esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Corpus Augmentation by Sentence Segmentation
### Correcting Segments' Correspondence Information Using Common Chinese Characters  
Word alignment errors can cause the above procedure to split sentences wrongly. Some of the errors can be avoided by considering the correspondence between Chinese characters in Japanese and Chinese sentences. For that reason, we improved Step 3 of the procedure described above to correct the correspondence rates of segments, as described below.  
After World War II, China and Japan respectively simplified their use of Chinese characters in different ways, although Taiwan and Hong Kong still use the traditional Chinese characters. In Unicode, the same character code is assigned to similar Chinese characters. For example, the Japanese kanji \(\mathbb{X}\) (copy) and the simplified Chinese hanzi \(\mathbb{Z}\) (write) are unified to U+5199. By contrast, different character codes are assigned to the following pairs, which were originally the same characters: \(\mathbb{H}\) (see) and \(\mathbb{H}\) (see), \(\mathbb{H}\) (depart) and \(\mathbb{Z}\) (send out), \(\mathbb{H}\) (wide) and \(\mathbb{J}^{\rightarrow}\) (wide). As a result, one cannot say whether they were originally the same characters based on their codes.  
[PERSON] et al. [24] produced a mapping table between Japanese kanji, simplified Chinese hanzi, and traditional Chinese characters. They showed that the table can improve accuracies of word alignment and example-based machine translation between Japanese and Chinese [25]. We replaced Japanese kanji in the parallel data with simplified Chinese hanzi employing [PERSON]'s mapping table to calculate the rates of common Chinese characters among segments and used them for correcting correspondence information between segments.  
Figure 1: Example of word alignment information and sentence segments by punctuation marks.  
Figure 2: Correspondence rates between segments and parallel partial sentences derived from the rates.  
The rate of common Chinese character \(\sigma\) for a Japanese and Chinese segment pair is defined as shown below.  
\[\sigma=\frac{2n_{s}}{n_{j}+n_{c}} \tag{1}\]  
Therein, \(n_{j}\) and \(n_{c}\) respectively denote the number of Chinese characters in the Japanese and Chinese segments, and \(n_{s}\) is the number of the common (shared) Chinese characters. Using the common character rate \(\sigma\), we updated the correspondence ratio \(\rho\) between segments as:  
\[\rho^{\prime}=\left\{\begin{array}{ll}\rho+\sigma\cdot w&(\sigma\geq\theta_ {2})\\ \rho&(\sigma<\theta_{2})\end{array}\right. \tag{2}\]  
where \(\theta_{2}\) and \(w\) respectively stand for a threshold value and a weight (\(\rho^{\prime}\) is no longer a ratio).  
An example of the correction is presented in Figure 3. In this figure, the correspondence of partial sentences including \"#\" (amperometric titration method) are corrected in the lower part.
# ARMONIA: A Unified Access and Metro Network Architecture
## 2 ARMONIA Architecture  
Existing network infrastructures are based on a three-tier hierarchy. The top level of the hierarchy consists of the core networks, which provide increased capacity and high-speed interconnection between the individual networks of the second level. The core networks use very long-distance fiber optic links and are designed to operate at country and/or continent level. The metropolitan networks are located at the second level of the hierarchy. They connect the access networks with the core networks covering areas of the order of a few tens of kilometers. In essence, metropolitan networks connect nodes on an urban or regional scale. At the lower level of the hierarchy, access networks are placed to provide connectivity to several users through various technologies.
## 2 Bending Loss
### Experimental Results  
The setup shown in Figure 4 is used to measure the light power that escaped from the optical fiber at different bend radii.  
The measured output power at different bend radius is shown in Figure 5. The power launched into the fiber is 3 mW. A light power sensor placed 1 mm from the fiber curvature is used to measure the light intensity in air. The fiber curvature is adjusted between R = 0.5 mm and R = 44 mm. The used optical fiber has core radius a = 4.5 \(\upmu\)m, the cladding radius b = 62.5 \(\upmu\)m, \(n_{0}\) = 1.0 (air), \(n_{1}\) = 1.48, \(n_{2}\) = 1.5.  
As shown in Figure 5, the relation between the bend radius and the transmitted power is very close to the derived one in Equation (24) and hence, we can assume that the approximations are valid.
## 3 Results  
No significant interactions (groups \(\times\) conditions) were observed for rowing time (F = 2.583; \(p\) = 0.116), BLa (F = 1.172; \(p\) = 0.285), RPP (F = 0.503; \(p\) = 0.482), RMP (F = 0.509; \(p\) = 0.48), HR\({}_{\text{WP}}\) (F = 0.295; \(p\) = 0.59), HR\({}_{\text{MEAN}}\) (F = 0.313; \(p\) = 0.579), or HR\({}_{\text{MAX}}\) (F = 0.16; \(p\) = 0.691) (Figures 2 and 3).  
Similarly, no significant main effect of the condition was found for rowing time (F = 3.482; \(p\) = 0.069), BLa (F = 1.152; \(p\) = 0.289), RPP (F = 1.383; \(p\) = 0.246), RMP (F = 2.378; \(p\) = 0.131), HR\({}_{\text{WP}}\) (F = 0.047; \(p\) = 0.829), HR\({}_{\text{MEAN}}\) (F = 0.012; \(p\) = 0.912), or HR\({}_{\text{MAX}}\) (F = 0.105; \(p\) = 0.748).  
However, the group main effect showed a significant difference in rowing time (F = 146.72; \(p<0.001\)), with shorter times observed among males compared to females. Additionally, males exhibited higher levels of BLa (F = 27.013; \(p<0.001\)), RPP (F = 70.701; \(p<0.001\)), and RMP (F = 97.49; \(p<0.001\)) compared to females (Table 1). No significant main effects were revealed for HR\({}_{\text{WP}}\) (F = 0.295; \(p\) = 0.59), HR\({}_{\text{MEAN}}\) (F = 0.681; \(p\) = 0.414), or HR\({}_{\text{MAX}}\) (F = 4.012; \(p\) = 0.052).  
Figure 3: Comparison of heart rate values in sham and ischemia preconditioning conditions. SHAM—sham condition; IPC—ischem preconditioning condition; HR_WM—heart rate after warm-up; HR_MEAN—mean heart rate recorded during 500 m rowing; HR_PEAK—maximum heart rate recorded during 500 m rowing; M—males; F—females.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **M (95\%CI)** & **F (95\%CI)** \\ \hline \hline \multicolumn{3}{c}{**Blood Lactate Concentration [mmol/L]**} \\ \hline \multirow{2}{*}{**SHAM**} & 19.4 \(\pm\) 2.7 & 15.7 \(\pm\) 4.2 \\  & (18.3 to 20.6) & (13.7 to 17.7) \\ \hline \multirow{2}{*}{**IPC**} & 19.5 \(\pm\) 1.9 & 14.7 \(\pm\) 3.3 \\  & (18.6 to 20.3) & (13.1 to 16.3) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of blood lactate concentration, relative power output values, and time to achieve peak power output in sham and ischemia preconditioning conditions.  
Figure 2: Comparison of 500 m rowing time in sham and ischemia preconditioning conditions. SHAM—sham condition; IPC—ischem preconditioning condition; M—males; F—females.  
[PERSON]'s signed-rank test showed a significantly shorter TPP in the SHAM compared to the IPC condition for females (\(Z=2.415\), \(p=0.017\)), but not for males (\(Z=1.914\), \(p=0.056\)).
## 4 Discussion
These overheads can be significant and might not be justified in some real-world applications where real-time computation is critical. Another drawback pertains to potential data distortion. While SMOTE creates synthetic samples by interpolating between existing minority samples, these newly generated instances might oversimplify the true data distribution, especially in complex, multimodal feature spaces. Although the Edited Nearest Neighbors step filters out some misaligned data points, it may also remove borderline examples that are actually representative of natural transitions in the dataset. This balancing act can sometimes create gaps in the data space, ultimately affecting a model's ability to generalize to unseen scenarios [19]. Beyond that, SMOTEENN's focus on minority-class oversampling and noise removal may not adequately address other subtleties in real-world datasets, such as mislabeled examples or concept drift, where the underlying data distribution can change over time. The primary focus of this study is to demonstrate the practical efficacy of SMOTE and SMOTEENN in addressing class imbalance within the context of regression-based machine learning models for fall risk assessments in bedridden patients. The study systematically evaluates the performance of these techniques across smaller, imbalanced datasets. The algorithm is specifically designed to address the challenges posed by class imbalance in such datasets by generating synthetic data for the minority class. In scenarios where large, balanced datasets are available, the need for synthetic data generation diminishes, as traditional ML algorithms can effectively learn from the abundant data. However, in real-world applications, particularly in domains such as healthcare where rare conditions or events are of high importance, smaller datasets with class imbalance are prevalent. The focus of future studies will be on comparing SMOTE and SMOTEENN with other techniques currently in use. This comparison is important for evaluating the practical application of these methods in real-world scenarios. The findings from such studies can guide practitioners in selecting the most appropriate resampling technique for their specific use case, particularly when dealing with imbalanced datasets and regression models.
## 2 Materials and Methods
### Field Trials
#### 2.4.2 Experimental Methodology  
To simulate wind erosion events, a commercially available electric blower was repetitively applied to each treated soil plot surface, and dust measurements were carried out at particular times over a period of 28 days. At each test plot, suspended dust concentrations were measured 3, 7, 14, 21 and 28 days after substrate treatment, whereby PM\({}_{2.5}\), PM\({}_{10}\), and total suspended particle (TSP) concentrations were determined in the air (i.e., mg per m\({}^{3}\)), using a DustIfrak DRX 8533 from TSI with a log interval of 1 s. The DustIfrak DRX 8533 records aerosol concentrations in a range of 0.001-150 mg/m\({}^{3}\) (\(\pm\)0.1% of reading). Isokinetic sampling was applied to avoid biased measurements of particulate concentration, using a 90-degree bent tubing with a streamlined inlet. A 2 mm inlet diameter was selected to match the probe inlet velocity to the generated airflow velocity. The isokinetic probe was placed centrally opposite the applied wind direction at the height of 450 mm above the wooden frame (Figure 3b). The duration of each measurement was 60 s, whereby the test plot surface was subjected to a continuous airstream from the electric blower for 45 s, which generated a constant wind speed of 14.65 m/s (i.e., 52 km/h). The wind speed was measured using an anemometer positioned at the test plot end. However, it should be noted that such blowers generate a turbulent flow and do not meet the aerodynamic criteria of portable field wind tunnels, simulating the atmospheric flow causing natural wind erosion [20]. Consequently, the measured dust emissions do not equate to dust emissions caused by natural wind erosion. Measured particulate matter concentrations were initially analyzed for the presence of outliers and extreme values using box-plot graphics. Implausible or illogical values (e.g., negative values) were removed from the data set before the mean concentration of TSP, PM\({}_{10}\), and PM\({}_{2.5}\) were calculated. In addition, photographic images were taken before and after each measurement to document changes in the soil plot surfaces.
## Appendix A Direct Time Integration
### Methods for 1 st Order vs. 2 nd Order ODEs
#### Explicit vs. Implicit  
A scheme is said to be explicit when the evaluation of \(\mathbf{q}\left(\tau_{n+1}\right)\) does not require the solution of any system of equations and can instead be deduced directly from the results at the previous time-steps:  
\[\mathbf{q}\left(\tau_{n+1}\right)=\mathbf{q}_{n+1}=f\left(\mathbf{q}_{n},\dot {\mathbf{q}}_{n},\ddot{\mathbf{q}}_{n},\mathbf{q}_{n-1},\dot{\mathbf{q}}_{n- 1},\ddot{\mathbf{q}}_{n-1},...\right). \tag{11}\]  
The computer storage requirements are typically low, but their main drawback is the small time step \(\Delta\tau\) required to ensure stability and accuracy. In the implicit schemes, the displacements and the velocities at the current time step are expressed not only in terms of the values of the previous time step but also in terms of the current time step.  
\[\mathbf{q}\left(\tau_{n+1}\right)=\mathbf{q}_{n+1}=f\left(\mathbf{q}_{n+1}, \mathbf{q}_{n},\dot{\mathbf{q}}_{n},\ddot{\mathbf{q}}_{n},\mathbf{q}_{n-1},.. \right). \tag{12}\]  
In this second case, if the system is linear, the solution simply requires a matrix inversion, i.e., Equation (12) is solved together with the reference equilibrium equations as shown in [45]. If the system is nonlinear, this is not enough: an iterative scheme, usually the Newton-Rapshon method, is required as well. Therefore, compared to explicit methods, the cost per time step is greater and more computer storage space is required. An advantage of implicit methods is the larger time step size allowed.
# The Influence of Arabinoxylan on the Properties of Sourdough Wheat Bread
###### Abstract
This is due to the addition of sourdough, which has a positive effect on the redistribution of moisture inside the bread, and thus causes less crumb moisture loss during storage. Moreover, sourdough metabolites such as organic acids, dextrins and exopolysaccharides, have affected the interaction between water molecules and bread components, such as starch, resulting in a lower crumb moisture loss of sourdough bread during storage compared to bread without sourdough [13; 14]. Arabinoxylans (AXs) belong to the group of non-starch polysaccharides that are part of the dietary fiber of cereal grains [21]. The highest amount of total AXs is found in rye grain, at 8-12% [22; 23; 24]. However, the content of total AXs in wheat grain has been found to range from 0.8% to 9% [25; 26]. The molar mass of natural AXs in rye grain is in the range of 197,800-2,000,000 g/mol [23; 24; 27; 28; 29], while in wheat grain it is 176,000-381,000 g/mol [28; 29; 30; 31]. There are indications that the molar mass of water-insoluble AXs decreases as a consequence of acid hydrolysis, and thus their negative effect on bread quality has not been reported, but this mechanism has not been completely investigated [6; 32; 33]. The effect of AXs on the sourdough bread baking process has not been investigated in the studies conducted so far. On the other hand, it has been shown that AX significantly influences the process of wheat bread baking using the direct method and shapes its quality parameters. However, the amount of added preparation and the molar mass of AXs are crucial in this case [34; 35]. The addition of AXs to wheat bread based using the direct method improves its quality characteristics, such as the volume and porosity of the bread crumb. In addition, AXs support the retention of water in the bread crumb, which consequently causes its lower hardness [34; 35]. According to the available knowledge, the influence of rye AXs significantly differing in molar mass, both natural and those with modified structures resulting from cross-linking and partial enzymatic hydrolysis, on the addition of water to the dough and the properties of sourdough wheat bread has not been studied so far. The aim of this study was to investigate the effect of AXs of different molar masses on the properties of sourdough wheat -head. Moreover, the effect of AXs on the aging of wheat bread baked with sourdough was studied.
## 4 Improved Fuzzy Analytic Hierarchy Process (IFAHP)  
The AHP method integrates qualitative analysis and quantitative calculation to achieve multi-objective decision-making. It is characterized by its simple structure and strong systematic approach, making it suitable for establishing a hierarchical analysis model to evaluate the performance of earthquake rescue robots. By decomposing the complex problem into objectives, criteria, and indexes at multiple levels and angles, the rationality and systematicness of ranking earthquake rescue robots are ensured. Simultaneously, fuzzy theory is employed to fuzzify the process of weight calculation in order to reduce the influence of subjective factors in expert evaluation. Moreover, an improved fuzzy analytic hierarchy process is utilized for determining the weight calculation of earthquake search and rescue robots, and the conversion between qualitative analysis and quantitative computation is illustrated in Table 7.  
It can be seen from Table 7 that the trapezoidal fuzzy complementary judgment matrix can be used to replace the judgment matrix of AHP, and the weight calculation can be fuzzified so as to improve the limitations of subjective decision-making of AHP and ensure the objectivity and rationality of the weight calculation of performance indexes of earthquake rescue robots.  
Assuming that the performance evaluation of the earthquake search and rescue robots consists of \(n\) (\(n>2\)) performance evaluation indexes \(y_{1},y_{2},\cdots,y_{n}\), denoted as set \(Y=\left\{y_{i},|i\in N\right\}\) with \(N=\left\{1,2,\cdots,n\right\}\). The trapezoidal fuzzy number complementary judgment matrix \(\widetilde{\mathbf{X}}=\left(\widetilde{\mathbf{x}}_{ij}\right)_{n\times m}\) is derived, where \(\widetilde{\mathbf{x}}_{ij}=\left[a_{ij},b_{ij},c_{ij},d_{ij}\right]\) represents the relative  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{4}{c}{**Test Results of the Samples**} & \multicolumn{4}{c}{**Normalization Results**} \\ \cline{2-7}  & **Sample A** & **Sample B** & **Sample C** & **Sample A** & **Sample B** & **Sample C** \\ \hline \(C_{11}\) (h) & 3.13 & 20 & 4.35 & 0.067 & 0.594 & 0.105 \\ \(C_{12}\) (m) & 3000 & 6000 & 4000 & 0.222 & 0.556 & 0.333 \\ \(C_{13}\) (\%) & 1  
importance of the index \(y_{i}\) compared to \(y_{j}\), with \(a_{ij}\leq b_{ij}\leq c_{ij}\leq d_{ij},\forall i,j\in N(i\
eq j)\) and satisfying \(a_{ii}=b_{ii}=c_{ii}=d_{ii}=0.5,\forall i\in N,a_{ij}+d_{ji}=1,b_{ij}+c_{ji}=1,c_ {ij}+b_{ji}=1,d_{ij}+a_{ji}=1\).
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 4 Experimental Results
### Qualitative Evaluation  
We will first perform the qualitative analysis. Comparing with SOWP [10], we found that our algorithm can track the target accurately in many challenging sequences, while the SOWP failed. As shown in Figure 4, the \"Basketball\", \"Car4\", \"Freeman1\", \"Girl2\", \"Lemming\", \"Suv\" and \"Human3\" sequences contain occlusion, background clutters, scale variation and deformation, for which many trackers cannot track the given object accurately. In this work, we compare our tracker with SOWP on OTB-100 and give some results in Figure 4. The illustration shows that the proposed approach effectively handled scenes' fast motion (\"Basketball\" and \"Lemming\"), background clutters (\"Car4\" and \"Basketball\"), occlusion (\"Freeman1\", \"Suv\" and \"Human3\") and deformation (\"Basketball\", \"Girl2\" and \"Human3\"). For example, SOWP lost the object on the sequence \"Basketball\", due to sudden illumination variation and another athlete with a similar appearance. In contrast, our tracker could track the target successfully. SOWP also did not perform well on the sequence \"Lemming\". When the object reappeared in frame 901, SOWP failed to find it, but our tracker could track it steadily. The performance of SOWP was not satisfactory for sequence \"Freeman1\" due to background blur. It considered some background as a part of the target and led to inaccurate tracking results. It is easy to find that our algorithm could achieve better results on these challenging videos due to the utilization of weighted patches with the MBD.
## Materials and Methods
### Remote Sensing Data and Preprocessing  
The HJ-1 satellite was launched on 6 September 2008, and the ZY-3 satellite was launched on 9 January 2012. Both the HJ-1 satellite and the ZY-3 satellite were launched from the Taiyuan Satellite Launch Center, Shanxi Province, China. The GF-1 satellite was launched on 26 April 2013 from the Jiuquan Satellite Launch Center, Gansu Province, China. The HJ-1, GF-1 and ZY-3 satellites are in sun synchronous orbits at altitudes of 649 km, 645 km, and 505 km, respectively. The technical specifications for the ZY-3 MUX, GF-1 WFV, and HJ-1 CCD sensors are shown in Table 1. Four spectral channels that are distributed in the visible and near-infrared (NIR) spectral domain ranging from 450 nm to 900 nm are identical in these three sensors. The radiometric resolutions of the GF-1 WFV and ZY-3 MUX sensors are higher than that of HJ-1 CCD by 2 bits, which improves the detectability of changes in the feature characteristics. The HJ-1 CCD data have a spatial resolution of 30 m, and the revisit time is approximately four days over China due to the combination of two satellites (HJ-1A and HJ-1B) with two cameras (CCD1 and CCD2) on each satellite. The GF-1 WFV data have a spatial resolution of 16 m and a revisit time of four days among the four combined cameras. The ZY-3 MUX data have the highest spatial resolution of 5.8 m, but the 51 km swath width is much narrower than that of the other two cameras.  
Figure 1: The geographic location of the study area of ZY-3 MUX based on the false color composite (NIR-red-green) (**a**), and the subset study area with leaf area index (LAI) field measurements for maize (yellow dots) (**b**).