esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependence of the Mechanical Behavior of Loess after Dry-Wet Cycles
## 2 Triaxial Creep Tests
### Testing Scheme  
According to the basic physical index properties listed in Table 1, samples with a moisture content of 16% were tested by using multi-level loading method [4; 43]. Considering the density (\(\rho=1858\) kg/m\({}^{3}\)) and the thickness (20 m) of the overlying loess, the confining pressure of 200 kPa was chosen for the triaxial creep test. The multi-level loading method used in this study is described as follows: the first level of deviatoric stress was applied to the specimen until the stability was attained when the deformation of the specimen was less than 0.01 mm in 24 h. To find an optimum time during which the deformation of the specimen was less than 0.01 mm, a series of preliminary tests were conducted, where 24 h, 36 h, and 48 h were used for specimens with the same testing conditions. The creep shear test results show that for the cases of 24 h, 36 h, and 48 h, the strength and strain-time curves of loess have no obvious difference, indicating that 24 h is enough for obtaining the stability state of the loess. Therefore, 24 h was selected here from the point of view of time saving. Then, the second level of deviatoric stress was applied to the specimen until the stability state was achieved. The specimen was subjected to shearing until creep failure occurred at some level of deviatoric stress. In this study, the failure occurred in the specimen when the strain of specimen reached 20% following the previous study concluded by [PERSON] et al. [44]. The samples after different dry-wet cycles were subjected to consolidation and shearing in the conventional triaxial compression test to calculate the instantaneous failure strength. Then, the loading scheme of the triaxial creep test (Table 2) was determined according to the instantaneous failure strength.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Number of Dry-Wet Cycles** & **Deviatoric Stress/kPa** \\ \hline
0 & 25, 50, 62.5, 75, 87.5, 100, 150, 250, 350 \\
5 & 50, 100, 150, 200, 250, 300 \\
10 & 50, 125, 200, 250, 300 \\
15 & 50, 100, 175, 250 \\
20 & 50, 100, 175, 250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loading scheme of the triaxial creep test.  
Figure 4: FSR-20 triaxial creep apparatus.
# An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
## 3 Improved Kernelized Correlation Filter Algorithm  
In the proposed IKCF, a base sample with a dynamically continuous scale is firstly presented to solve the problem, which is the poor performance of fixed-scale filters in detecting targets with varying scales. Then, to solve the problem of large computation and filter drift caused by the filter's frequent updating, an adaptive filter update strategy based on PSR is adopted, so as to obtain a better tracking result.
## 1 Introduction
### Electric Solar Wind Sail and Plasma Brake in the Context of Propellantless Propulsion
Section 1.2 presents our heritage and recent advances in the quest to demonstrate the E-sail in orbit. There are several related and notable _propellantless propulsion_ developments globally and which are important in the context of the Special Issue \"Advances in CubeSat Sails and Tethers\", where the current article belongs. In the United States, the most prominent E-sail development has been the two-phase NASA Innovative Advanced Concepts (NIAC) \"Heliopause Electrostatic Rapid Transit System\", or HERTS project, which prepared a particle-in-cell (PIC) simulation and analysed the potential E-sail missions, scenarios and spacecraft designs [15; 16]. In Australia, the ionospheric plasma drag on objects in LEO has been analysed with PIC simulations and experiments [17]. A Finnish startup company named Aurora Propulsion Technologies is implementing a European Innovation Council (EIC) grant for developing the plasma brake [18], as well as a European Space Agency (ESA) project, DragLiner, to develop a Coulomb drag-based telecommunication satellite deorbiting device, in collaboration with the Finnish Meteorological Institute [19]. In terms of propellantless propulsion in general, technologies have already advanced in successful in-orbit demonstration and utilisation. The JAXA's IKAROS spacecraft made the world's first solar sail demonstration of photon propulsion while en route to Venus in 2010 [20]. The NanoSAIL-D2 was deployed in 2011, but orbit was too low to observe the drag effect of the small sail which the satellite carried [21]. While similar results were achieved by the LightSail 1 mission [22], the LightSail 2 mission has demonstrated solar sail control and deorbiting. The electrodynamic tether is developed by the E.T.PACK consortium who are preparing an in-orbit demonstration mission supported by the EIC [23].
## 2 Preliminaries and Problem Description  
### Appointed-Time Performance Constraint Function  
Appointed-time means that the time of system completion to track the targets can be pre-specified offline in terms of mission-oriented demands. The appointed-time performance constraint function can guarantee that trajectory tracking is achieved in a user-appointed time within the prescribed convergence rates and maximum overshoot.  
**Definition 1**.: _A first-order continuously differentiable function \(k(t):R_{\geq 0}\to R_{+}\) is described as an appointed-time performance constraint function if there is an appointed time \(T>0\) such that the following two properties are true:_  
_(1)_ \(k(t)\) _is a positive and decreasing function over the set_ \(t\in[0,T)\)_;_  
_(2)_ \(k(t)\equiv k_{b}\) _for all_ \(t\geq T\)_, where_ \(k_{b}\) _is a positive constant._  
According to Definition 1, a new first-order continuously differentiable appointed-time performance constraint function is designed as:  
\[k\left(t\right)=\left\{\begin{array}{l}\frac{k_{b}-k_{b}}{T^{2}}t^{2}-2\frac {k_{b}-k_{b}}{T}t+\bar{k}_{b},t\in[0,T)\\ k_{b},t\in[T,+\infty)\end{array}\right. \tag{1}\]  
where \(T\) and \(k_{b}\) denote the prescribed settling time and maximum steady error, respectively. Parameter \(\bar{k}_{b}\) needs to be selected such that the initial tracking error \(e\) satisfies \(\left|e\right|<\bar{k}_{b}\).  
**Remark 1**.: _The appointed-time performance constraint function is inspired by the appointed-time performance function in [28], with some differences. The biggest difference is that the appointed-time performance function is a higher-order polynomial in [28], so that the proposed appointed-time performance constraint function has a simple form and is easy to implement._
## 6 Error Estimates: Semi-Discrete (SD) Problems
### Error Estimates: SD Problem in \(\Omega\)
\begin{split}\varepsilon\|\theta_{t}(t)\|_{\Omega}^{2}+a(\theta (t),\theta(t))&\leq 4\Big{(}\int_{0}^{T}\|\rho_{ss}\|_{\Omega} \Big{)}^{2}\\ &\leq 4\Big{(}Ch^{2}\int_{0}^{T}\|E_{ss}\|_{H^{2}(\Omega)}\,ds\Big{)}^{ 2}\leq 4\varepsilon\Big{(}Ch^{2}\int_{0}^{T}\|E_{ss}\|_{H^{2}(\Omega)}\,ds \Big{)}^{2}.\end{split}  \tag{56}\]  
Consequently,  
\[\|\theta_{t}(t)\|_{\Omega}\leq Ch^{2}\int_{0}^{T}\|E_{ss}\|_{H^{2}(\Omega)}\,ds,\]  
and  
\[[\|\theta(t)\|]_{\Omega}\leq Ch^{2}\int_{0}^{T}\|E_{ss}\|_{H^{2}(\Omega)}\,ds \leq Ch^{2}\int_{0}^{T}\|E_{ss}\|_{H^{2}(\Omega)}\,ds.\]  
Note further that  
\[ \begin{split} 2\|\theta\|_{\Omega}\frac{d}{dt}\|\theta\|_{\Omega}& =\frac{d}{dt}\|\theta\|_{\Omega}^{2}=\frac{d}{dt}\int_{\Omega}\| \theta|^{2}\,dx\\ &=2\int_{\Omega}\theta\cdot\theta_{t}\,dx\leq 2\|\theta\|_{\Omega}\| \theta_{t}\|_{\Omega}.\end{split}  \tag{57}\]  
Which integrating yields\[\left\|\theta(t)\right\|_{\Omega}\leq\int_{0}^{t}\left\|\theta_{s}(s)\right\|_{ \Omega}ds\leq Ch^{2}t\int_{0}^{T}\left\|E_{ss}\right\|_{H^{2}(\Omega)}ds. \tag{58}\]  
Summing up, we get the desired result. **Remark 4**: _Note that the errors in initial data, \(f_{0}-f_{0,h}\) and \(f_{1}-f_{1,h}\), do not appear in the above estimates. In general, and for sufficiently regular \(f_{0}\) and \(f_{1}\), the initial data errors can always be dominated by other error terms in appropriate norms._
## 2 Materials and Methods
### Spectrophotometric Studies  
#### 2.4.1 Total Phenolic Content (TPC)  
The total phenolic content (TPC) of the flower extracts was determined by applying a modified micromethod of the Folin-Ciocalteu (FC) colorimetric assay, as described by [PERSON] et al. [27]. The results were expressed as mg of gallic acid equivalents (GAE) per 1 g of flower, using standard solutions with a range of 25-500 mg L\({}^{-1}\) of gallic acid. The photometric measurements were performed at 750 nm using a Vis spectrophotometer (Spectro 23 Digital Spectrophotometer, Labomed, Inc., Los Angeles, CA, USA).  
2.4.2 Scavenging Activity on 2,2\({}^{\prime}\)-Azino-Bis-(3-Ethylbenzothazoline-6-Sulfonic Acid)  
Radical (ABTS\({}^{\bullet+}\))  
The antiradical activity of flower extracts was determined according to the method described by [PERSON] et al. [28]. Trolox was used as the standard compound and the antiradical activity of samples was expressed as mg of Trolox Equivalents (TE) per 1 g of flower, using standard solutions with a concentration range of 0.20-1.5 mM. Absorbance was measured at 734 nm with a Vis spectrophotometer (Spectro 23 Digital Spectrophotometer, Labomed, Inc., Los Angeles, CA, USA).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Common Name and Flower Color** & **Scientific Name** & **Family Name** \\ \hline Purple Mexican petunia & _Ruellia simpex_ [PERSON]. [PERSON] & Acanthaceae \\ Yellow black-eyed Susan vine & _Thunberga al alata_ [PERSON] & \\ Pink periwinkle & _Catharantibus vesues_ [PERSON]. [PERSON] & Apocynaceae \\ Orange Cape honeysuckle & _Tecomaria capensis_ (Thunb.) [PERSON] & Begoniaaceae \\ Pink carnation & _Diamthus carnychyplulus_ L. & Caryophyllaceae \\ Orange marigold & _Tagetes_ erca L. & \\ Purple Indian chrysanthemum & _Chrysanthemum indicum_ L. & Compositae \\ Dark red Indian chrysanthemum & _Chrysanthemum indicum_ L. & \\ Pink azalea & _Rhododendron simsii_ Planch. & Ericaceae \\ Pink lily & _Lilium candidium_ L. & Liliaceae \\ Red hibiscus & _Hibiscus rosa-sinensis_ L. & \\ White hibiscus & _Hibiscus rosa-sinensis_ L. & Malvaceae \\ Red wax mallow & _Malvoxiscus arboreus_ Cav. & \\ White jasmine & _Jasminum Officinale_ L. & Oleaceae \\ Purple common snapdragon & _Antirthinum majus_ L. & Plantaginaceae \\ Blue plumbago & _Plumbago auriculata_ [PERSON]. & Plumbaginaceae \\ Red spring sowbread & _Cyclamen repandum_ Sm. & \\ White spring sowbread & _Cyclamen herderifolium_ Aiton & Primulaceae \\ Fuchsia spring sowbread & _Cyclamen herderifolium_ Aiton & \\ Red China rose & _Rosa chinensis_ Jacq. & Rosaceae \\ Yellow lantana & _Lantana camara_ L. & \\ White lantana & _Lantana camara_ L. & Verbenedaceae \\ Pink lantana & _Lantana camara_ L. & \\ Yellow heartsease & _Viola tricolor_ L. & \\ Purple heartsease & _Viola tricolor_ L. & Violaaceae \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of flower samples used in the present study.
# Robust Control for a Slung-Mass Quadcopter Under Abrupt Velocity Changes
## 2 System Modeling and Robust Control Design
### Longitudinal Dynamics Modeling  
The goal of the control design is to dampen oscillations of the slung mass when the velocity changes abruptly. The quadrotor displacement on the \(y\)-axis is neglected due to the quadrotor navigation being always in forward direction. The slung mass is connected to the aircraft through a bar and the \(\beta\) angle of the slung mass is locked by a bearing which only allows oscillations at \(\alpha\) angle. It is assumed that the velocity abrupt change forward navigation of the quadcopter induces oscillations on the angular position of the slung mass. These oscillations could cause catastrophic scenarios. Reduction in the overall dynamic model to a longitudinal dynamics is possible by considering only the forward navigation and using a bearing for locking the \(\beta\) angle of the bar. Thus, the planar mathematical model contains only the longitudinal and vertical displacements \(x\) and \(z\), angular displacement \(\theta\) and the angular position of the slung-mass \(\alpha\). Then, the longitudinal model is expressed as follows:  
\[(M+m)\ddot{x} =-ml(\cos\alpha\dot{x}-\sin\alpha\dot{x}^{2})+\sin\theta u+d_{1} \tag{2a}\] \[(M+m)\ddot{z} =-ml(\sin\alpha\dot{x}-\cos\alpha\dot{x}^{2})-(M+m)g+\cos\theta u +d_{2}\] (2b) \[ml^{2}\ddot{x} =-ml\cos\alpha\ddot{x}-ml\sin\alpha\ddot{z}-mlg\sin\alpha\] (2c) \[J\ddot{\theta} =\tau+d_{3} \tag{2d}\]  
where \(d_{i}\) for \(i=1,2,3\) are perturbations due to wind, gust, etc. The natural behavior of the \(\alpha\) is oscillatory and depends on the initial conditions (small \(\alpha\) angle):  
\[\ddot{\alpha}=-\frac{g}{l}\alpha \tag{3}\]
## 3 Results
### PARNet Experimental Results on NEU-Dataset  
The experimental results of the algorithm in the NEU-dataset are shown in Table 3. Due to the three attributes recognition of Upcolor 1, Upcolor 2 and Lowercolor as a multi-label problem, as a result, we only statistics the Acc of three attributes. Besides, ROC and PR curves of different attributes (except the above three attributes) in the NEU-dataset are provided in Figure 4. As shown in Table 3, compared with other attributes, the accuracy of the Hat attribute in our algorithm is relatively high, and the Calling attribute has higher PRR and Recall than other attributes. The Backpack attribute has higher NRR than other attributes and the Carrying attribute Prec and F1 are higher than other attributes. In addition, the color of the coat and the color of underwater are the multi-label situation, and the recognition rate is stated only for the correct recognition situation. The average recognition rate of the three color-related multi-label attributes is 75.54%, which is lower than the overall attribute recognition rate. Therefore, the recognition of multi-label attributes needs to be further improved.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attributes** & _Acc_ & _PRR_ & _NRR_ & _mA_ & _Prec_ & _Rec_ & \(F1\) \\ \hline
**Gender** & 85.25 & 90.43 & 82.00 & - & 75.95 & 90.43 & 82.56 \\
**Hat** & **90.70** & 75.51 & 91.46 & - & 30.66 & 75.51 & 43.62 \\
**Glasses** & 72.81 & 72.16 & 72.94 & - & 35.51 & 72.16 & 47.60 \\
**Backpack** & 89.53 & 71.23 & **91.93** & - & 53.68 & 71.23 & 61.23 \\
**Shoulderbag** & 74.04 & 79.61 & 72.81 & - & 39.31 & 79.61 & 52.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attribute recognition results (%) on NEU-dataset.
## 5 Performance Evaluation Results and Discussion  
We evaluated the effectiveness of the proposed method. Specifically, we used simulations to evaluate the reduction in cellular traffic, the mitigation of the effects of misbehaving REPs that fail to fulfill the role of REP, and the effect of the configuration parameter \(T_{b}\) on successful bidders' bids. Two movement scenarios for mobile hosts, that is, a pedestrian case and vehicle case, were simulated. Mobile hosts move in a 500 m by 500 m square area according to the random waypoint model (RWP) [22] for the pedestrian case and a 1000 m by 1000 m square area by RWP for the vehicle case. The purpose of the vehicle case is to evaluate how the higher velocity of the mobile hosts affects the effectiveness of the proposed method. The amount of reward requested by each mobile host is set at a fixed value determined for each mobile host by a uniform random number at the start of a simulation. Each mobile host tenders a bid under the condition that it fulfills a data request at a location nearest the POI in its planned trajectory. A data request for a randomly selected POI is generated every 5 s. The evaluation score of a bid is calculated based on the distance between the POI and the location where the mobile host obtains the data, the time that it takes for the mobile host to obtain the data, and the reward requested by the mobile host. The event-driven simulation program was written in the C programming language.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Feature Selection and Preprocessing
#### 2.2.4 Normalization of Input Features  
A normalization is also required, because the dataset includes features with different scales. Spectral bands, for example, have different ranges than VIs. By transforming features to have a mean equal to 0 and standard deviation equal to 1, it ensures that each feature makes an equal contribution to the model. The normalized value \(Z\) of data point \(X\) is calculated by:  
\[Z=\frac{X-\mu}{\sigma}, \tag{14}\]  
Figure 2: [PERSON]’s correlation matrix of input features to identify potential redundancy among the features.  
where \(\mu\) stands for the mean of the feature and \(\sigma\) represents the standard deviation of it. All the input features are normalized as a means of ensuring consistent scaling across variables. However, the target variable (yield) was not normalized, as normalizing the target can distort its interpretability, which is critical for practical applications.
## 4 Results and Discussion
### Predictive ML Models Created over a Set of Predictors Optimized by the RReliefF Algorithm
ML Predictive Models Created over a Set of Predictors Optimized by the Backward Selection via the Recursive Feature Elimination Algorithm  
In accordance with the first step of the algorithm shown in Figure 6, all 17 independent variables are used as inputs to the ML models. Automatic training and testing of predictive models based on MLP, SVM and k-NN techniques are performed using the Auto Numeric method. As one of the results of this step, Figure 9 shows the input variables ranked by PI value [47]. The first two variables with the highest PI value are related to packet retransmission, which directly and negatively affects the end-to-end delay. By multiple executions of the loop of the algorithm given in Figure 6, the \(RE\) values of the model testing are obtained, as shown in Figure 10. From the figure, it can be concluded that the best predictive performance is shown by the model based on k-NN, which has the smallest relative error (\(RE\) = 0.04) for the five most influential input variables sorted according to Figure 10. Nevertheless, due to lower complexity, the k-NN model with four inputs is selected as the best solution; its relative error is slightly higher and amounts to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & \(RE\) & **Correlation** \\ \hline
1. k-NN & 0.109 & 0.944 \\
2. MLP & 0.159 & 0.917 \\
3. SVM & 0.205 & 0.893 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of testing the models created over the data set optimized by the RReliefF algorithm. Figure 9: Independent variables ranked by PI. \(RE=0.041\). In addition, it is evident that the prediction performance decreases drastically with a further reduction in the number of inputs to three and two variables. The performance of the tested predictive models, in addition to the relative error, can also be expressed by correlation, which is shown in Figure 11. It is concluded that the values of the Pearson correlation coefficients of the prediction results with the real data from the test set are \"inverse\" in relation to the \(RE\) values shown in Figure 10. Accordingly, the model with five inputs has the highest correlation coefficient (0.98), but due to the reasons mentioned above, the k-NN model with four inputs, whose correlation coefficient is equal to 0.979, was selected as the best solution. One of the main reasons for the better performance of the k-NN model when compared to the SVM and MLP models lies in the fact that the k-NN model is oriented towards simpler data sets, such as the one observed.
# Identifying Human Daily Activity Types with Time-Aware Interactions
## 4 Experiment & Results  
### Dataset  
To examine the proposed model performance, we built a new dataset extracted from the open Yelp dataset ([[https://www.yelp.com/dataset](https://www.yelp.com/dataset)]([https://www.yelp.com/dataset](https://www.yelp.com/dataset))) that includes interactions on post content and users. We selected 300 posters (users) from the downloaded dataset and removed two users whose attributed data are missing. Then, the 298 user items and their posts were collected. The collected data consisted of 23,701 posts published by the 298 users. Finally, each post was labeled into one activity type over 14 types, which are \"Food\", \"Beauty&spa\", \"Entertainment\", \"Travel\", \"Shopping\", \"Service\", \"Sports\", \"Health\", \"Car\", \"Nightlife\", \"Pets\", \"Education\", \"Religious\" and \"Mass media\", and the dataset was built.  
As illustrated in Table 1, each item composes of a post text, date, an activity type, and the id of the user who published the post. The average word count of one post was 84.51.  
Social media activity is closely connected with human daily behavior, which means that the number of each type of activity is not balanced. As shown in Table 2, food and shopping are the two most popular activities. The unbalanced distribution between them challenges the accurate identification of HATs and requires a powerful identity model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Value** & **Posts (Description)** \\ \hline post & The pizza was ok. Not the best I’ve had. & The post that user published on YELP \\ User id & msQe1u7Z\_XuqjCoqB0J5g & Unique id for every user \\ Activity type & Food & The activity type of the post \\ Date & 2011-02-25 & Date when the post was published \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of an item in the dataset, which consists of the post text, user id, and activity type.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activity Type** & **Train** & **Test** & **Total** \\ \hline Food & 11,577 & 4962 & 16,539 \\ Beauty and spa & 146 & 62 & 208 \\ Entertainment & 661 & 283 & 944 \\ Travel & 497 & 213 & 710 \\ Shopping & 1602 & 687 & 2289 \\ Service & 566 & 242 & 808 \\ Sports & 501 & 214 & 715 \\ Health & 83 & 36 & 119 \\ Car & 221 & 95 & 316 \\ Nightlife & 616 & 264 & 880 \\ Pets & 47 & 20 & 67 \\ Education & 24 & 10 & 34 \\ Religious & 26 & 11 & 37 \\ Mass media & 25 & 10 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of posts in each HAT.