esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Nitrogen and Phosphorus Removal
## 4 Discussion
### Nitrogen Removal Performance
#### 4.2.2 bioreactors  
Cumulatively over the sampling season, TN mass load was reduced by 76% (Table 2). Average TN concentrations decreased within the bioreactors despite low concentrations of NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N entering the bioreactors from the Tank Outflow (Figure 4, Supplementary Materials Table S2). This overall decrease indicates that N removal via denitrification occurred within the beds, preceded by nitrification in the aerobic zones of the bioreactor, which has been described as a possible removal mechanism of NH\({}_{4}\)\({}^{+}\)-N in in other woodchip bioreactor studies [21; 25]. However, there was likely mineralization of organic N to NH\({}_{4}\)\({}^{+}\)-N, due to the increase in median NH\({}_{4}\)\({}^{+}\)-N concentration between the Tank Outflow and WB and EB Outflows, which was not consistently followed by nitrification and denitrification processes.  
Most woodchip bioreactor studies sample influent and effluents on regular intervals when flow rates are relatively constant, however treatment of storm flows within bioreactors is thought to be less effective due to disturbance of steady-state conditions [36]. A meta-analysis of bioreactor studies found that performance was significantly lower with a hydraulic retention time (HRT) of less than 6 h compared to longer times between 6 and 20 h and \(>\)20 h [20]. For this system, the steady-state HRT within each bioreactor during storm events ranged from 29 to 309 hr and HRT between storm events varied from 1 day to 12 days. This variation in HRT and flow rates through the system may have inhibited performance by disrupting stable microbial populations, especially at rapid flow rates. Future work could modify outflow structures to control flow rates and HRT.  
Bioreactor performance was limited by low NO2\({}^{-}\)+NO3\({}^{-}\)-N concentrations entering the woodchip bioreactors from the tanks (Figure 4), which has also been reported in other studies [27; 37]. A consequence of low NO2\({}^{-}\)+NO3\({}^{-}\)-N concentrations in the bioreactors can be undesirable redox reactions such as sulfate (SO4\({}^{2-}\)) reduction, which produces hydrogen sulfide (H2S). There is the potential for these reactions to occur in woodchip bioreactors if complete NO2\({}^{--}\)+NO3\({}^{-}\)-N removal is achieved and the NO2\({}^{-}\)+NO3\({}^{-}\)-N concentration falls below 0.5-1 mg L\({}^{-}\)[22]. However, SO4\({}^{2-}\) concentrations were not measured in this study, so future work could investigate if sulfate reduction occurs in this system due to low NO2\({}^{--}\)+NO3\({}^{-}\)-N concentrations that were measured (below 0.05 mg L\({}^{-}\) to 0.13 mg L\({}^{-}\)). The release of nitrous oxide (N2O) due to incomplete denitrification is a further 'pollution swapping' concern, which should be investigated for this system. Generally, bioreactor studies have found low levels of N2O production [21; 38], but more work on greenhouse gas emissions from bioreactors is needed [20].
###### Abstract  
This study was conducted to investigate the efficacy of a self-expandable retainer (SER) for endoscopic visualization of the external auditory canal (EAC). Trypanomeatal flap (TMF) elevation was performed in six cadaveric heads. Two different types of SER were placed. The procedural feasibility was assessed by endoscopic images. Technical success rate, procedure time, endoscopy lens cleaning, and presence of mucosal injuries were analyzed. TMF elevation and SER placement were successful in all specimens and there were no procedure-related complications. The mean procedure time with the SERs was significantly shorter than without (\(p<0.001\)). The mean number of times the endoscopy lens was cleaned during the procedure was significantly lower in the SER group (\(p<0.001\)). In the SER group, endoscopy insertion into the EAC was easier without tissue contact with the lens during the TMF elevation compared with the non-SER group. There were no mucosal injuries. SER placement is effective for endoscopic visualization via the expanded and straightened EAC. A fully covered type of SER is preferable. The device can be useful for endoscopic ear surgery, reducing procedure time and reducing the need for endoscopy lens cleaning during the procedure.  
self-expandable metallic stent; ear canal; cadaver; ear speculum; endoscopy +
Footnote †: journal:  
since the endoscope is positioned medial to the curvature [8]. However, the cartilaginous and bony portions need to be aligned to insert the endoscope. The cartilaginous portion of the EAC is often pushed posteriorly using the shaft of the endoscope. Certain techniques such as the Tarabichi's stitch can be used to straighten the EAC and increase the working space for the endoscope [9; 10].  
Because the space in the EAC and middle ear is limited, a clean surgical field is required to assure patient safety and benefit from the wide-angle vision offered by the endoscope [11]. The lens of the endoscope is easily smeared by the hair and earwax in the EAC and blood, however minimal, can obscure the view. The authors, therefore, devised a self-expandable retainer (SER) which can be placed in the EAC to aid surgery.  
An ideal retainer would provide straight and clean entry to the middle ear without causing damage to the soft tissues of the EAC and, further, protect the tissues from trauma by the surgical instruments. The aim of this study was to investigate the efficacy and technical feasibility of SERs for endoscopic visualization in the EAC of cadaveric heads. SER placement was found to be effective for endoscopic visualization due to the expansion and straightening of the EAC.
# Simulation and Experimental Research on the Energy Loss of Confluence Pipelines
## 3 Experimental System for the Energy Loss of the Arc Confluence Pipeline
### The Effect of Curvature Radius on the Energy Loss of the Arc Confluence Pipe  
Figure 7 shows that, with the gradual increase in the curvature radius of the arc confluence pipeline, the value of its energy loss coefficient first decreases and then tends to be stable, and its change law is similar to that of energy loss along the path. For arc confluence pipelines with different curvature radii at the same Reynolds number, the energy loss coefficient decreases first and then increases gradually with an increase in curvature radius. When the curvature radius is 65 mm, the energy loss coefficient is at its minimum value. In the range of 4000 \(<\) Re \(<\) 56,000, the value of the energy loss coefficient increases with an increase in the curvature radius of the arc confluence pipeline. When the radius of curvature is constant, the energy loss coefficient decreases with an increase in Re, and when Re \(>\) 20,000, the energy loss coefficient ultimately remains unchanged.  
When Re \(>\) 15,000, the energy loss coefficient of the circular manifold with a radius of curvature R = 65 mm is the minimum for an arc confluence pipeline with different pipe diameters. Therefore, it can be judged that the radius of curvature corresponding to the minimum energy loss should be R = 65 mm and should meet the requirements of 30 mm \(<\) R \(<\) 100 mm. These findings are due to the fact that local energy loss dominates when the radius of curvature is small in the arc confluence pipeline with the same pipe diameter. However, with the increase in the radius of curvature of the circular manifold, its energy loss along the path is dominant; therefore, there will be a radius of curvature corresponding to the lowest energy loss.  
Figure 6: Schematic diagram of the experimental system. 1. Submersible pump; 2. bypass ball valve; 3 and 9. intelligent electromagnetic flowmeter; 4 and 8. intelligent pressure transmitter; 5. arc manifold; 10 and 13. branch ball valve; 11 and 12. fine adjustment ball valve; and 14. water tank.
## 2 Materials and Methods
### Cell Morphologic, Viability and Yield  
The osteoblasts were seeded at a density of 2000 cells/cm\({}^{2}\) in six-well plates and cultured in DMEM/F12 medium (Sigma-Aldrich) supplemented with 10% (\(v/v\)) human platelet lysates (HPLs) along with 1% antibiotic-antimycotic solution (Sigma-Aldrich). Morphological changes, growth patterns, and confluency of the cultured cells were monitored every 2-3 days using an inverted microscope at 40\(\times\) magnification. Images were captured at five random points per well for consistency and analyzed using ImageJ software (version 1.53k) to measure cell length, width, and size. Meanwhile, another set of cultured cells was grown to 70-80% confluence and then released from the culture plate using 0.05% trypsin-EDTA (Sigma-Aldrich) to manually quantify cell number, viability and yield using a hemocytometer and trypan blue. The cell suspension was diluted, and an aliquot of the suspension was placed in the hemocytometer to count the number of cells under a microscope. The total cell number was then calculated by multiplying the count by the dilution factor, which gives the final cell yield per well or per culture dish. For each group, six samples were analyzed. The tests were repeated three times. Viability was determined using the following formula:  
\[\text{(Total live cells/total live + dead cells) }\times 100\%.\]  
The group growth rate, i.e., population doubling time (PDT), was calculated using the following formula:  
\[\text{PDT}=\text{t}\,\text{log2/(\text{logN2}-\text{logN1})},\]  
where t denotes time in culture (hour), N2 denotes the cell number at the end of the passage and N1 denotes the cell number seeded at the beginning of the passage.
# Comparison of SEVIRI-Derived Cloud Occurrence Frequency and Cloud-Top Height with A-Train Data
## 4 Discussion
### Cloud Occurrence Frequency
In this study, we found that the UK Met Office SEVIRI algorithm detects clouds very strictly over the ocean surface with mean COF of 64.32%, which is even larger than CALIOP products (63.65%). Over estimation of SEVIRI COF is dominant over the high-latitude oceans, where the satellite zenith angle of Meteosat-10 became larger. On the contrary, SEVIRI algorithm has a relative weakness in detecting clouds over the land surface with the COF bias of \(-12.14\%\) compared to CALIOP products. From passive infrared measurements, the cloud detection of low level clouds over land is challenging for the following reasons: first, the thermal contrast between them and the surface is small. Second, surface type, emissivity and temperature in the satellite field of view are complex and inhomogeneous, which is normally neglected in the cloud detection algorithm. Thus, further investigation is required for cloud detection over land.
# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
## 3 Methodology
### Our Model
#### 3.4.3 Subgraph Modeling Based on a Dual Attention Mechanism  
For subgraph modeling, in order to improve the accuracy of the subgraph information utilization and distinguish the role played by different structures in modeling, we propose to integrate edge and relation dual attention mechanisms into a GCN. Generally, the entity embedding for updating subgraphs employs the traditional iterative message passing strategy of a GCN.  
\[\mathbf{h}_{v}^{k+1}=\sum_{(u,r)\in N_{k}(j)}\alpha_{(u,r,v)}^{k}\mathbf{W}_{ 11}^{k}\phi\Big{(}\mathbf{h}_{u}^{k},\mathbf{e}_{r}^{k}\Big{)}+\mathbf{W}_{ 22}^{k}\mathbf{h}_{v}^{k}, \tag{9}\]  
where \((u,r,v)\) is the example triple and \(N_{k}(\cdot)\) is the set of neighbors containing entity relationship pairs. \(\mathbf{W}_{11}^{k}\) and \(\mathbf{W}_{22}^{k}\) are transformation matrices, and \(\mathbf{h}_{v}^{k}\) represents the embedding of entity \(v\) at layer \(k\).  
In actuality, there are differences between the neighbors in entity modeling. For example, neighbors \((h_{3},r_{2})\), as well as \((h_{4},r_{6})\), have different effects on the representation of \(h_{2}\). Meanwhile, predicting different target relationships affects the representation of \(h_{2}\). In order to distinguish these effects, we propose two types of attention, edge-aware \(\beta_{u,r}^{k}\) and relation-aware \(\gamma_{r,r_{t}}^{k}\), to compute the joint neighbor attention values.  
\[\beta_{u,r}^{k}=\sigma\Big{(}\mathbf{W}_{1}^{k}\Big{(}\mathbf{h}_{u}^{k} \oplus\mathbf{e}_{r}^{k}\oplus\mathbf{h}_{v}^{k}\Big{)}\Big{)}, \tag{10}\]  
\[\gamma_{r,r_{t}}^{k}=\sigma\Big{(}\mathbf{W}_{2}^{k}\Big{(}\mathbf{e}_{r}^{k} \oplus\mathbf{e}_{r_{t}}^{k}\Big{)}\Big{)}, \tag{11}\]  
\[\alpha_{(u,r,p)}^{k}=\text{softmax}\Big{(}\beta_{u,r}^{k}+\gamma_{r,r_{t}}^{k} \Big{)}, \tag{12}\]  
where \(\mathbf{W}_{1}^{k}\) and \(\mathbf{W}_{2}^{k}\) are transformation matrices.  
To enable information sharing and improve representation, we integrated updated entity embeddings to enhance the representation as follows:  
\[\mathbf{H}_{index(v)}^{k+1}=\lambda_{1}\mathbf{H}_{index(v)}^{k}+(1-\lambda_{ 1})\mathbf{h}_{v}^{k+1}, \tag{13}\]  
where \(index(v)\) indicates the actual index of entity \(v\).  
Based on this, we can obtain the embedding matrix \(\mathbf{H}_{L}\), which consists of the embedding vectors of all the entities in the subgraph of the last GCN. To further improve the representational power of the model, a two-way gated recursive unit GRU, as inspired by [17; 32], was added after the last layer of the GCN. Since the entities in the subgraph are sorted according to the distances to the target entities, the GRU that handles the sequences can be used to increase the inter-entity interactions, thereby updating the embedding representation as follows:  
\[\mathbf{H}_{L}{}^{\prime}=\text{BiGRU}(\mathbf{H}_{L}). \tag{14}\]Finally, the average readout function (1) was used to process \(\mathbf{H_{L}}^{\prime}\) to obtain the representation \(\mathbf{h}_{G}\) of the subgraph G.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 4 Discussion  
### Datasets and Evaluation Metrics  
The training set is the Bappy synthetic dataset with 11k splicing tampered images [21], where the tampered regions are scaled and rotated in different factors. To compare with state-of-art methods, we also evaluated on standard datasets NIST16 [33] and CASIA [34]. NIST16 contains three tampered operations: splicing, copy-move and removal. CASIA is a commonly splicing dataset, where the ground-truth masks are obtained by the subtraction of tampered images and corresponding host images. This paper used CASIA2.0 for training and CASIA1.0 for testing. The division of the dataset is shown in Table 1.  
In order to compare the experimental results more objectively, this paper uses the average precision of the image-level index Average Precision (_AP_) and the pixel-level index \(F_{1}\) score to quantitatively evaluate the model performance. The definition of the image-level index AP is the same as the evaluation standard of the CoCO dataset [35], which can be used as an evaluation indicator for blind evidence  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & \begin{tabular}{c} **Tampered** \\ **Images** \\ \end{tabular} & \begin{tabular}{c} **Most Image Size** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Train** \\ **and Test** \\ \end{tabular} \\ \hline Synthesized & \(11\)k & \(512\times 512\) &
\begin{tabular}{c} Train: 10k \\ Test: 1k \\ Train: 404 \\ Test: 160 \\ Train: 5123 \\ Test: 921 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of image datasets.  
The pixel-level \(F_{1}\) score can be used to evaluate the accuracy of the location of the tampered area. We calculate the \(F_{1}\) score of each picture and then take the average as the final score for each data set. For each tampered picture, the definition of the \(F_{1}\) is as follows:  
\[F_{1}(M_{\text{out}},M_{\text{gt}})=\frac{2 TP}{2 TP+FN+FP} \tag{11}\]  
where \(M_{\text{out}}\) and \(M_{\text{gt}}\) represent the final predicted mask and ground-truth mask, \(TP\) represents the number of tampered pixels that are correctly predicted, \(FP\) represents the number of tampered pixels that are incorrectly predicted, \(FN\) represents the number of genuine pixels that are incorrectly predicted.
## 2 Methods
### The Dirichlet-to-Neumann Operator  
The Dirichlet-to-Neumann (DtN) operator, \(\Lambda(u)\), relates the values assumed by a scalar unknown function, \(u\), on the boundary, \(\Sigma\), (i.e., Dirichlet-type conditions), into the values of its normal derivative, \(\partial_{n}u\), assumed on the same boundary, \(\Sigma\) (i.e., Neumann-type conditions):  
\[\Lambda:\ u|_{\Sigma}\to\partial_{n}u|_{\Sigma}, \tag{9}\]  
\(u\) being the solution of the free-space Helmholtz equation outside \(\Sigma\) (i.e., in the domain \(V_{ext}\), see Figure 1b). By applying the DtN operator to \(\Sigma\) it is possible to decouple the scattering problem (Equations (3)-(7)) into two problems: One problem defined in \(V_{in}\) and another one defined in \(V_{ext}\). In other words, the inner problem in \(V_{in}\) can be closed exactly, without the need of introducing absorbing conditions or artificial absorbing layers.  
In principle, the DtN may be numerically evaluated for any shape of the boundary. However, for canonical geometries it can be analytically evaluated. This entails better insight and more efficient numerical computations. Here we briefly recall its derivation for a circular boundary (details may be found in [15; 26; 27]). Introducing a polar coordinate system \((r,\theta)\) in the \(x,y\) plane, the scalar wave equation can be solved in \(V_{ext}\) by separation of variables as:\[u(r,\theta)=\sum_{n=-\infty}^{\infty}U_{n}H_{n}^{(2)}(kr)e^{jn\theta}, \tag{10}\]  
where \(H_{n}^{(2)}\) is the Hankel function of second kind [28], and the coefficients \(U_{n}\) are given by:  
\[U_{n}=\frac{1}{2\pi H_{n}^{(2)}(kr)}\int_{0}^{2\pi}u(r,\theta\prime)e^{-jn \theta\prime}d\theta\prime. \tag{11}\]  
Note that the \(U_{n}\)s do not depend on \(r\). The DtN operator for a circular boundary \(\Sigma\) of radius \(R\) may be obtained by taking the normal derivative (\(\partial_{n}=\partial_{r}\)) of \(u\) at \(r=R\). Thus, from Expressions (10) and (11) we have:  
\[\Lambda(u|_{\Sigma})=\frac{1}{2\pi}\sum_{n=-\infty}^{\infty}\frac{H_{n}^{(2) \prime}(kR)}{H_{n}^{(2)}(kR)}\int_{0}^{2\pi}u(R,\theta\prime)e^{-jn\theta \prime}d\theta^{\prime}, \tag{12}\]  
where \(H_{n}^{(2)\prime}\) is the derivative of \(H_{n}^{(2)}\) with respect to \(r\), that can be also expressed as [28]:  
\[H_{n}^{(2)\prime}(kR)=\frac{H_{n-1}^{(2)}(kR)-H_{n+1}^{(2)}(kR)}{2}. \tag{13}\]  
The map is therefore known in analytical form, in terms of a series expansion.  
It is worth noting that the position of the circular boundary may be arbitrarily chosen, even in close proximity to the scatterers. Therefore, the size of the solution domain may be strongly reduced, compared to the use of classical absorbing conditions such as PML, that are more efficient when the boundary is placed in the far-field region.
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 3 Results and Discussion
### The Influence of Ionizing Radiation on Thermal Properties of Polyesters
This may therefore explain the overall decrease in \(M_{\rm n}\) in the case of both types of irradiation, together with the contradictory results of the microstructure of the polymer, which suggests the slight elongation of lactidyl segments (from 4.33 to 4.61) in the copolymer chain irradiated with EB. Contrary to the non-irradiated copolymer (\(\Delta H_{\rm c}\)2 nd\({}^{\rm heating}\) = 48.13 J g\({}^{-1}\) and \(\Delta H_{\rm m}\)2 nd\({}^{\rm heating}\) = 49.84 J g\({}^{-1}\)), in the case of both irradiated samples, recrystallization occurred to a much lesser extent during the second heating (as shown by the lower values of \(\Delta H_{\rm c}\) being equal to 22.86 J g\({}^{-1}\) and 29.17 J g\({}^{-1}\) in the case of the \(\gamma\)-irradiated and EB-irradiated samples, respectively). However, even though the melting enthalpies were lower (31.90 J g\({}^{-1}\) and 39.68 J g\({}^{-1}\) in the case of \(\gamma\)-irradiated and EB-irradiated PLGA samples), this indicated a lower crystalline content. Some of the melting crystalline phase was present in the irradiated samples before the start of this heating cycle (as shown by the lower values of \(\Delta H_{\rm c}\) compared to \(\Delta H_{\rm m}\)), indicating that it was formed during the preceding cooling cycle (desmeared curves). Simultaneously, the appropriate values of \(T_{\rm c}\) were equal to 115.0 \({}^{\circ}\)C, 117.8 \({}^{\circ}\)C and 116.2 \({}^{\circ}\)C, and the values of \(T_{\rm m}\) were equal to 139.4 and 147.2 \({}^{\circ}\)C (PLGA non-irr), 139.0 and 146.1 \({}^{\circ}\)C (\(\gamma\)-irr PLGA), and 139.1 and 146.6 \({}^{\circ}\)C (EB-irr PLGA). Therefore, it can be concluded that irradiation also influences the recrystallization and melting processes taking place during cooling and the second heating. Similarly to other cases, the effect was more evident after radiation treatment performed in the gamma chamber compared to EB.
# Cutting Performance and Wear/Damage Characteristics of PCBN Tool in Hard Milling
## 4 Conclusions and Outlook  
The cutting performances (cutting force, chip, surface quality, tool life) and the wear/damage characteristics of PCBN tools were investigated for the intermittent machining of hardened steel (Cr12 MoV, 59 HRC). The main conclusions were as follows:  
1. Based on the evaluation indexes of cutting force, surface quality, chip morphology, and cutting temperature, it was concluded that the cutting performance of the PCBN tool was better than that of the coated cemented carbide tool.
2. A combination of oxidation wear, adhesive wear, and diffusion wear was believed to control the flank wear of the PCBN cutting tool.
3. The main failure modes of the PCBN tool in the machining of hardened steel at low speed were tool micro-chipping, and low-rate impact damage of the rake face and the flank face. The main failure modes of the PCBN tool in the machining of hardened steel at high speed were flank wear and high-rate fatigue damage.
4. The PCBN tool's life is affected by the cutting speed. When the cutting speed was greater than 150 m/min, the PCBN tool's life was better than that of the carbide tool. Meanwhile, PCBN tools should be avoided at low cutting speeds.
5. The sawtooth pitch of chip increased with the increase in cutting speed. The reason was that the increase in cutting speed made the shear slip more easily during chip formation, which led to the increase in sawtooth degree.
6. When the PCBN tool was used to machine hardened steel, the cutting temperature was higher at the smaller chip thickness. The reason was that the action mechanism between the cutter and the workpiece was mainly ploughing and extrusion in the place where the chip thickness was small. Because the size of chip section was small, the excessive heat generated by ploughing and extrusion could not be discharged in time and, thus, the chip color was deepened.  
Figure 17: The macrostructure of the tool rake face.  
**Author Contributions:** The idea of this project was conceived by [PERSON]; [PERSON] consulted to relevant high-level papers on aspect of cutting performance and wear/damage characteristics of PCBN tool in hard milling, and wrote this project; [PERSON] and [PERSON] reviewed this project and proposed constructive guidance to make the article more complete.  
**Funding:** This project was supported by the Projects of International Cooperation and Exchanges NSFC (51720105009).  
**Conflicts of Interest:** The authors declare no conflict of interest.