esa-sceva/satcom-chunk-collection huggingface dataset:
# Does Pressurized Carbon Dioxide Lavage Improve Bone Cleaning in Cemented Arthroplasty?
###### Abstract
A reliable method for this crucial cleaning procedure of cancellous bone is using saline high-pressure pulsatile lavage and subsequent drying of bone bed with abdominal cloth and suction. However, after this procedure liquid and fatty material often remains and may prevent cement from penetrating in the desired depth into the cancellous bone. Many studies have shown, that cleaning cancellous bone using pulsatile lavage is more efficient than manual rinse cleaning by bladder syringe alone [21; 22; 23; 24; 25]. For the application of pulsatile lavage, different rinsing pressures are recommended in the literature depending on the area of application or tissue type, since pulsatile lavage is used to clean contaminated wounds as well. When comparing pressures of these procedures, it is important to distinguish whether the pressure specifications are \"output pressure\" or \"impact pressure\". The US Department of Health and Human Services recommends an impact pressure between 0.03 N/mm\({}^{2}\) (4 PSI) and 0.10 N/mm\({}^{2}\) (15 PSI) for cleaning wounds [26]. This recommendation is based on a number of studies that have shown a pressure of less than 0.03 N/mm\({}^{2}\) being inefficient in removing surface pathogens. Also, pressure of more than 0.10 N/mm\({}^{2}\) can potentially facilitate bacterial contamination in deeper tissue layers or cause traumatic soft tissue damage. Regarding the topic of this study, much higher pressures for cleaning cancellous bone have been reported in the literature. They reach from 0.48 N/mm\({}^{2}\) (70 PSI) to 0.59 N/mm\({}^{2}\) (85 PSI) [27; 28]. A novel type of high-pressure carbon dioxide lavage, which is recommended for usage after standard saline lavage, is designed to advance cleaning and simultaneous drying of the bone [29]. A cadaveric study demonstrated that a combination of pressurized carbon dioxide and common pulsatile saline lavage produced a significantly deeper bone cement penetration than saline lavage alone [30]. Another study from 2019 showed that by using sterile compressed CO\({}_{2}\) in addition to the established pulsatile lavage, a significantly deeper cement penetration in three of seven defined zones (Knee Society Radiographic Evaluation System) could be achieved in 303 total knee endoprosthesis [31]. In addition bone cement interface was stronger in an experimental study using human radii [32]. However, cases of embolic events using carbon dioxide lavage in intramedullary cleaning during cemented hip arthroplasty have been reported [33; 34]. Aim of this prospective laboratory study was to compare impact pressure and cleaning effects of pulsatile saline lavage to novel carbon dioxide lavage in a standardized carbon foam setup and to determine whether or not additional use of carbon dioxide lavage has any impact on cleaning volume or cleaning depth in cancellous bone.
# Optimization of Valorization of Chicken MDCM to Produce Soluble Protein and Collagen Peptides
## 3 Results and Discussion
### MDCM Protein Hydrolysate Production
(20) also found that the recovery yield was higher when the time of hydrolyses was increased from 1 h to 3 h; however, little improvement in recovery was observed beyond the 3 h time. There were no considerable differences in protein concentrations between the hydrolysate fractions prepared with ERM1 or ERM 1 + 2, and ERM 1 alone was effective. The fat concentrations showed more differences between treatments that might be due to an incomplete separation in the 4 h ERM 1 + 2 treatments rather than analytical error. The protein yield depends on the extraction method, raw material type, and the deboning machine [21]. Studies have shown that, depending on the extraction method, 6.7-17.6% of total protein from chicken bone may be recovered. [PERSON] and [PERSON] [21] extracted chicken bone with a water solution of NaCl with the addition of NaNO\({}_{2}\) and ascorbate, and they obtained extracts containing 3.4% of protein, which constituted 18% of total chicken bone protein. [PERSON] [22] reported a yield of 11-17% from total protein content in animal bone, while [PERSON] and [PERSON] [23] reported that more than 18% of the protein in chicken bone was alkali-extractable. [PERSON] et al. [14] observed that 80.25% of the protein in the chicken bone could be extracted using the hot-pressure extraction method. Papain was used for enzymatic hydrolysis for its highest protein extraction rate (46%) of chicken bone protein compared to other enzymes (trypsin 44%, protamex 40%, alkaline protease 35%, neutral protease 32%, pepsin 28%, flavored proteinase 19%). Additionally, ultrasonic treatment was shown to gradually increase the protein yield as the processing temperature increased, reaching the highest yield (56.49% \(\pm\) 0.67%) at 45 \({}^{\circ}\)C [24]. In a recent study, [PERSON] et al. [25] investigated the impact of three commercial proteases, Alcalase 2.4 L, Corolase 2 TS, and Flavourzyme, on the quality of chicken and turkey carcasses and MDCM. The results showed that the choice of protease and industrially relevant variations in poultry raw material composition both had a major effect on product composition and protein yield, nutrient and amino-acid composition, degree of hydrolysis, size distribution of peptides, and rheological properties. Alcalase was the most efficient protease, while Flavourzyme hydrolysis resulted in substantially lower nitrogen yields [25]. According to the present study's findings, around 87% of the proteins from the chicken MDCM were extracted with the Ermitase enzymes, indicating their superiority to other enzymes.
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 3 System Model and Problem Formulation
### Network Model  
We consider a 6G V2X Open RAN computing infrastructure consisting of sets of vehicles and RSUs that provides new radio communication capabilities to the vehicles. RSUs are densely deployed closer to vehicles and provide computing and communication  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(P_{p}\) & Transmission power of vehicle \(\
u\) \\ \(d_{n,\
u}\) & Distance between vehicle \(\
u\) and RSU \(n\) \\ \(\alpha\) & Attenuation factor: calculated from the COST 231–Hata Model [40] \\ \(N_{0}\) & Power spectral density of white noise source \\ \(I_{\text{tot}}\) & Aggregated uplink ICI representing interference power level of RSUs \\ \(I_{i}\) & Mean value of uplink ICI for RSU \(i\) \\ \(C\) & RSU coverage (in kilometers) \\ \(\kappa\) & Distance between the vehicle and the interfering RSU (in kilometers) \\ \(\rho\) & Distance from transmitter to receiver (in kilometers) \\ \(\theta\) & Vehicle’s angle to direct line connecting target RSU to interfering RSU \\ \hline \(N\) & Set of RSUs \\ \(V\) & Set of vehicles \\ \(M_{n}\) & Available uplink RBs per time slot for RSU \(n\) \\ \(F_{n}\) & Maximum available computation capacity of RSU \(n\) \\ \(\mathcal{L}_{\text{p}}\) & Maximum allowed latency for vehicle \(\
u\) \\ \(R_{n,\
u}\) & Required RBs per time slot to send data \\ \(H_{n,\
u}\) & Threshold value for \(\Gamma_{n,\
u}\) \\ \(U_{n,\
u}\) & Required uplink data rate of vehicle \(\
u\) \\ \(\mathcal{U}_{n,\
u}\) & Link capacity between vehicle \(\
u\) and RSU \(n\) \\ \(\psi_{n}^{\
u}\) & Energy coefficient associated with chip architecture of RSU server \\ \(x_{n}\) & Decision variable to turn on/off RSU \(n\) \\ \(y_{n}^{n}\) & Decision variable indicating whether vehicle \(\
u\) is associated with RSU \(n\) or not \\ \(z_{\text{p}}\) & Decision variable indicating whether vehicle \(\
u\) is not allocated \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mathematical notations used throughout this paper.  
capabilities to end users. In our model of the system under study, as shown in Figure 2, vehicles need to upload their tasks to the RSUs for processing. In particular, we investigate a scenario consisting of a set of RSUs (\(n=1,2,\ldots,N\)) and a set of vehicles (\(v=1,2,\ldots,V\)). Each RSU \(n\) has a number of RBs available per time slot, indicated by \(M_{n}\). Each vehicle \(v\), if it is associated with RSU \(n\), will require several RBs per time interval to send the sensing data, indicated by \(R_{n,v}\). The required number of RBs depends on the SINR values and the uplink data rates.
## 3 Signal Analysis
### Conventional Time-Frequency Analysis
That is to say, it is difficult to identify series arc faults from the load states. Generally speaking, arc fault high frequency signals are random, nonlinear and non-Gaussian. Since bispectrum is a powerful technique in processing random signals, it will be introduced to analyze arc fault signals in the following section.
# Study on the Absorbing Properties of V-Doped MoS2
## 2 Experiment  
The experimental preparation process is depicted in Figure 1. High-purity (99.999%) starting materials were employed, consisting of ammonium metavanadate (NH\({}_{4}\)VO\({}_{3}\); 0.1755 g), sodium molybdate dihydrate (Na\({}_{2}\)MoO\({}_{4}\).2H\({}_{2}\)O; 0.3629 g), and thioacetamide (C\({}_{2}\)H\({}_{5}\)NS; 2.2539 g). The powder was dissolved in 30 mL of deionized water, followed by ultrasonic treatment for 1 h. The solution was transferred to a 100 mL hydrothermal reactor and placed it in an oven to heat at 220 \({}^{\circ}\)C for 24 h. Since C\({}_{2}\)H\({}_{5}\)NS decomposes under hydrothermal conditions to produce H\({}_{2}\)S, this can effectively inhibit the formation of oxidation products. After cooling to room temperature, it was washed with deionized water and ethanol and then centrifuged. The filtered powder was then dried in a vacuum oven at 60 \({}^{\circ}\)C for 12 h. The as-synthesized V-doped MoS\({}_{2}\) sample was designated as VMS. The VMS sample was placed in a crucible and transferred to a vacuum tube furnace for annealing at 600\({}^{\circ}\)C for 2 h (heating rate of 5\({}^{\circ}\)C/min). Prior to annealing, Ar was introduced into the vacuum tube at a flow rate of 300 mL/min for one hour to remove oxygen. Continuous argon flow was maintained during both the heating and cooling stages to ensure an oxygen-free environment. The annealed sample was labeled as VMSA. The EM wave absorption properties of both VMS and VMSA were systematically characterized, with corresponding mechanisms elucidated.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 2 Probabilistic Forecasting Models
### Examples of Non-Parametric Probabilistic Forecasting Models  
A wide range of probabilistic forecasting models fall into the non-parametric category. The non-parametric probabilistic forecasting models do not use the existing known PDFs. Instead, they build predictive distributions of the response variable based on different factors or construct quantiles/ensembles considering historical data. Since they do not limit probability distributions to specifically known distributions, the non-parametric methodsare more flexible. However, compared to parametric models, they often need larger datasets to be able to estimate the response variable distributions [13].  
Random forecast (RF) and quantile regression forecast (QRF) models are two examples. RF models aim to forecast the conditional mean of the response variable given the input data and without associating any known distribution functions with the variables [14]. Similarly, QRF models predict quantiles of the response variable regardless of any known parametric distributions. To obtain the quantiles, QRF minimizes the sum of errors of the mean values, considering some asymmetric weights [15; 16]. Kernel density estimation (KDE) is another non-parametric method estimating the probabilistic density of the response variables using kernel functions. The kernel density function can be mathematically written as follows [17; 18]:  
\[F_{y}(h)=\frac{1}{Nh}\sum_{i-1}^{n}K(\frac{y-y_{i}}{h}) \tag{12}\]  
where \(K\) indicates the kernel function, \(y_{i}\) are the sample points, \(N\) refers to the total number of sample points and \(h\) is the bandwidth referring to the smoothing parameter. In the KDE method, a proper kernel function should be selected according to the type of response variables.  
Another way of obtaining ensemble forecasts is considering various initial states or different boundary conditions for the response variable, such as the lower upper bound estimate (LUBE) [19]. This method provides forecasters with prediction intervals (PI). LUBE employs artificial intelligence tools to build the PIs. In addition to LUBE, the bootstrap method can fall into this category, since it resamples the data and constructs a distribution of residuals accordingly [20]. Short range ensemble forecast (SREF), as another example of non-parametric forecasts, takes into account the uncertainties of initial states [21].  
A set of different types of machine learning-based and numerical forecasts can build a non-parametric ensemble forecast. This method uses \(N\) different data-driven and numerical forecasting models to predict the response variable. These forecasts are then considered as quantiles. The corresponding distribution \(F(y)\) can be mathematically modeled as follows [2]:  
\[F(y)=\frac{1}{N}\sum_{i=1}^{N}l(y-y_{i,t}) \tag{13}\]  
where \(l(y-y_{i,t})\) denotes a Heaviside step function that shifts \(y\) to the \(i\)th ensemble member.
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### DNA Nanomechanics
#### 5.3.3 Biophysical Studies  
DNA nanomechanics can also be used to study biophysical processes. Since the material properties of DNA are well-understood, their physical deformation can be easily converted into values of force and energy. For instance, a hinged DNA nanostructure allowed [PERSON] et al. to measure the attractive forces between two nucleosomes [175]. A hinged DNA nanostructure was designed to have a preferred open configuration. A pair of nucleosomes were then placed on the top and bottom platforms, and their attractive force would work against the tension of the hinge, reducing its angle. This new angle could be observed under transmission force microscopy or by a transduced FRET signal, and the attractive force between the nucleosomes could be calculated from the known values required to produce the measured displacement of the DNA nanostructure. [PERSON] et al. designed a DNA origami nanostructure resembling helicopter blades which was used to characterize genome-processing reactions that generate DNA rotation [171] (Figure 8A). A fluorophore attached to the end of one blade could be optically tracked over time to reconstruct the sequence of biochemical events. [PERSON] et al. engineered a DNA tetrahedron into a nanoscale pump, showing that dynamic DNA nanostructures could induce physical changes into its environment, rather than only measure them. In reaction to pH changes, an i-motif sequence in one edge of the tetrahedron causes the structure to collapse, creating a downwards piston pumping motion, and reversing the pH conditions could straighten the edge again to push the piston upwards. It was shown that this DNA nanodevice could pump water and ferricynide [176].  
Constructions of DNA origami nanosprings, which are DNA origami bundles with consistent negative superhelical strain causing the bundle to coil, have been shown to measure and exert forces. [PERSON] et al. used i-motifs to implement pH-dependent coiling, which could then be modulated to exert piconewton forces. Integrin-binding peptide domain RGD (arginyl-glygly-aspartic acid acid) regularly placed along the nanospring could transduce pH control into clustering and declustering of cell surface integrin, which in turn controls membrane deformations that occur for cell motion [177]. [PERSON] et al. used a DNA nanospring to instead measure the mechanical forces of myosin IV, which can function as a vesicle transporter along actin filament pathways. A DNA nanospring was attached between anchored and active myosin IV and labelled with fluorospheres to measure any displacement. The rigidity of DNA nanostructures ensured that determinedmechanical properties of the DNA nanospring, such as its spring constant, would be consistent, which would be sufficient to deduce the forces applied as evaluated from the observed displacement of the nanospring [178].
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
## 3 Results and Discussion
### Analysis of Stress-Strain Characteristics in Rare Earth Mine Slopes  
Once the seepage field of the slope is determined with the Seep/W module, the results are transferred to the Sigma/W module to simulate the stress and displacement fields during the leaching process. According to engineering practice and research, the strain magnitude in the slope is key to analyzing slope stability. The position of maximum shear strain is generally where the slope is most susceptible to failure and where initial deformations appear.  
Figures 7 and 8 display the maximum shear strain distribution during leaching in the slope. It is evident from Figures 7 and 8 that the regions with the highest shear strain occur at the bottom of the slope, the middle to upper parts of the slope, the fully weathered granite range, and the interfaces between different layers. As the leaching progresses, these regions see a continuous increase in shear strain, eventually forming continuous shear strain zones that are susceptible to landslides. Consequently, the targeted landslide prevention measures are essential for these areas in in situ leaching mining operations.  
The shear strength of soil can be effectively assessed through effective stress. This study simulates the changes in the effective stress field distribution within the slope's soil mass. The analysis in this section is centered on the 15 th day, characterized by the most significant deformation. The maximum and minimum effective principal stresses on the slope on the 15 th day of leaching are shown in Figure 9a,b. From the figures, it is evident that after leaching in the rare earth mine, reductions in effective stress on the slope primarily concentrate at the bottom of the slope, the middle to upper parts of the slope, and at the interface between the leaching agent injection wells and the ore-hosting horizon of deeply weathered granite layer, extends to the topsoil and the fully weathered granite layer, indicating that the influence of leaching on effective stress is predominantly localized in these areas. Overall, the results of the effective stress field demonstrate the overall stability of slopes in this study.  
Figure 6: Comparison of the variation in safety factor with leaching time using three methods.  
Figure 7: Distribution of maximum shear strain at different times during leaching (The contour map represents the variation of maximum shear strain).  
Figure 8: Distribution of maximum shear strain at different times during the push-water operation (The contour map represents the variation of maximum shear strain).
###### Abstract  
Abundant infrared remote sensing images and advanced information processing technologies are used to predict earthquakes. However, most studies only use single long-wave infrared data or its products, and the accuracy of prediction is not high enough. To solve this problem, this paper proposes a statistical method based on connected domain recognition to analyze multi-channel anomalies. We extract pre-seismic anomalies from multi-channel infrared remote sensing images using the relative power spectrum, then calculate positive predictive values, true positive rates and probability gains in different channels. The results show that the probability gain of the single-channel prediction method is extremely low. The positive predictive value of four-channel anomalies is 41.94%, which is higher than that of single-channel anomalies with the same distance threshold of 200 km. The probability gain of the multi-channel method is 2.38, while that of the single-channel method using the data of any channel is no more than 1.26. This study shows the advantages of the multi-channel method to predict earthquakes and indicates that it is feasible to use multi-channel infrared remote sensing images to improve the accuracy of earthquake prediction.  
Keywords:earthquake prediction; infrared remote sensing; multi-channel; pre-seismic anomaly; relative power spectrum; connected region +
Footnote †: journal: Applied Sciences
## 3 Test Results
### Crack Development and Quality Degradation Results  
The durability of MOCRC was analysed using the relative dynamic modulus of elasticity evaluation parameters \(\omega_{1}\) and relative mass evaluation parameters \(\omega_{2}\)[22]. The formula for \(\omega_{1}\) is  
\[\omega_{1}=\frac{E_{r}-0.6}{0.4} \tag{8}\]  
where \(E_{r}=\frac{E_{t}}{E_{0}}=\frac{V_{0}^{2}}{V_{0}^{2}}\), with \(E_{0}\) being initial dynamic elastic modulus, \(E_{t}\) being the dynamic elastic modulus at time \(t\), \(V_{0}\) being the initial wave velocity (in km/s), and \(V_{t}\) being the wave velocity (km/s) at time \(t\). When \(\omega_{1}\) < 0, the specimen reaches the failure state; when \(0\leq\omega_{1}\) < 1, the specimen undergoes damage but does not reach the failure state; and when \(\omega_{1}\geq 1\), the specimen does not reach the failure state and is in good condition. The formula for \(\omega_{2}\) is  
\[\omega_{2}=\frac{M_{r}-0.95}{0.05} \tag{9}\]  
where \(M_{r}=\frac{M_{t}}{M_{0}}\), with \(M_{0}\) being the initial mass and \(M_{t}\) being the mass at time \(t\). When \(\omega_{2}\) < 0, the specimen reaches a failure state; when \(0\leq\omega_{2}\) < 1, the specimen undergoes damage but does not reach a failure state; and when \(\omega_{2}\geq 1\), the specimen does not reach a failure state and is in good condition.  
As shown in Figure 19a, at 2610 days, the relative dynamic modulus of the elasticity evaluation parameter \(\omega_{1}\) for group A specimens is 0.942, and that of group B specimens is 0.946, indicating that there is crack development in the specimens. From Figure 19b, the masses of groups A and B began to decrease after 90 days. At 2610 days, \(\omega_{2}\) for group A was 0.959, and that for group B was 0.962. However, on the whole, the quality loss was not substantial, being 0.2% and 0.3%, respectively. The development of cracks and quality loss interact with each other, and they promote each other. At the same time, the corrosion of steel bars will also cause the development of cracks. However, the mass loss of concrete is due to the continuous hydrolysis of 5 Mg(OH)\({}_{2}\cdot\)MgCl\({}_{2}\cdot\)8H\({}_{2}\)O and 3 Mg(OH)\({}_{2}\cdot\)MgCl\({}_{2}\cdot\)8H\({}_{2}\)O, which produce Mg(OH)\({}_{2}\) and MgCl\({}_{2}\), and continuously decreases the concrete's compactness. The specific chemical path is as follows [22]:  
\[\left[\text{Mg}_{3}(\text{OH})_{5}(\text{H}_{2}\,\text{O})_{\text{x}}\right]^{ +}\cdot\text{Cl}^{-}\cdot(4-x)\text{H}_{2}\text{O}+\text{H}_{2}\text{O} \to 3\text{Mg(OH)}_{2}+\text{H}^{+}+\text{Cl}^{-}+4\text{H}_{2}\text{O} \tag{10}\]  
\[\left[\text{Mg}_{2}(\text{OH})_{3}(\text{H}_{2}\,\text{O})_{\text{x}}\right]^{ +}\cdot\text{Cl}^{-}\cdot(4-x)\text{H}_{2}\text{O}+\text{H}_{2}\text{O} \to 2\text{Mg(OH)}_{2}+\text{H}^{+}+\text{Cl}^{-}+4\text{H}_{2}\text{O} \tag{11}\]  
Figure 19: \(\omega_{1}\) and \(\omega_{2}\) results for group A and B specimens.  
HCl and some of the Mg(OH)\({}_{2}\) generate water-soluble MgCl\({}_{2}\), the chemical equation of which is  
\[2\mathrm{HCl}+\mathrm{Mg}(\mathrm{OH})_{2}\rightarrow\mathrm{MgCl}_{2}+2\mathrm{H }_{2}\mathrm{O} \tag{12}\]