esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Capture of Sr\({}^{2+}\) Ions by a Layered Potassium Neodymium Phosphate
###### Abstract  
\({}^{90}\)Sr has a long half-life, strong radioactivity, and high mobility. The removal of radioactive strontium from the water environment is of great significance to human safety and the sustainable development of nuclear energy. In this study, a two-dimensional rare earth phosphate K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) efficiently captured Sr\({}^{2+}\) ions in aqueous solutions. At room temperature, the adsorption isotherm, kinetics, and pH dependence experiments of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) for Sr\({}^{2+}\) ions were examined (\(V/m=1000\) mL/g, 12 h contact time). The experimental results show that the maximum adsorption capacity of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) for Sr\({}^{2+}\) (\(q_{m}\)Sr) was 42.35 mg/g. The removal efficiency for Sr\({}^{2+}\) (\(R^{8\mathrm{Sr}}\)) was 87.47% within 24 h. It had a good affinity with Sr\({}^{2+}\) ions in neutral or even high alkaline environments (distribution coefficient \(K_{d}^{\mathrm{Sr}}=1.46\)\(\times\) 10\({}^{6}\) mL/g, \(R^{8\mathrm{Sr}}=99.93\%\)). The adsorption mechanism was attributed to the ion exchange between Sr\({}^{2+}\) and K\({}^{+}\) ions by batch adsorption experiments combined with multiple characterizations, including XPS, EDS, and PXRD. This is the first report of Sr\({}^{2+}\) removal by ion exchange via rare earth phosphate materials with a two-dimensional structure. This work provides insight into the future development of rare earth phosphates as ion exchange materials for radionuclide remediation.  
layered material; phosphate; Nd; strontium; radionuclide remediation; ion exchange +
Footnote †: journal: Physics Letters B
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 3 Scalable TCP for 5G MmWave Networks  
### Scalable TCP  
S-TCP [29], proposed in 2003 by [PERSON], provides high throughput and scalability. In the past, NewReno TCP [30] reduced cwnd by half for each packet loss, so link utilization was significantly lower in high bandwidth-delay product (BDP) networks. For example, it takes half an hour, which is a relatively long time, for a NewReno TCP flow with an RTT of 200 ms to reach a bandwidth of 1 Gbps [29]. S-TCP can improve link utilization because it increases cwnd more quickly than does NewReno TCP. Like NewReno TCP, the cwnd increment and decrement mechanism of S-TCP that operates based on packet loss and ACK is as follows:  
1. For each ACK received: \[\text{cwnd}\leftarrow\text{cwnd}+0.01\] (1)
2. For each packet loss: \[\text{cwnd}\leftarrow\text{cwnd}-[0.125\times\text{cwnd}]\] (2)  
After each ACK is received during the RTT, S-TCP increases the cwnd size in proportion to the defined constant (0.01). NewReno TCP increases cwnd by 1/cwnd for each ACK reception, while S-TCP increases cwnd size by 1 MSS if 100 ACKs are successfully received. That is, even in a high-speed network having a large cwnd size, S-TCP increases the delivery rate at a fixed speed. For packet loss events, S-TCP reduces the cwnd size less than the amount reduced by standard TCP congestion control. This reduces the time required for standard TCP to recover to a 10 Gbps link from more than 4 h to less than about 15 s.
# CDA-Net for Automatic Prostate Segmentation in MR Images
## 4 Experiments and Discussion
### Experiment Setting and Evaluations
#### 4.2.1 Quantitative Comparison with State-of-the-Art Algorithms  
We compared the results of our CDA-Net against several other algorithms, which have also been applied to the PRIMOSE12 dataset. Since most methods do not give open source implementation code, we directly used the test results given by these algorithms except the FCN and U-Net. The comparison results are listed in Table 2. Due to the inconsistent evaluation mechanisms adopted by different approaches, we utilized the most commonly used DSC in the field of medical image segmentation to compare the segmentation effects. The comparison results indicate that the value of DSC of our algorithm on the PROMISE12 dataset is significantly higher than other popular algorithms, which means that the predicted segmentation result of our algorithm is closest to the real segmentation mask.
# Gamma-ray Bursts: 50 Years and Counting!
## 4 The BeppoSAX Era (1996-2000/2004)
In particular, the supranova model posited that GRBs are emitted when a supramassive neutron star (a neutron star with a mass greater than the Tolman-Oppenheimer-Volkoff limit, supported by fast rotation) loses so much angular momentum that centrifugal support against self-gravity becomes impossible, and the star implodes into a black hole [56; 57]. The interaction of the material ejected from the collapse with an almost baryon-clean environment would produce the GRB signals. A more radical proposal was put forward by [PERSON] and [PERSON] with the Cannonball model, an alternative to the paradigm of the forward-and-backward-shocks model, where each GRB pulse is given by the expulsion of \"cannonballs\" of relativistic plasmas from the supernova shell rather than by the interactions of the fronts in a collimated jet [58]. This era can be summarized as follows:  
* In 1996, the BeppoSAX mission was launched with large-FoV gamma-ray instruments and X-ray instruments with great sensitivity, which allowed GRB signals to be localized with unprecedented precision. * The discovery of afterglows led to the first multiwavelength observations, the measurement of redshifts (and therefore distances and energies), and the identification of host galaxies. * The Amati, Yonetoku, and Ghirlanda correlations for long GRBs were discovered. * In this era were the first indications of the GRB-supernova association. * Since the X-ray telescopes and WFCs were not aligned, BeppoSAX had to be slewed after locating each GRB. The procedure had to be carried out from the ground, leading to slow replacements. For this reason, in this era were predominantly (almost exclusively) observed long GRBs.
# Asymmetric Protocols for Mode Pairing Quantum Key Distribution with Finite-Key Analysis
## Appendix B Simulation Formulas for Asymmetric MP-QKD  
The average response probability during each round \(p\) can be formulated as  
\[p=\sum_{k_{a}^{i}k_{b}^{i}}p_{k_{a}^{i}}p_{k_{a}^{i}}q_{k_{b}^{i}k_{b}^{i}}^{ \mathrm{Z}},\] (A2)  
where \(q_{k_{a}^{i}k_{b}^{i}}=2y[I_{0}(  
\[t^{Z}_{(k_{a},k_{b}),0}=\frac{Nr_{p}}{p^{2}}\sum_{(k_{a},k_{b}),k_{b}^{ \prime}=-k_{b}^{\prime}-0}p_{k_{b}}p_{k_{b}^{\prime}}p_{k_{b}^{\prime}}q_{k_{b}^ {\prime},k_{b}^{\prime}}q_{k_{b}^{\prime},k_{b}^{\prime}}+\frac{Nr_{p}}{p^{2}} \sum_{(k_{a},k_{b}),k_{a}^{\prime}=k_{b}^{\prime}=0}p_{k_{b}^{\prime}}p_{k_{b} ^{\prime}}p_{k_{b}^{\prime}}q_{k_{b}^{\prime},k_{b}^{\prime}}q_{k_{b}^{\prime}, k_{b}^{\prime}}q_{k_{b}^{\prime},k_{b}^{\prime}}. \tag{34}\]  
For the X-pair, where \((k_{a},k_{b})\in\{(2\mu_{a},o_{b})\,,\,(o_{a},2\mu_{b}),\,(2v_{a},o_{b}),\,(o_{a },2v_{b})\}\), the count of effective detections can be expressed as  
\[n^{X}_{(k_{a},k_{b})}=\frac{Nr_{p}}{p^{2}}\sum_{(k_{a},k_{b})}p_{k_{b}^{\prime }}p_{k_{b}^{\prime}}p_{k_{b}^{\prime}}p_{k_{b}^{\prime}}q_{k_{b}^{\prime},k_{ b}^{\prime}}q_{k_{b}^{\prime},k_{b}^{\prime}}q_{k_{b}^{\prime},k_{b}^{\prime}}. \tag{35}\]  
The number of the error effective detections in this part can be represented as \(t^{X}_{(k_{a},k_{b}),0}=\frac{n^{X}_{(k_{a},k_{b})}}{2}\).  
For \((k_{a},k_{b})\in\{(2\mu_{a},2\mu_{b})\,,\,(2\mu_{a},2v_{b})\,,\,(2\
u_{a},2\mu _{b}),\,(2\
u_{a},2\
u_{b})\}\) in the X-pair, the count of effective detections can be expressed as  
\[n^{X}_{(k_{a},k_{b})}\approx\frac{Nr_{p}}{p^{2}}\frac{2\Delta}{\pi}p_{k_{a}^{ \prime}}p_{k_{b}^{\prime}}p_{k_{b}^{\prime}}p_{k_{b}^{\prime}}\Big{(}4y^{4}-8 y^{3}I_{0}(x)+2y^{2}\Big{(}I_{0}\Big{(}x\sqrt{2-2\cos\Delta}\Big{)}+I_{0} \Big{(}x\sqrt{2+2\cos\Delta}\Big{)}\Big{)}\Big{)}, \tag{36}\]  
where \(I_{0}(\cdot)\) is the modified Bessel function of the first kind. The error-effective detections corresponding to this section can be expressed as  
\[t^{X}_{(k_{a},k_{b}),0}\approx\frac{Nr_{p}}{p^{2}}\frac{2\Delta}{\pi}p_{k_{a}^ {\prime}}p_{k_{b}^{\prime}}p_{k_{b}^{\prime}}p_{k_{b}^{\prime}}\Big{(}2y^{4}- 4y^{3}I_{0}(x)+2y^{2}I_{0}\Big{(}x\sqrt{2-2\cos\Delta}\Big{)}\Big{)}. \tag{37}\]  
Considering the misalignment error, the error-effective detections of the \(Z\)-pair and \(X\)-pair can be rewritten as  
\[t^{Z}_{(k_{a},k_{b})} =\Big{(}1-e_{d}^{Z}\Big{)}t^{Z}_{(k_{a},k_{b}),0}+e_{d}^{Z}\Big{(} n^{Z}_{(k_{a},k_{b})}-t^{Z}_{(k_{a},k_{b}),0}\Big{)}, \tag{38}\] \[t^{X}_{(k_{a},k_{b})} =\Big{(}1-e_{d}^{X}\Big{)}t^{X}_{(k_{a},k_{b}),0}+e_{d}^{X}\Big{(} n^{X}_{(k_{a},k_{b})}-t^{X}_{(k_{a},k_{b}),0}\Big{)},\]  
where \(e_{d}^{Z}\) and \(e_{d}^{X}\) is the misalignment-error of the \(Z\)-pair and \(X\)-pair, respectively.
## 2 Materials and Methods  
The materials used in the present study are listed in Table 1.  
Five CAD-CAM feldspathic ceramic (CEREC Blocs; Dentgly Sirona, PA, USA), A2 shade, were used for the present study (10 \(\times\) 12 mm). The ceramic blocks were cut into slices with thicknesses of 0.5 and 0.8 mm (ninety slices for each thickness) with a water-cooled diamond saw (Isomet 1000; Buehler, Lake Bluff, IL, USA) at a speed of 450 rpm. To ensure a uniform surface roughness, both sides of the samples were polished with a sequence of 400-, 600-, and 1200-grit SiC paper for 15 s at a constant speed of 100 rpm using a grinding machine (LabolPol-4; Struers, Madrid, Spain) under water cooling. To ensure a uniform thickness of the samples (\(\pm\)0.05 mm), we employed a precision digital caliper (Heavyware Tools) at three different points. The samples were then randomly assigned to the following experimental groups according to the resin-based luting agent and its shade (n = 10): two resin cements Variolink Esthetic in Light, Neutral, and Warm shades (Ivoclar Vivadent, Schaan, Liechtenstein) and RelyX Veneer, in B0.5/white, Translucent and A3 Opaque/yellow opaque shades (3M Oral Care, Seefeld, Germany); a flowable composite resin (G-aenial Flo in A2 and A3 shades (GC Europe, Leuven, Belgium); and a preheated composite resin (Filtek Supreme XTE, A3 Body shade (3M Oral Care, St. Paul, MN, USA)). The latter composite resin was used for the control group. Composite resin discs (Filtek Supreme XTE (3M Oral Care, St. Paul, MN, USA,)) (n = 180) with a thickness of 1 mm (\(\pm\)0.05 mm) were used as substrate. The composite discs were prepared using a resin former (sample ref. 7015 Smile Line Porcelain, St-Imier, Switzerland) and light-cured with a LED unit (Elipar S10; 3M Oral Care, Seefeld, Germany) for 40 s at high intensity (1000 mW/cm\({}^{2}\)) according to the manufacturer's instructions. Resin discs were also calibrated using a digital caliper (Heavyware Tools). The ceramic samples were randomly paired with the resin disks to make 18 groups with 10 samples per group. Surface treatment of the ceramic was carried out according to the manufacturer's instructions. Firstly, application of 9.6% hydrofluoric acid (PulpDent Corporation, Watertown, MA, USA) for 90 s, then rinsed for 60 s and followed by application of 37% phosphoric acid (R&S Supraetch; R&S, Paris, France) making vigorous circular movements for 60 s and using a microbrush. The ceramic samples were washed with distilled water, followed by an ultrasonic bath for 4 min. The surfaces were dried with 96% alcohol, and a silane coupling agent (Ultradent, South Jordan, UT, USA) was applied for 20 s and evaporated for 60 s. Finally, an adhesive system (Optibond(tm) FL; Kerr Dental, Orange, CA, USA) was applied without curing. Each ceramic sample was cemented maintaining a constant force of 50 Newtons for 60 s [32] to standardize the luting agent thickness. In the control group, the composite resin used for luting (Filtek Supreme XTE, A3 Body shade) was previously heated in a resin oven (55 \({}^{\circ}\)C) (Micerium, Avegno, Italy) before its application.
# Multi-Point Deformation Prediction Model for Concrete Dams Based on Spatial Feature Vector
## 4 Conclusions  
In this paper, combined with a convolutional neural network, a dam deformation prediction model (2D-1D-CNN) based on spatial feature vectors is proposed. After an experimental analysis of the measured data of a concrete dam, the following conclusions are obtained:  
1. The multi-point model based on the fusion of spatial feature vectors can effectively extract the spatial connection between the measuring points and, compared with the other models, the model proposed in this paper shows a great improvement in the prediction accuracy.In addition, the experimental results indicated that the prediction accuracy of the deep-learning-based prediction model is higher than that of the machine learning model and the statistical model in most cases.
2. The case study shows that the proposed 2D-1D-CNN model is able to accurately predict the deformation of concrete dams so that it is effective and feasible.
3. In this paper, 2D-CNN and 1D-CNN are fused into the same network model, which, on the one hand, improves the fitting ability of the network for dam deformation sequences, and, on the other hand, it also increases the hyperparameters that need to be adjusted to a certain extent. Since this paper only adopts the artificial coarse tuning method, the optimization algorithm can be combined in future studies to further improve the prediction performance of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Monitoring Points**} & \multicolumn{1}{c}{**Statistical Indexes**} & \multicolumn{1}{c}{**2D-1D-CNN**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **SVM (Space** \\ **Coordinate)** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **SVM (Spatial** \\ **Feature Vector)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{EX3} & RMSE/mm & 0.121 & 0.5472 & 0.238 \\  & MAE/mm & 0.0971 & 0.4782 & 0.1406 \\  & MAPE & 11.75\% & 46.82\% & 10.52\% \\  & \(R^{2}\) & 0.9658 & 0.3010 & 0.8678 \\ ine & RMSE/mm & 0.1635 & 0.4263 & 0.364 \\ EX5 & MAE/mm & 0.1241 & 0.3245 & 0.1946 \\  & MAPE & 6.47\% & 21.04\% & 7.45\% \\  & \(R^{2}\) & 0.9694 & 0.7923 & 0.8486 \\ ine & RMSE/mm & 0.1296 & 0.5349 & 0.2949 \\ EX7 & MAE/mm & 0.1045 & 0.4501 & 0.1671 \\  & MAPE & 4.95\% & 17.27\% & 6.42\% \\  & \(R^{2}\) & 0.9772 & 0.6110 & 0.8817 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of SVM prediction statistical indexes with different input factors.  
Author Contributions: Conceptualization, Z.C.; methodology, Z.C.; writing--original draft preparation, Z.C.; writing--review and editing, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant numbers 42171437).  
Not applicable.  
The authors declare no conflict of interest.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 3 Simultaneous Target Localization and Unmanned Aerial Vehicle (UAV) Trajectory Optimization
### Simultaneous Target Localization and UAV Trajectory Optimization  
The attitude angles of LoS are measured with random errors, and thus there is unavoidable error in the localization result. However, the localization performance to the moving target is not only influenced by the measurement noises, but also significantly influenced by the trajectory of the UAV, which determines the sensor-target geometry at each time instant. Therefore, to best localize the target position, an optimized trajectory should be generated automatically according to the desired Sensor-target geometry. As the true position of the target is unknown, the localization uncertainty minimization objective of the moving target relies closely on the estimate of the target. In other words, the position estimation for the target and trajectory optimization for the UAV are closely coupled, which makes the simultaneous target localization and UAV trajectory optimization problem complicated, as shown in Figure 4a.  
For the discrete measurement case, one solution to decoupling the problem is to optimize the next waypoint of the UAV immediately each time the estimation task of the target position is completed, as shown in Figure 4b. The existing methods use the current estimate of the target position to optimize the next waypoint of the observer. However, when the measurement time interval is large, the current estimate of the target position deviates largely from the actual position at the next time instant. This will make the next optimized waypoint of the UAV invalid. To overcome this problem, the predicted estimate of the target position was utilized in this paper. This can be expressed as follows. In the estimation phase of time step \(k\), the target position is estimated using the optimized waypoint of UAV at time step \(k-1\), and then, in the trajectory optimization phase, the predicted position of the target given the obtained estimate is exploited to optimize the waypoint of UAV at time step \(k+1\). In this way, the target localization and UAV trajectory optimization are implemented concurrently, leading to the localization performance being maximized and the UAV trajectory is also generated automatically.  
To facilitate the simultaneous target localization and UAV trajectory optimization, the EIF target state estimation method was exploited, which can express the predicted FIM information of the target state explicitly and iteratively. Using the predicted FIM obtained from the target motion model and the current estimate, the UAV waypoint optimization objective can be established. The sequel will first introduce the EIF method for moving target localization, and then present the UAV trajectory optimization algorithm.
# Well-Distributed Feature Extraction for Image Registration Using Histogram Matching
## 2 Datasets  
In this work, four different datasets of multi-spectral satellite images are used for experiments and comparative analysis. A multi-spectral satellite image consists of a panchromatic and several spectral bands images. In each dataset, the size of panchromatic image is \(15000\times 16000\), while multi-spectral images are of size \(4000\times 4000\). The multi-spectral images contain four bands: blue, green, red, and NIR. The spatial resolution of the panchromatic image is 1 m and the multispectral image is 4 m. Geometric distortion is included in all images. The sample images are shown in Figure 1. The responses in terms of wavelengths for panchromatic and spectral band images are shown in Figure 2. The shortest band is the visible band, ranging from 0.4 \(\upmu\)m to 0.7 \(\upmu\)m, called red-green-blue (RGB) region. The infrared band with wavelength from 0.7 \(\upmu\)m to 10 \(\upmu\)m is classified as near infrared (NIR).  
Figure 1: Study area of reference images: (**a**) set 1; (**b**) set 2.
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 1 Introduction
In this paper, a high-accuracy surface reconstruction system is established with telecentric lenses, a line laser generator and 1D displacement platform, which can be mainly applied in a circuit board test, surface roughness measurement, flatness measurement and other industrial inspections. The main contributions of the work are listed as follows. (1) Theoretical analysis is proposed to analyze the systematic error for the triangulation with a line laser, and the result of the analysis guides the system parameters selected for certain applications. (2) A gray scale-weighted centroid algorithm is proposed to realize the sub-pixel location of the laser line to obtain the center line for the laser line in the practical image. (3) A telecentric intersection process is presented only with the knowledge of the normal direction of the laser plane, and the intersection results are transferred into a global coordinate system with the motor direction of the displacement platform. The principal part of the paper is organized as noted below. At first, the design of the system is introduced in Section 2, in which the systematic error based on the design is analyzed. Next, a complete calibration procedure is presented for the telecentric surface metrology system in Section 3, including the single camera calibration and the calibration of system structure parameters. Then, this is followed by the measurement procedure in Section 4, in which a weighted centroid algorithm based on gray scale is used to achieve laser stripe extraction, and the telecentric intersection process is presented. At last, experiments are presented to indicate the performance of the system in Section 5, and the evaluation of uncertainty is analyzed. Some concrete applications of surface detecting are realized in the experiments part, including defect detecting and circuit board tests.
# Potential Role of Vitamins A, B, C, D and E in TB Treatment and Prevention: A Narrative Review
## 2 Results
### Vitamin B Complex
#### 2.2.6 Vitamin B12 (Cobalamin)  
_M. tuberculosis_ is one of a few number of microorganisms possessing the capacity for de novo biosynthesis of vitamin B12 (VB12). VB12 and related corrinoids can also be scavenged by the bacillus using an ATP-binding cassette [37]. While the modest numbers of tuberculosis bacteria are unlikely to have an effect on overall VB12 levels in infected people, vitamin B12 generated or ingested by mycobacterial pathogens may have a local effect on the metabolic environment of human granulomatous lesions. As highlighted by [PERSON] et al. [38], the cobF gene (encoding a precorrin-6a-synthase required for B12 synthesis) was lost during evolution from the M. canettii-like ancestor. Despite these deletions, B12 biosynthesis was not entirely depleted [39]. The MTB regulates its core metabolic functions according to B12 availability, through the acquisition of B12 via endogenous or through uptake from the environment. A role of vitamin B12 in pathogenesis may be implicated, but this finding is still poorly understood [37]. In a previous study by [PERSON] et al., the incidence of tuberculosis in vegetarians was 133 per 1000, while it was 48 per 1000 in patients on varied diets. There is a second intriguing hypothesis that may be relevant: in cobalamin deficiency there is a failure to convert methylmalonic acid to succiniacid as cobalamin is a coenzyme in the enzyme methylmalonyl CoA-mutase. One of the components of the cell wall of _M. tuberculosis_ is mycocerosic acid, and methylmalonic acid is the obligatory substrate for its synthesis. The increased concentrations of methylmalonic acid present in vitamin B12-deficient vegetarians could provide a particularly favorable climate for the multiplication of _M. tuberculosis_.  
Vitamin B12 concentration in the blood could be a biomarker in the identification of active tuberculosis with and without HIV infection, according to a recent study. Detecting the concentration level of these micronutrients in HIV-uninfected patients could be used as a TB treatment response monitoring biomarker [40].  
In addiction, authors showed potent antim mycobacterial activity with a minimum inhibitory concentration (MIC) of 6 \(\mathrm{\SIUnitSymbolMicro g}\)/mL [33]. Furthermore, Vitamin B12 insufficiency has also been linked to neuropathy in patients with ileal TB [41].