esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Validity Experiments
However, the proposed adaptive pavement distress detection models effectively mitigate this issue, leading to enhanced pothole identification accuracy. Overall, by employing the framework proposed in this study to transfer a pavement distress detection model trained on the source domain to the target domain, there was an improvement of about 28% in _Precision_, about 20% increase in _Recall_, about 20% rise in _mAP_, and about 23% improvement in \(F\)1-score. In the unsupervised setting, the framework proposed in this paper achieves 93.09% of the performance attained by transfer learning in the supervised setting. These results affirm the efficacy of the proposed framework. In summary, the test-time adaptive framework for pavement distress detection proposed in this paper achieves performance close to that of transfer learning without the need for target domain data annotation. Although transfer learning can significantly enhance a model's performance in the target domain by utilizing target domain data and labels to teach the model how to detect pavement distress in that domain, it requires extensive data annotation in the target domain. By employing self-supervised learning, the proposed framework enables the original pavement distress detection model to adapt from the source domain to the target domain and achieves better pavement disease detection results.
## 3 Numerical Evaluation of the Dominant Terms of the Doubly Averaged Lunisolar Perturbation
### The Dominant Terms in the Long-Term Dynamics

Table 5: Largest periods (\(>\)7 years) for the lunar and solar arguments appearing in the second-order doubly averaged disturbing potential. The first column helps to identify which third body the arguments belong to. The periods (in years) are computed assuming \(\hat{h}\approx\hat{h}_{I_{2}}\) and following the assumptions listed in Table 4 for \(\hat{g}\), as specified at the top of each column. tion chosen. The same feature also emerges from Table 6, where the groups are of four arguments in the lunar case and of two arguments in the solar case. The solar third-order critical arguments \(g\pm g_{\odot}\) and \(3g\pm g_{\odot}\) (top of Table 6) behave as the main resonant angle. Conversely, the third-order lunar arguments \(3g-g_{\zeta\ }-2h+2h_{\zeta\ }\) and \(3g+g_{\zeta\ }+h+h_{\zeta\ }\) behave in the opposite way: by increasing the lunisolar perturbation, the arguments may even become critical. In Table 7, all the harmonics whose \(R>1\) km\({}^{2}\)/s are shown, from the highest to the lowest, taking as reference the assumption of the fourth column. At the top of the table, we find clearly the main resonant argument \(2g\), which is the lunisolar term dominating the dynamics of the eccentricity for Molniya orbits, as is well known. Subsequently, the harmonics corresponding to \(2g\pm h\) and \(h\) show \(R>200\) km\({}^{2}\)/s; thus, they produce a significant contribution to the dynamics; precisely, \(2g\pm h\) affects both \(e\) and \(i\), while \(h\) only perturbs \(i  
In [5], the role played by \(2g\pm h\) was analyzed by means of a double resonance model, even though the resonance \(2g+h\) does not produce any resonances overlapping. Moreover, in [2], the authors proved that the terms corresponding to \(2g\), \(2g\pm h\) and \(h\) capture the main features of the long-term eccentricity variation, by comparing the pseudo-observation data given by the Two-Line Element (TLE) with ad hoc numerical propagations. Following the results in [2], in this work, the _dominant terms_ are chosen as the ones associated with \(R>100\) km\({}^{2}\)/s. Indeed, as long as the hypothesis of a constant semi-major axis holds true, a model including the lunar harmonics with \(l=2\), \(m=0,1\) and \(s=0,1\) in Equation (2) and the solar harmonics with \(l=2\), \(m=0,1\) in Equation (1) seems to be the \"poorest\" model capable of representing the third-body effect in the actual mean eccentricity of many Molniya spacecraft. Such terms of the quadrupolar expansion occupy the first 14 lines of Table 7, having only 2 of them \(R<100\) km\({}^{2}\)/s significantly.
## 4 Experiments  
### Datasets and Experimental Setup  
To evaluate our method comprehensively and verify its effectiveness, we conducted extensive experiments and compared it with DIP and eight other state-of-the-art image denoising methods, including BM3D [2], NCSR [7], WNNM [5], DnCNN [11], FFDNet [12], TWSC [23], RED-Net [24], and CsNet [13]. We conducted denoising experiments on four datasets. In the first dataset, as shown in Figure 7, 10 images commonly used in the literature were selected, comprising six images with a size of 512 \(\times\) 512 (Barbara, Boat, Couple, Hill, Lena, and Man) and four images with a size of 256 \(\times\) 256 (Cameraman, House, Monarch, and Peppers). For the second dataset, as illustrated in Figure 8, we randomly selected 50 natural images from the Berkeley segmentation dataset (BSD) [25]. The third dataset contains 10 images obtained randomly from the Flickr1024 database, [26] which consists of 1024 high-quality images covering diverse scenarios. Figure 9 shows some examples. The 10 images in the fourth dataset were randomly selected from Urban100, which contains 100 high-resolution images with various real-world structures; Figure 10 shows some representative images in this dataset.  
Figure 6: Performance comparison between the adaptive termination step and optimal step.  
To test the denoising performance of the proposed method objectively, we utilized three widely accepted image quality evaluation criteria [27; 28; 29], including PSNR, structural similarity index (SSIM) [30], and the feature similarity index measurement (FSIM) [31]. In addition, we compared the results visually to assess the quality of the denoising effects subjectively. We performed our experiments using a Lenovo desktop with a 4.00 GHz eight-core Intel Core i7-6700K CPU and 16 GB of RAM.
## 5 Performance Evaluation
### Cluster Mode  
In this subsection, we compare and analyze the performance according to the Cluster mode. The previous experiments were performed in the environment where Cluster mode is Quadrant mode. In this subsection, experiments are performed in SNC-4 mode. Then, we compared and analyzed the performance differences according to Cluster mode. The data type in the experiment was Integer, and the data size of Broadcast and AllReduce was 256 MB, but the data size of Gather was 16 MB. In the experiment graph of Figure 11, C represents MPI communication in C environment; \(\text{Bcast\_B}\), \(\text{Gather\_B}\), and All_B represent MPI buffer communication in Python environment; and \(\text{Bcast\_O}\), \(\text{Gather\_O}\), and All_O represent MPI object communication in Python environment. The comparison method is the same as in the previous sections; however, the Memory mode experimented with is DDR4 in Flat  
Figure 10: AllReduce communication time results according to the experiment environment: (**a**) Host; (**b**) one Docker Container; (**c**) two Docker Containers; (**d**) four Docker Containers.  
mode and MCDRAM in Flat mode. This section differs from previous ones in that we compare just two environment types, Host and four Docker Containers.  
Figure 11: _Cont._  
is Host environment In AllReduce, and Figure 11f is AllReduce in four Docker Containers environment. As explained in the background of the previous section, SNC-4 mode divides the chip into quadrants, and each quadrant is mapped to a NUMA node. In this mode, SNC-4 mode has a memory controller and TD in the same area and has a shorter path than Quadrant mode. For this reason, it was expected that SNC-4 mode might perform better than Quadrant mode. However, the experimental results showed little difference in performance between SNC-4 mode and Quadrant mode. Due to the characteristics of the algorithms of Broadcast, Gather, and AllReduce performed in this paper, it seems that the characteristics of SNC-4 Cluster mode cannot be utilized. In SNC-4 mode, MCDRAM NUMA node and DDR4+Core node are physically close. SNC-4 mode shows the lowest latency when communication is maintained within the NUMA domain. But, if in SNC-4 mode cache traffic crosses NUMA boundaries, this path is longer than in the Quadrant mode. In this experiment, MPI collective communication creates a graph topology with a distributed method, and each node defines a neighbor. Therefore, due to this MPI communication characteristic, it seems difficult to utilize the advantages of SNC-4 mode when the process to be communicated is not in the same domain. According to our experiment, MPI communication in Quadrant mode is effective, which can maintain transparency without code modification.
## 3 Results
### Treatment of Tailing Leadates  
The sorbent was tested for the treatment of the tailing leachate and further extraction of REEs using DOWEX 50X8 resin. This step recovered around 75% of total REEs and up to 38% of uranium leached from tailings. In addition, this pre-treatment process allowed the removal of 90% of cadmium and about 69% of nickel. Table S5 reports the composition of the ore leachate (before and after pH control to 4) and after sorption step. Figure S15 reports the sorption capacities for selected metals and the selectivity coefficients for these metals (with reference to Cd, as in Section 3.2.6.). For Al and Cu (whose initial concentrations were -103.8 mmol Al L\({}^{-1}\) and 14.58 mmol Cu L\({}^{-1}\)), the occurrence of precipitation at pH 5.5 greatly overestimates the sorption capacities (Figure S15a). In the case of Fe(III), the initial concentration is close to 1.42 mmol Fe L\({}^{-1}\), making possible the formation of hydrocolloids that facilitate the recovery of the metal; the sorption capacity reaches up to 1.39 mmol Fe g\({}^{-1}\). Other than the three metal ions present in elevated concentrations (subject to hydrolysis and precipitation mechanisms), the other metal ions show sorption capacities below 1 mmol g\({}^{-1}\). The initial concentration of Zn(II) reaches up to 7.30 mmol Zn L\({}^{-1}\); this may explain the high sorption capacity (close to 0.99 mmol Zn g\({}^{-1}\)). Nickel is another major element in the leachate (concentration: \(\sim\)1.37 mmol Ni L\({}^{-1}\)); the sorption capacity is close to 0.41 mmol Ni g\({}^{-1}\). Much lower sorption levels (below 0.1 mmol g\({}^{-1}\)) are reported for other metal ions, which is consistent with their relative initial concentrations in the leachate. Apparently, the sorbent has a wide reactivity for different types of metal ions. The apparent selectivity coefficients (i.e., SC\({}_{\text{metal/Cd}}\)) are relatively limited (varying between 3 and 0.3); these values are not sufficient to effectively separate these metals. It is noteworthy that the levels of concentration are drastically different, making the comparison of SC values only indicative of some trends in the preference of sorbent for given metal ions. High initial concentrations (large excess) may contribute to minimizing the SC values, while precipitation phenomena induce the overestimation of SC values (Al, Cu and to a certain extent Fe). The sorbent shows a preference for metal ions according to the series:  
\[\text{Nd(III)}>\text{Sm(III)}>\text{Pb(II)}>\text{Y(III)}>\text{Cd(II)}>\text{ U(VI)}>\text{Ni(II)}>\text{Zn(II)}\]  
This ranking is difficult to correlate with the physico-chemical properties of selected metals as reported in Table S3. External criteria (metal concentration, speciation of the metals, etc.) may interfere in the effective affinity of the sorbent for these metal ions.
## 3 Results and Discussion  
### Screening of Phase Ratio in ATPS System  
#### 3.1.1 Selection of Ethanol Mass Fraction  
Single-factor conditions were fixed to allow analysis of other factors. As shown in Table 2, the effect of the ethanol mass fraction on the yield of total flavonoids was studied. Before the ethanol mass fraction reached 38% (\(w/w\)), the yield of total flavonoids was positively correlated with it; then, the yield of flavonoids showed a downward trend with increasing ethanol concentration. The reason for this is that as the mass fraction of the ethanol in the system increased, the concentration of the ethanol in the top phase increased, and the polarity decreased. Under these conditions, it was more conducive to perform the extraction of flavonoids. As the mass fraction of ethanol exceeded 38% (\(w/w\)), the polarity of the upper phase decreased. This resulted in the immiscibility of the flavonoids and the salt of the precipitation [30]. At this time, the amount of some fat-soluble organic compounds also increased, which inhibited the leaching of total flavonoids. Therefore, the optimal parameter for the ethanol concentration was 38% (\(w/w\)).
## 3 Experimental Results and Analysis
### Grasping Algorithm Experiments  
The experiments were conducted on the Visual Studio 2019 platform with an R7-5800H CPU. The robotic arm used was the SD-700E model from Xinshida. The grasping environment is illustrated in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **R\%** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]:0.95\%** & **Size (MB)** \\ \hline YOLOv8n & 79.7 \(\pm\) 0.8 & **88.4 \(\pm\) 0.6** & 62.3 \(\pm\) 1.0 & 6.5 \\ YOLOv7-tiny & 80.2 \(\pm\) 0.7 & 86.9 \(\pm\) 0.8 & 54.3 \(\pm\) 1.2 & 11.7 \\ YOLOv4 & 75.2 \(\pm\) 0.9 & 84.9 \(\pm\) 0.7 & 51.9 \(\pm\) 1.3 & 17.76 \\ YOLOv3 & 70.3 \(\pm\) 1.1 & 82.0 \(\pm\) 1.0 & 47.6 \(\pm\) 1.5 & 117.7 \\ YOLOv5s & 74.1 \(\pm\) 0.6 & 86.3 \(\pm\) 0.7 & 55.2 \(\pm\) 1.1 & 13.78 \\ Faster R-CNN & 71.3 \(\pm\) 0.8 & 87.2 \(\pm\) 0.6 & 56.9 \(\pm\) 1.3 & 108 \\ SSD & 64.8 \(\pm\) 1.2 & 75.9 \(\pm\) 1.5 & 47.8 \(\pm\) 1.4 & 92.6 \\ YOLOv8-URE & **80.4 \(\pm\) 0.5** & 88.3 \(\pm\) 0.6 & **62.4 \(\pm\) 0.8** & **4.46** \\ \hline \hline \end{tabular}  
* The best results for each metric are highlighted in bold; all results are reported as means \(\pm\) standard deviation over five independent experiments.  
\end{table}
Table 8: Comparison of algorithm generalization.  
Figure 15: Comparison of detection results in blurred scenes between YOLOv8-URE and YOLOv8n: (**a**) detection results of YOLOv8-URE in blurred scenes; (**b**) detection results of YOLOv8n in blurred scenes. Note: red bounding boxes are generated by the algorithm, green boxes indicate missed detections, and yellow boxes indicate false detections.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 4 Discussion  
### RGB Imagery Classification  
In this study, a proximal and low-cost sensor combined with an enhanced open-source software image tool highlighted the opportunity to digitally determine the percentage of weeds in a fodder crop and compute a VRA of a herbicide. Compared to other studies using different classification methods, our kappa value of 78% is similar to those with 77% using object-based algorithms [27] or 73% using the Random Forest (RF) model [28]. Regarding the accuracy of weed identification, our method reaches 45%, less than [PERSON] et al.'s [29], which reached values of 96% and 94%, for the RF and Support Vector Machine models, and 63% for the K-Nearest Neighbors model. Although having a lower value, our proposal using the RGB sensor of a smartphone as a low-cost sensor computed an image for later classification in Geographic Information System (GIS) software, allowing moderate distinction of the soil, weeds, and crops, even considering its earlier growth stage. This method provides a friendly and light computational solution for farmers to find out the quantities of weeds in a plot, making it possible to know their location in space, as well as facilitating the adjustment of the doses of herbicide in the zoning areas to the needs of each specific point.  
Impact of VRA of Herbicide on % of Crop Plants, Weed Plants, and Final DM Productivity (t ha\({}^{-1}\))  
As expected, the non-application of herbicide (Plot I) results in the continued presence of a large percentage of weeds. The percentage of weeds clearly decreases in Plots II and III with the application of herbicide, and there is also an increase in crop production and  
Figure 7: Bivariate analysis of dependent variables under study: (a) b-plots; (b) correlation matrix.  
homogeneity of the crop shown by the reduction of the value of the CV. Both of these plots demonstrate the benefits of herbicide in crop production. Despite a lower production than Plot III, the results of Plot II with was an average reduction of 0.22 l ha\({}^{-1}\) demonstrate that a clear reduction in the weed population was achieved, even in the areas with a lower percentage of herbicide (0.50 l ha\({}^{-1}\)). This finding corresponds to a reduction in the total herbicide applied and certainly contributes to a greater economic and environmental impact than the injudicious weed control in a fixed-rate application system. A similar contribution was made by [PERSON] and [PERSON] [30], who have implemented variable-rate herbicide applications based on soil physical properties in grain sorghum.
## 1 Introduction
The IXS technique is generally implemented at synchrotron sources at dedicated beamlines, equipped with instruments ensuring good energy resolution and efficient X-ray detection [3; 23; 24; 25]. In order to obtain good quality spectra with relatively short acquisition times, the IXS signal is often recorded by scanning the incident X-ray energy rather than scanning the emission spectrometer. Such an approach, called inverse-IXS, allows the efficiency of the IXS experiments to be improved by building X-ray spectrometers equipped with multiple analyzer crystals. Such spectrometers, arranged in the Johann geometry, are operated at a fixed Bragg angle close to 90 degrees. A large solid angle can be achieved by using even a few tens of analyzer crystals. To date, this experimental approach is the best possible approach for use at 3 rd-generation storage rings, which are characterized by very stable beam parameters and allow for fast, reproducible scanning of the incident X-ray beam energy using fixed-exit double-crystal monochromators synchronized to insertion device gaps. This approach cannot be applied at XFELs, where the pulse and photon energy stability are poor and vary significantly from pulse to pulse [26; 27]. This means the application of the IXS technique at an XFEL should avoid, if possible, the necessity of scanning the incident photon energy. An alternative approach to scanning the incident X-ray photon energy is to use a dispersive X-ray spectrometer at a fixed incident X-ray beam energy. This allows the full and direct IXS spectrum to be obtained in a single acquisition, and each acquisition can then be simply summed up, removing the necessity of both signal normalization and changing the incident beam photon energy. This approach is clearly advantageous for sources where the stability of the incident X-ray beam parameters varies from pulse to pulse. Due to the properties of X-ray radiation delivered by XFEL sources, the implementation of X-ray spectroscopy techniques commonly applied and established at synchrotron sources is not straightforward due to the inherent instabilities of the XFEL beam. In the present work, we report on the application of a dispersive von Hamos spectrometer to measure the IXS signals from solid and liquid samples. Thanks to the dispersive detection, the entire IXS signal may be measured on a shot-to-shot basis. Moreover, the shape of the IXS signal does not depend on the incident beam energy, and only requires shifting the IXS spectra according to the energy conservation rule to be centered on the spectrometer acquisition bandwidth. Therefore, combining a dispersive-type spectrometer with the self-seeded operation of an XFEL source [26; 28] will allow one to record IXS signals for every X-ray pulse, improving the efficiency of the experiment.
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 5 Discussion
### The Relationship of the SUS Score, Tourist's Satisfaction, Loyalty, and Engagement
Younger tourists (18-30 years old) exhibited comparatively lower loyalty, indicating potential differences in preferences and behaviors regarding tourism safety management and trip recommendation systems. Factors such as risk perception, travel experience, and decision-making processes may influence this disparity. Conversely, tourists aged over 45 displayed the highest level of loyalty, regardless of gender, suggesting their prioritization of safety in the travel decision-making process and inclination to utilize tourism safety management and trip recommendation systems for a secure and enjoyable travel experience. Gender disparities in loyalty towards tourism safety management and trip recommendation systems were evident, showcasing varying degrees of loyalty between males and females across different age groups. Male tourists aged 18-30, 31-45, and over 45 years displayed loyalty scores of 4.67, 4.65, and 4.76, respectively. In contrast, their female counterparts in the same age groups exhibited slightly higher loyalty scores of 4.75, 4.77, and 4.87. These findings suggest that females might be more inclined to utilize such systems, regardless of age. The interpretation of the results reveals that gender and age play significant roles in understanding tourist behavior and preferences towards tourism safety management and trip recommendation systems. The fact that female tourists showed higher loyalty scores in comparison to males indicates that safety and security are crucial factors for female travelers, potentially influencing their choice of destinations and activities. These findings highlight the importance for tourism businesses and managers to consider gender-specific preferences when designing safety measures and recommendation systems to effectively cater to their diverse customer base. By acknowledging these disparities, the industry can tailor its services better and enhance the overall experience for all tourists. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sources** & **DF** & **Seq.SS** & **Adj.SS** & **Adj.MS** & **F** & \(\mathbf{p}\) \\ \hline Age range & 2 & 1.65790 & 1.33192 & 0.66596 & 297.78 & 0.000 \\ Gender & 1 & 1.30402 & 1.30693 & 1.30693 & 584.38 & 0.000 \\ Age range\({}^{\text{t}}\)gender & 2 & 0.03736 & 0.03736 & 0.01868 & 8.35 & 0.000 \\ Error & 511 & 1.14281 & 1.14281 & 0.00224 & & \\ Total & 516 & 4.14209 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Statistical test of the data from Table 9.