esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Savenging Assay  
The ABTS\({}^{+}\bullet\) is reactive towards most antioxidants and the blue ABTS\({}^{+}\bullet\) with an absorption maximum of 734 nm is converted back to its colorless neutral form during this reaction [3]. Therefore, ABTS\({}^{+}\bullet\) scavenging assay is one of the most widely assay used to screen anti-radical peptides. As shown in Figure 8D, eight isolated peptides (TJP1 to TJP8) showed strong ABTS\({}^{+}\bullet\) scavenging activities in a dose-effect manner with EC\({}_{50}\) values of 1.925, 2.496, 1.652, 0.831, 3.527, 2.835, 8.752, and 0.586 mg/mL, respectively. TJP4 and TJP8 showed the strongest ABTS\({}^{+}\bullet\) scavenging activity among eight isolated peptides, but still weaker than glutathione (GSH) did at the same concentration. The EC\({}_{50}\) values of TJP4 and TJP8 were significantly lower than those of peptides from protein hydrolyses of salmon (FLNEFLAHV: 1.548 mg/mL) [41], skate cartilages (FIMGPY: 1.04 mg/mL; IVAGPQ: 1.29 mg/mL) [24], blueinf leatherjacket heads (WEGPK: 5.407 mg/mL; GPP: 2.472 mg/mL; GVPLT: 3.124 mg/mL) [8], and corn gluten meal (FLPF: 1.497 mg/mL; LPF: 1.013 mg/mL; LLPF: 1.031 mg/mL) [49]. However, The EC\({}_{50}\) values of TJP4 and TJP8 were significantly higher than those of peptides from protein hydrolyses of skate cartilages (GPAGDY: 0.77 mg/mL) [24], grass carp skin (GFGL: 0.328 mg/mL; VGGRP: 0.465 mg/mL) [40], scalloped hammerhead cartilage (GPE: 0.24 mg/mL; GARGPQ: 0.18 mg/mL; GFTGPPGFNG: 0.29 mg/mL) [12], and _Sphyrna lewini_ muscle (WDR:0.34 mg/mL; PYFNK: 0.12 mg/mL) [33]. These results indicated that eight isolated peptides (TJP1 to TJP8), especially TJP4 and TJP8, have the strong ability to convert ABTS\({}^{+}\bullet\) to its colorless neutral form and hold back the radical chain reaction.
# Enhanced Path Planning by Repositioning the Starting Point +
## 4 Experimental Evaluation
### Evaluation Results
#### 4.2.4 Employing the Best Centering Approach in the mTSP  
As previously discussed, the geometric median approach emerged as the optimal choice for centering the starting point in the mTSP. To further examine its effectiveness, we conducted a thorough analysis of how adjusting the starting point impacts performance when the number of drones or stations varies. This analysis follows the same experimental procedure described in Section 4.2.2.  
The experimental design involved applying the mTSP to each flight plan using the default starting point and then employing the geometric median approach to refine the starting location for subsequent mTSP optimization. This two-step process resulted in 130,000 tests across the 65,000 scenarios. The simulation model generated comprehensive data, which is presented in Figures 15 and 16.  
The \(Y\)-axis values represent the percentage improvement over the vanilla mTSP for three key metrics: SPD (as defined in Equation (3)), total flight distance, and SPF (as defined in Equation (4)). The \(X\)-axis shows the variation in the number of drones and stations for each respective analysis.  
Analysis of Drone Variation Effects  
When examining the impact of varying drone numbers in Figure 15, several clear patterns emerge. For SPD, there is a consistent upward trend in improvement, ranging from 20% to 24% as the number of drones increases. This pattern is mirrored in the distance analysis, which shows a positive correlation between drone count and improvement, with gains ranging from 7% to 18%.  
An increase in improvement is anticipated for both SPD and distance as more drones are engaged in the flight plan. Each additional drone introduces new starting point distances (as detailed in Section 3.1.1), which increases the total distance and consequently provides greater opportunities for optimization through strategic starting point repositioning.  
The SPF analysis reveals a contrasting pattern, showing a consistent decrease in improvement as drone numbers grow. This decline is initially more pronounced but gradually levels off with additional drones. However, this apparent contradiction can be explained by examining the relationship between the components. Considering the almost constant change in Diff (Figure 15d) when comparing \"Vanilla mTSP\" and \"After Centering\", along with the observation that SPF increases with the addition of drones, the apparent decrease in SPF improvement is anticipated, as the improvement is proportional to the evolution of SPF.  
Figure 15: Centering using geometric median for varying number of drones. (**a**) Improvement by drones. (**b**) SPD analysis. (**c**) Distance analysis. (**d**) SPF analysis.
# A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
###### Abstract  
The Internet of things (IoT) provides the possibility of communication between smart devices and any object at any time. In this context, wireless nodes play an important role in reducing costs and simple use. Since these nodes are often used in less accessible locations, recharging their battery is hardly feasible and in some cases is practically impossible. Hence, energy conservation within each node is a challenging discussion. Clustering is an efficient solution to increase the lifetime of the network and reduce the energy consumption of the nodes. In this paper, a novel hybrid unequal multi-hop clustering based on density (HCD) is proposed to increase the network lifetime. In the proposed protocol, the cluster head (CH) selection is performed only by comparing the status of each node to its neighboring nodes. In this new technique, the parameters involving energy of nodes, the number of neighboring nodes, the distance to the base station (BS), and the layer where the node is placed in are considered in CH selection. So, in this new and simple technique considers energy consumption of the network and load balancing. Clustering is performed unequally so that cluster heads (CHs) close to BS have more energy for data relay. Also, a hybrid dynamic-static clustering was performed to decrease overhead. In the current protocol, a distributed clustering and multi-hop routing approach was applied between cluster members (CMs), to CHs, and CHs to BS. HCD is applied as a novel assistance to cluster heads (ACHs) mechanism, in a way that a CH accepts to use member nodes with suitable state to share traffic load. Furthermore, we performed simulation for two different scenarios. Simulation results showed the reliability of the proposed method as it was resulted in a significant increase in network stability and energy balance as well as network lifetime and efficiency.  
Internet of things; Wireless nodes; Hybrid clustering; Multi-hop routing; Network lifetime; Artificial intelligence +
Footnote †: journal: Journal of Network Science
## 3 Results
### Classification Results and Accuracy Assessment
#### 3.1.3 Accuracy Assessment
CART is a decision-tree algorithm that can be used for classification and regression. During classification, CART recursively divides the feature space based on the purity of the nodes until the number of samples is less than a predetermined threshold, or the number of the samples in the nodes is homogeneous [53; 58]. Figure 6: Changes in various assessment indicators with SVM, RF, and CART. (**a**) The result of SVM; (**b**) the result of RF; (**c**) the result of CART. See Table S1 and Table S2 for detail. Since there are many classification features, the whole classification may produce a large number of child nodes. The information contained in these child nodes may be noisy and may thus affect the classification results. Therefore, the classification tree is pruned to improve the classification accuracy. In the growth of the classification tree, the tree growth depth (maxD) and the minimum samples of split (minSP) affect the classification results [53; 61]. Generally, default parameters can be selected to achieve classification results. In this study, we obtained the hyperparameters maxD: 20 and minSP: 10. For RF, the algorithm assembles many decision trees. In the classification process, each tree votes on the classification results, and then the classifier selects the result with the most votes as the final classification result [50; 57] In addition, RF employs bootstrap for randomly sampling. Each node randomly selects a subset of features to split. By following these steps, randomness can be achieved to the greatest extent. In addition, the number of RF-assembled trees (tree) and the largest split node feature (split) will impact the classification results [9; 22]. Consequently, the hyperparameters for RF are tree: 300 and split: 3.
# On Computational Hardness of Multidimensional Subtraction Games +
## 2 Concepts and Results
### Turing Machines and Cellular Automata  
Turing machines are a well-known universal model of computation. For the formal definition we refer to [PERSON]'s book [25].  
Although the cellular automata are also well-known, for the reader's convenience we provide the definition. Informally, a cellular automaton operates on a tape consisting of _cells_. The tape is infinite in both directions. Each cell carries a symbol from some fixed set which is called the _alphabet_. At each step all the symbols in cells are changed simultaneously. The change is governed by the same rule for all cells. This rule specifies a new symbol in a cell depending on a content of a fixed neighborhood of the cell. Formally, a cellular automaton (CA) \(\mathcal{C}\) is a pair \((A,\delta)\), where \(A\) is an alphabet, and \(\delta\colon A^{2r+1}\to A\) is the _transition function_. The number \(r\) is called _the size of a neighborhood_. \(A\)_configuration_ of \(\mathcal{C}\) is a function \(c\colon\mathbb{Z}\to A\).  
An operation of a CA starting at a configuration \(c_{0}\) generates the infinite sequence of configurations. The sequence is defined by the recurrence  
\[c_{t+1}(u)=\delta\big{(}c_{t}(u-r),c_{t}(u-r+1),\ldots,c_{t}(u),\ldots,c_{t}(u +r-1),c_{t}(u+r)\big{)}.\]  
Please note that the changes are local: the content of a cell depends only on the content of \(2r+1\) neighbor cells. It is easy to see that these formal definitions fit the informal description of an operation of a CA.  
We assume that there exists a blank symbol \(\Lambda\) in the alphabet and the transition function satisfies the condition \(\delta(\Lambda,\ldots,\Lambda)=\Lambda\) (\"nothing will come of nothing\"). If a CA with the blank symbol starts from a configuration containing only a finite number of non-blank symbols, then its operation generates the sequence of configurations with the same property.  
A 2 CA (a binary CA) is a CA with the binary alphabet \(\{0,1\}\). We assume that \(1\) is the blank symbol in 2 CAs. This non-standard assignment is convenient due to connections with games.  
**Example 4**.: _Let \(C=(\{0,1\},\delta(x_{-1},x_{0},x_{1})=x_{-1}\oplus x_{1}\oplus 1)\). It is a 2 CA with \(r=1\) and the blank symbol \(1\). Indeed, \(1\oplus 1\oplus 1=1\). If \(C\) starts from the configuration \(\ldots 11011\ldots\), where the dots substitute \(1\)s, then the next configuration is \(\ldots 1101011\ldots\)._  
It is well-known that Turing machines can be simulated by CA with \(r=1\) and any CA can be simulated by a 2 CA (with a neighborhood of a larger size).  
We will need some specific requirements on these simulations; see Section 5 for more details.
# Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
## 2 Comparative Analysis of Transient Responses Based on Different Damping Models  
The viscous damping model and hysteretic damping model are the two most common damping models, and the comparative analysis is very important. The dynamic response consists of transient and steady responses [26]. Therefore, the transient and steady responses of the two damping models were compared and analyzed, respectively.
# Using Fluorescence Spectroscopy to Detect Rot in Fruit and Vegetable Crops
## 2 Materials and Methods
### Experiments with a 527 nm Pulsed Laser  
A compact Raman lidar was used to record the fluorescence spectra in the second part of experiment. Lidar is based on a diode-pumped pulsed Nd:YLiF4 solid-state laser (Laser Compact, DTL-319 QT: 527 nm, 5 ns, 1 kHz, 200 \(\upmu\)J/pulse) [17]. The laser beam was directed to the sample using two rotating prisms. The laser spot diameter was 0.5 mm (measured by burn paper). The radiation scattered from the object was collected using a quartz lens. The signal recording system consists of the compact spectrograph (Spectra Physics, MS127i) (Andor Technology Ltd., Belfast, Northern Ireland) (with an optical resolution of 0.22 nm) equipped with the intensified CCD (ICCD) camera (Andor iStar) (Andor Technology Ltd., Belfast, Northern Ireland). To increase the signal-to-noise ratio, a diffraction grating with a small dispersion (400 lines/mm) and a 250 \(\upmu\)m micron wide input slit were used. In order to avoid damage of the detector by scattered laser radiation, a band-pass light filter OS-13 was used for signal registration (Figure 3). The use of Lidar does not require sample darkening from external illumination since a pulsed source and a gated detector are used; however, this is necessary for a continuous laser. All gating was performed with the help of a built-in pulse generator in an ICCD camera. Chlorophyll fluorescence delay depends on different environments, but is generally less than 3 ns for in situ measurements [18]. Consequently, the detection gate was set to 10 ns in order to fit the pumping laser pulse duration, fluorescence delay, and jitter of gating (3 ns). The delay between the laser pulse and detection gate was varied in order to maximize backscattered elastic scattering; fluorescence spectrum intensity was also maximized (due to chosen gate duration).  
Figure 3: Scheme of an experimental setup based on pulsed fluorescence spectroscopy: 1—laser, 2 —prisms, 3—Al mirror, 4—lens, 5—the sample, 6—motorized stage, 7—spectrograph with intensified CCD ICCD camera, 8—fluorescence spectrum.  
Figure 2: Apples and potatoes were used in experiments with continuous-wave fluorescence spectroscopy. Healthy (**left**) and rotten (**right**) samples.
## 6 Conclusions and Prospect  
The disease mechanism of FSHD was once a mystery. With the in-depth study of FSHD in recent years, considerable progress has been made in the study of the disease mechanism. The treatment of the disease has shifted from relieving symptoms to targeted treatment and is moving towards clinic, making it possible to cure FSHD. The treatment based on oligonucleotide and Crispr have proved the feasibility of DUX4 knock-down in reversing the pathology of FSHD, but these therapies still face many problems before they are applied to patients. In this paper, we elaborate on the emerging therapies in recent years and discuss the pathway to clinical application of these approaches, which needs to consider the drug delivery, efficacy, and safety before they can be truly applied to patients. In addition, the early exploration of symptomatic relief strategies in FSHD has also achieved certain results that are equally important. Stem cell therapy has become an important tool in the field of regenerative medicine due to its potential for self-renewal and multi-lineage differentiation. It also plays a significant role in the advancement of treating myopathy. In recent years, numerous studies have focused on developing stem cell therapies to repair damaged muscles and restore muscle function, particularly with mesenchymal stem cells (MSCs) and induced pluripotent stem cells (iPSCs), which have gained attention in FSHD treatment research. MSCs and iPSCs hold promise for replacing damaged muscle of FSHD patients with healthy muscle cells.  
In fact, FSHD patients exhibit significant individual variability; therefore, we believe that all treatment plans should fully consider these individual differences. Based on the severity of the patient's symptoms, treatment should not only involve editing the DUX4 gene or inhibiting DUX4 expression but also focus on alleviating the patient's muscle symptoms, such as using \(\beta\)2 agonists or myostatin inhibitors or employing stem cell therapies to replace and rejuvenate damaged muscle.  
Although we know that the underlying cause of FSHD may be genetic defects, a combination of various treatment options can provide the greatest benefits for patients. In a word, although many treatment strategies, including gene therapy and stem cell therapy, are still in development or preclinical stages, any ultimate treatment plan for rare diseases requires substantial, reliable preclinical data. Therefore, these studies and data are of great value. In the process of overcoming challenges associated with gene therapies mentioned in this review, the combined use of multiple therapies will bring more significant improvement to FSHD patients and comprehensively enhance their muscle function and self-care ability.  
G.M. and Q.X.: writing and editing. Original Draft. Y.S.: conceptualization, methodology, resources, supervision. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China. Number: 82071413.  
The authors declare no conflicts of interest.
## 4 Numerical Analysis and Experimental Verification
### Experimental Verification and Real-Time Diagnosis
In essence, this research demonstrates the efficacy of the combined physical model and machine learning approach in identifying multiple faults. The integration of the physical model ensures an accurate representation of the Jeffcott rotor system, while the FNN provides efficient and reliable diagnosis capabilities. This research can provide new possibilities for advanced rotor system diagnosis and maintenance strategies in various industrial applications.
## 3 Results and Discussion
### Spatial and Temporal Patterns of ET
However, spatial heterogeneity of annual total ET had a huge diversity even within the same vegetation stand in the Upper Heihe watershed. The spatial average values of annual ET in deciduous and conifer stands had little discrepancy (131-145 mm) in both years. Annual potential ET losses in the watershed were much more reliant upon soil texture, topography and LAI distribution. Figure 6: (**a**) Time-series comparison of modeled and observed evapotranspiration (ET) for 2013 and 2014 in the Arou footprint area. (**b**) Combined comparison between simulated and measured ET for 2013 and 2014. Figure 7: Simulated magnitudes of ecosystem ET sub-components in the Arou footprint area in 2013 and 2014, between DOY 1 and 365. In terms of three major soil textures in Upper Heihe, the annual average modeled ET of silt loam was 340 mm in 2013 and 320 mm in 2014. For loam, it was 237 mm in 2013 and 219 mm in 2014, while clay only had average values of 162 mm in 2013 and 147 mm in 2014 even if it was the most widely distributed soil type in the watershed. One reason for the minimum ET loss of clay was because it had a lower vertical hydraulic conductivity that restricted the water infiltration from soil surface and further limited vegetation transpiration rate. And from HWSD, soil depth of clay was much shallower than that of others. As a result, under the same amount of water input, the rhizosphere of clay was more likely to be located in the saturated zone in the modeling domain. This could cause a lower ET efficiency because the transpirational losses in the saturated zone are much lower than that in unsaturated zone, as shown in Figure 7.  
Additionally, annual ET was also highly in concordance with the DEM and LAI distributions in the simulation. [PERSON] (2014) pointed out that topography is one of the most important factors affecting hydrological behavior in watershed scale, and closely related with distribution of plant functional types, soil properties and climate factors. The watershed with a relatively higher ET loss (\(>\)200 mm) is mainly concentrated in the south-eastern part of the watershed and near the river outlet area, which has a relatively lower altitude and gentle slope. Comparing with a high elevation mountainous area, the climate of a lower and outlet contiguous area was much warmer and wetter. However, it is important to note that the spatial distribution of soil texture and LAI were obtained from 1 km datasets. Although they were upscaled to 100 m to fit the model, they cannot provide such detailed spatial information. Considering the high dependence of ET on these two variables, the coarse spatial datasets might lead to unrealistic driving forces to some extent. For instance, the inaccurate classification of clay might over/underestimate total ET because of its restricted effect on vegetation transpiration.
## 3 Experiment
### Classification Features Extraction
#### 3.3.2 Wavelet-Based Features  
Wavelet Transform is a powerful tool for analysing and classifying the time series signal data. Daubechies wavelets was selected because it is the most commonly used set of discrete wavelet transforms [24]. Among the extremal phase wavelet of Daubechies family, db4 wavelet was chosen in this study, where the number 4 refers to the number of vanishing moments [25].  
In this study, the signal from each sensor collected during each sailing section was subjected to wavelet decomposition into N levels, and the result of the decomposition is divided into two parts as one set (of the \(N^{th}\) level) of approximation coefficients (cA) and N set (from 1 to \(N^{th}\) level) of and detail coefficient (cD). The cA represents low-frequency signal and the cDs represent high-frequency signal. The original signal usually can be decomposed to several levels, and each layer decomposition coefficients are obtained from the previous decomposition. In other words, the original signal S is decomposed into (see Equation (11)):  
\[S=cD1+cD2+\ldots+cDN+cAN \tag{11}\]where \(S\) is the dataset, \(cD1,cD2,\ldots,cDN\) are high-frequency signal obtained by decomposition from the first layer, the second layer and the \(N\) layers respectively, \(cAN\) is the low-frequency signal obtained by decomposition of the \(N^{th}\) layer.  
In the wavelet decomposition, the greater the gain, the more obvious the performance of the different characteristics of noise and signal is, and the more conducive to the separation of the noise and signal. On the other hand, the greater the number of decomposition levels, the greater the distortion of the reconstructed signal, which affects the final denoising effect to a certain extent. In this study, in order to handle the contradiction and choose an appropriate decomposition level, the highest six values of both cA and cD from the first level decomposition were selected. In addition, the mean, standard deviation, entropy of cD and cA are added into the feature vectors.