esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 2 Materials and Methods
### Algorithm Design
#### 2.4.2 Stage 2: Combined Acceleration Variance Analysis Method  
In the proposed approach, the combined acceleration variance analysis method deduces specific variance patterns under different motion states by computing the variance of the combined acceleration data within intervals associated with the three activity levels and examining the variations in variance, as illustrated in Figure 7.  
Analysis of test data from horses in different activity levels shows a characteristic range for the variance of combined acceleration. Through approximately 50 experiments, the boundaries for combined acceleration variance between low activity and medium activity (Table 4) and between medium activity and high activity (Table 5) were determined. This study defines the combined acceleration variance of horses in different activity levels as follows:  
* When the horse is in a low-activity state, it ranges from 0 to 1.2.
* During the slow, medium activity, it ranges from 1.2 to 29.
* When the horse is moving rapidly in a high activity state, it exceeds 29.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Interval Analysis Threshold** & **Accuracy** \\ \hline \(\frac{1N}{2N}\) & 86.92\% \\ \(\frac{2N}{3N}\) & 87.19\% \\ \(\frac{4N}{5}\) & 78.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The relationship between interval analysis threshold and classification accuracy.  
Figure 7: Distribution range of the combined acceleration variance during low-activity, medium-activity, and high-activity behaviors.  
These findings serve as crucial features for identifying the horse's activity levels.  
Specific analysis methods:  
* Periodically collecting combined accelerometer data via a timer. \(A=[a_{1},a_{2},a_{3},\ldots,a_{n}]\) is a set of combined acceleration values, where \(a_{n}\) represents the \(n\)th combined acceleration.
* Equation (4) calculates the variance with a data collection window size of \(n\). \(\sigma^{2}\) represents the variance, and \(\mu\) stands for the mean, calculated as \(\mu=\frac{1}{n}\sum\limits_{i=1}^{n}A_{i}\).  
\[\sigma^{2}=\frac{1}{n}\sum\limits_{i=1}^{n}(A_{i}-\mu)^{2} \tag{4}\]  
* Equation (5) determines the activity levels of the horse based on the variance \(\sigma^{2}\) of the combined acceleration data.  
\[f(A)=\left\{\begin{array}{ll}\text{``}low\ activity^{\prime\prime}&if\ 0\leq \sigma^{2}\leq 1.2\\ \text{``}medium\ activity^{\prime\prime}&if\ 1.2<\sigma^{2}\leq 29.0\\ \text{``}high\ activity^{\prime\prime}&if\ \sigma^{2}>29.0\end{array}\right\} \tag{5}\]
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Feature Extraction Based on HOG and LBP
#### 3.2.2 LBP  
The LBP feature is used as a local texture feature descriptor to extract spatial texture features of ship images [36], which has the advantages of rotation invariance and gray invariance, and has been widely used in texture classification [37] and ship classification. Given a pixel, its grayscale value is \(g_{c}\). Its \(m\) neighborhood pixels are on a circle of radius equal to \(r\) equidistant from a given pixel. The LBP value of \(g_{c}\) is defined as:  
\[LBP_{m,r}(g_{c})=\sum_{j=0}^{m-1}s(g_{j}-g_{c})2^{j}, \tag{3}\]  
\[s(x)=\left\{\begin{array}{ll}1,&x\geq 0\\ 0,&x<0\end{array}\right., \tag{4}\]  
where \(g_{j}\) is the gray value of the neighbors, and \(g_{j}-g_{c}\) represents the difference between the center pixel and each neighbor. \(m\) is the total number of involved neighbors. LBP records the difference between the pixel at the center and the pixel in the neighborhood. When the illumination transformation causes the same increase or decrease in pixel gray value, LBP changes slightly, so it is insensitive to the illumination change. In this study, in order to describe ship image features more accurately, LBP features were combined with CNN features to supplement spatial texture features. The extraction process of LBP feature is shown in Algorithm 4.  
```
Input: A well-prepared training dataset. Step 1: Convert the training set images to grayscale images. Step 2: Tune parameters (\(m,r\)) and select the optimal parameters. Step 3: Calculate the LBP feature. Output: LBP feature.
```  
**Algorithm 4** The extraction process of LBP feature  
Figure 8: Illustration of HOG feature using the VAIS dataset: (**a**) Original image; (**b**) grayscale image; and (**c**) HOG feature.  
Figure 7: Illustration of HOG feature using the self-built dataset: (**a**) Original image; (**b**) grayscale image; and (**c**) HOG feature.  
In this study, the classification performance was best when (_m,r_) = (8, 1) for the two datasets. The LBP features obtained by using two datasets are shown in Figures 9 and 10. As can be seen, the spatial texture features of the ship images are well extracted.
## 4 DC Characterization and Model Validation
### UTC Photodiode Compact Model Validation
#### 4.3.2 Photocurrent Characterization  
The photocurrent measurements were performed under an optical power ranging between 0 and 9 dBm by illuminating the UTC-PDs with a laser wavelength of 1.55 \(\upmu\)m. Figure 7 shows the measured and simulated photocurrents for all geometries under test. We estimated a high responsivity of 0.6 A/W for 15 \(\upmu\)m long photodiodes and 0.79 A/W  
Figure 5: (a) Collector current at different temperatures; output characteristics at forced \(V_{BE}\) (a) and at forced \(I_{B}\) (b), for the InP DHBT with an area of 0.7 \(\times\) 5 \(\upmu\)m\({}^{2}\).  
Figure 6: Measured (symbol) and simulated (line) current–voltage–temperature of UTC-PD with an active area of (a) 5 \(\times\) 15 \(\upmu\)m\({}^{2}\) (b) 5 \(\times\) 30 \(\upmu\)m\({}^{2}\) (c) 7 \(\times\) 25 \(\upmu\)m\({}^{2}\).  
for 25 and 50 \(\upmu\)m-long photodiode. Good accuracy of the photocurrent model, depicted in (4), has been observed. Moreover, good photocurrent scalability is also achieved.
## 4 Experiments and Results
### Robotic Arm Teleoperation Execution  
In this experiment, two Geomagic Touch were used as the master devices, while each of them was responsible for one UR10 control. To illustrate the tracking performance of the proposed incremental-based method for robotic arm teleoperation, corresponding joint values of the master and slave devices were recorded for comparison. The experiments were implemented on a PC platform with MATLAB/Simulink development environment. The OpenHaptics toolkit API was used as the software development kit for the Geomagic Touch. As the two sets of the robotic arm teleoperation subsystems share the same configuration, here we only list the joint values of one Geomagic Touch and the corresponding slave robot, which are shown in Figure 7.  
Figure 7: _Cont._  
In Figure 7, the joint values of the slave and master devices are the incremental values of the robotic arm (\(\Delta_{ir}(t)=\theta_{i}(t)-\theta_{i}(t-1)\)) and the corresponding scaled incremental values of the Geomagic Touch (\(\Delta_{ih}(t)\)), respectively. To evaluate the tracking performance of the slave device, the RMSE (root-mean-square-error) was introduced to evaluate the difference between the corresponding joint values, which is defined as:  
\[\text{RMSE}_{i}=\sqrt{\frac{1}{n}\sum_{t=1}^{n}(\Delta_{ir}(t)-\Delta_{ih}(t)) ^{2}} \tag{5}\]  
where \(n\) is the sample size (number of sample time step) and \(i\) is the \(i\)th joint (\(i=1,2,\ldots,6\)).  
As shown in Figure 7, the RMSEs were calculated as 0.0071, 0.0149, 0.0348, 0.0115, 0.0389, and 0.0154 for Joints 1-6, respectively. The average value is 0.0204 for the six joints. Therefore, it can be concluded that the proposed incremental-based robotic arm control strategy can properly perform the joint space mapping between the master device and the slave robot during the teleoperation.
Although computer simulation offers a potential alternative to measurements in wind tunnels, to our knowledge, in the case of alpine skiing, this approach has only been used to investigate the effects of wind on various body postures during the flight phase of downhill skiing jumps [16]. Recently, [PERSON] and colleagues [14] concluded that virtually any reduction in aerodynamic drag improves skiing performance significantly. Technological advances, and in particular Global Navigation Satellite Systems (GNSS), now allow parameters related to performance to be monitored along an entire ski course [17]. In an attempt to achieve a more detailed analysis of performance that also includes energy losses due to aerodynamic drag and ski-snow friction, an individualized experimental model has been combined with GNSS measurements to estimate the aerodynamic drag on a skier in different positions [11]. Although this approach is potentially of considerable value, it requires measurements in different skiing positions and at different wind speeds in expensive wind tunnels prior to the measurements on-snow, which limits its applicability in practice. Such measurements in a wind tunnel could be avoided if a reliable model of the aerodynamic drag on skiers performing the various technical disciplines in different positions were available. To our knowledge, because of the nature of the postures investigated, models of this kind that are presently available are applicable to the speed disciplines only [12; 14; 15] or \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable/Abbreviation** & **Description** \\ \hline Cd & Coefficient of aerodynamic drag \\ Cd-S & Product of the coefficient of aerodynamic drag and cross-sectional area \\ CV & Coefficient of variation \\ Fd & Aerodynamic drag \\ H & Body height \\ h & Shoulder height, i.e., the distance between the shoulders and ground. \\ hn & Relative shoulder height = h/H \\ LoA & Limits of agreement \\ Sfr & Reference cross-sectional area \\ S & [PERSON]’s cross-sectional area \\ Sn & Normalized cross-sectional area = S/S, \\ V & Relative wind speed \\ \(\rho\) & Air density \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The variables examined and their abbreviations. are based on monitoring the trajectories of multiple body segments of recreational skiers, which requires more complex technology [10]. Accordingly, the present investigation was designed to develop and assess a model of the aerodynamic drag on elite alpine skiers competing in the technical disciplines in order to enhance analyses of performance based on GNSS data alone.
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 3 Results and Discussion  
In this paper, we apply the methodology for wind farm monitoring by designing a customized head-mounted fault display system that integrates predictive capabilities (through deep learning models) with anomaly explanations (focusing on model transparency and comprehensibility) for effective predictive maintenance tasks.  
Wind farms are essentially composed of a set of wind turbines which convert kinetic wind energy into mechanical energy, which is then transformed into electrical energy for various uses (Figure 7). These turbines are crucial for the renewable energy infrastructure, providing a sustainable and environmentally friendly alternative to fossil fuels. They play a major role in the transition to renewable energy by harnessing wind power to generate clean electricity, contributing to a sustainable and resilient energy future. Despite facing challenges, advancements in technology and increasing efficiency continue to make wind energy an integral part of the global energy landscape. However, wind turbines are complex systems susceptible to faults that can impact performance and increase maintenance costs. For example, issues in the hydraulic system, such as pressure drops or air content in the oil, and sensor failures that measure various parameters can stem from electrical or mechanical problems, each affecting turbine operation differently. In this context, it is essential to assist maintenance managers and operators by simplifying and targeting their tasks to implement the appropriate maintenance strategy for specific issues. That involves first determining the persistent features that allow the observation of each component's behavior, which is necessary for generating explanations and drawing specific inferences about the failure.  
The dataset provided by Energias De Portugal ([[https://www.edp.com/en/innovation/open-data](https://www.edp.com/en/innovation/open-data)]([https://www.edp.com/en/innovation/open-data](https://www.edp.com/en/innovation/open-data)) (accessed on 25 March 2024)) is utilized for the computation test of the methodology. It includes measurements from multiple wind turbines, recorded every ten minutes over a two-year period, from 1 January 2016 to 1 September 2017, totaling 87,208 samples per turbine. The dataset features 81 sensors distributed across 12 turbine components (such as the gearbox, generator, and transformer) as well as environmental factors (including wind speed, wind direction, and ambient temperature). Statistical metrics like standard deviation, minimum and maximum values, average sensor observations, and signal descriptors provide comprehensive insights.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components  
#### 4.1.1 Raptor (Regulatory Associated Protein of mTOR)  
Was Identified by TOR-IPs  
Identification of Raptor was independently achieved by two research groups by distinctive purification procedures. [PERSON] et al. utilized an anti-mTOR antibody to immunoprec mTOR binding proteins derived from \(3\times 10^{6}\) HEK293T cells radiolabeled with \({}^{35}\)S-methionine followed by DSP (dithiobis (succinimidyl propionate)) cross-linking. Raptor was detected by autoradiography [30]. In another study, [PERSON] et al. took a three-step purification procedure to find Raptor, including precipitating mTOR-containing proteins from \(5\times 10^{9}\) HeLa cells by 30% (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), followed by cation-exchange chromatography, and lastly, an mTOR antibody-aided immunoprecipitation of mTOR containing fractions [27]. In yeast, Raptor homolog Kog1 was identified through biochemical purification procedures as well. Specifically, 5 L of yeast cultured in YPD medium expressing either \(2\times\) Myc-tagged TOR1 or \(3\times\) HA-tagged TOR2 at endogenous levels were lysed in buffer containing 0.5%Tween 20, followed by ion-exchange chromatography and immunoprecipitations by either anti-Myc or anti-HA antibodies. A mock purification with non-tagged TOR was included as the negative control. Gel bands from purifications were silver stained, excised, and analyzed by MALDI-TOF mass spectrometry [29]. This led to identification of yeast TOR1 and TOR2 complex components including KOG1 (Raptor), LST8 (G\(\beta\)L), AVO1 (Sin1), AVO2, and AVO3 (Rictor).
# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
## 6 Conclusions  
Our proposed method addresses the limitations of existing image editing techniques in terms of precise control and detail preservation by applying control over the attention mechanisms in diffusion models. Our approach enables high-precision image editing and supports style transformations. By comparing existing techniques, we demonstrate superior performance in quantitative and qualitative evaluations, especially in the critical metric of CLIP scores. This shows that our method has a better understanding and execution of user editing instructions. Furthermore, by introducing dual visual attention control, our method demonstrates zero-shot characteristics and extremely high flexibility across various image editing scenarios. Compared to a scenario with no control, adopting dual control has led to improvements: the CLIP Score increased by 6.19%, LPIPS decreased by 29.3%, and FID was reduced by 24.7%. These enhancements verify the effectiveness of our proposed dual attention control. Additionally, the zero-shot nature of our method allows it to be easily applied to different image editing scenarios without additional training or fine-tuning, significantly enhancing its practicality and flexibility. Although our method performs  
Figure 8: Self-attention control in fine layers. Using only cross-attention control will cause a large change in the entire image (column 3, for example, the shape of the tree and the layout of the street), while using dual attention control can achieve the desired style transfer while keeping the content and details consistent with the original image.  
well in various situations, it still has limitations when dealing with blurry images. Future research will focus on overcoming these limitations and further exploring the application of self-attention mechanisms in image editing and other image-processing tasks.  
Conceptualization, Z.P.; funding acquisition, Y.K., J.L. and L.Z.; methodology, Z.P.; resources, Y.K. and L.Z.; validation, Z.P.; writing--original draft, Z.P.; writing--review and editing, Y.K., J.L. and L.Z. 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 under Grant 61972147 and the Open project fund of Dongting Lake Basin Ecological Protection and Restoration Engineering Technology Innovation Center of the Ministry of Natural Resources under Grant DTB.TICECR-2024-06.  
The original contributions presented in this study are included in the article. The data in the article were generated by diffusion modeling. For further inquiries, please contact the corresponding author.  
The authors declare no conflicts of interest.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 5 Conclusions  
This paper presented an initial prototype and the associated methodologies for a sensor package that can be deployed via a drone onto civil structures for structural health monitoring. Alongside this demonstration, Level 1 damage detection (the presence of damage in the structure) was performed using a novel BIRF technique to extract the DSR from data collected by reference accelerometers. Through manual flights, the feasibility of delivering and retrieving a sensor package from the underside of a structure was proven. Similarly, experimental validation showed that the BIRF method is an effective method for determining damage based on data collected from any sensor package.  
In future work, the landing mechanism of the UAS system will be fully automated, instead of relying on manual flight controls. The sensitivity of the sensor package will be improved, and wireless communication and data transfer will be added. The use of elastomers will be considered in an effort to reduce the effect of drone vibrations on the measurements. As for the damage detection algorithm, future work will focus on expanding the methodology to include damage quantification and damage localization, which relate to Level 2 and 3 damage detection, respectively.  
The authors confirm the contributions to the paper as follows: study conception and design: N.V., A.D. and D.R.; data collection: S.C., J.S. and S.A.; analysis and interpretation of results: S.C., J.S., S.A., N.V., A.D. and D.R. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the University of South Carolina (UofSC) Office of Undergraduate Research (Magellan Awards 80003124 and 80003491), the UofSC College of Engineering and Computing startup fund and the Advanced Railway Technology Group at UofSC.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Study Area and Data  
Our study investigates Shenzhen Metro network, which consisted of five lines and 118 stations in the year of 2013. The Shenzhen Metro ridership used in this study was collected and aggregated from the transit entry-exit smart cards records between 14 October (Monday) 2013 and 20 October (Sunday) 2013, which are provided by Shenzhen Metro Corporation (Shenzhen Metro Corporation: [[http://www.szmc.net/](http://www.szmc.net/)]([http://www.szmc.net/](http://www.szmc.net/))) in China, which obtains the smart cards records from AFC system.
## 5 The Mode of Action/Plant Response Mechanisms to the Crude Oil-Induced Abiotic Stresses
### Nutrient Deficiency
#### 5.3.1 Plants' Responses to Nutrient Deficiency  
1. Biosynthesis of stress-induced phenylpropanoids: Environmental (biotic and abiotic) stresses like pathogen attack, wounding, nutrient deficiencies, and temperature, among others (Figure 8), are capable of enhancing the levels of phenylpropanoids in plants [99; 100].  
These phenylpropanoids are plant secondary metabolites [99; 101; 102] derived from _trans_-cinnamic acid, produced from the deamination of L-phenylalanine via the action of the enzyme--phenylalanine  
Figure 8: Examples of stress-induced phenylpropanoids. Source: [PERSON] and [PERSON] [100].  
ammonia-lyase (PAL) [99; 100]. The other two enzymes involved in the first three steps that brings about the synthesis of these secondary metabolites (phenylpropanoloid-derived compounds), which together are referred to as the general phenylpropanoloid pathway (GPP) (Figure 9), are cinnamate 4-hydroxylase (C4H) and \(p\)-coumaroyl coenzyme A ligase (4 CL) [101].  
In line with [PERSON] et al. [103], the shikimate pathway is found in plants and provides phenylalanine for the synthesis of protein and secondary metabolites (like lignin and flavonoids). This shikimate pathway, with distinct patterns of organ-specific as well as tissue-specific activity, however, depends on developmental regulation and environmental stimuli [104]. The enzymes of this pathway also respond to nitrogen and amino acid starvation [104].  
On the other hand, [PERSON] [105] indicated that nitrogen, phosphorus, potassium, and sulphur deficiencies most times lead to higher concentrations of phenolic compounds.  
Figure 9: Biosynthesis pathway of some plant phenylpropanoloid compounds (PAL = phenylalanine ammonia lyase; C4H = cinnamate 4-hydroxylase; 4 CL = \(p\)-coumaroyl coenzyme A ligase). Adapted from: [101; 102].  
(b) Oxidative stress: Following the acknowledgment of the relationship between macronutrient deficiency and oxidative stress by [PERSON] et al. (2016); [PERSON] et al. (2018) in their study found that N-, P-, and S-nutrient deprivation triggered redox changes and induced oxidative stress.