esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussion
### Mean Acquisition Time Using Raster Scan  
Figure 10a depict the mean acquisition time \(E[T_{\mathrm{r}}]\) to scan the uncertainty area when the raster scan method is used for various standard deviations of the receiver satellite position error. As expected, the curves show behavior corresponding to the scan path. For the same value of uncertainty scan area, i.e., 50 \(\upmu\)rad, \(E[T_{\mathrm{r}}]\) is approximately [0.38, 0.21, 0.07] seconds for \(\sigma=[10,20,40]\) \(\upmu\)rad, respectively. This behavior is clearly illustrated in Figure 10b, where the mean acquisition time decreases as the receiver satellite moves away from the center, and vice versa. The scan time decreases as the satellite moves away from the center, from regions of high probability to regions of low probability. Since the raster scan begins at the edges (low probability) and scans the area in a zigzag fashion toward the center (high probability), the curves eventually converge to the time required for the entire square area to be scanned by the raster scan. The acquisition time \(E[T_{\mathrm{r}}]\) increases  
Figure 9: Mean acquisition time analysis for spiral scan path. (**a**) Mean acquisition time vs. uncertainty scan area size for spiral scan path. (**b**) Mean acquisition time vs. receiver satellite position error for spiral scan path.  
with the size of the scan area but decreases with an increase in the standard deviation of the satellite's position. Specifically, \(E[T_{\mathrm{r}}]\) is approximately \([0.46,\,1.42,\,4.80]\) seconds for \(\sigma=[10,20,40]\) \(\upmu\)rad when \(P_{c}\) is set to \(0.98\) in Equation (81).
## 3 Results and Discussions
Noticeably, even though \(\Delta(\text{Ms})_{sn}\) and \(\Delta(\text{E1s})_{sn}\) are both based on the experimental element \(sn\) and weighted by the intrinsic state (s), they have opposite relationships with the response variable. Therefore, a balance between these descriptors is responsible for determining the toxicity of the PPCPs. More importantly, the 2D and 3D topology of the compounds largely influences their toxicity. It is observed that two closely related descriptors, \(\Delta(\text{MATS7v})_{sn}\) and \(\Delta(\text{MATS8v})_{sn}\), have opposite relationships with the dependent parameter. Both MATS7v and MATS8v are Moran 2D autocorrelation descriptors weighted by van der Waals volume [37]. The difference lies in their topological path lengths, which are 7 and 8 for these two descriptors, respectively. As shown in the correlation matrix (cf. Figure 2), these two descriptors (i.e., \(\Delta(\text{MATS7v})_{sn}\) and \(\Delta(\text{MATS8v})_{sn}\)) do not show high inter-collinearity. However, the difference in their contributions to the model indicates that a slight change in the chemical structure may significantly alter its environmental toxicity. On the other hand, another Moran autocorrelation descriptor \(\Delta(\text{MATS1e})_{te}\) also contributed positively to the model. Among different atomic properties, atomic mass depicted consistency in contributions for the descriptors weighted by it. For example, both \(\Delta(\text{GATS3m})_{sg}\) and \(\Delta(\text{Dm})_{te}\) were found to be positively correlated to toxicity. Therefore, it is inferred that PPCPs with higher molecular weights are more prone to elicit aquatic toxicity when the species groups and time of experiments are varied. **Comparison of present findings to prior investigations. ** Previous investigations into the toxicity of pharmaceuticals and personal care products have focused on specific compounds or limited experimental conditions [1; 2; 3; 4; 5]. This study aims to develop an mt-QSTR model that encompasses a broader range of experimental conditions and diverse chemical structures from the ECOTOX database. Consequently, the scope of this model surpasses previous reports. A key advantage of mt-QSTR modeling lies in its applicability to a wide array of chemicals and experimental settings. Recognizing the continuous growth of chemical databases like ECOTOX, this work emphasizes reproducible methodologies. Open-access tools and readily adaptable codes are provided to facilitate the development of future  
Figure 6: Examples of data points to explain the contribution of \(\Delta(\text{Dm})_{te}\) to aquatic toxicity. QSTR models not only for PPCPs but also for other types of chemicals with environmental toxicity risks.
## 3 Results and Discussion
### RSM for Furfural Production from Xylose Standard Solution with Extracting Solvent
Beyond this time, yield decreased due to furfural decomposition [29] and self-condensation [30]. The xylose concentration was considered a less influential variable when compared to reaction temperature and time, as shown in Figure 1b,c. This inference was also supported by the p-value of the variables (Table 3). The sources related to xylose concentration (X\({}_{3}\), X\({}_{1}\)X\({}_{3}\), X\({}_{2}\)X\({}_{3}\), X\({}_{3}\)\({}^{2}\)) had high \(p\)-values, indicating that xylose concentration was not as significant a factor for furfural yield. This result differs from previous research, which has demonstrated that furfural yield generally has an inverse relationship with xylose concentration. Higher xylose concentrations are able to produce more furfural at once, leading to a higher collision possibility that causes  
Figure 1: Three-dimensional plot and detailed contour of response surface methodology of furfural yield from xylose standard solution with extraction solvent (THF): (**a**) fixed xylose concentration (10 g/L); (**b**) fixed reaction time (120 min); (**c**) fixed reaction temperature (160 \({}^{\circ}\)C); (**d**) contour of furfural yield depending on reaction temperature and time. condensation of the furfural reaction [31]. It was assumed that the 10-30 g/L xylose concentration range used in this study was too narrow of a range to affect furfural yield. This was unlike a study by [PERSON] [32] where they produced furfural at high xylose concentrations. [PERSON] [32] varied the xylose concentration from 40 to 120 g/L and found that furfural yield had been relatively stable when the xylose concentration was from 40 to 70 g/L. Based on the xylose concentration range used in this study (10-30 g/L), the effect on furfural yield was negligible compared to reaction time and temperature. With a fixed xylose concentration there was greater clarity in terms of the optimal conditions to maximize furfural yield. Additionally, the reaction temperature and time were expanded compared to the experimental range (Figure 1d). The optimal conditions to maximize furfural yield was calculated based on Equation (3). The maximum furfural yield in the predicted reaction conditions was 75.1%, where the reaction temperature was 170 \({}^{\circ}\)C, reaction time was 120 min, and xylose concentration was 10 g/L. To verify the model, actual furfural production was carried out using these predicted optimal conditions, rendering a furfural yield of 72.39%, similar to the predicted yield.
# Properties of Vermicomposts Derived from Cameroon Sheep Dung
## 3 Results and Discussion
### Changes in C:N Ratio  
Total carbon content did not differ in both vermicomposts produced (CSD: CSDH) (\(p>0.05\)), but it was significantly lower compared to the initial material (\(p<0.05\)). A decrease in the organic carbon content is associated with the process of mineralization, which is the transformation of organic carbon into CO\({}_{2}\) through the respiration of organisms [19; 25].  
As a result of the vermicomposting process, a statistically significant increase in nitrogen content in the obtained fertilizers was observed (CSD: from 14.0 to 17.0 g kg\({}^{-1}\) d.m.; CSDH: from 14.5 to 18.0 g kg\({}^{-1}\) d.m.). This resulted in a significant reduction in the C:N mass ratio (respectively, from 34.7 and 35.1 to 20.0 in both vermicomposts). [PERSON] and [PERSON] [75], studying the composting of poultry manure with or without plant residues, also observed that at the end of the composting process, the total organic carbon decreased, while the total nitrogen increased in contrast with initial values.  
The basis of rational nutrient management with the use of composts or vermicomposts in agriculture is to determine C:N ratio and the general forms of mineral ingredients. The C:N ratio in vermicomposts indicates their maturity, so it is commonly used for the assessment of quality of organic fertilizers [76; 20; 77; 78; 79]. The C:N ratio depends on many factors: e.g., on vermicomposting technology and type of baseline waste. In the vermicomposts produced, C:N ratios ranged from 19 to 22, which indicated the appropriate level of substrate maturity. [PERSON] et al. [18] suggested that vermicompost produced from cattle manure was characterized by a C:N ratio of 27.86; whereas raw manure--of 89.37. In turn, [PERSON] and [PERSON] [19] processed other types of waste mixtures (composed of cattle manure, rice straw and paper) and obtained a broader range of C:N ratios (from 12.23 to 38.85), thus demonstrating a varying degree of vermicompost maturity. The results indicated that C:N ratio is more appropriate in vermicompost (12.8) compared to sheep manure (28) [30]. Thus, using vermicompost as a fertilizer would provide better and more facilitative conditions for plants to absorb nitrogen.
## 4 Establishment of Sequence Stratigraphic Framework in the Study Area
### Establishment of Sequence Stratigraphic Framework  
To establish the sequence stratigraphic framework, it is necessary to identify stratigraphic sequence interfaces at all levels within the study interval, including unconformity surfaces, sedimentary conversion surfaces, and large-scale flooding surfaces. The formation of sequence boundary represents the existence of erosion unconformity or sedimentary discontinuity with different scales. Its upper and lower sedimentary strata will produce some special responses in lithology, sedimentary facies combination, logging curve characteristics, and seismic reflection characteristics, which can be used independently or together as indicators to identify sequence boundaries. In seismic, the thickness of the third-order sequence has little change, showing good horizontal continuity.  
According to the sedimentary characteristics of the drilling wells and related seismic characteristics of these boundaries, the high-resolution sequence stratigraphic framework of the study area is established in seismic and well ties to explore the stratigraphic characteristics of the target strata in the whole study area (Figures 4-8).  
Figure 3: Sequence Stratigraphic column of well A*, showing the stacking patterns of the lithology indicated by different well loggings, as well as the key core features of sandstone reservoirs. Blue and red triangles correspond coarsening and fining upward sequence, respectively.  
Figure 4: RMS amplitude map of the top SQ3-4 showing the locations of the two well tie profiles.  
Figure 5: Well tie profile showing the sequence correlation feature (G-C-D-E).  
Figure 6: Well-seismic tie profile showing the sequence correlation feature (G-C-D-E).  
Comparatively, the fourth-order sequences have great lateral changes (Figures 5-8). Specifically, the relationship between strata and the fourth sequence is as follows: Bazhenov: corresponds to the ascending half cycle of SQ1-1. J2-4: corresponds to the descending half cycle of the fourth sequences SQ3-1 and SQ3-2. J5-6: equivalent to the ascending half cycle and SQ3-3 of the fourth-order sequence SQ3-2. J7-8: equivalent to the fourth-order sequence SQ3-4 and SQ3-5. The drilling situation of SQ4-1 (J9) in each well is different, and the top interface is not uniform in a well-seismic correlation as some wells may not encounter J9.
## 4 Discussion
### Comparison of CPU Running Time of Different Inverse Algorithms  
The inverse method proposed in this study used a CNN to replace the FEMU used in the conventional inverse method. The efficiencies of these two methods were compared by considering the case of \(T\) = 0.5 in Table 2 as an example. The inverse problems were simulated on a computer configured with a quad-core 2.6 GHz CPU, but only one CPU was used without parallel computing. Table 3 presents the time required for the CNN- and FEMU-based methods to complete one iteration. The time used for the FEMU-based method was 21 s, while the CNN-based method only required 0.08 s, which is almost negligible compared to the former. Totally, \(10^{6}\) iterations were needed for the CNN-based method to obtain the converged result. Including the training and sampling time, the whole optimisation task took about 82.76 h for the CNN-based method, while it seemed to take forever for the FEMU method. If the same number of iterations was assumed for the two methods, the estimated time for the FEMU-based method would be 5833.33 h, which is far more than that of the CNN-based method. Therefore, it can be concluded that the proposed CNN-based inverse method significantly outperformed the conventional one in terms of computational efficiency.
# Intrusion Detection Model Based on Improved Transformer
## 4 Results
### Experimental Results and Discussion
#### 4.3.1 Comparison Experiments of Different Dimensionality Reduction Methods  
To validate the effectiveness and applicability of the feature dimensionality reduction method employed in this paper, we conducted comparative experiments under the same experimental conditions with different dimensionality reduction methods: we compared the dimensionality reduction method used in this paper (SAE) with existing dimensionality reduction methods such as PCA [20] and AE [19]. Finally, we used the proposed overall model for classification and selected the highest accuracy and _F1-score_ for each dimensionality reduction method for comparison. The comparative results are shown in Table 1.  
According to Table 1, the accuracy of the proposed model using SAE for dimensionality reduction achieved 88.7% in binary classification with an _F1-score_ of 88.2%, which is a 3.6% and 0.9% improvement over using PCA and AE, respectively. In multi-class classification, the accuracy reached 84.1% with an _F1-score_ of 83.8%, which is a 2.1% and 0.6% improvement over using PCA and AE, respectively. Analysis of the reasons for this shows that PCA relies more on variance when reducing data, and non-principal components with low variance may contain important information on sample differences. This leads to a reduction in the amount of information contained in the features during the dimensionality reduction process. AE trains a single-layer encoder directly during training, and excessive reduction can result in a loss of information in the reduced data features. However, SAE uses greedy layer-wise training and initializes the parameters for each layer, ensuring control over the reduced features. The layer-wise dimensionality reduction ensures that the amount of information contained in the data features remains unchanged, allowing the model to obtain the most amount of information and achieve the highest accuracy and _F1-score_ in classification.
# Near-Nature Ecological Technique for Pier Scour Countermeasure in a Submerged Overfall
## 4 Results and Discussions
### Protective Effect Due to Length of Strips of CKAW
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Case 0** & **Case 2** & **Case 5** & **Case 6** & **Case 7** \\ \hline Length of strip (cm) & N/A & 25 & 37.5 & 50 & 75 \\ \hline Flexibility index \(F_{i}\) & & 46 & 46 & 46 & 46 \\ \hline Flexibility parameter \(F_{p}\) & & 6.1 & 9.2 & 12.2 & 18.3 \\ \hline Maximum scour depth at pier \(d_{s}\) (cm) & \(-11.0\) & \(-9.1\) & \(-7.3\) & \(-7.1\) & \(-6.0\) \\ \hline Protective effect of scour depth at pier \(P_{s}\) (\%) & – & 17.3 & 33.6 & 35.5 & 45.5 \\ \hline Half length of surface wave (cm) & 10.5 & 9.5 & 13.5 & 11.5 & 11.0 \\ \hline Protective effect of surface wave’s half length \(P_{l}\) (\%) & – & 9.5 & \(-28.6\) & \(-9.5\) & \(-4.8\) \\ \hline Magnitude of surface wave (cm) & 3.4 & 2.7 & 2.4 & 2.1 & 1.7 \\ \hline Protective effect of surface wave’s magnitude \(P_{m}\) (\%) & – & 20.6 & 29.4 & 38.2 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Protective effect with different lengths of strips. effect when the strip length covers half the distance to the pier location. Figure 17 shows the change in water surface with different strip lengths. The magnitude reduced when the strip length increased. In the case of \(L/L_{d}=1\), the reduced percentage of magnitude was up to 50%, as listed in Table 5, showing the influence of CKAW on stabilizing the water surface. When \(L/L_{d}\) exceeded 0.5, the wave transferred further downstream than in the case without CKAW.
## 2 Materials and Methods
### Eligibility Criteria  
The PECOs (Population, Exposure, Comparator, Outcomes, Study design) framework was followed to establish the eligibility criteria [48].  
Inclusion criteria:  
* P (population): adults (>18 years) diagnosed with chronic musculoskeletal pain or chronic spine pain according to the ACTION-APS Pain Taxonomy [49]. This includes individuals with chronic low back pain, lumbosacral radiculopathy, fibromyalgia and myofascal widespread pain, gout, osteoarthritis, rheumatoid arthritis, and spondyloarthritis.
* E (exposure): participants underwent an mHealth-based exercise program, alone or within a multimodal intervention. mHealth was defined as 'health practice supported by mobile devices such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices' [32].
* C (comparator): any control group, with no restriction, or no control group.
* O (outcomes): pain-related measures (e.g., pain intensity) and physical function.
* S (study design): observational studies and controlled clinical trials that provide results from a correlation or regression analysis between clinical or demographic variables and the impact of mHealth exercise-based therapy on pain and function.  
Exclusion criteria:  
* Patients with chronic pain were analyzed together with participants with other chronic disorders.
* Studies where exercise was provided using a digitally delivered modality other than mHealth, e.g., web-based tools.
* Participants with cancer-related pain or with pain associated with the central or peripheral nervous systems.
* Cluster analysis.
* Outcome measures were evaluated before and after surgery, e.g., knee arthroplasty.
* Studies written in a language other than English or Spanish.
* Reviews, editorials, letters, commentaries, thesis dissertations, grey literature, and conference abstracts.
## 2 System Description and Data Collection
### System Design
Two electronic TTL-driven shutters (INLINE-TTL, 140 mm \(\times\) 50 mm \(\times\) 50 mm) are employed to connect the bifurcated optical fiber and two 5 m long bare fibers, which means that light path can be blocked without disturbing the measurement process. The temperature control unit is designed to maintain an ideal temperature using a thermoelectric cooler, a temperature transducer, and a Proportion Integration Differentiation (PID) controller. The SIFSpec system is designed to be mounted at different sites covering a variety of terrestrial ecosystems, meaning that the climate may differ greatly at different sites. In order to guarantee that the spectrometer's CCD can operate normally, as well as to control a main source of the thermal noise, the temperature inside the system has to be controlled. The thermoelectric cooling system consists of a TE cooler; two heatsinks; and two fans, one placed outside and one placed inside the thermotank. The TE cooler works to keep the temperature inside the thermotank at \(25\pm 1^{\circ}\). In addition, the TE cooler inside the spectrometer can cool the CCD detector down to \(-15\,^{\circ}\)C.
## 2 Method Description  
### Principle of the Method  
Smoothing of cutting force variation requires that the area of the detached chip section remains quasi-constant during the cutting process (see the previous section). In gear teeth machining, there are two potential solutions to achieve this result:  
* The constructive modification of the cutting tool;
* The appropriate variation in the cutting tool feed relative to the worked piece.  
Both solutions start from finding the variation law for the area of the detached chip section, which can be found by modeling the toothing process. Such modeling was realized by analytical and specific numerical methods [19] in the cases of gear teeth machining by slotting (with a rack tool or with a pinion cutter) and by hobbing.  
For sampling the tool constructive modification, in the case of the rack tool, it may consist of giving different angles of inclination to the successive teeth flanks or adopting different heights or different thicknesses for the successive teeth [19; 20]. However, this solution is difficult to apply due to the complex geometry of the tool, which becomes expensive, while the induced results have shown only a partial smoothing of the chips section area, hence a partial smoothing of the cutting force.  
Concerning cutting force smoothing by appropriate variation in the cutting tool feed rate, there are few mentioned results. When toothing by employing the rolling method on conventional equipment, the tool feed motion is mechanically controlled through elements of the rolling kinematical chain in a discrete manner. The pre-established increment of motion is impossible to modify throughout the process.  
The principle of the proposed method is to perform the teeth-cutting process by rolling with variable increments, according to an appropriate, previously established law. This can be reached by modifying the rolling kinematical chain, such as the tool feed motion, which is independently driven by a numerically controlled stepper motor.  
The implementation of the proposed method requires the steps presented below.