esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Attention Analysis from Coordination
Blue and red cells labeled _A_’ and \(R\) represent intelligent coworker and roaming agent, respectively. Besides the discussion on the standard attention, we discuss the explanation based on the class and positional attention. In the class attention heatmap in Case 1, _agent_\(A\) assigns a high attention weight of 0.304 on both objects, 0.009 on itself and 0.004 elsewhere. In the positional attention, values of attention weight around _agent_\(A\) are relatively higher (0.075, 0.078, 0.041, and 0.125); this implies that agents are slightly but always aware of their surroundings. Note that because only standard attention heatmaps are generated by DA3-X [18], the detailed explanation provided by class and positional attentions is the interpretation made possible by eDA3-X. Figure 4(c) (Case 2) is a demonstration of _agent_\(A\) behaving cooperatively when it observes _agent_\(A^{\prime}\), which is an intelligent coworker, along with two objects. Accordingly, _agent_\(A\) yields _agent_\(A^{\prime}\) for the closer object and moves downward to approach a slightly farther object. The standard attention heatmap shows high intensity on _agent_\(A^{\prime}\) (0.231) and a decrease in the attention weight on the closer object as it changes from 0.221 in Case 1 to 0.181 in Case 2. We can verify that such differences in attention weight are derived from class attention. Once _agent_\(A\) sees _agent_\(A^{\prime}\) nearby, it puts 0.395 of attention weight on the coworker and relatively less attention (0.187) on objects, indicating the high impact of the existence of _agent_\(A^{\prime}\) for _agent_\(A^{\prime}\)s decision-making process. Lastly, _agent_\(A\) ignores _agent_\(R\), which is a roaming agent, and approaches a closer object in Case 3 (Figure 4c). In other words, _agent_\(A\) successfully recognizes _agent_\(R\) as an irrational agent and learns that it is unworthy to coordinate with _agent_\(R\). Unlike in Case 2 shown in Figure 4b, _agent_\(A\) assigns only 0.078 of attention weight on _agent_\(R\), as shown in the standard attention heatmap in Figure 4c. This decrement of attention weight on _agent_\(R\) is derived from the class attention because it indicates a higher attention weight on objects than that on _agent_\(R\) (0.248 and 0.188).
# Nanoparticles--Plant Interaction: What We Know, Where We Are?
## 2 Apoplast
### Documented Entrance of NP into Apoplast  
The available literature data indicate that NP, even if their diameter is much larger than the determined average pore size, can cross the cell walls (for review see: [18; 30; 31; 32; 33; 34; 35]). For example, studies on wheat showed the presence of 20 nm titanium dioxide NP (TiO\({}_{2}\)-NP) in different plant tissues indicating the possibility of this NP to cross the wall barrier [36]. In soybean and alfalfa, the iron oxide (Fe\({}_{3}\)O\({}_{4}\)-NP) of about 18 nm diameter were taken up by roots, but translocation to the aerial parts was not detected [37]. For jasmine rice treated with silver NP (Ag-NP) of different diameter (20, 30-60, 70-120 and 150 nm) the uptake of NP was described [38]. It was shown that the lead sulfide NP (PbS-NP) in a size of about 20 nm can penetrate the maize roots, because they were detected in intercellular spaces and cytoplasm of the cortical cells [33]. In radish treated with cerium oxide NP (CeO\({}_{2}\) NP; diameter 10-40 nm) the presence in plant tissues was also described indicating that even such large NP can cross the wall [32]. Treatment of _Cucurbita pepo_ with superparamagnetic iron oxide NP (SPIONs; 12.5 nm in size) also showed that NP pass the cell wall [39]. Moreover, it was shown that zinc oxide NP (ZnO-NP) of 8 nm in diameter may enter cytoplasm of root cells of _Brassica_[40].  
So, the question is, what mechanism allows NP to cross the cell wall barrier? It can be assumed that NP primarily cause changes in the chemical composition and physical parameters of the wall. The cell wall is the part of the plant cell structure, which is actively involved in the plant response to changing environmental conditions [41]. Modifications in the chemical composition of cell walls in response to biotic and abiotic factors are increasingly being investigated as an adaptation mechanism to altering environmental conditions [28; 42; 43; 44; 45; 46; 47; 48; 49].
## 1 Introduction  
Bilateral deficit (BLD) occurs when the sum of the strength produced by each limb individually is greater than the strength produced when both limbs apply force simultaneously [1, 2]. This phenomenon has been observed in both the upper and lower limbs, regardless of whether the contraction is static or dynamic [3]. From a performance perspective, athletes should focus on training for optimal performance in the specific tasks of their sport, rather than aiming to equalize bilateral index values [4].  
Since [PERSON] and [PERSON] described BLD in 1961 [5], many studies have attempted to elucidate the cause of BLD [6, 7, 8]. In 2016, [PERSON] et al. [9] compiled possible mechanisms underlying the production of BLD. Specifically, they divided the classification into psychological, task-related, physiological, and neurophysiological factors. Despite the many mechanisms described, the most contrasted explanation for the origin of BLD is the neurophysiological factors; specifically, the inability to bilaterally activate fast-twitch muscle fibers at a maximal level [6, 7, 8]. BLD has been widely investigated in lower limbs [10, 11, 12, 13, 14]. In contrast, less scientific evidence exists for the upper limbs. The primary studies have focused on the finger [1, 10, 11, 12, 15], shoulder [16], and elbow [3, 8, 17, 18, 19, 20] joints. According to [PERSON] et al. [21], the elbow joint is the most suitable joint for assessing upper limb strength. However, there is limited literature on BLD in this joint.  
The presence of BLD has been confirmed in analytically assessed elbow flexion movements in healthy young adults [3; 8; 17; 18]. Several studies agree on both the evaluation protocol of gesture and the fact that BLD is produced by a decrease in the electromyographic activity of the dominant limb [3; 17; 18]. These studies have shown that physiological and neurophysiological factors are responsible for BLD production in analytical gestures. However, other factors related to global and functional gestures, such as psychological and task-related factors, have been neglected.  
With new technologies, it is possible to evaluate gestures both functionally and analytically. The assessment of functional movements can provide more practical conclusions regarding their application to daily life. This allows us to study other factors that trigger BLD in the upper limbs, such as the influence of postural stability or core action. Specifically, functional electromechanical dynamometry is a valid and reliable tool for performing functional assessments [22; 23; 24; 25; 26].  
To the best of our knowledge, no study has functionally assessed elbow flexion movement. Therefore, new studies are needed to elucidate the effect of task-related factors, psychological factors, and other physiological factors on BLD. Therefore, the main objectives of this study were to (a) identify the influence of postural stability and core function modification on BLD and (b) compare the assessment of strength generated during elbow flexion functionally vs. analytically in the dominant (D) and nondominant (ND) upper-limbs.
## 10 The HED Instrument
### HED Instrumentation and Capabilities
These detectors will be coupled, e.g., to crystal spectrometers. Three EPIX10k modules [123] have identical chip size, 100 \(\upmu\)m pixel pitch, but offer 10\({}^{4}\) dynamic range by gain switching. With the same dynamic range, four Jungfrau modules offer 40 \(\times\) 80 mm\({}^{2}\) chips each with 75 \(\upmu\)m pixel pitch and 10\({}^{4}\) dynamic range at 12 keV [124]. The latter two gain-switching detectors are ideally suited to record dedicated parts of an X-ray diffraction pattern. For both, IA1 and IA2, a detector bench at the end of EH will offer a possibility to place large area detectors, e.g., for imaging or SAXS type experiments. This bench allows adjusting the distance from IA1 and IA2 to the detector. On this bench, the HIBEF consortium plans to integrate an AGIPD 1M detector [31], a Perkin-Elmer 4343 CT flat-panel large-area detector, and high-resolution CCD cameras for X-ray phase contrast imaging and ptychography applications. Several drivers to generate extreme states of matter will be available at HED, e.g., two high energy optical lasers, diamond anvil cells, and pulsed magnetic fields, contributed and operated by the international HIBEF user consortium. The all-diode pumped high energy (HE) nanosecond DiPOLE-100X laser is developed by STFC CLF (UK) [125]. It delivers up to 80 J at 515 nm wavelength with pulse durations of 2-15 ns with a maximum repetition rate of 10 Hz. This laser will be primarily used for shock compression experiments and its pulses can be temporally shaped to enable isentropic ramp compression techniques. The multi-100 TW Ti:Sapphire (HI) laser system, currently under construction by Amplitude (France), will deliver 4-10 J of 800 nm light in ultrashort pulses of less than 25 fs at a repetition rate of 10 Hz. The pulses of this laser can be focused to a few \(\upmu\)m\({}^{2}\) spot by means of an off-axis parabola, reaching on-target intensities of the order of 10\({}^{20}\) W/cm\({}^{2}\). This laser will primarily be used for relativistic laser-matter interaction experiments. In addition, the standard PP laser of the European XFEL will be available. All three lasers have to be precisely timed with respect to the X-ray pulses and are synchronized to the master oscillator. The timing jitter between the PP laser and the incident X-rays is monitored by photon-arrival diagnostics with a precision on the order of a few femtoseconds. Timing between the HI laser and the X-rays is realized indirectly using the characterized PP laser in an optical-optical balanced cross-correlator. Timing between the HE laser and the X-rays is less demanding and achieved via fast photo diodes that detect both X-rays and optical light with a resolution of few 10 ps. Matter in magnetic fields of up to 60 T can be studied in a solenoid coil. The timescale of the field build-up of 0.6 ms is perfectly adapted to the length of a 4.5 MHz pulse train of the facility.
## 3 Materials and Methods
### Data Pre-Processing  
Both datasets were initially provided as ECG recordings. The CE dataset comprised 24 h recordings, while the AF dataset consisted of 3-min segments, manually chosen based on data quality, basically those segments where the PQRS components were clearly visible and were not affected by additional, spurious spikes. HRV features were extracted from both datasets using Kubios Premium software. Kubios Premium 3.4.1. is a specialized tool for HRV analysis, recognized for its scientific and professional utility [34]. This same tool could be used to detect segments of high quality data in case researchers are not acquainted with visual inspection of ECG recordings. It incorporates a built-in Pan-Tompkins QRS detector and advanced options for correcting RR interval artifacts, efficiently rectifying missed, additional, and ectopic beats with high precision. In the CE dataset, we utilized 5-min samples from the 24 h recordings for each subject, selecting segments with minimal beat corrections and the least noise. For the AF dataset, clinical personnel manually selected 3 min segments of high-quality data, eliminating the need for a window selection phase. Upon selecting the appropriate ECG time window, Kubios automatically extracted time-based, frequency-based, and non-linear features. The resulting HRV features were extracted as tabular data. We used ultra-short term recordings to test a system that, among other advantages, has the potential to become a cheap, easy, and fast tool for screening. In [35], the authors highlighted the risk of ultra-short-term HRV analysis, but also suggested that further development and validation are needed, because this could provide new tools for overcoming the high cost associated with long-term recordings. They suggested that this is a new challenge for HRV-based applications, and we believe that we are contributing more evidence on the effectiveness of using very short ECG recordings; to give another recent example, [PERSON] et al. [36] successfully predicted increased risk of cardiovascular events by extracting HRV from tracks shorter than 15 s. Next, to address class imbalances, both datasets underwent undersampling, a simple procedure that removes random samples from the majority class until a balance is reached. In the CE dataset, we balanced the classes by undersampling the larger class 0 (with 122 records) to match the size of class 1 (with 17 records). For the AF dataset, we achieved a balance by randomly selecting 42 records from the available 60 in class 0 to match the size of class 1, also with 42 records. We acknowledge that our dataset is small and that undersampling further reduced its size. However, balanced datasets are essential for achieving unbiased data augmentation with a generative algorithm. For this reason, we applied undersampling before generating the synthetic data. Subsequently, we retained all features with complete data across the patient records, resulting in 64 feature columns. The final datasets were shuffled and divided into a 70% training set and a 30% test set. All features were normalized to values between 0 and 1.
## 5 Research Progress on Membranes for Redox Flow Batteries
### Membrane Evaluation Methods
#### 5.1.5 Cell Performance Tests  
The performance of the membrane in the flow cell is one of the most important considerations while selecting a membrane. To study the performance of different membranes, they are exposed to repeated charge/discharge cycles and their efficiencies are monitored. The performance of the cell is gauged in terms of coulombic, energy and voltage efficiencies. After several cycles of charging and discharging, the membranes are analyzed for stability and physical properties. An ideal membrane should exhibit good stability and capacity over extended periods of cycling [17; 68].
## 4 Results and Discussion
### Results
#### 4.1.2 Results in Zhaosu  
The crop classification map resulting from the proposed method and local comparative details from the five methods are displayed in Figure 7. In general, _'spring rapeseed'_ was  
Figure 6: Confusion matrix for the XGB-FR classification. Light orange, light yellow, light green, and light blue indicate winter crops, spring crops, summer crops, and other crops, respectively.  
widely distributed in the study area, with the exception of the eastern river valley. _'Spring rapeseed'_ was planted on larger parcels in the western and northern regions and on tiny parcels in the southern region where residents reside.  
Figure 7: _Cont._  
Also, the classification accuracies derived from the five comparative methods were summarized, as illustrated in Table 3.  
As in the first experiment, classifications based on time-series feature representation yielded the highest accuracy, while classifications based solely on clean image sequences yielded the worst results. When comparing the precision and recall scores of _'rapeseed'_ and _'other'_ types, it was discovered that the _'other'_ type (the major category in the binary schema) had a higher score than _'rapeseed'_ (minor category). Moreover, for _'rapeseed'_, there was a more remarkable improvement in precision scores (approximately 5.00%) than in recall scores (approximately 2.00%) over classification using raw time series.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **XGB-Clear** & **LSTM-TS** & **XGB-TS** & **LSTM-FR** & **XGB-FR** \\ \hline OA & 92.42\% & 95.01\% & 94.96\% & 96.74\% & 96.92\% \\ F1 & 0.8984 & 0.9307 & 0.9306 & 0.9544 & 0.9570 \\ \hline Precision & 88.79\% & 93.10\% & 92.81\% & 95.57\% & 96.16\% \\ (W/R/O) & 81.35\% & 89.50\% & 88.67\% & 94.26\% & 94.74\% \\  & 96.22\% & 96.71\% & 96.95\% & 97.48\% & 97.57\% \\ \hline Recall & 90.92\% & 93.05\% & 93.31\% & 95.02\% & 95.25\% \\ (W/R/O) & 88.07\% & 89.33\% & 90.19\% & 91.78\% & 92.07\% \\  & 93.76\% & 96.76\% & 96.44\% & 98.27\% & 98.42\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of classification accuracies in the Zhaosu study area (W: overall, R: rapeseed, O: other; the underline indicates the best scores).  
Figure 7: Results of parcel-based crop mapping in the Zhaosu study area. (**a**) presents the whole crop classification using XGB-FR method, (**b**) and (**c**) present the local details of crop classification from five comparison methods.
## 3 Results and Discussion
Figure 3: Kinetic results on the adsorptive removal of acetaminophen from waste water by adsorption onto (**a**) the activated carbon GPP20 and (**b**) the polymeric resin Sepabeads SP207. Experimental results are shown together with fittings to the pseudo-first and pseudo second order kinetic equations. Error bars stand for standard deviation of three experimental replications. Note: for a better visualization of fittings, the scale of axis Y has been adjusted to results. Figure 2: Kinetic results on the adsorptive removal of ibuprofen from ultrapure water by adsorption onto (**a**) the activated carbon GPP20 and (**b**) the polymeric resin Sepabeads SP207. Experimental results are shown together with fittings to the pseudo-first and pseudo second order kinetic equations. Error bars stand for standard deviation of three experimental replications. Note: for a better visualization of fittings, the scale of axis Y has been adjusted to results. The equilibrium experimental results on the adsorption of acetaminophen and ibuprofen from ultrapure water are shown in Figures 5 and 6, respectively. Those on the adsorption of acetaminophen and ibuprofen from waste water are respectively depicted in Figures 7 and 8. Equilibrium data are represented in these figures, together with fittings to the considered isotherm models (Equations (5)-(7)); the corresponding fitted parameters are depicted in Table 5.  
Figure 4: Kinetic results on the adsorptive removal of ibuprofen from waste water by adsorption onto (**a**) the activated carbon GPP20 and (**b**) the polymeric resin Sepabeads SP207. Experimental results are shown together with fittings to the pseudo-first and pseudo second order kinetic equations. Error bars stand for standard deviation of three experimental replications. Note: for a better visualization of fittings, the scale of axis Y has been adjusted to results. Figure 5: Equilibrium results on the adsorptive removal of acetaminophen from ultrapure water by adsorption onto (**a**) the activated carbon GPP20 and (**b**) the polymeric resin Sepabeads SP207. Experimental results are shown together with fittings to the Freundlich, to the Langmuir and to the Langmuir-Freundlich isotherm models. Error bars stand for the standard deviation of three experimental replications. Note: for a better visualization of fittings, the scale of axis Y has been adjusted to results.
## 2 Data and Methods
### SSD Network Framework
#### 2.2.3 Loss Function  
The loss (objective) value was calculated as a combination of the confidence of the predicted class scores and the accuracy of the location. The total loss value (localization loss + confidence loss) given in Equation (1) is an indication of the pairing of the \(i\)-th default box with \(j\)-th ground truth box of class \(p\), such that \(x_{ij}^{p}=\{1,0\}\):  
\[L(x,c,l,g)=\frac{1}{N}\left(L_{conf}(x,c)+\alpha L_{loc}(x,l,g)\right), \tag{1}\]  
where \(N\) corresponds to the number of matching default boxes. If there is no match (\(N=0\)), the total loss is determined as zero directly. The \(\alpha\) value is the balance of two types of losses, and it is equal to 1 during the cross-validation phase. The localization loss is calculated as the Smooth L1 loss between the offsets of the predicted box (\(l\)) and the ground truth box (\(g\)). If the center location of the boxes denoted as \(cx\), \(cy\), the default boxes \(d\), width \(w\), and height as \(h\):  
\[L_{loc}(x,l,g)=\sum\
olimits_{i\in Po_{\text{smo}}[cx,cy,w]_{i}}\sum x_{ij}^{k }smooth_{L1}(l_{i}^{m}-\hat{g}_{j}^{m}), \tag{2}\]  
in which:  
\[smooth_{L1}(x)=\begin{cases}0.5x^{2}&if\ |x|<1\\ |x|-0.5&otherwise,\end{cases}\]  
\[\hat{g}_{j}^{cx}=(g_{j}^{cx}-d_{i}^{cx})/d_{i}^{w}\quad\hat{g}_{j}^{cy}=(g_{j }^{cy}-d_{i}^{cy})/d_{i}^{h}\]  
\[\hat{g}_{j}^{w}=\log\frac{g_{j}^{w}}{d_{i}^{w}}\quad\hat{g}_{j}^{h}=\log\frac {g_{j}^{h}}{d_{i}^{h}}.\]  
Figure 5: Illustration of the \(5\times 5\) feature map and generated default boxes with six aspect ratios.  
Additionally, the confidence loss (\(c\)) was calculated as a softmax loss of the predicted class relative to other classes:  
\[L_{conf}(x,c)=-\sum_{i\in\text{Pos}}^{N}x_{ij}^{p}\log\hat{c}_{i}^{p}-\sum_{i\in \text{Neg}}^{N}\log(\hat{c}_{i}^{0}) \tag{3}\]  
\[\hat{c}_{i}^{p}=\frac{\exp(c_{i}^{p})}{\sum_{p}\exp(c_{i}^{p})}. \tag{4}\]  
The above-mentioned equations are detailed in [PERSON] et al.'s article [37].
# A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
## 5 Statistical Analysis of Wilcoxon
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Noise-Based K-Means Versus K-Means**} \\ \cline{3-6}
**Taxi GPS Dataset** & **Clustering Result** & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{\(h\)} & \multicolumn{2}{c}{_Stats_} \\ \cline{4-6}  & **SC** & & & & \(Zval\) & _Ranksum_ \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Aracaju \\ (Brazil) \\ \end{tabular} } & **SC** & \multirow{6}{*}{120} & 3.41557 \(\times\)\(10^{-7}\) & 1 & 5.0989 & 599 \\  & SSE & & 9.17277 \(\times\)\(10^{-8}\) & 1 & \(-\)5.3424 & 212 \\  & DBI & & 9.0900 \(\times\)\(10^{-2}\) & 0 & 1.6906 & 473 \\  & PBM & & 1.06456 \(\times\)\(10^{-7}\) & 1 & 5.3153 & 607 \\  & SC & & 1.06456 \(\times\)\(10^{-7}\) & 1 & 5.3153 & 607 \\ San Francisco & SSE & & 6.79561 \(\times\)\(10^{-8}\) & 1 & 5.3965 & 210 \\ (USA) & DBI & & 8.95744 \(\times\)\(10^{-6}\) & 1 & 4.4497 & 575 \\  & PBM & & 6.79561 \(\times\)\(10^{-8}\) & 1 & 5.3965 & 610 \\  & SC & & 1.60981 \(\times\)\(10^{-4}\) & 1 & 3.7735 & 550 \\ Roma & SSE & & 6.79561 \(\times\)\(10^{-8}\) & 1 & \(-\)5.3965 & 210 \\ (Italy) & DBI & & 1.23463 \(\times\)\(10^{-7}\) & 1 & 5.2883 & 606 \\  & PBM & & 6.79561 \(\times\)\(10^{-8}\) & 1 & 5.3965 & 610 \\  & PBM & & 4.32000 \(\times\)\(10^{-3}\) & 1 & 2.8538 & 516 \\ Chongqing & SSE & & 6.91658 \(\times\)\(10^{-7}\) & 1 & 4.9637 & 226 \\ (China) & DBI & & 3.98735 \(\times\)\(10^{-6}\) & 1 & 4.6120 & 581 \\  & PBM & & 9.74797 \(\times\)\(10^{-6}\) & 1 & 4.4227 & 574 \\  & SC & & 3.36910 \(\times\)\(10^{-1}\) & 0 & \(-\)0.9603 & 374 \\ Beijing & SSE & & 6.61044 \(\times\)\(10^{-5}\) & 1 & \(-\)3.9899 & 262 \\ (China) & DBI & & 7.89803 \(\times\)\(10^{-8}\) & 1 & 5.3694 & 609 \\  & PBM & & 2.13000 \(\times\)\(10^{-3}\) & 1 & 3.0702 & 524 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistical analysis results of Wilcoxon testing for noise-based K-means vs. K-means. As can be seen from Tables 8-10, there are significant differences among noise-based K-means, FCM, and K-means clustering, which indicates that they are distributed differently in space; however, the differences between noise-based K-means and K-means plus clustering are not significant, especially in Roma (Italy), which rejects the significance level and indicates that their spatial distribution is similar. Furthermore, as \(p\)\(\rightarrow\)0 for each group, it indicates that it is feasible and effective to automatically capture the cluster number and initialize the center cluster by use of a noise algorithm, and the urban hotspots captured by the noise-based K-means clustering algorithm more effectively represent reality.