esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Segmentation by Searching for Image Feature Density Peaks
## 3 Experiment
### Evaluation
#### 3.2.3 Qualitative Evaluation  
To prove the effectiveness of the proposed method, we compare our method with existing segmentation methods on the Berkeley datasets: BSDS300. We compute the results of our method  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Image Type** & **FCM-S** & **K-Means** & **Our Method** \\ \hline human & 0.510 & 0.674 & 0.672 \\ transport & 0.503 & 0.563 & 0.592 \\ intensity inhomogeneity & 0.608 & 0.653 & 0.651 \\ building 1 & 0.715 & 0.694 & 0.712 \\ animal & 0.816 & 0.803 & 0.837 \\ landscape & 0.704 & 0.783 & 0.818 \\ building 2 & 0.819 & 0.885 & 0.897 \\ Mean & 0.668 & 0.722 & 0.740 \\ average computation time & 5.782s & 4.237s & 5.331s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different method for Berkeley image dataset, Probabilistic Rand Index (PRI).  
Figure 8: Performance with different parameter values, \(k\) is an constant between 0.04 and 0.06.  
on entire datasets, and then the results of FCM-S and K-means are computed by following the optimal settings in the previous work [41; 42], respectively. Figure 9 shows the qualitative results of 7 images, with each one is randomly selected from one of 7 image types (corresponding to first 7 rows of Table 1). These results suggest that our method can achieve superior performance in preserving the salient features of the input images due to its excellent accuracy of pixel classification. For example, from Figure 9, it can clearly be observed that there are considerable number of pixels in the white kerchief or human face (1 st row), body of the bird (5 th row), and the church clock (end row) are falsely clustered as background or the part of the other objects. Comparing with the other two segmentation approaches, the proposed scheme accomplishes the segmentation requests by avoiding these classification errors through reasonably classifying these pixels. Moreover, as we can see in the 3 rd row of Figure 9, there is an obvious intensity inhomogeneity occurring around the image. By contrast, the proposed scheme outperforms the FCM-S and K-means by producing more homogeneous background.  
Figure 9: Results for different segmentation algorithms based on clustering. (**a**) Original image (**b**) FCM-S based segmentation (**c**) K-means based segmentation (**d**) The method we proposed.
## 3 Pharmacological Properties of GP
### Anti-Cancer Effect
#### 3.1.8 Relationship between Anti-Cancer Activity and Structure of GPS  
The anti-cancer activity of GPS was associated with its structure. [PERSON] et al. [19] evaluated the cytotoxic activities of several dammarane saponins (gypenoside L, gypenoside LI, gypenoside LVI, and gypenoside XLVI) on A549 cells with ginsoside as a positive control by CCK-8 assay and found that the loss of sugar might be related to the enhanced inhibition of cancer cell proliferation. Once the free hydroxyl in the C20 position was glycosylated, the anti-cancer activity was extremely decreased. As shown in Figure 5, gypenoside stereoisomers, gypenoside L (S configuration at C20), and gypenoside LI (R configuration at C20), both with free hydroxyls in the C20 position, showed stronger activity with IC50 values of 29.38 \(\pm\) 2.52 \(\upmu\)M and 21.36 \(\pm\) 0.78 \(\upmu\)M respectively against A549 cells compared with gypenoside LVI and gypenoside XLVI glycosylated in the C20 position. [PERSON] et al. [45] evaluated the cytotoxic activities of damulin E and damulin F (their structures are shown in Figure 5) against A549 cells and showed that after being incubated for 24 h, compared with positive control (ginsenoside Rg3), damulin F showed stronger cytotoxic activity against A549 cells with the IC50 value of 19.8 \(\pm\) 0.4 \(\upmu\)M, whereas damulin E appeared to have weaker activity with the IC50 value of 38.9 \(\pm\) 0.6 \(\upmu\)M. Therefore, saponins with a double bond in C20 (21) exhibited stronger activities than saponins with a double bond in C20 (22). However, some studies [44; 46; 47; 48] did not mention the IC50 value or positive control, and the cytotoxic activities could not compare, so the relationship of the structure and activities needed further investigation.
## 3 Results
It is worth noting that the presence of barium was highlighted only in one fragment. These results allowed us to be fairly sure that samples 1 and 3, which were red samples, were mainly composed of cinnabar (HgS), because of the contemporary presence of mercury and sulphur. In all the other cases, the actual nature of the pigment on the surfaces could be only be hypothesised, since numerous might be the reasons to find iron, lead and copper in a painted layer. In fact, matching the information coming from the colour and the iron content, the presence of red ochre or hematite on the red samples 2 and 4 seemed quite probable, and of yellow ochre on sample 6; the same could not be stated for the light red/orange pigment, where the coexistence of such minerals could be hypothesised. An analogous argument can be proposed for the blue copper-based pigments 8, 9 and 10; in fact, it is well known that a lot of green and blue shades in painting, especially in ancient artifacts, are due to copper compounds, and that is rather impossible to determine their actual nature by means of elemental analysis [16; 17]. As regards samples 7, since on the basis of its EDS spectrum the presence of copper and/or other elements that might be undeniably associated with green, blue and yellow pigments was excluded,  
Figure 3: Representative SEM images showing that the fragments surfaces are generally homogeneous. (a,b) sample 1; (c,d) sample 3.  
it was supposed it could be green earth; on that note, it is worth recalling that sometimes a green hue can be obtained by mixing blue with yellow or also blue with pale green (such as green earth) pigments [5; 18]. As regards samples 11 and 12, the presence of almost nothing but calcium led us to suspect the presence of calcite in the white sample and to exclude the presence of iron or manganese-based pigments in the black one; as a consequence, the presence of a carbon-based pigment has been supposed, but of course without true evidence--only on the basis of elemental analysis. It is evident that even though the outcomes of SEM-EDS analyses were quite significant, there were some residual doubts on the actual nature of almost all the pigments, so the samples underwent FTIR and Raman spectroscopic analyses. Depending on the sample, the Raman spectrum has been acquired with the bench tool (Thermo Fisher instrument), with the portable one (BW Tek instrument) or with both of them. Thus, in each figure, the excitation line is indicated. The results inferred from the study of the vibrational spectra are synthetically reported for each sample in Table 3 and are discussed in detail in the following paragraphs, depending on the hues. It is worth noting that no band ascribable to the presence of organic materials was detected.
# Nonlinear Analysis and Closed-Form Solution for Overhead Line Magnetic Energy Harvester Behavior
## Appendix A Calculation of the Power Transfer Window  
In the following, to keep the solutions manageable, the variables are normalized with respect to the following base quantities:  
Base time  
\[T_{base}=\frac{T}{2}=\frac{\pi}{\omega} \tag{1}\]  
here, as usual, \(T\) is the line period and \(\omega\) is the line angular frequency.  
Base flux density:  
\[B_{base}=a \tag{2}\]  
Base field intensity:  
\[H_{base}=b \tag{3}\]  
Base current, referred to secondary:  
\[I_{base}=H_{base}\frac{l_{c}}{N}=\frac{bl_{c}}{N} \tag{4}\]Base voltage  
\[V_{base}=\frac{NA_{c}B_{base}}{T_{base}}=\frac{\omega aNA_{c}}{\pi}\] (A5) Normalized time \[t_{N}=\frac{t}{T_{base}}=\frac{\omega t}{\pi}\] (A6) Normalized flux density: \[B_{N}=\frac{B}{B_{base}}=\frac{B}{a}\] (A7) Normalized field intensity: \[H_{N}=\frac{H}{H_{base}}=\frac{H}{b}\] (A8) Normalized secondary current \[I_{N}=\frac{I_{2m}}{I_{base}}=\frac{NI_{2m}}{bl_{c}}=\frac{I_{1m}}{bl_{c}}\] (A9) Normalized CVL voltage \[V_{N}=\frac{V_{b}}{V_{base}}=\frac{\pi V_{b}}{\omega aNA_{c}}\] (A10) Applying the condition, \(v_{in}(t_{1})=V_{b}\) to (8) and normalizing: \[V_{N}=\frac{\pi I_{N}}{\left(1-\pi I_{N}t_{N}\right)^{2}}\] (A11) Whence, the normalized time instant, \(t_{1N}\), is: \[t_{1N}=\frac{1}{\pi I_{N}}-\frac{1}{\sqrt{\pi I_{N}V_{N}}}\] (A12) The normalized flux density at the rectifier's cut-in instant is: \[B_{1N}=\frac{B(t_{1})}{a}\approx\frac{\frac{NI_{2m}\omega t_{1}}{bl_{c}}}{1- \frac{NI_{2m}\omega t_{1}}{bl_{c}}}=\frac{\frac{\pi I_{N}\omega t_{1}}{\pi}}{1 -\frac{NI_{3m}\omega t_{1}}{\pi}}=\frac{\pi I_{N}t_{1N}}{1-\pi I_{N}t_{1N}}\] (A13) The normalized instant when flux density crosses zero is: \[t_{ZN}=t_{1N}-\frac{B_{1N}}{V_{N}}\] (A14) Equation (17) can be first transformed into \[\frac{V_{b}(t_{2}-t_{Z})}{aNA_{c}-V_{b}(t_{2}-t_{Z})}=\frac{NI_{2m}}{bl_{c}} \frac{K_{1}(\pi-\omega t_{2})}{K_{2}-\omega t_{2}}\] (A15) then normalized \[\frac{V_{N}(t_{2N}-t_{ZN})}{1-V_{N}(t_{2N}-t_{ZN})}=I_{N}\frac{K_{1}\pi(1-t_{ 2N})}{K_{2}-\pi t_{2N}}\] (A16) and after additional manipulation put into the form of a quadratic equation: \[\alpha t_{2N}^{2}+\beta t_{2N}+\gamma=0\] (A17)where  
\[\alpha=\pi K_{1}V_{N}I_{N}+\pi V_{N} \tag{118}\]  
\[\beta=-[\pi K_{1}I_{N}+\pi K_{1}(1+t_{ZN})V_{N}I_{N}+(\pi t_{ZN}+K_{2})V_{N}] \tag{119}\]  
\[\gamma=\pi K_{1}I_{N}+\pi K_{1}t_{ZN}V_{N}I_{N}+K_{2}t_{ZN}V_{N} \tag{120}\]  
Only a positive solution for \(t_{2N}\) has a physical meaning:  
\[t_{2N}=\frac{-\beta+\sqrt{\beta^{2}-4\alpha\gamma}}{2\alpha} \tag{121}\]
# Application of Image Recognition Methods to Determine Land Use Classes
## 3 Materials and Methods  
This section presents the proposed hybrid ML, cloud interpolation, and vegetation indices-based approach for the classification of Sentinel-2 satellite imagery. The methodology is structured into three main components--data acquisition, pre-processing, and classification--followed by post-processing (see Figure 1). Data acquisition involves retrieving Sentinel-2 satellite images and filtering them based on cloud cover and resolution criteria. Pre-processing steps focus on preparing the data for analysis, including image merging, cloud removal, and spectral index calculations to enhance feature representation. The classification stage utilizes a random forest (RF) model to assign land use labels, leveraging training datasets constructed from accurately labeled reference data. Finally, post-processing techniques are then applied to refine classification results by filtering out low-confidence predictions and improving spatial consistency [46]. These methodological components work together to enhance the reliability and precision of the classification outcomes.  
Below, the description of each sub-process denoted by a plus symbol (+) in Figure 1 is presented.
## 3 Results and Discussion
### Surface Morphology Analysis of [CuZn]Fe\({}_{2}\)O\({}_{4}\)  
Figure 6 shows the surface morphologies of Cu\({}_{0.7}\)Zn\({}_{0.3}\)Fe\({}_{2}\)O\({}_{4}\) at different sintering temperatures, in an air atmosphere at 5 \({}^{\circ}\)C/min, held at a constant temperature for 2 h, and lastly cooled. It can be seen that the non-sintered powder had very small particulate diameter, and the average particulate diameter was about 10 nm. When sintering temperature increased to 500 \({}^{\circ}\)C, the SEM image showed that there was no significant difference, and the average particulate diameter was still about 20 nm. When sintering temperature was 600 \({}^{\circ}\)C, there was also no significant difference, as the average particulate diameter was still about 25 nm. At 700 \({}^{\circ}\)C, it can be seen that the primary particle started to grow significantly larger, and the average particulate diameter approached 50 nm. When the sintering temperature was 800 \({}^{\circ}\)C, the primary particles started to crystallize to form secondary particles, and the average diameter approached 70 nm. At 900 \({}^{\circ}\)C, the phenomenon of crystallization into secondary particles was more significant than that observed at 800 \({}^{\circ}\)C, and the average particulate diameter reached about 150 nm. In the observation of non-sintered Cu\({}_{0.7}\)Zn\({}_{0.3}\)Fe\({}_{2}\)O\({}_{4}\) micro-structure, since the non-sintered powder has a very fine average particulate diameter, even on the SEM images with a 50,000\(\times\) amplification the average particulate diameter could not be seen. From the 100,000\(\times\) SEM image, it can be clearly seen that the average particulate diameter was about 10 nm. The electronic data systems(EDS) composition analysis showed that ferrite powder was an alloy formed by four elements; Mn, Zn, Fe, and O, and that no other metal atoms were present. Meanwhile, the proportion was correct, proving that, when neutralized, the metal ions were completely precipitated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Properties Magnetic** & **Hc** & **Ms** & **Mr** & **B** \\
**Composition/Temperature, \({}^{\circ}\)C** & & **(Oe)** & **(emu/g)** & **(emu/g)** & **(gauss/g)** \\ \hline  & 500 & 3.32 & 44.56 & 0.16 & 562.99 \\  & 600 & 23.92 & 52.24 & 1.33 & 680.05 \\ Cu\({}_{0.7}\)Zn\({}_{0.3}\)Fe\({}_{2}\)O\({}_{4}\) & 700 & 75.69 & 54.39 & 4.58 & 758.83 \\  & 800 & 47.78 & 58.40 & 3.16 & 781.28 \\  & 900 & 17.42 & 60.19 & 1.33 & 773.41 \\ \hline  & 500 & 1.55 & 63.70 & 0.09 & 801.62 \\  & 600 & 31.98 & 74.60 & 2.43 & 968.96 \\  & 700 & 71.79 & 80.20 & 6.70 & 1079.1 \\ Ni\({}_{0.5}\)Zn\({}_{0.5}\) Fe\({}_{2}\)O\({}_{4}\) & 800 & 69.54 & 85.28 & 6.64 & 1140.91 \\  & 900 & 45.74 & 91.40 & 5.08 & 1193.72 \\  & 1000 & 22.29 & 84.68 & 2.17 & 1085.87 \\  & 1100 & 4.06 & 80.72 & 0.43 & 1017.9 \\  & 1200 & 8.11 & 69.92 & 0.67 & 886.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The effect of sintering temperature on the magnetism of ferrite.  
Figure 6: _Cont._
## 2 Research Approach
### Control Charts
#### 2.2.1 Individual Control Chart  
The centerline (CL) of an individual control chart corresponds to the sample average.  
\[\text{CL}=\hat{\mu}=\overline{x}=\frac{1}{T}\sum_{t=1}^{T}x_{t} \tag{3}\]  
As described in reference [16], an unbiased estimate \(\hat{\sigma}\) of sample standard deviation is obtained as follows:  
\[\hat{\sigma}=\frac{\overline{\text{MR}}}{d_{2}} \tag{4}\]where \(\text{MR}_{t}=|x_{t}-x_{t-1}|\) is the individual moving range for the pair of consecutive measurements at \(t-1\) and \(t\), and \(\overline{\text{MR}}=\sum_{t=2}^{T}\text{MR}_{t}/\left(T-1\right)\) is the mean individual moving range. When two consecutive measurements are used to calculate the moving ranges, \(d_{2}=1.128\)[18].  
Following common practice in SPC, a distance \(L\) from the CL is set to 3 to specify the upper and lower control limits of individual control charts, as follows:  
\[\text{UCL}=\hat{\mu}+L\hat{\sigma} \tag{5}\]  
\[\text{LCL}=\hat{\mu}-L\hat{\sigma} \tag{6}\]
# An Optimization Framework for Wind Farm Design in Complex Terrain
## 3 Optimization Framework  
### Problem Formulation  
For a wind farm with \(N_{wt}\) turbines, its design can be specified by the turbines' locations, i.e., \(\mathbf{X}=[x_{1},\ x_{2},\ \dots,\ x_{N_{wt}}]\) and \(\mathbf{Y}=[y_{1},\ y_{2},\ \dots,\ y_{N_{wt}}]\). Then, a general optimization problem of the wind farm design can be formulated as:  
\[\begin{array}{ll}\min&f_{m}(\mathbf{X},\ Y),\
# Modification of Hand Muscular Synergies in Stroke Patients After Robot-Aided Rehabilitation
## 2 Materials and Methods
### Experimental Protocol  
As reported in Figure 1, each patient carried out a robot-aided rehabilitation treatment with the Gloreha Sinfonia, composed of five sessions per week for 4 weeks. At the beginning and at the end of the course of treatment with robot (i.e., before rehabilitation and after rehabilitation, BR and AR, respectively), sEMG signals were recorded from injured and healthy hand, by using the Delsys Trigno EMG wireless system, in order to extract muscular synergies of each subject and evaluate patient rehabilitation outcome. Moreover, each subject was evaluated using Fugl-Meyer motor assessment of the upper extremity (FM) and motor power (MP) assessment before and after the therapy program with the Gloreha robot [31,32].  
As showed in Figure 2, the recording of the EMG signals was performed with the patient sitting in a comfortable position, in front of a screen that showed him/her the grip to perform. The subject performed four different grasps selected randomly, shown by means of a purposely developed user interface and performed similarly to the FM assessment. He/she had to grab and hold the following objects: a can, a pencil, a sheet, and a tennis ball (Figure 3). The subjects were required to perform the grasp and to hold the object for ten seconds, followed by a pause of at least ten seconds. The sEMG data were recorded during the ten seconds of grasp execution. The subjects were asked to perform the same task with the injured limb before and after rehabilitation treatment (BR and AR) and subsequently with the healthy limb in order to obtain the healthy muscle patterns (H), i.e., a comparison term for each subject. Furthermore, at the beginning and at the end of the treatment, the subjects were assessed by means of the FM and MP scales to evaluate the performance through clinical validated tools.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Age** & **Sex** & **Stroke** & **Time from Stroke [Months]** \\ \hline
1 & 56 & M & ischemic & 68 \\
2 & 53 & F & hemorrhagic & 96 \\
3 & 40 & M & ischemic & 75 \\
4 & 79 & F & ischemic & 18 \\
5 & 61 & M & hemorrhagic & 49 \\
6 & 56 & M & hemorrhagic & 30 \\
7 & 72 & M & ischemic & 33 \\ \hline \multicolumn{4}{c}{\(59.6\pm 12.8\)} & & \multicolumn{2}{c}{\(52.7\pm 28.1\)} \\ \hline \end{tabular}
\end{table}
Table 1: Summary table of patients involved in this study.  
Figure 1: An overall view of the experimental protocol.  
The study was conducted under Ethical Committee approval (Ethical Approval N. 41/17 OSS Com Et CBM) and in accordance with the Declaration of Helsinki. All patients were adequately informed about the purpose of the study and gave their written informed consent.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Evaluation of the Classifiers  
In this section, we present the results of traffic classification by the four selected DL models, utilizing the established hyperparameters from the modeling phase and sequence lengths defined in the previous section. The evaluation metrics include accuracy, precision, recall, and F1 score. We present detailed results with all evaluation metrics in Table 10.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time (s)** & **GPU Utilization (\%)** \\ \hline \multirow{4}{*}{GRU} & \multirow{4}{*}{0.9965} & Class 0: 1.00 & Class 0: 0.94 & Class 0: 0.97 & \multirow{4}{*}{339} & \multirow{4}{*}{28} \\  & & Class 1: 1.00 & Class 1: 0.99 & Class 1: 0.99 & & \\  & & Class 2: 1.00 & Class 2: 0.93 & Class 2: 0.96 & & \\  & & Class 3: 1.00 & Class 3: 0.99 & Class 3: 1.00 & & \\  & & Class 4: 1.00 & Class 4: 0.98 & Class 4: 0.99 & & \\ \hline \multirow{4}{*}{BiGRU} & \multirow{4}{*}{0.9860} & Class 0: 1.00 & Class 0: 0.94 & Class 0: 0.97 & \multirow{4}{*}{626} & \multirow{4}{*}{45} \\  & & Class 1: 1.00 & Class 1: 0.98 & Class 1: 0.99 & & \\ \cline{1-1}  & & Class 2: 1.00 & Class 2: 0.85 & Class 2: 0.92 & & \\ \cline{1-1}  & & Class 3: 1.00 & Class 3: 0.95 & Class 3: 0.97 & & \\ \cline{1-1}  & & Class 4: 1.00 & Class 4: 0.86 & Class 4: 0.92 & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Evaluation metrics of the proposed models.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & \begin{tabular}{l} **Sequence** \\ **Length** \\ \end{tabular} & \begin{tabular}{l} **Training** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{l} **Test Accuracy** \\ \end{tabular} &
\begin{tabular}{l} **Training Time (s)** \\ \end{tabular} \\ \hline GRU & 50 & 0.9711 & 0.9744 & 244 \\ BiGRU & 50 & 0.9824 & 0.9492 & 382 \\ LSTM & 50 & 0.9639 & 0.9582 & 229 \\ BiLSTM & 50 & 0.9607 & 0.6023 & 424 \\ \hline GRU & 75 & 0.9894 & 0.9834 & 251 \\ BiGRU & 75 & 0.9900 & 0.9553 & 390 \\ LSTM & 75 & 0.9825 & 0.9782 & 317 \\ BiLSTM & 75 & 0.9859 & 0.8644 & 540 \\ \hline GRU & 115 & 0.9927 & 0.9955 & 339 \\ BiGRU & 115 & 0.9904 & 0.9854 & 626 \\ LSTM & 115 & 0.9853 & 0.9951 & 382 \\ BiLSTM & 115 & 0.9843 & 0.8777 & 646 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Sequence length analysis.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 6 Conclusions  
This paper presents Pido, a predictive delay optimization route selection mechanism for intertidal wireless sensor networks. Based on our observations in the field test, IT-WSN suffers from poor link quality and dynamic connections, which lead to intolerable delays. Pido considers delay introduced both by the link and the node comprehensively, which decreases the end-to-end delay efficiently with low overhead. Pido utilizes implicit system indicators to predict nodes future status accurately, and helps nodes to select routes wisely. Extensive evaluation is conducted in both real systems and large-scale simulation. The results indicate that Pido can decrease end-to-end delay up to 73% with little overhead.  
**Author Contributions:** [PERSON] and [PERSON] proposed the idea, performed the experiments and wrote this paper. [PERSON] and [PERSON] provided the simulation testbed for this paper. [PERSON] designed the hardware platform of the sensor system.  
Figure 11: The prediction accuracy of node status with three classification algorithms: Logistic Regression, Naive Bayesian and Ada Booster Classifier. (**a**) Training classifiers for each node with three classification algorithms; (**b**) Training a universal classifier for the network with three classification algorithms.  
[PERSON] revised the manuscript. [PERSON] and [PERSON] provided guidance for the deployment of the test bed. [PERSON] offered overall guidance on the model building and revised the manuscript.  
This research was funded by [the China NSFC] grant number [61472358, 61702451] and [the Fundamental Research Funds for the Central Universities] grant number [2017 QNA4017].  
We'd like to thank the supports from all members in USSLAB (www.usslab.org), Zhejiang University.  
The authors declare no conflict of interest.