esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Fluctuating Wind Characteristics and Non-Stationarity at U-Shaped Canyon Bridge Site
## 3 Analysis of Fluctuating Wind Characteristics
### Stationarity Tests at Different Heights  
To investigate the variation in non-stationary phenomena with height, specifically focusing on the U-component of wind velocity, the stationarity test described in Section 3.1 was conducted at five measurement points along the height, with the results shown in Figure 5. The proportion of non-stationary samples increases from 9.0% at the 20 m measurement point to 31.1% at the 50 m bridge deck height. The overall trend indicates that non-stationarity gradually increases with height.  
More non-stationary samples are observed at the 10 m measurement point, likely due to strong non-stationarity near the surface, influenced by objects such as vegetation and stones. The high proportion of non-stationary sample sequences in the canyon area can be attributed to the following reasons: First, terrain features such as ridges and canyons induce rapid changes in wind speed and direction, leading to the generation of non-stationary winds. Secondly, the mountainous wind field contains both large-scale eddy circulations and small-scale turbulence, with this multi-scale characteristic exhibiting non-stationarity in wind speed analysis. In general, sudden changes in wind speed and gusts caused by mountainous terrain may result in the non-stationary characteristics of wind speed.  
Figure 4: Relationship between gust factor and turbulence intensity.  
Figure 3: Relationship between turbulence intensity and wind speed.
## 4 Mobile Localization Algorithms
### Distributed Localization Algorithms
#### 4.2.2 Dive and Rise (DNR) Positioning Scheme  
In [45], an interesting idea of Dive and Rise (DNR) positioning is presented. DNR anchor nodes are used to replace static ones. Each DNR anchor node is equipped with GPS. While sinking and rising, they broadcast their positions. Unknown nodes are localized by passively listening to DNR anchor nodes' messages. Range measurement is done by using one-way ToA. After hearing from several anchor nodes, unknown nodes estimate their coordinates.  
DNR scheme reduces communication overhead and energy consumption by the passively listening method. Unknown nodes spend energy only in receiving and processing localization message. Furthermore, DNR scheme can localize unknown nodes in deep water. However, DNR anchor nodes diving and rising takes longer time than message propagation. Therefore, localization performance heavily depend on frequency of location updates and number of anchor nodes. Compared with LSHL, DNR has higher localization accuracy and less energy consumption and communication overhead [46].
## 4 Materials and Methods
### In Vivo Analysis of the Antifungal Activity of B. atrophaeus Strain HF1 against V. pyri  
The ability of _B. atrophaeus_ strain HF1 to control _V. pyri_ growth in pear twigs was assessed using protocol from a previously published study [9]. Briefly, healthy 1-year-old pear twigs (Zhongli 1) were cut from a tree, treated for 1 min with 75% ethanol, and each \(\sim\)10 cm long twig was punched in the center using a 5 mm diameter punch, after which it was sprayed with 1 mL of a strain HF1 cell suspension (1 \(\times\) 10\({}^{8}\) CFU mL\({}^{-1}\)) from an overnight culture in liquid NA medium (Peptone 10 g L\({}^{-1}\), Beef extract 3.0 g L\({}^{-1}\), NaCl 5.0 g L\({}^{-1}\)) at 180 rpm, 28 \({}^{\circ}\)C. Sterile water (CK) and Tebuoconazole (0.086 g L\({}^{-1}\); Anhui Zhongshan Chemical Industry Co., Ltd, Anhui, China) treatments were, respectively, used for negative and positive control treatments. Twigs were allowed to dry naturally, after which mycelial plugs (diameter: 5 mm) isolated from the edge of a 5-day-old colony were used for twig inoculation. Mycelial plugs were covered with moistened medical absorbent cotton to maintain local moisture levels, and these inoculated twigs were then transferred into a transparent plastic box for incubation at 25 \({}^{\circ}\)C. At 7 days post-inoculation (dpi), Vernier calipers were used to measure lesion length. This experiment was repeated three times, with six inoculation sites per replicate.  
Analyses of the Antifungal Activity of B. atrophaeus Strain HF1 Culture Filtrate against V. pyri Mycelial Growth  
After culturing strain HF1 for 4 days in liquid NA medium at 28 \({}^{\circ}\)C, 180 rpm, cultures were centrifuged and a crude culture filtrate was collected. This filtrate was then passed through a filter (0.22 \(\upmu\)m) to yield the final filtrate. PDA medium (\(\sim\)50 \({}^{\circ}\)C) was mixed with different concentrations of culture filtrate (2%, 5%, or 10%, \(v/v\)) and used for _V. pyri_plugs (diameter: 5 mm) inoculation. PDA medium without culture filtrate was used as CK. Following a 6-day culture period at 25 \({}^{\circ}\)C, colony diameter values were measured. This testing was repeated three times with three replicates per test.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Datasets
#### 3.2.3 GZ-CD  
The GZ-CD dataset is another widely adopted benchmark dataset in the field of change detection, focusing on urbanization-related changes in Guangzhou, China. The dataset contains 19 pairs of high-definition images collected from 2006 to 2019, focusing on monitoring the seasonal evolution characteristics of Guangzhou's rapidly urbanizing suburbs over a 14-year period. As buildings are the primary drivers of change in urbanization, the dataset emphasizes building-related changes, making it particularly suitable for studying urban development and transformation.  
During the training phase, a standard sample unit of 256 \(\times\) 256 pixels is generated based on the original image pairs. This process generated a total of 1067 image block pairs. To prevent overfitting and ensure a balanced representation of change patterns, we randomly sampled the dataset and divided it into training, validation, and test sets in a 7:1:2 ratio. Specifically, 70% of the dataset (approximately 747 image pairs) was allocated to the training set, 10% (approximately 107 image pairs) to the validation set, and 20% (approximately 213 image pairs) to the test set. This split ratio allows for thorough training, effective validation, and robust evaluation of the model's performance.  
Figure 9 provides a visual representation of the dataset, showcasing five representative examples of change detection. In columns a to c, the changes primarily involve the construction of buildings on previously undeveloped land, highlighting the rapid urbanization in the region. Column d illustrates the emergence of a swimming pool. Finally, column e depicts the transformation of green areas into industrial facilities, emphasizing the dataset's capacity to detect land-use changes. These examples collectively demonstrate the dataset's utility in studying diverse urbanization processes, from large-scale building  
Figure 8: Visualization of the SYSU-CD dataset. T1_img and T2_img are images at different times, and columns (**a–e**) are the corresponding instances.  
constructions to subtle land-use modifications, making it a valuable resource for both research and practical applications in urban planning and change detection.
## 4 Conclusions  
In this paper, a novel two-step de-noising method for effectively extracting random PD pulse from complex and strong interferences is proposed and applied to robustly estimate the arrival time of the noisy PD pulse in power cables. Firstly, an improved adaptive VMD is presented to remove out periodic narrowband interference due to its advantage of minimal sum of estimated bandwidths for each mode decomposed from input noisy signal. To avoid over-decomposition or mode loss, the correlation coefficient method is used to determine the optimal intrinsic mode number adaptively. In order to effectively filter out white noise, a scale adaptive wavelet packet decomposition method based on correlation coefficient is proposed, which decomposes the signal into high, middle, and low-frequency components. The components with low frequency, small amplitude are removed out as the white noise interference according to the threshold method, and the residual is the de-noising PD signal. Simulation results show that the proposed method can robustly suppress the interference of periodic narrowband signal and white noise and effectively preserve the essential characteristics of the real PD signal. In the multi-sensor travelling wave based localization system using time-varying kurtosis, accurate estimation of first arrival time of PD pulse can be achieved by the de-noising results.  
Conceptualization, K.S.; data curation, T.W.; formal analysis, T.W.; investigation, X.L.; methodology, K.S.; project administration, K.S.; software, T.W. and J.Z.; supervision, K.S.; writing--original draft, J.Z.; writing--review & editing, T.W., X.L., and J.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, Grant No. 61403130.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Evaluation of Different Extraction Techniques on the Yield and Purity of Obtained Extracts
#### 3.1.2 Extraction of Phycobiliproteins by Homogenisation  
Homogenisation is another widely used cell disruption technique to obtain PBP by mechanical cell disruption that leads to the release of cell contents [16]. In this part of the study, extraction by homogenisation was performed to investigate the influence of homogenisation time on PBP extraction yield and purity. Four different extraction times (5, 15, 30, and 45 min) under constant conditions were performed. Figure 3 shows that the time of homogenisation had an impact on the extraction yield and purity. The highest total extraction yield was obtained at 45 min of extraction--(37.01 \(\pm\) 0.7 mg/g of biomass of PC and 30.4 \(\pm\) 0.8 mg/g of biomass of APC); however, the total yield for 30 min of homogenisation was 36.5 \(\pm\) 0.6 mg/g of biomass of PC and 30.9 \(\pm\) 0.2 mg/g of biomass of APC, which was not significantly different from extraction at 45 min. Similar results were obtained in a previous study, where the increase in PC and APC yield extracted from wild cyanobacteria was no longer significantly different (\(p\) > 0.05) after 30 min of homogenisation [41]. Considering the differences in time consumption and energy requirements between the two different extraction conditions, 30 and 45 min of homogenisation, 30 min of extraction seems more efficient for PC extraction. Although the values obtained in this study were high enough for the extracts to be considered food-grade (>0.7), all extracts were  
Figure 2: Effect of maceration time on phycocyanin and allophycocyanin content (**A**) and purity (**B**). Image of obtained extract after 2 h of extraction (**C**). Data are presented as the mean \(\pm\) standard deviation (n = 3). Different lowercase letters indicate statistically significant differences (\(p\) < 0.05).  
green, with the extract after 30 min of homogenisation pictured in Figure 3C), compared to the characteristic blue colour extract of phycocyanin, which indicated the presence of impurities, particularly chlorophyll, in the extract after the use of homogenisation to disrupt the cells of _A. platensis._ Although there was no significant difference (\(p\) > 0.05) between the purity of the extracts from 15, 30, and 45 min of homogenisation, the gradual increase of purity could be explained by the increase in the absorbance values obtained, leading to higher purity ratios calculated because of the high shear homogenisation releasing chlorophyll and other compounds from the cells.
## 3 Methodology
### Future Scenarios and Downscaling  
Four future scenarios comprehensively consider shared socio-economic paths (SSPs) and typical concentration paths (RCPs), namely SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. After evaluating the GCMs of CMIP6 in the Yangtze River Basin, four climate models (NorESM2-MM, ACCESS-CM2, IPSL-CM6A-LR, MPI-ESM1-2-LR) were selected by calculating correlation coefficient and standard deviation. Then, they were downscaled by quantile correction after collective average. The specific evaluation and correction methods are as follows:  
\[S=\frac{4{(1+R)}^{4}}{{\left({\sigma_{f}+1/\sigma_{f}}\right)}^{2}{(1+R_{0})} ^{4}} \tag{15}\]  
where \(R\) is the correlation coefficient and R\({}_{0}\) is the maximum correlation coefficient, \(\sigma_{f}\) is the specific value of standard deviation. The Quantile Mapping method is designed to reduce the difference between the empirical cumulative distribution functions (CDFs) of the model data and that of the observation data by establishing transfer functions. Then, these transfer functions are used to correct the future climate model data. The transfer function can be expressed as  
\[x_{mc}=F_{o,c}^{-1}\left\{F_{m,c}[x_{m,o}(t)]\right\} \tag{16}\]  
where \(x_{mc}\) is the corrected climate model data; \(x_{m,o}(t)\) is the original climate model data of \(t\) period; \(F_{m,c}\) is the CDFs of original climate model data for historical periods; \(F_{o,c}^{-1}\) is the inverse empirical CDFs of observation data.
## 2 Methodology
### Simulation Model
#### 2.3.3 Maneuvers Module  
The maneuvers module calculates reconfiguration time which is one of the multiple objectives in ReCon optimization. It is defined as a time elapse from the identification of a target (Figure 6a) until the alignment of GOM and ROM groundtracks (Figure 6b). Once aligned, the satellite is ready for switching observation modes; the time required for trajectory switching through a Hohmann transfer (less than an hour) is negligible compared to the waiting time for groundtracks to be naturally aligned (several days).  
Given a ROM altitude of \(h\) and a GOM altitude of \(h+\Delta h\), their orbital periods differ by:  
\[\Delta T=2\pi\left(\sqrt{\frac{\left(R_{E}+h+\Delta h\right)^{3}}{\mu_{E}}}- \sqrt{\frac{\left(R_{E}+h\right)^{3}}{\mu_{E}}}\right)\enspace, \tag{10}\]  
If \(\Delta h>0\), a satellite in GOM will take a longer time to complete one revolution than a satellite in ROM. This lagging causes a westward drift of the NRGTs relative to the ground-fixed RGTs. The distance by which NRGTs deviate from RGTs at equator after one orbit is [13]:  
\[\Delta d=\left(\omega_{E}-\dot{\Omega}\right)R_{E}\Delta T\enspace, \tag{11}\]  
Figure 6: Reconfiguration time: (**a**) Target identification at t = T\({}_{0}\) (satellite not aligned with repeat groundtracks); (**b**) Orbit transfer from GOM to ROM at t = T\({}_{0}\) + T\({}_{\mathrm{R}}\) (satellite aligned with repeat groundtracks).  
If a satellite in ROM orbits the Earth \(N_{p}\) times in \(N_{d}\) days (RGT ratio \(N_{p}/N_{d}\)), the deviation distance along the equator in is \(N_{p}\Delta d\) in \(N_{d}\) days, or \(N_{p}\Delta d/N_{d}\) per day. Finally, the reconfiguration time measured in days for separation distance \(d\) or separation angle \(\varphi\) is:  
\[T_{R}=\frac{N_{d}d}{N_{s}\Delta d}=\frac{N_{d}R_{E}\Delta\varphi}{N_{s}\left( \omega_{E}-\hat{\Omega}\right)R_{E}\Delta T}[\text{day}]\;, \tag{12}\]  
Finally, \(\Delta t\) is also used in this module to calculate delta-v (metric of fuel consumption). In Equation (12), the reconfiguration term grows if \(\Delta t\) is increased. The expressions for the reconfiguration term and others are provided in Appendix C [51].  
\[\Delta V=\Delta V_{commissioning}+\Delta V_{reconfig}+\Delta V_{stationkeeping }+\Delta V_{decompositioning}[\text{day}]\;. \tag{13}\]
# Task-Oriented Adversarial Attacks for Aspect-Based Sentiment Analysis Models
## 5 Experimental Setup
The selected articles were initially proposed for the attack text classification task; however, in [19], these works were evaluated for the sentiment analysis task at the aspect level. We consider the results presented in [19] to compare our proposed approach:  
* Baseline 3. [PERSON] et al. [37]: BERT-Attack employs BERT-MLM to generate word perturbations through replacement operations and utilizes the leave-one-out (LOO) strategy to determine word importance rankings. The method does not differentiate between context words and aspect terms. Baseline 4. [PERSON] and [PERSON] [38]: This BERT-based attack utilizes BERT-MLM to perturb words through both replacement and insertion operations, with word importance determined using the leave-one-out (LOO) method. The insertion operation is performed after the replacement operation. The method does not differentiate between context words and aspect terms. * Baseline 5. [PERSON] et al. [16]: Textbugger employs both character-level and word-level perturbations and identifies the nearest neighbors in the embedding space using the GloVe model. The method does not differentiate between context words and aspect terms. A sentiment analysis model is commonly assessed using the accuracy metric, which represents the proportion of correctly classified instances relative to the total number of instances ([PERSON] and [PERSON] [2]). To measure the effectiveness of our attacks, we observe the before-attack accuracy (BA) and after-attack accuracy (AA). BA is calculated before applying the attack, while AA is determined after modifying the inputs. To evaluate the quality of adversarial examples generated, we measured the semantic similarity (SS) between the original opinion and its adversarial example using the cosine similarity. In addition, we calculated the perturbation ratio (PR), which quantifies the proportion of modified terms relative to the total number of terms in the sentence.
## 3 Power Performance Assessment Methodology
### Health Value Based on the Probabilistic Area Metric  
#### 3.3.1 Health Value in the MWPTR  
According to the interval size \(\delta\), the MWPTR can be divided into \(t\) intervals, and the \(i\)th wind speed interval can be expressed as \(\left[v_{i}^{mean}-\delta,v_{i}^{mean}+\delta\right]\). The assessment data can be expressed as \(\left\{(v_{1},P_{1}),(v_{2},P_{1}),\cdots,(v_{n},P_{n})\right\}\), in which the points falling within the \(i\)th wind speed interval are expressed as \(\left\{v_{i,j},P_{  
where \(\overline{P}_{i}^{0}\) is the mean value of power in the \(i\)th interval, \(M_{i}\) is the ratio of the probabilistic area and \(\overline{P}_{i}^{0}\). To explain the probabilistic area metric (PAM), the actual ECDF and baseline ECDF are shown in Figure 3. There is a contact point of two ECDF curves. The probabilistic area is cyan region of two ECDF curves, which can be calculated by the integral method in the Equation (5). It is possible that actual ECDF and baseline ECDF have no contact point. In other words, the actual ECDF is always above or under the baseline ECDF. The probabilistic area is also computed by the Equation (5).  
After calculating the deviations in \(t\) intervals, the standard power curve is used to compound the overall variations. Assuming the standard power curve can be expressed as \(P=g(v)\), where \(v\) is the wind speed, \(P\) is the power and \(g(\cdot)\) is the standard power curve function. The power of the mean point in the \(i\)th speed interval should be:  
\[P_{i}^{mean}=g\Big{(}v_{i}^{mean}\Big{)} \tag{6}\]  
where \(v_{i}^{mean}\) is mean of the \(i\)th wind speed interval, \(p_{i}^{mean}\) is the power of the standard power curve. In the \(i\) interval, the weighting value \(\kappa_{i}\) is calculated by:  
\[\kappa_{i}=\frac{P_{i}^{mean}}{\sum\limits_{i=1}^{t}P_{i}^{mean}} \tag{7}\]  
The HV in the MWPTR is:  
\[\gamma_{\textsc{MWPTR}}=\kappa_{1}M_{1}+\kappa_{2}M_{2}+\cdots\cdots+\kappa_ {t}M_{t} \tag{8}\]  
In the MWPTR, the control objective of wind turbine is to track the maximum wind power. \(\gamma_{\textsc{MWPTR}}\) represents the derivation that the actual power data from the baseline data. The HV with an approximate zero value means that the individual wind turbine is close to the optimal power performance. Conversely, a higher HV represents a poorer health condition. The reasons may be equipment fault, machine degradation, weather and so on. By analyzing \(\gamma_{\textsc{MWPTR}}\) and its change trend, it is beneficial to condition assessment and monitoring.  
Figure 3: Illustration of probabilistic area metric.
## 2 Materials and Methods
### Statistical Analysis  
The statistical values were expressed as mean \(\pm\) standard deviation. After testing each variable for the normality of the distribution (Shapiro-Wilks test), the differences in anthropometric (BM and circumference of the thigh), bioelectrical (R, Xc, Z, R/h, Xc/h, Z/h, and PhA) and hematological data (CK) at PRE, POST, and POST24H were analyzed through the non-parametric Friedman test and, in the case of differences, the Wilcoxon test. The magnitude of ratio changes was computed as delta percent values (\(\Delta\)%). Following this, the correlation was analyzed (Spearman coefficient) between increments or decrements (delta values) of BM, whole-body and localized R/h, Xc/h, and PhA, against CK. Whole-body individual bioimpedance vectors were analyzed by the standard reference RXc score graph, according to a healthy Italian reference population [40]. The RXc mean graph was performed to compare the whole-body mean vector of hockey players to the reference population. One and two-sample [PERSON]'s T\({}^{2}\) tests were used to determine the BIA vector differences between checkpoints and between hockey players vs. the reference population, respectively. An alpha value of 0.05 for statistical significance was set. IBM SPSS Statistics software v.20 was used for data management and statistical analysis.