinqiao Mine is illustrated in Figure 7a. To ensure a safe, efficient, and smooth transition between open-pit and underground mining, and to resolve the series of challenges associated with the closure of the open pit, detailed connection measures and technical solutions must be developed.  
Figure 6: Capacity transition diagram of Xinqiao Mine.  
Figure 7: Overview of mining operations at Xinqiao Mine: (**a**) overview of open-pit mining operations at Xinqiao Mine; (**b**) underground mining site at Xinqiao Mine. Source: Photographs by [PERSON].  
Post-closure treatment: Following the closure of the open pit, the pit floor is promptly treated to prevent seepage. Following the open-pit waste rock management plan, the pit is then backfilled to the \(-106\) m level. This procedure ensures the stability of the mining area while meeting environmental protection standards.
2. Safety isolation layer: The ore body above the 180 m level, with a thickness of 24 m, serves as a safety isolation layer between surface and underground mining. This layer acts as a buffer, preventing potential safety risks between the surface and underground operations. The underground mining site is illustrated in Figure 7b.
3. Open-pit slope monitoring system: An open-pit slope monitoring system is established to closely monitor the impact of underground mining activities on the stability of the open-pit slopes. This system provides real-time data to assess and manage risks during the transition period.
4. Comprehensive drainage system planning: During the transition period, water from the open-pit excavation is discharged through the existing open-pit drainage facilities. Once the open pit is closed and backfilled to the 106 m level, a collection pit (\(10\) m \(\times\) 6 m \(\times\) 6 m) is constructed at the lowest point. Water is then directed through the existing drainage infrastructure to a permanent pumping station located at the 48 m level.
# Improving Particle Swarm Optimization Analysis Using Differential Models
## 4 Differential Evolution PSO
### How to Choose the Step Size \(h\)
#### 4.4.1 Step Size Selection Range of the Euler Method  
First, consider the conditions that need to be satisfied for the absolute stability of the velocity direction. From the absolute stability of [PERSON]'s method, it can be seen that its absolute stability interval satisfies:  
\[|1+\lambda h|<1 \tag{57}\]  
where \(h\) is the algorithm step size, \(\lambda=\frac{\partial f_{1}}{\partial v_{jk}}\). From the definition of \(f_{1}\left(v_{jk},x_{jk}\right)\), it can be known that:  
\[\lambda=\frac{\partial f_{1}}{\partial v_{jk}}=\omega-1 \tag{58}\]Substituting into the absolute stability interval shows that the step size \(h\) is sufficient:  
\[0<h<\frac{2}{1-\omega} \tag{59}\]  
According to the definition of absolute stability, the parameter \(\lambda=\frac{\partial f_{1}}{\partial\varphi_{jk}}<0\), which means \(\omega<1\). Therefore, for the velocity vector, the condition of its absolute stability is:  
\[\omega<1,\ 0<h<\frac{2}{1-\omega} \tag{60}\]  
Next, we continue to consider the absolute stability of the position vector defined by:  
\[\frac{dx_{\bar{k}}(t)}{dt}=f_{2}\Big{(}t,v_{jk},x_{jk}\Big{)} \tag{61}\]  
\[\ln f_{2}\Big{(}t,v_{jk},x_{jk}\Big{)}=\omega v_{jk}-\varphi x_{jk}+\Big{(} \varphi_{1}p_{jk}+\varphi_{2}p_{\bar{g}\bar{k}}\Big{)},\]  
\[\lambda=\frac{\partial f_{2}}{\partial x_{jk}}=-\varphi<0 \tag{62}\]  
According to the definition of absolute stability, the absolute stability interval of the step size \(h\) is obtained as:  
\[0<h<\frac{2}{\varphi} \tag{63}\]  
Therefore, for [PERSON]'s method with a step size \(h\), in order to ensure the absolute stability of the evolution equations of the velocity vector and the position vector, the step size \(h\) needs to satisfy:  
\[0<h<min\{\frac{2}{1-\omega},\frac{2}{\varphi}\} \tag{64}\]
## 1 Introduction
The main limitations of using coherent sources for the transmission water-window microscopy are the huge and expensive pump lasers in the case of XRL and LWFA, and low flux in the case of HHG [23; 24]. However, a newly developed TW-class mid-infrared laser, together with a loose focusing geometry for HHG, resulted in increased photon flux, opening a new possibility for the transmission water-window imaging [25]. However, the laser-produced and discharge-produced plasma sources remain the more compact solution. The up-to-date overview of laboratory water-window X-ray microscopy is described in the review article [22], where the table-top sources, optics, detectors, and sample preparation for SXR microscopy are discussed. Although table-top sources are accessible and easy to operate, they come with limitations in terms of the required exposure times, and in terms of the comparable spatial resolution with large-scale facilities. To improve the brilliance of the compact sources, a jet with a so-called barrel shock system was introduced [26], enhancement of the soft X-ray radiation by inserting a low-pressure nitrogen atmosphere to increase the conversion efficiency of the laser-produced plasma sources was demonstrated [27; 28]. Nevertheless, the future development of laboratory-scale sources is necessary to perform a single-shot imaging experiment as the photon flux of these sources is a few orders of magnitude below the requirements for a single shot imaging micrometer-sized specimen [29]; a current perspective of a single shot imaging using compact SXR sources is discussed in [29]. In this paper, we present a new design of a table-top water-window transmission microscope using a Z-pinching capillary discharge source, operating at 2.88 nm (430 eV) corresponding to the 1s\({}^{2}\)-1s2p quantum transition of helium-like nitrogen ion. The working principle of the source is described, as well as the sample preparation. Images of _Desmodesumumis_ (green algae) showing their organelles are demonstrated.
###### Abstract  
Chemical agents pose a significant threat to social security, highlighting the crucial role of representative chemical agents adsorption in ensuring the safety our environment. This study explored the application of Magnetic Graphene Oxide Nanoplatelet Composites (MGONCs) in adsorbing the representative chemical agents such as Lewisite (L), O-ethyl S-2-disopropylaminoethyl methylphosphonothioolate (VX), Sarin (GB), and Soman (GD). MGONCs were synthesized through a physical blending method, with the combination of graphene oxide (GO) and Fe\({}_{3}\)O\({}_{4}\) nanoparticles at a mass ratio of 1:1. Optimization of the adsorption process involved investigating the effects of contact time, temperature, and adsorbent dosage. Remarkably, the adsorption rate of L and VX exceeded 99% when the dosage of MGONCs was 2.5 mg, with a contact time of 30 s at room temperature. Furthermore, GB and GD achieved maximum adsorption rates after a contact time of 20 min, with the dosages of MGONCs at 10 mg and 20 mg, respectively. Characterization of the magnetic composite was accomplished through XRD, TEM, VSM, FTIR, TGA, and BET analyses. Kinetical analysis revealed that the adsorption mechanism of GB and GD on MGONCs followed pseudo-second-order (PSO) kinetics, exhibiting a high regression coefficient. The calculated \(\mathrm{d}_{\mathrm{e}}\) values were 0.103125 mg/g and 0.081349 mg/g, respectively. This research demonstrated the feasibility of utilizing MGONCs as highly efficient adsorbents for representative chemical agents, particularly in on-site sampling scenarios.  
graphene oxide; Fe\({}_{3}\)O\({}_{4}\); absorption; chemical agents +
Footnote †: journal: Applied Physics Letters  
Article
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 2 Data and Methods  
### General Observation Model  
For satellite \(s\), receiver \(r\) and frequency \(i\), the original code and phase observation can be expressed as:  
\[\left\{\begin{array}{l}p_{r,i}^{s}=u_{r}^{s}\cdot x+dt_{r}-dt^{s}+m_{r}^{s} \cdot Z_{r}+\mu_{i}\cdot I_{r,1}^{s}+b_{r,i}-b_{i}^{s}+e_{r,i}^{s}\\ I_{r,i}^{s}=u_{r}^{s}\cdot x+dt_{r}-dt^{s}+m_{r}^{s}\cdot Z_{r}-\mu_{i}\cdot I_ {r,1}^{s}+\hat{N}_{r,i}^{s}+B_{r,i}-\hat{B}_{i}^{s}+\xi_{r,i}^{s}\end{array}\right. \tag{1}\]  
where \(p_{r,i}^{s}\) and \(l_{r,i}^{s}\) represent the code and phase observed minus computed (OMC) values, respectively; \(u_{r}^{s}\) denotes the unit directional vector from the satellite to the receiver; \(x\) denotes the vector of position correction relative to the a priori position; \(dt_{r}\) and \(dt^{s}\) represent the receiver and satellite clock offset, respectively; \(Z_{r}\) denotes the tropospheric zenith wet delay and wet mapping function \(m_{r}^{s}\); \(I_{r,1}^{s}\) denotes the ionospheric delay on the frequency, \(f_{1}\), with the scale factor \(\mu_{i}=f_{1}^{2}/f_{i}^{2}\); \(\hat{N}_{r,i}^{s}=\lambda_{i}\cdot N_{r,i}^{s}\) is the phase ambiguity in meters, where \(N_{r,i}^{s}\) is the integer phase ambiguity in the cycle and \(\lambda_{i}\) is the carrier wavelength; \(b_{r,i}\) and \(b_{i}^{s}\) represent the receiver and satellite uncalibrated code delays (UCDs), respectively; \(B_{r,i}\) and \(B_{i}^{s}\) represent uncalibrated phase delays (UPDs) of the receiver and satellite, respectively; and \(\xi_{r,i}^{s}\) and \(\xi_{r,i}^{s}\) represent the code and phase measurement noise, respectively.
## 5 Analysis of Results and Field Testing  
### Dataset and Experimental Environment  
Due to the lack of a publicly available standard water gauge dataset in the field of hydrology, we collected raw data over time from three hydrological stations in the same basin to ensure that the experimental data reflect the real application environment and guarantee the reliability of the model. To ensure the diversity of the data, we randomly extracted 1029 photos containing water gauges from the original data, comprising 833 images in the training set, 93 images in the validation set, and 103 images in the test set. The semantic annotation labels in the dataset were divided into two groups, water body and background, as illustrated in Figure 9.  
This paper's experimental platform is Ubuntu 22.04, with the following hardware configuration: Intel(R) Core(TM) i5-12490F CPU and NVIDIA GeForce RTX 3060 12G GPU. The software versions are Python 3.10, Pytorch 1.13, and Cuda 11.7.  
To ensure the comparability of the experimental results, uniform hyperparameters are used in the network training process in this paper. The network parameter optimizer used is stochastic gradient descent (SGD) with momentum, where the initial learning rate is set to \(1\times 10^{-2}\), the minimum learning rate is \(1\times 10^{-4}\), the momentum parameter is set to \(0.9\), and the weight decay parameter is set to \(1\times 10^{-4}\). The learning rate is adjusted using the Cosine Annealing algorithm [32], with the frequency of learning rate adjustment being the number of epochs per training round. For the binary classification task, the Binary Cross Entropy Loss (BCE Loss) combined with the Dice Coefficient Loss is used as the loss function, and its expression is calculated as Equation (6):  
\[\begin{split}\text{L}_{\text{loss}}\,=\text{L}_{\text{BCE}}+ \text{L}_{\text{Dice}}\\ \text{L}_{\text{BCE}}=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\log(\tilde {y}_{i})+(1-y_{i})\log(1-\tilde{y}_{i})]\\ \text{L}_{\text{Dice}}=1-\frac{2|X\cap Y|}{|X|+|Y|},\end{split} \tag{6}\]  
Figure 8: Water level value calculation.  
where \(N\) denotes the number of all pixel points, \(y_{i}\) denotes the true label value for the \(i\)-th point, and \(\tilde{y}_{i}\) denotes the predicted label value for the \(i\)-th point. \(X\) denotes the set of true labels, \(Y\) denotes the set of predicted labels, \(|X\cap Y|\) is the number of elements of the intersection between labels and predictions, and \(|X|\) and \(|Y|\) denote the number of elements of labels and predictions, respectively.
## 3 Methodology
### Identify the Challenges for Assuring WBAN Data Security and Privacy  
The goal of this step was to identify the challenges faced by developers for assuring data security and privacy for WBAN based healthcare applications and complying with regulations. A two-step process was utilized to identify the challenges. The first step involved a literature review, while the second step involved an interview with the Chief Technology Officer (CTO) and the tech lead of an organization that develops a WBAN based fitness tracking application. The findings from the literature review and interview have been published here [24], and are summarised in Section 4. The following steps were utilised by the lead author of this paper to conduct the literature review:* Conduct a search on IEEEXplore, ScienceDirect and Google scholar using the search string; _\"healthcare AND (security OR privacy) AND (standard OR regulation OR compliance) AND (barrier OR challenges OR difficulties)\"_.
* Set inclusion criteria as follows: (1) presented the challenges for assuring security and privacy of healthcare applications that comply with regulations; (2) publication year: 2010-2020; (3) language is English and full text available.
* The initial search resulted in a total of 320 research papers.
* In the first screening each paper was analyzed by reviewing the abstract and conclusion. If the paper addressed any challenges, then it was selected for the second screening. A total of 125 papers out of 320 were selected for the second screening.
* In the second screening each paper was analyzed by reading the full text and checking whether the paper presented any challenges for assuring security and privacy of healthcare applications that comply with regulations. The second screening resulted in a total of 19 papers out of 125.
* Finally, a list of challenges was recorded from those papers which is presented in Section 4.
## 3 Geopolymer Concrete (GPC)
### Fresh Properties of Geopolymer Concrete  
#### 3.1.1 Geopolymer Concrete Workability  
[PERSON] et al. [29] highlighted that the workable flow of the GPC increases as the fly ash/sand ratio increases, and the sodium silicate solution's cohesion and slump ability improve when the SiO\({}_{2}\)/Na\({}_{2}\)O ratio rises [14]. [PERSON] et al. [23] also used the IS-1199:1959 slump cone test to find out how the different workable samples of GPC were. Due to its dense flow character, the slump value of GPC specimens was 72% higher than that of cement concrete. Steel fibres limit workability because they block the flow of concrete. [PERSON] et al. [30] studied the workability of GPP, and GPC was investigated at different molarities of NaOH and varied SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) ratios by mass. Because the particles on the alkaline activator mixture grow as the dosage of NaOH increases, it is also noted that the dosage of NaOH considerably impacts the workability of GPC.  
Figure 13: Flexural strength of GPM [55].  
[PERSON] et al. [31] evaluated the workability of FA-based GPC using the slump cone test. Coarse and fine aggregate, alkaline liquid, FA, and water are used in GPC. Sodium silicate and sodium hydroxide are combined to make the alkaline liquid. As shown in Figure 14, the findings of the experiments showed that adding GGBS makes GPC less workable.  
[PERSON] et al. [16] examined the feasibility of fly ash-based GPC for curing in ambient conditions. The experimental results showed that the slump and flow parameters are reduced in FA-based GPC when the slag blend increases. The behaviour becomes more visible as the percentage of the blend increases. As shown in Figure 15, S00, S10, S20, and S30 represent combinations with slag contents of 0%, 10%, 20%, and 30%, respectively.  
The steel and iron industries produce ground granulated blast furnace slag (GGBS). According to [PERSON] et al. [57], using GGBS in concrete increases the material's workability, among other benefits. Geopolymer concrete made from ground-granulated blast furnace slag was developed by [PERSON] et al. [58].  
Figure 14: Variation in slump for different mixes (mm) [31].  
Figure 15: Slump value variation with slag content [16].  
Fumed silica is a byproduct of the silicon and ferrosilicon alloy industries. [PERSON] [59] states that due to its spherical shape, adding fumed silica in concrete increases its density and workability. With the activation of sodium silicate, fumed silica can form dihydrogen during synthesis and can be involved in modifying the chemistry and porosity of specimens.