esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The CeO\({}_{2}\) nanorods (CeNR) promote the oxidation of ethylbenzene (PhEt) and cyclohexene with _t_-BuOOH, at temperatures as low as 55 \({}^{\circ}\)C. The reaction of PhEt gives 1-phenyl-ethyl-_tert_-butyl-peroxide (39% yield after 48 h, 55 \({}^{\circ}\)C), which may be converted into acetophenone (70% selectivity at 89% conversion, 105 \({}^{\circ}\)C). The reaction of cyclohexene with _t_-BuOOH, at 55 \({}^{\circ}\)C, gives mainly 1-_(tert-butyl-peroxy)_-2-cyclohexene (86% selectivity at 22% conversion). For both substrates the saturated C-H bonds are preferentially activated over the unsaturated ones.  
The liquid-phase oxidation catalysis with CeNR/_t_-BuOOH may be associated with the Ce\({}^{3+}\)/Ce\({}^{4+}\) inter-conversion in the one-electron redox processes mediating the formation of _tert_-butyl-(peroxy) radicals. The _tert_-butyl-peroxy products of the reactions of cyclohexene and ethylbenzene are mostlikely formed _via_ the homolytic addition of _tert_-butyl-peroxy radicals (these results contribute to the list of the main applications of _t_-BuOOH as a source for _tert_-butyl derivatives, including alkyl peroxides). CeNR is fairly stable towards leaching of cerium and the catalytic results obtained for the used CeNR are similar to those observed in the first batch run.  
Pre-treatment of CeNR with H\({}_{2}\)O\({}_{2}\) or _t_-BuOOH prior to reaction of PhEt with _t_-BuOOH leads to faster reaction than CeNR. While ATR-FTIR does not show major differences in the bulk features of CeNR (before and after exposure to the oxidants), UV-vis and photoluminescence spectroscopies shows noticeable differences in the case of CeNR-H\({}_{2}\)O\({}_{2}\), which are probably associated to the defect sites present in the materials (supported by the powder XRD data and TEM images): UV-vis and photoluminescence spectroscopy confirm the presence of both Ce\({}^{3+}\) and Ce\({}^{4+}\) in all investigated materials. Thermal treatment at 600 \({}^{\circ}\)C of CeNR has a detrimental effect on catalytic activity. While the powder XRD and TEM data are similar for CeNR and CeNR600, the emission spectra of these materials shows changes in the distribution of Ce\({}^{3+}\)/Ce\({}^{4+}\) ions probably in defect sites. Although exposure to H\({}_{2}\)O\({}_{2}\) seems to give a more defective material (CeNR is very effective in the disproportionation of H\({}_{2}\)O\({}_{2}\)) than that with _t_-BuOOH, the PhEt reaction is faster for CeNR treated with _t_-BuOOH. The enhanced catalytic activity associated with the generation of defect sites may be levelled off by the concomitant morphological changes (extensive fragmentation) leading to defect sites with different structures and electronic properties (and expectedly different intrinsic catalytic activity). A detailed study using XPS spectroscopy could give further insights on structure-activity relationships.
# Analysis of Movement Law and Influencing Factors of Hill-Drop Fertilizer Based on SPH Algorithm
There are two prominent problems in the actual application of hill-drop fertilization: one is insufficient fertilization depth, and the other is the easy accumulation of fertilizer. Both are caused by the fertilizer movement in the soil, making the actual fertilization depth less than the theoretical requirement. Fertilizer cannot be fully utilized, and sufficient nutrients are not obtained in the early stages of plants' growth, which affects crop yield and causes environmental pollution [PERSON] et al. (2019). In order to solve the problems of insufficient fertilization depth and easy accumulation of fertilizer, and to improve the qualification rate of fertilization, a hill-drop fertilizer device was designed, and a 3D model of the hill-drop fertilizer device and soil was established by the SPH algorithm in this paper. The disturbance process of fertilizer in the soil under the action of the designed cover was simulated and analyzed. The fertilizer movement law, as well as the effect on fertilization quality, were studied. Through the simulation based on the SPH algorithm, studying the movement law and influencing factors of fertilizer in the soil can provide a scientific reference for solving problems such as fertilizer accumulation, and provide a theoretical basis for improving the hill-drop fertilizer device and the fertilization quality.
## 2 Governing Equations
### Transport Equation for the Turbulent Kinetic Energy  
The governing transport equation for the turbulent kinetic energy of turbulent flow with FENE-P fluids is given by,  
\[\rho\frac{\partial k}{\partial t}+\rho U_{i}\frac{\partial k}{ \partial x_{i}}= \rho\frac{\partial}{\partial x_{i}}\left[\left(v_{\rm s}+\frac{f _{\rm t}v_{\rm T}}{\sigma_{k}}\right)\frac{\partial k}{\partial x_{i}}\right]+ P_{k}-\rho(\varepsilon^{N}+D)\] \[+Q^{V}-\rho\varepsilon^{V}, \tag{19}\]  
with  
\[D=2v_{\rm s}\left(\frac{d\sqrt{k}}{dx_{i}}\right)^{2}. \tag{20}\]  
\(P_{k}=-\rho\overline{u_{i}u_{j}}\frac{\partial U_{i}}{\partial x_{j}}\) is the rate of production of \(k\).  
The Newtonian closures of Equation (19) are those present in the [PERSON] et al. [35; 36] models. To increase numerical stability, a modified Newtonian rate of dissipation of \(k\) is applied instead of the true dissipation, which are related by \(\varepsilon^{N}=\varepsilon^{N}+D\). For better model performance and to correct for the turbulent diffusion near walls, a turbulent variable Prandtl number is added of the form, \(f_{t}/\sigma_{k}=1+3.5\exp(-(Re_{\rm T}/150)^{2})\) with \(Re_{\rm T}=k^{2}/(v_{\rm s}\varepsilon)\) and model constant \(\sigma_{k}=1.1\). More details of the form of Equation (19) can be found in [PERSON] et al. [20] and [PERSON] et al. [24].  
The last two terms on the right side of the Equation (19) are:  
\[Q^{V}=\frac{\partial\overline{\tau^{\prime}_{ik,p}u_{i}}}{\partial x_{k}}\quad \text{and}\quad\varepsilon^{V}=\frac{1}{\rho}\overline{\tau^{\prime}_{ik,p} \frac{\partial u_{i}}{\partial x_{k}}}, \tag{21}\]  
which are the viscoelastic turbulent transport and the viscoelastic stress work, respectively. They represent the fluctuating viscoelastic turbulent part of the \(k\) transport equation and require suitable closure models.  
A budget analysis for each term in the \(k\) transport equation was performed by [PERSON] et al. [20] for different regimes of DR. They demonstrated that the magnitude of \(Q^{V}\) has more impact on the overall budget in the IDR, and also developed a closure. In the HDR, the amplitude of \(Q^{V}\) is the same as \(\varepsilon^{V}\) but has a different location in the buffer layer, in which the effects of \(Q^{V}\) are overcome by turbulent diffusion, thus, revealing negligible effects to overall flow predictions. [PERSON] et al. [28] had chosen to neglect the \(Q^{V}\) contributions in the \(k-\varepsilon-v^{2}-f\) model and is also not included here as well.
## 3 Path Planning Algorithm Based on the Offset Curve
### Process of the OCPP  
Figure 5 shows the process of the OCPP. The first step is to generate offset curves on the surface, and then printing points are generated along the offset curves. Finally, the coordinates and color information of the printing points can be obtained based on the triangular facets where the printing points are located.  
The OCPP focuses on the offset curve generation. As shown in Figure 5a, firstly, the first contour line is generated at the bottom of the model by the section method (slicing the model on a horizontal plane). Based on the first contour line, the first offset curve is generated by an offset algorithm. Then, the next offset curve is calculated according to the previous offset curve until the offset curves cover the entire surface. How to generate the next offset curve from a known contour is detailed below.
# Potential of Liquid-Crystal Materials for Millimeter-Wave Application
## 2 Measurement of Complex Refractive Indices in Millimeter Wave  
Most simple and common measurement methods of the refractive index in the MMW region involve introducing an LC material into any type of excellent waveguide and extracting the influence of LC materials by comparing the transmission properties with and without the test sample. The differential measurement of the transmission property caused by the successive small changes of the LC propagation length is a more precise measurement method called the cutback technique, which can eliminate the influence due to aspects other than the LC test material. We developed a measurement method for LC materials using a rectangular waveguide by inserting and moving a metal-tip electrical short, which can precisely change the propagation length of the LC material. A magnetic field was applied during the measurement to align the LC molecules uniformly, which is important for the LC measurement to evaluate its anisotropy [25; 26]. Although the size of the waveguide becomes small in the MMW region, the mm-size cavity is still extremely large, based on common knowledge of the LC display application. If we utilize aplanar-type waveguide, such as a CPW, the test sample volume can be greatly reduced because a thinner sandwich cell structure can be introduced, such as in an LC display [27]. However, a metal plate, which works as a reflector of MMW and can change the LC length, must be in contact with the inner surface of the LC cell. The mechanical contact with the internal surface disrupts the LC molecular alignment layer after one measurement. In this section, we try utilizing an optical short, which can work as a moving reflector of the MMW without any mechanical contacts, where the CPW was fabricated using an intrinsic semiconductor, and the electrical short was created by photo-excited carriers. Experiments for the verification of the novel measurement method were performed using the Si substrate itself.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 4 Proposed Optimizations
### Optimizing Storage and Execution Cycles  
The effective execution of the differential addition law requires \(14\times m\) storage elements. These elements store various values, including the initial projective point values (\(W_{1}\), \(Z_{1}\), \(W_{2}\), \(Z_{2}\)) and intermediate results (A, B, C, \(T_{1}\) to \(T_{3}\)). Additionally, they hold the computed values of the final projective point, which are dynamically updated during the execution \(W_{a},Z_{a},W_{d},Z_{d}\). The allocation of values to these storage elements is presented in column 3 of Table 5. It can be shown in Table 5 that each operation requires a total of 14 cycles when the read (R), execute (E), and write-back (WB) stages are completed within a single clock cycle as indicated in column 3 of Table 5. RAW hazards are present  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Without Pipelining** & **With 2-Stage Pipelining** \\ \hline Initial States & 6 & 12 \\ Length of Key & 233 & 233 \\ \(13\times(m-1)\) & 3016 & - \\ \(16\times(m-1)\) & - & 3712 \\ Inversion States & 638 & 1276 \\ Total Clock Cycles & 3660 & 4994 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Information about clock cycles.  
for some specific instructions \((Instr_{6},Instr_{7},Instr_{8},Instr_{10},Instr_{11},Instr_{13})\) as shown in column seven. For instance, \(Instr_{6}\) experiences a one-cycle delay due to a write operation, requiring two cycles to compute the new value of \(T_{1}\). Taking into account RAW hazards, each unified PA and PD operation in this pipelined architecture necessitates 20 cycles for completion. These additional cycles account for hazard resolution and ensure proper execution.
## 4 Digital Platform--DNS/SWAT--New Modules
### Fingerprinting  
The effectiveness of the SWAT module in simulating the transport of sediment particles, especially in the land phase of the basin, has been repeatedly confirmed. However, the problem was the absorption capacity of the finer sediment particles, which are responsible for the transport of pollutants such as heavy metals [148]. SWAT itself has limited ability to simulate such pollutants, as well as the ability to use them to determine the contribution of individual sediment sources. The fingerprinting method [72] has already been successfully combined with SWAT, but without using all the possibilities offered by these tools [30; 149; 150]. The effectiveness of such integration of tools can be appreciated, especially in areas under the pressure of large emitters of pollutants [151]. This is one of the reasons why the Wolnica basin (a fragment of the Raba basin) was selected for study (Figure 2), located only 23 km from one of the largest urbanized areas in Poland. The Krakow agglomeration covers an area of approximately 3231 km\({}^{2}\) and is inhabited by approximately 1400 million people [152]; its concentration of particulate matter is one of the highest in Europe [153]. In the land phase of the Wolnica basin area, soil sampling sites were designated, and a suspended sediment sampler BS-2 (Figure 4) was installed in the riverbed phase at the estuary [72].  
The results of these measurements broadened existing knowledge about this area and became part of the data module, which then fed the fingerprinting module. It was based on inputs from four sources of suspended sediments: grassland, forest, arable land, and residential areas, occurring in the Wolnica river basin. The research led to the selection of Pb, Zn, Cd, Cu, Ni, and Hg as the optimal fingerprints to solve the mass balance equation for suspended sediment and soil samples [72]. The temporal pattern of sediment yields for specific land-use types followed the SYLD distribution for the entire basin, with maximum values of 1.52 t/ha in May for grassland. The land-use sediment yields mainly reflected differences between surface areas for the particular land-use types, displaying the highest values of 139 t/m and 156 t/m for arable and residential land, respectively, and the lowest (2.3 t/m) for forest. Overall, adding the fingerprinting module to the digital platform--DNS/SWAT made it possible to demonstrate the dependence of SYLD distribution on meteorological and topographic conditions. Only in the case of forest areas can low SYLD values be expected. The fingerprint module shows that forests significantly change the signals of pollution sent by the system. Their location is important as they buffer the terrain-dependent flow of pollutants directly into the river.
## 2 Materials and Methods
### Dataset
However, the slight difference in each dataset's number and type of features imposes some challenges in this merging process, since it cannot be done by simply concatenating all the files into a single one. That could be one of the reasons for the existing literature gap, since it may have prevented the exploration of this repository's full potential to investigate in-company cross-project and cross-language generalizability. When multiple datasets have some features in common and others distinct, such as NASA MDP datasets, a proper dataset merging strategy must first identify all the features present in all the datasets (features in common). Then, all the other features that are not contained in at least one dataset must be removed from all the datasets they are contained in. Finally, with all the datasets containing the same number and type of features, all their contents can be copied into a single new file. That process makes all the datasets compatible by ensuring they contain only features in common among all of them before they are merged. Therefore, first, all the distinct features from all the 12 NASA MDP datasets were identified, as shown in Table 1. Then, each dataset containing each one of those 39 distinct features was identified (marked with \"x\" in a light gray background cell in Table 1). Right after, all the features contained in the 12 NASA MDP datasets were identified (i.e., with all the corresponding cells marked with \"x\" in a light gray background cell in Table 1). As a result, a total of 20 features were identified as present in all datasets (marked with \"X\" in a darker gray background in Table 1). All the other 19 features were removed from the datasets containing them. Finally, all the now compatible 12 NASA MDP datasets were merged into a new single file. The \"NASA MDP CROSS-PROJECTS DATASET\" was the resulting single cross-project and cross-language dataset, which was used to support the present study and will be opened to the research community for future investigations. While the dataset used in the original study had 9593 instances with 21 features of a single project's software modules in C language, the assembled dataset contains 43,023 instances and 20 features based on source-code static metrics corresponding to software modules in C, C++, and Java, with no missing values. As expected, the assembled dataset is imbalanced because only 7.4% of modules were defective. Although that imbalance is more aggressive than the original study's (18.33% of the classes defective), no technique, such as oversampling [65], under-sampling [66, 67], case weighting [68], or synthetic minority oversampling technique (SMOTE) [69], was used to balance the dataset classes to follow the same protocol used by the original research.
## 2 Staggered BPMR Channel Model and PRML Detection
### PRML Detection  
The 2D interference that occurs when the readback signal is affected by surrounding bits is usually equalized to target the response by PRML, which is usually employed in data storage systems. The PRML detector consists of a PR equalizer and a ML channel decoder based on a Viterbi algorithm. A PR equalizer reshapes the channel response to the PR pulse shape according to the PR target. Thus, a suitable PR target for channel response is important for achieving a better performance. However, when an unsuitable PR target is used, the equalizer output can be an inaccurate value because of noise enhancement. The received data \(r_{p,q}\) influenced by the BPMR channel and noise are entered into the 2D equalizer. The equalizer output \(e_{p,q}\) is calculated by  
\[\begin{array}{l}e_{p,q}=\sum_{n=1}^{L}r_{p,q-\lceil\frac{L}{2}\rceil+n}c_{ \lceil\frac{L}{2}\rceil,n}+\sum_{m=1}^{\lceil\frac{L}{2}\rceil}\sum_{n=m+1}^{ L-m+1}r_{p-(2m-1),q-\lceil\frac{L}{2}\rceil+n}\cdot c_{\lceil\frac{L}{2} \rceil-(2m-1),n}+\\ \sum_{m=1}^{\lceil\frac{L}{2}\rceil}\sum_{n=-N+m}^{L-m+1}r_{p+(2m-1),q-\lceil \frac{L}{2}\rceil+n}\cdot c_{\lceil\frac{L}{2}\rceil+(2m-1),n}+\sum_{m=1}^{ \lceil\frac{L}{2}\rceil}\sum_{n=-N+m}^{L-m+1}r_{p-2m,q-\lceil\frac{L}{2} \rceil+n}\cdot c_{\lceil\frac{L}{2}\rceil-2m,n}+\\ \sum_{m=1}^{\lceil\frac{L}{2}\rceil}\sum_{n=-N+m}^{L-m+1}r_{p+2m,q-\lceil\frac {L}{2}\rceil+n}\cdot c_{\lceil\frac{L}{2}\rceil+2m,n},\end{array} \tag{5}\]  
where \(c_{m,n}\) is the equalizer coefficient, \(L\) is the equalizer length, and \(\lceil x\rceil\) is a ceiling function, which maps \(x\) to the least integer greater than or equal to \(x\). In this study, \(L=5\) was set. When \(L=5\), the equalizer coefficients are as follows:  
Figure 2: Block diagram of the proposed system model.  
\[\left[\begin{array}{rrrrr}c_{1,2}&c_{1,3}&c_{1,4}\\ c_{2,1}&c_{2,2}&c_{2,3}&c_{2,4}\\ c_{3,1}&c_{3,2}&c_{3,3}&c_{3,4}&c_{3,5}\\ c_{4,1}&c_{4,2}&c_{4,3}&c_{4,4}\\ c_{5,2}&c_{5,3}&c_{5,4}\end{array}\right]. \tag{6}\]  
The least mean square algorithm was used for updating equalizer coefficients.  
\[c_{m,n}^{k+1}=c_{m,n}^{k}+\mu(e_{p,q}-\sum\
olimits_{n=-1}^{1}d_{p,q-n}.f_{n +2})\cdot r_{p,q}, \tag{7}\]  
where \(c_{m,n}^{k+1}\) and \(c_{m,n}^{k}\) are updated and current equalizer coefficients, respectively, \(\mu\) is an adaptation gain, and \(f_{n}\) is a PR target coefficient in the down-track direction. To calculate the reliability or soft value of the input data, a soft output Viterbi algorithm (SOVA) was used. The equalizer output is input to the one-dimensional (1D) SOVA detector for the down-track direction. The branch metric of 1D SOVA is calculated using the following equation:  
\[\lambda_{p,q}\big{(}s_{i},s_{j}\big{)}=\left\{e_{p,q}-\Big{(}f_{1}\cdot d_{p,q -1}(s_{i})+f_{2}\cdot d_{p,q}(s_{i})+f_{3}\cdot a_{p,q+1}\big{(}s_{j}\big{)} \Big{)}\right\}^{2}, \tag{8}\]  
where \(s_{i}\) and \(s_{j}\) are the current and next state, and \(\hat{a}\big{(}s_{j}\big{)}\) and \(a(s_{k})\) are decisions at \(s_{i}\) and \(s_{j}\), respectively.
## 3 Methods
### Classification with a Linear Prediction Head  
For accurate classification, the extracted features are projected into label space using a prediction head composed of linear layers before softmax output.  
Given an input feature map \(x\) from Swin Transformer with shape [B, L, C], layer normalization is first applied to normalize the features:  
\[\hat{\mathbf{x}}=\text{LayerNorm}(x) \tag{29}\]  
Figure 3: Swin Transformer Block.  
Then global average pooling is performed across the spatial dimensions to aggregate spatial information. The input is transposed to [B, C, L] and average pooled on L, outputting a feature map \(y\) with shape [B, C, 1]:  
\[y=\text{AvgPool}(\hat{\mathbf{x}}^{T})\in\mathbb{R}^{B\times C\times 1} \tag{30}\]  
Next, flatten is applied to squeeze y to [B, C]:  
\[z=\text{Flatten}(y)\in\mathbb{R}^{B\times C} \tag{31}\]  
Finally, a fully-connected layer is used to map z to classification outputs \(\hat{y}\):  
\[\hat{y}=\text{Linear}(z) \tag{32}\]  
This module applies normalization, spatial aggregation through pooling, flattening and a linear classifier on top of Swin Transformer features to effectively perform image classification with simplified computations.
## 3 Results and Discussion
### Phosphorus Removal in Batch Adsorption Experiments
#### 3.2.2 The Effect of Ionic Strength on Phosphorus Removal Efficiency  
The results of the test are presented in (Figure 5).  
Figure 4: The effect of pH on phosphorus removal by HAOPs (adsorbent dose = 1 g/L, agitation speed = 200 rpm, temperature = 25 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C, contact time = 60 min).  
Only a small change in removal efficiency was observed for the ionic strength of 0.003 M. The removal efficiency decreased slowly with the increase in ionic strength. The most notable decrease was 37% for the highest concentration. Compared to lower concentrations this decrease was much smaller, e.g., for the concentration of 4.34 mg/L the decrease was only 3%. This indicates that for the adsorption at different ionic strengths and constant pH value, the sole variable is ionic strength. Possible changes in the electrical double layer surrounding a charged metal oxide surface in HAOPs can explain the results. The effect of changing the surface charge from positive to negative as the phosphorous saturation of the surface increases was reported by [PERSON] et al. [41]. They also revealed that the maximum phosphorous adsorption was achieved when all sorption sites became occupied.
## 3 Results
### Multiple Sequence Alignments  
In silico results showed a susceptibility of the _E. faecalis_ strain against the _C. sativa_ extract (gallic acid, quinic acid, protocatechuic acid, brevifolia acid, and ellagic acid), differently, the other strains (_P. aeruginosa_, _S. aureus_, and _E. coli_) did not exhibit any vulnerability after the treatment with the extract.  
Based on the evidence that our extract was active only on the _E. faecalis_ strain, we detected the potential _E. faecalis_ targets, involved in the interaction with our compounds, by performing an MSA of the entire _E. faecalis_ protein pool against the entire \"Non-redundant protein sequences (nr)\" database of the _P. aeruginosa_, _S. aureus_, and _E. coli_ strains to identify the _E. faecalis_ non-homologue sequences aligned with the sequences of the other strains.  
A Python script written in-house analyzed 150.305.287 alignment of sequences and provided 38 _E. faecalis_ protein sequences that showed appropriate coverage and a similarity/identity value of less than 20% (non-homologous sequences). The sequences were explored on the UniProt database and all sequences that were annotated as \"obsolete\" were discarded; our analysis identified the endocarditis and biofilm-associated pilus tip protein EbpA (EbpA) as the only _E. faecalis_ non-homologue sequence.